Can't start to finetune and can't pull nvcr.io/nvidia/nemo:24.01 or nvcr.io/ea-bignlp/ga-participants/nemofw-training:24.01
I also meet the problem can't pull nvcr.io/ea-bignlp/ga-participants/nemofw-training too. @thangdt277 Can you start to train with other images?
 This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.
**Describe the bug**

Follow [NeMo Framework PEFT with Mistral-7B](https://docs.nvidia.com/nemo-framework/user-guide/latest/playbooks/mistralpeft.html)

First error , I can't pull image although I logged in nvcr.io successfully:
```
12345i@ai_pc:~/NEMO_TRAIN$ docker login nvcr.io
Authenticating with existing credentials...
WARNING! Your password will be stored unencrypted in /ldap/home/12345/.docker/config.json.
Configure a credential helper to remove this warning. See
https://docs.docker.com/engine/reference/commandline/login/#credentials-store

Login Succeeded
```
```
12345@ai_pc:~/NEMO_TRAIN$docker pull nvcr.io/ea-bignlp/ga-participants/nemofw-training:24.01
Error response from daemon: pull access denied for nvcr.io/ea-bignlp/ga-participants/nemofw-training, repository does not exist or may require 'docker login': denied: requested access to the resource is denied
```

So I change docker image to  `nvcr.io/nvidia/nemo:23.10` which is mentioned in [README](https://github.com/NVIDIA/NeMo#)

When I start to [Step 2: Run PEFT training](https://docs.nvidia.com/nemo-framework/user-guide/latest/playbooks/mistralpeft.html#step-2-run-peft-training)

Here is the error :
```
Traceback (most recent call last):
  File "/usr/src/app/./NeMo-main/examples/nlp/language_modeling/tuning/megatron_gpt_peft_tuning.py", line 59, in <module>
    @deprecated(
TypeError: deprecated() got an unexpected keyword argument 'wait_seconds'
```
So I commented out  lines below in `megatron_gpt_peft_tuning.py`:
```
# @deprecated(
#    wait_seconds=20,
#    explanation=f"\n{banner}\nmegatron_gpt_peft_tuning.py is renamed to megatron_gpt_finetuning.py with the "
#    f"same functionality. \nPlease switch to the new name.\n{banner}\n",
)
```



Then it starts to work , Here is process:
```
appuser@ai_pc:/usr/src/app$ python3 ./NeMo-main/examples/nlp/language_modeling/tuning/megatron_gpt_peft_tuning.py     trainer.devices=1     trainer.num_nodes=1     trainer.precision=bf16     trainer.val_check_interval=20     trainer.max_steps=50     model.megatron_amp_O2=False     ++model.mcore_gpt=True     model.tensor_model_parallel_size=1     model.pipeline_model_parallel_size=1     model.micro_batch_size=1     model.global_batch_size=8     model.restore_from_path=${MODEL}     model.data.train_ds.num_workers=0     model.data.validation_ds.num_workers=1     model.data.train_ds.file_names=${TRAIN_DS}     model.data.train_ds.concat_sampling_probabilities=[1.0]    
 model.data.validation_ds.file_names=${VALID_DS}     model.peft.peft_scheme=${SCHEME}     exp_manager.checkpoint_callback_params.mode=min
[NeMo W 2024-02-29 13:43:09 nemo_logging:349] /usr/local/lib/python3.10/dist-packages/hydra/_internal/hydra.py:119: UserWarning: Future Hydra versions will no longer change working directory at job runtime by default.
    See https://hydra.cc/docs/1.2/upgrades/1.1_to_1.2/changes_to_job_working_dir/ for more information.
