seed: 0
output_dir: './output' # path to save checkpoint/strategy
load_checkpoint: './internlm2.ckpt'
src_strategy_path_or_dir: ''
auto_trans_ckpt: False  # If true, auto transform load_checkpoint to load in distributed model
only_save_strategy: False
resume_training: False
use_parallel: False
run_mode: 'predict'

# trainer config
trainer:
  type: CausalLanguageModelingTrainer
  model_name: 'internlm2_7b'

# runner config
runner_config:
  epochs: 1
  batch_size: 2
  sink_mode: True
  sink_size: 1

# optimizer
optimizer:
  type: FP32StateAdamWeightDecay
  beta1: 0.9
  beta2: 0.999
  eps: 1.e-8
  weight_decay: 0.1

# lr schedule
lr_schedule:
  type: CosineWithWarmUpLR
  learning_rate: 1.e-5
  warmup_ratio: 0.03
  total_steps: -1 # -1 means it will load the total steps of the dataset

# dataset
train_dataset: &train_dataset
  data_loader:
    type: MindDataset
    dataset_dir: ""
    shuffle: True
    num_samples: 256
  input_columns: ["input_ids"]  # "input_ids", "labels" , labels are used in instruction finetune.
  num_parallel_workers: 8
  python_multiprocessing: False
  drop_remainder: True
  repeat: 1
  numa_enable: False
  prefetch_size: 1
train_dataset_task:
  type: CausalLanguageModelDataset
  dataset_config: *train_dataset
# if True, do evaluate during the training process. if false, do nothing.
# note that the task trainer should support _evaluate_in_training function.
do_eval: False
eval_step_interval: -1        # num of step intervals between each eval, -1 means no step end eval.
eval_epoch_interval: 50        # num of epoch intervals between each eval, 1 means eval on every epoch end.

# eval dataset
eval_dataset: &eval_dataset
  data_loader:
    type: MindDataset
    dataset_dir: ""
    shuffle: False
  input_columns: ["input_ids"]
  num_parallel_workers: 8
  python_multiprocessing: False
  drop_remainder: False
  repeat: 1
  numa_enable: False
  prefetch_size: 1
eval_dataset_task:
  type: CausalLanguageModelDataset
  dataset_config: *eval_dataset

# default parallel of device num = 8 for Atlas 800T A2
parallel_config:
  data_parallel: 1
  model_parallel: 1
  pipeline_stage: 1
  micro_batch_num: 1
  vocab_emb_dp: False
  gradient_aggregation_group: 4
# when model parallel is greater than 1, we can set micro_batch_interleave_num=2, that may accelerate the train process.
micro_batch_interleave_num: 1

# recompute config
recompute_config:
  recompute: False
  parallel_optimizer_comm_recompute: False
  mp_comm_recompute: False
  recompute_slice_activation: False

# callbacks
callbacks:
  - type: MFLossMonitor
  - type: CheckpointMonitor
    prefix: "internlm2_7b"
    save_checkpoint_steps: 500
    keep_checkpoint_max: 3
    integrated_save: False
    async_save: False
  - type: ObsMonitor

# mindspore context init config
context:
  mode: 0 #0--Graph Mode; 1--Pynative Mode
  device_target: "Ascend"
  enable_graph_kernel: False
  max_call_depth: 10000
  max_device_memory: "59GB"
  save_graphs: False
  save_graphs_path: "./graph"
  device_id: 0

# parallel context config
parallel:
  parallel_mode: 1 # 0-dataset, 1-semi, 2-auto, 3-hybrid
  gradients_mean: False
  enable_alltoall: False
  full_batch: True
  search_mode: "sharding_propagation"
  enable_parallel_optimizer: True
  strategy_ckpt_save_file: "./ckpt_strategy.ckpt"
  parallel_optimizer_config:
    gradient_accumulation_shard: False
    parallel_optimizer_threshold: 64

# model config
model:
  model_config:
    type: InternLM2Config
    batch_size: 1 # add for increase predict
    seq_length: 2048
    hidden_size: 4096
    num_layers: 32
    num_heads: 32
    n_kv_heads: 8
    vocab_size: 92544
    rms_norm_eps: 1.0e-5
    intermediate_size: 14336
    theta: 1000000
    bos_token_id: 1
    eos_token_id: [2, 92542]
    pad_token_id: 2
    ignore_token_id: -100
    compute_dtype: "float16"
    layernorm_compute_type: "float32"
    softmax_compute_type: "float32"
    rotary_dtype: "float32"
    param_init_type: "float16"
    qkv_concat: True
    use_past: True
    use_flash_attention: True
    block_size: 16
    num_blocks: 512
    is_dynamic: False
    scaling_factor: 1.0
    extend_method: "None"
    offset: 0
    checkpoint_name_or_path: "internlm2_7b"
    repetition_penalty: 1.00
    max_decode_length: 512
    top_k: 3
    top_p: 0.8
    do_sample: False
    auto_map:
      AutoModel: internlm2.InternLM2ForCausalLM
      AutoConfig: internlm2_config.InternLM2Config
      AutoTokenizer: [internlm2_tokenizer.InternLM2Tokenizer, null]
  arch:
    type: InternLM2ForCausalLM

processor:
  return_tensors: ms
  tokenizer:
    unk_token: '<unk>'
    bos_token: '<s>'
    eos_token: '</s>'
    pad_token: '</s>'
    type: InternLM2Tokenizer
    vocab_file: './tokenizer.model'
  type: LlamaProcessor

# metric
metric:
  type: PerplexityMetric

# wrapper cell config
runner_wrapper:
  type: MFTrainOneStepCell
  scale_sense:
    type: DynamicLossScaleUpdateCell
    loss_scale_value: 16384
    scale_factor: 2
    scale_window: 1000
  use_clip_grad: True

eval_callbacks:
  - type: ObsMonitor

auto_tune: False
filepath_prefix: './autotune'
autotune_per_step: 10

profile: False
profile_start_step: 1
profile_stop_step: 10
init_start_profile: False
profile_communication: False
profile_memory: True
layer_scale: False
layer_decay: 0.65
lr_scale_factor: 256

# aicc
remote_save_url: "Please input obs url on AICC platform."