      ret = run_job(
    
[NeMo I 2024-02-29 13:43:09 megatron_gpt_peft_tuning:66] 
    
    ************** Experiment configuration ***********
[NeMo I 2024-02-29 13:43:09 megatron_gpt_peft_tuning:67] 
    name: megatron_gpt_peft_${model.peft.peft_scheme}_tuning
    trainer:
      devices: 1
      accelerator: gpu
      num_nodes: 1
      precision: bf16
      logger: false
      enable_checkpointing: false
      use_distributed_sampler: false
      max_epochs: 9999
      max_steps: 50
      log_every_n_steps: 10
      val_check_interval: 20
      gradient_clip_val: 1.0
    exp_manager:
      explicit_log_dir: null
      exp_dir: null
      name: ${name}
      create_wandb_logger: false
      wandb_logger_kwargs:
        project: null
        name: null
      resume_if_exists: true
      resume_ignore_no_checkpoint: true
      create_checkpoint_callback: true
      checkpoint_callback_params:
        monitor: validation_${model.data.validation_ds.metric.name}
        save_top_k: 1
        mode: min
        save_nemo_on_train_end: true
        filename: ${name}--{${exp_manager.checkpoint_callback_params.monitor}:.3f}-{step}-{consumed_samples}
        model_parallel_size: ${model.tensor_model_parallel_size}
        always_save_nemo: false
        save_best_model: true
      create_early_stopping_callback: true
      early_stopping_callback_params:
        monitor: val_loss
        mode: min
        min_delta: 0.001
        patience: 10
        verbose: true
        strict: false
    model:
      seed: 1234
      tensor_model_parallel_size: 1
      pipeline_model_parallel_size: 1
      global_batch_size: 8
      micro_batch_size: 1
      restore_from_path: ./model/mistral.nemo
      resume_from_checkpoint: null
      save_nemo_on_validation_end: false
      sync_batch_comm: false
      megatron_amp_O2: false
      sequence_parallel: false
      activations_checkpoint_granularity: null
      activations_checkpoint_method: null
      activations_checkpoint_num_layers: null
      activations_checkpoint_layers_per_pipeline: null
      answer_only_loss: true
      gradient_as_bucket_view: false
      hidden_dropout: 0.0
      attention_dropout: 0.0
      ffn_dropout: 0.0
      peft:
        peft_scheme: lora
        restore_from_path: null
        adapter_tuning:
          type: parallel_adapter
          adapter_dim: 32
          adapter_dropout: 0.0
          norm_position: pre
          column_init_method: xavier
          row_init_method: zero
          norm_type: mixedfusedlayernorm
          layer_selection: null
          weight_tying: false
          position_embedding_strategy: null
        lora_tuning:
          target_modules:
          - attention_qkv
          adapter_dim: 32
          alpha: ${model.peft.lora_tuning.adapter_dim}
          adapter_dropout: 0.0
          column_init_method: xavier
          row_init_method: zero
          layer_selection: null
          weight_tying: false
          position_embedding_strategy: null
        p_tuning:
          virtual_tokens: 10
          bottleneck_dim: 1024
          embedding_dim: 1024
          init_std: 0.023
        ia3_tuning:
          layer_selection: null
        selective_tuning:
          tunable_base_param_names:
          - self_attention
          - word_embeddings
      data:
        train_ds:
          file_names:
          - ./pubmedqa/pubmedqa_train.jsonl
          global_batch_size: ${model.global_batch_size}
          micro_batch_size: ${model.micro_batch_size}
          shuffle: true
          num_workers: 0
          memmap_workers: 2
          pin_memory: true
          max_seq_length: 2048
          min_seq_length: 1
          drop_last: true
          concat_sampling_probabilities:
          - 1.0
          label_key: output
          add_eos: true
          add_sep: false
          add_bos: false
          truncation_field: input
          index_mapping_dir: null
          prompt_template: '{input} {output}'
          truncation_method: right
        validation_ds:
          file_names:
          - ./pubmedqa/pubmedqa_val.jsonl
          names: null
          global_batch_size: ${model.global_batch_size}
          micro_batch_size: ${model.micro_batch_size}
          shuffle: false
          num_workers: 1
          memmap_workers: ${model.data.train_ds.memmap_workers}
          pin_memory: true
          max_seq_length: 2048
          min_seq_length: 1
          drop_last: false
          label_key: ${model.data.train_ds.label_key}
          add_eos: ${model.data.train_ds.add_eos}
          add_sep: ${model.data.train_ds.add_sep}
          add_bos: ${model.data.train_ds.add_bos}
          write_predictions_to_file: false
          output_file_path_prefix: null
          truncation_field: ${model.data.train_ds.truncation_field}
          index_mapping_dir: null
          prompt_template: ${model.data.train_ds.prompt_template}
          tokens_to_generate: 32
          truncation_method: right
          metric:
            name: loss
            average: null
            num_classes: null
        test_ds:
          file_names:
          - ./pubmedqa/pubmedqa_test.jsonl
          names:
          - pubmedqa
          global_batch_size: ${model.global_batch_size}
          micro_batch_size: ${model.micro_batch_size}
          shuffle: false
          num_workers: 0
          memmap_workers: ${model.data.train_ds.memmap_workers}
          pin_memory: true
          max_seq_length: 2048
          min_seq_length: 1
          drop_last: false
          label_key: ${model.data.train_ds.label_key}
          add_eos: ${model.data.train_ds.add_eos}
          add_sep: ${model.data.train_ds.add_sep}
          add_bos: ${model.data.train_ds.add_bos}
          write_predictions_to_file: false
          output_file_path_prefix: null
          truncation_field: ${model.data.train_ds.truncation_field}
          index_mapping_dir: null
          prompt_template: ${model.data.train_ds.prompt_template}
          tokens_to_generate: 32
          truncation_method: right
          metric:
            name: loss
            average: null
            num_classes: null
      optim:
        name: fused_adam
        lr: 0.0001
        weight_decay: 0.01
        betas:
        - 0.9
        - 0.98
        sched:
          name: CosineAnnealing
          warmup_steps: 50
          min_lr: 0.0
          constant_steps: 0
          monitor: val_loss
          reduce_on_plateau: false
      mcore_gpt: true
    
[NeMo W 2024-02-29 13:43:09 nemo_logging:349] /usr/local/lib/python3.10/dist-packages/lightning_fabric/connector.py:554: UserWarning: bf16 is supported for historical reasons but its usage is discouraged. Please set your precision to bf16-mixed instead!
      rank_zero_warn(
    
GPU available: True (cuda), used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
[NeMo W 2024-02-29 13:43:09 exp_manager:754] No version folders would be created under the log folder as 'resume_if_exists' is enabled.
[NeMo W 2024-02-29 13:43:09 exp_manager:611] There were no checkpoints found in checkpoint_dir or no checkpoint folder at checkpoint_dir :/usr/src/app/nemo_experiments/megatron_gpt_peft_lora_tuning/checkpoints. Training from scratch.
[NeMo I 2024-02-29 13:43:09 exp_manager:394] Experiments will be logged at /usr/src/app/nemo_experiments/megatron_gpt_peft_lora_tuning
[NeMo I 2024-02-29 13:43:09 exp_manager:835] TensorboardLogger has been set up
[NeMo W 2024-02-29 13:43:09 exp_manager:931] The checkpoint callback was told to monitor a validation value and trainer's max_steps was set to 50. Please ensure that max_steps will run for at least 1 epochs to ensure that checkpointing will not error out.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: context_parallel_size in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: expert_model_parallel_size in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: tp_comm_overlap in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: tp_comm_split_ag in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: tp_comm_split_rs in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: tp_comm_bulk_wgrad in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: tp_comm_bulk_dgrad in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: finalize_model_grads_func in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: pipeline_model_parallel_split_rank in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: barrier_with_L1_time in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo I 2024-02-29 13:43:32 megatron_init:234] Rank 0 has data parallel group: [0]
[NeMo I 2024-02-29 13:43:32 megatron_init:237] All data parallel group ranks: [[0]]
[NeMo I 2024-02-29 13:43:32 megatron_init:238] Ranks 0 has data parallel rank: 0
[NeMo I 2024-02-29 13:43:32 megatron_init:246] Rank 0 has model parallel group: [0]
[NeMo I 2024-02-29 13:43:32 megatron_init:247] All model parallel group ranks: [[0]]
[NeMo I 2024-02-29 13:43:32 megatron_init:257] Rank 0 has tensor model parallel group: [0]
[NeMo I 2024-02-29 13:43:32 megatron_init:261] All tensor model parallel group ranks: [[0]]
[NeMo I 2024-02-29 13:43:32 megatron_init:262] Rank 0 has tensor model parallel rank: 0
[NeMo I 2024-02-29 13:43:32 megatron_init:276] Rank 0 has pipeline model parallel group: [0]
[NeMo I 2024-02-29 13:43:32 megatron_init:288] Rank 0 has embedding group: [0]
[NeMo I 2024-02-29 13:43:32 megatron_init:294] All pipeline model parallel group ranks: [[0]]
[NeMo I 2024-02-29 13:43:32 megatron_init:295] Rank 0 has pipeline model parallel rank 0
[NeMo I 2024-02-29 13:43:32 megatron_init:296] All embedding group ranks: [[0]]
[NeMo I 2024-02-29 13:43:32 megatron_init:297] Rank 0 has embedding rank: 0
24-02-29 13:43:32 - PID:2242 - rank:(0, 0, 0, 0) - microbatches.py:39 - INFO - setting number of micro-batches to constant 8
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: context_parallel_size in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: expert_model_parallel_size in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: tp_comm_overlap in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: tp_comm_split_ag in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: tp_comm_split_rs in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: tp_comm_bulk_wgrad in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: tp_comm_bulk_dgrad in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: finalize_model_grads_func in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: pipeline_model_parallel_split_rank in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: barrier_with_L1_time in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo I 2024-02-29 13:43:32 tokenizer_utils:191] Getting SentencePiece with model: /tmp/tmpdiuso3r4/49ec005ed7e34fdab15e18659ba3b65a_tokenizer.model
[NeMo I 2024-02-29 13:43:32 megatron_base_model:315] Padded vocab_size: 32000, original vocab_size: 32000, dummy tokens: 0.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: context_parallel_size in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: expert_model_parallel_size in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: tp_comm_overlap in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: tp_comm_split_ag in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: tp_comm_split_rs in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: tp_comm_bulk_wgrad in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: tp_comm_bulk_dgrad in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: finalize_model_grads_func in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: pipeline_model_parallel_split_rank in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_base_model:821] The model: MegatronGPTSFTModel() does not have field.name: barrier_with_L1_time in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_gpt_model:1554] apply_query_key_layer_scaling is only enabled when using FP16, setting it to False and setting NVTE_APPLY_QK_LAYER_SCALING=0
[NeMo W 2024-02-29 13:43:32 megatron_gpt_model:1619] The model: MegatronGPTSFTModel() does not have field.name: num_moe_experts in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_gpt_model:1619] The model: MegatronGPTSFTModel() does not have field.name: fp8_wgrad in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
[NeMo W 2024-02-29 13:43:32 megatron_gpt_model:1619] The model: MegatronGPTSFTModel() does not have field.name: clone_scatter_output_in_embedding in its cfg. Add this key to cfg or config_mapping to make to make it configurable.
Initializing distributed: GLOBAL_RANK: 0, MEMBER: 1/1
----------------------------------------------------------------------------------------------------
distributed_backend=nccl
All distributed processes registered. Starting with 1 processes
----------------------------------------------------------------------------------------------------

Loading distributed checkpoint with TensorStoreLoadShardedStrategy
[NeMo I 2024-02-29 13:44:52 nlp_overrides:752] Model MegatronGPTSFTModel was successfully restored from /usr/src/app/model/mistral.nemo.
[NeMo I 2024-02-29 13:44:52 megatron_gpt_peft_tuning:82] Adding adapter weights to the model for PEFT
[NeMo I 2024-02-29 13:44:52 nlp_adapter_mixins:182] Before adding PEFT params:
      | Name  | Type     | Params
    -----------------------------------
    0 | model | GPTModel | 7.2 B 
    -----------------------------------
    0         Trainable params
    7.2 B     Non-trainable params
    7.2 B     Total params
    28,966.928Total estimated model params size (MB)
[NeMo I 2024-02-29 13:44:55 nlp_adapter_mixins:195] After adding PEFT params:
      | Name  | Type     | Params
    -----------------------------------
    0 | model | GPTModel | 7.3 B 
    -----------------------------------
    10.5 M    Trainable params
    7.2 B     Non-trainable params
    7.3 B     Total params
    29,008.871Total estimated model params size (MB)
[NeMo W 2024-02-29 13:44:55 nemo_logging:349] /usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/configuration_validator.py:153: UserWarning: The `batch_idx` argument in `MegatronGPTSFTModel.on_train_batch_start` hook may not match with the actual batch index when using a `dataloader_iter` argument in your `training_step`.
      rank_zero_warn(
    
[NeMo W 2024-02-29 13:44:55 nemo_logging:349] /usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/configuration_validator.py:153: UserWarning: The `batch_idx` argument in `MegatronGPTSFTModel.on_train_batch_end` hook may not match with the actual batch index when using a `dataloader_iter` argument in your `training_step`.
      rank_zero_warn(
    
[NeMo I 2024-02-29 13:44:55 megatron_gpt_sft_model:752] Building GPT SFT validation datasets.
[NeMo I 2024-02-29 13:44:55 text_memmap_dataset:116] Building data files
[NeMo I 2024-02-29 13:44:55 text_memmap_dataset:525] Processing 1 data files using 2 workers
[NeMo I 2024-02-29 13:44:55 text_memmap_dataset:535] Time building 0 / 1 mem-mapped files: 0:00:00.098850
[NeMo I 2024-02-29 13:44:55 text_memmap_dataset:525] Processing 1 data files using 2 workers
[NeMo I 2024-02-29 13:44:55 text_memmap_dataset:535] Time building 0 / 1 mem-mapped files: 0:00:00.053183
[NeMo I 2024-02-29 13:44:55 text_memmap_dataset:158] Loading data files
[NeMo I 2024-02-29 13:44:55 text_memmap_dataset:249] Loading ./pubmedqa/pubmedqa_val.jsonl
[NeMo I 2024-02-29 13:44:55 text_memmap_dataset:161] Time loading 1 mem-mapped files: 0:00:00.000691
[NeMo I 2024-02-29 13:44:55 text_memmap_dataset:165] Computing global indices
[NeMo I 2024-02-29 13:44:55 megatron_gpt_sft_model:755] Length of val dataset: 50
[NeMo I 2024-02-29 13:44:55 megatron_gpt_sft_model:759] Building GPT SFT test datasets.
[NeMo I 2024-02-29 13:44:55 text_memmap_dataset:116] Building data files
[NeMo I 2024-02-29 13:44:55 text_memmap_dataset:525] Processing 1 data files using 2 workers
[NeMo I 2024-02-29 13:44:56 text_memmap_dataset:535] Time building 0 / 1 mem-mapped files: 0:00:00.047882
[NeMo I 2024-02-29 13:44:56 text_memmap_dataset:525] Processing 1 data files using 2 workers
[NeMo I 2024-02-29 13:44:56 text_memmap_dataset:535] Time building 0 / 1 mem-mapped files: 0:00:00.047631
[NeMo I 2024-02-29 13:44:56 text_memmap_dataset:158] Loading data files
[NeMo I 2024-02-29 13:44:56 text_memmap_dataset:249] Loading ./pubmedqa/pubmedqa_test.jsonl
[NeMo I 2024-02-29 13:44:56 text_memmap_dataset:161] Time loading 1 mem-mapped files: 0:00:00.000603
[NeMo I 2024-02-29 13:44:56 text_memmap_dataset:165] Computing global indices
[NeMo I 2024-02-29 13:44:56 megatron_gpt_sft_model:762] Length of test dataset: 500
[NeMo I 2024-02-29 13:44:56 megatron_gpt_sft_model:766] Building GPT SFT traing datasets.
[NeMo I 2024-02-29 13:44:56 text_memmap_dataset:116] Building data files
[NeMo I 2024-02-29 13:44:56 text_memmap_dataset:525] Processing 1 data files using 2 workers
[NeMo I 2024-02-29 13:44:56 text_memmap_dataset:535] Time building 0 / 1 mem-mapped files: 0:00:00.050147
[NeMo I 2024-02-29 13:44:56 text_memmap_dataset:525] Processing 1 data files using 2 workers
[NeMo I 2024-02-29 13:44:56 text_memmap_dataset:535] Time building 0 / 1 mem-mapped files: 0:00:00.049478
[NeMo I 2024-02-29 13:44:56 text_memmap_dataset:158] Loading data files
[NeMo I 2024-02-29 13:44:56 text_memmap_dataset:249] Loading ./pubmedqa/pubmedqa_train.jsonl
[NeMo I 2024-02-29 13:44:56 text_memmap_dataset:161] Time loading 1 mem-mapped files: 0:00:00.000572
[NeMo I 2024-02-29 13:44:56 text_memmap_dataset:165] Computing global indices
[NeMo W 2024-02-29 13:44:56 nemo_logging:349] /usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/data/language_modeling/megatron/dataset_utils.py:1332: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/pytorch/pytorch/torch/csrc/tensor/python_tensor.cpp:83.)
      counts = torch.cuda.LongTensor([1])
    
[NeMo I 2024-02-29 13:44:57 dataset_utils:1341]  > loading indexed mapping from ./pubmedqa/pubmedqa_train.jsonl_pubmedqa_train.jsonl_indexmap_402mns_2046msl_0.00ssp_1234s.npy
[NeMo I 2024-02-29 13:44:57 dataset_utils:1344]     loaded indexed file in 0.000 seconds
[NeMo I 2024-02-29 13:44:57 dataset_utils:1345]     total number of samples: 450
make: Entering directory '/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/data/language_modeling/megatron'
make: Nothing to be done for 'default'.
make: Leaving directory '/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/data/language_modeling/megatron'
> building indices for blendable datasets ...
 > sample ratios:
   dataset 0, input: 1, achieved: 1
[NeMo I 2024-02-29 13:44:57 blendable_dataset:67] > elapsed time for building blendable dataset indices: 0.03 (sec)
[NeMo I 2024-02-29 13:44:57 megatron_gpt_sft_model:768] Length of train dataset: 402
[NeMo I 2024-02-29 13:44:57 megatron_gpt_sft_model:773] Building dataloader with consumed samples: 0
[NeMo I 2024-02-29 13:44:57 megatron_gpt_sft_model:773] Building dataloader with consumed samples: 0
[NeMo I 2024-02-29 13:44:57 megatron_gpt_sft_model:773] Building dataloader with consumed samples: 0
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
[NeMo I 2024-02-29 13:44:57 nlp_overrides:150] Configuring DDP for model parallelism.
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 adapter_mixins:435] Unfrozen adapter : lora_kqv_adapter
[NeMo I 2024-02-29 13:44:57 nlp_adapter_mixins:245] Optimizer groups set:
      | Name  | Type     | Params
    -----------------------------------
    0 | model | GPTModel | 7.3 B 
    -----------------------------------
    10.5 M    Trainable params
    7.2 B     Non-trainable params
    7.3 B     Total params
    29,008.871Total estimated model params size (MB)
[NeMo I 2024-02-29 13:44:57 modelPT:728] Optimizer config = FusedAdam (
    Parameter Group 0
        betas: [0.9, 0.98]
        bias_correction: True
        eps: 1e-08
        lr: 0.0001
        weight_decay: 0.01
    )
[NeMo I 2024-02-29 13:44:57 lr_scheduler:910] Scheduler "<nemo.core.optim.lr_scheduler.CosineAnnealing object at 0x7ff9feffc070>" 
    will be used during training (effective maximum steps = 50) - 
    Parameters : 
    (warmup_steps: 50
    min_lr: 0.0
    constant_steps: 0
    max_steps: 50
    )

  | Name  | Type     | Params
-----------------------------------
0 | model | GPTModel | 7.3 B 
-----------------------------------
10.5 M    Trainable params
7.2 B     Non-trainable params
7.3 B     Total params
29,008.871Total estimated model params size (MB)
```


But there is another error:
```
Sanity Checking: 0it [00:00, ?it/s][NeMo W 2024-02-29 13:45:11 nemo_logging:349] /usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/connectors/data_connector.py:438: PossibleUserWarning: The dataloader, val_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 224 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
      rank_zero_warn(
    
[NeMo W 2024-02-29 13:45:11 nemo_logging:349] /usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/utilities.py:148: UserWarning: Found `dataloader_iter` argument in the `validation_step`. Note that the support for this signature is experimental and the behavior is subject to change.
      rank_zero_warn(
    
[NeMo W 2024-02-29 13:45:25 nemo_logging:349] /usr/local/lib/python3.10/dist-packages/apex/transformer/pipeline_parallel/utils.py:81: UserWarning: This function is only for unittest
      warnings.warn("This function is only for unittest")
    
Sanity Checking DataLoader 0:   0%|                                                                                                                                                                       | 0/2 [00:00<?, ?it/s]Error executing job with overrides: ['trainer.devices=1', 'trainer.num_nodes=1', 'trainer.precision=bf16', 'trainer.val_check_interval=20', 'trainer.max_steps=50', 'model.megatron_amp_O2=False', '++model.mcore_gpt=True', 'model.tensor_model_parallel_size=1', 'model.pipeline_model_parallel_size=1', 'model.micro_batch_size=1', 'model.global_batch_size=8', 'model.restore_from_path=./model/mistral.nemo', 'model.data.train_ds.num_workers=0', 'model.data.validation_ds.num_workers=1', 'model.data.train_ds.file_names=[./pubmedqa/pubmedqa_train.jsonl]', 'model.data.train_ds.concat_sampling_probabilities=[1.0]', 'model.data.validation_ds.file_names=[./pubmedqa/pubmedqa_val.jsonl]', 'model.peft.peft_scheme=lora', 'exp_manager.checkpoint_callback_params.mode=min']
Traceback (most recent call last):
  File "/usr/src/app/./NeMo-main/examples/nlp/language_modeling/tuning/megatron_gpt_peft_tuning.py", line 87, in main
    trainer.fit(model)
  File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 532, in fit
    call._call_and_handle_interrupt(
  File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/call.py", line 42, in _call_and_handle_interrupt
    return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 93, in launch
    return function(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 571, in _fit_impl
    self._run(model, ckpt_path=ckpt_path)
  File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 980, in _run
    results = self._run_stage()
  File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 1021, in _run_stage
    self._run_sanity_check()
  File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/trainer.py", line 1050, in _run_sanity_check
    val_loop.run()
  File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/utilities.py", line 181, in _decorator
    return loop_run(self, *args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/evaluation_loop.py", line 115, in run
    self._evaluation_step(batch, batch_idx, dataloader_idx)
  File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/loops/evaluation_loop.py", line 376, in _evaluation_step
    output = call._call_strategy_hook(trainer, hook_name, *step_kwargs.values())
  File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/call.py", line 293, in _call_strategy_hook
    output = fn(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/strategies/ddp.py", line 338, in validation_step
    return self.model(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/parallel/distributed.py", line 1521, in forward
    else self._run_ddp_forward(*inputs, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/parallel/distributed.py", line 1357, in _run_ddp_forward
    return self.module(*inputs, **kwargs)  # type: ignore[index]
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/overrides/base.py", line 102, in forward
    return self._forward_module.validation_step(*inputs, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/models/language_modeling/megatron_gpt_sft_model.py", line 387, in validation_step
    return self.inference_step(dataloader_iter, batch_idx, 'validation', dataloader_idx)
  File "/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/models/language_modeling/megatron_gpt_sft_model.py", line 403, in inference_step
    loss = super().validation_step(itertools.chain([batch]), batch_idx)
  File "/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py", line 963, in validation_step
    loss = self.fwd_bwd_step(dataloader_iter, batch_idx, True)
  File "/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/models/language_modeling/megatron_gpt_sft_model.py", line 347, in fwd_bwd_step
    losses_reduced_per_micro_batch = fwd_bwd_function(
  File "/usr/local/lib/python3.10/dist-packages/megatron/core/pipeline_parallel/schedules.py", line 327, in forward_backward_no_pipelining
    output_tensor = forward_step(
  File "/usr/local/lib/python3.10/dist-packages/megatron/core/pipeline_parallel/schedules.py", line 183, in forward_step
    output_tensor, loss_func = forward_step_func(data_iterator, model)
  File "/usr/local/lib/python3.10/dist-packages/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py", line 863, in fwd_output_and_loss_func
    output_tensor = model(**forward_args)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/megatron/core/models/gpt/gpt_model.py", line 166, in forward
    hidden_states = self.decoder(
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/megatron/core/transformer/transformer_block.py", line 311, in forward
    hidden_states, context = layer(
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/megatron/core/transformer/transformer_layer.py", line 153, in forward
    attention_output_with_bias = self.self_attention(
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/megatron/core/transformer/attention.py", line 263, in forward
    core_attn_out = self.core_attention(
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/megatron/core/transformer/custom_layers/transformer_engine.py", line 427, in forward
    return super().forward(
  File "/usr/local/lib/python3.10/dist-packages/transformer_engine/pytorch/attention.py", line 1235, in forward
    qkv_layout = _get_qkv_layout(query_layer, key_layer, value_layer,
  File "/usr/local/lib/python3.10/dist-packages/transformer_engine/pytorch/attention.py", line 464, in _get_qkv_layout
    raise Exception("The provided qkv memory layout is not supported!")
Exception: The provided qkv memory layout is not supported!

Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
```

**Additional context**
GPU : H100*1