seed: 0
output_dir: './output'  # path to save checkpoint/strategy
load_checkpoint: './internlm.ckpt'
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: 'internlm_20b'

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

# 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: 8
  pipeline_stage: 1
  micro_batch_num: 1
  vocab_emb_dp: True
  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: True
  parallel_optimizer_comm_recompute: False
  mp_comm_recompute: True
  recompute_slice_activation: True

# callbacks
callbacks:
  - type: MFLossMonitor
  - type: CheckpointMonitor
    prefix: "internlm_20b"
    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: False
  strategy_ckpt_save_file: "./ckpt_strategy.ckpt"
  parallel_optimizer_config:
    gradient_accumulation_shard: False
    parallel_optimizer_threshold: 64

# model config
model:
  model_config:
    type: InternLMConfig
    batch_size: 1 # add for increase predict
    seq_length: 2048
    hidden_size: 5120
    num_layers: 60
    num_heads: 40
    vocab_size: 103168
    multiple_of: 256
    rms_norm_eps: 1.0e-5
    bos_token_id: 1
    eos_token_id: 2
    pad_token_id: 2
    ignore_token_id: -100
    compute_dtype: "float16"
    layernorm_compute_type: "float16"
    softmax_compute_type: "float16"
    rotary_dtype: "float16"
    param_init_type: "float16"
    has_bias: False
    use_past: True
    use_flash_attention: True
    block_size: 16
    num_blocks: 512
    is_dynamic: True
    scaling_factor: 1.0
    extend_method: "None"
    offset: 0
    checkpoint_name_or_path: "internlm_20b"
    repetition_penalty: 1.0
    max_decode_length: 512
    top_k: 3
    top_p: 0.8
    do_sample: False
    auto_map:
      AutoModel: internlm.InternLMForCausalLM
      AutoConfig: internlm_config.InternLMConfig
      AutoTokenizer: [internlm_tokenizer.InternLMTokenizer, null]
  arch:
    type: InternLMForCausalLM

processor:
  return_tensors: ms
  tokenizer:
    unk_token: '<unk>'
    bos_token: '<s>'
    eos_token: '</s>'
    pad_token: '</s>'
    type: InternLMTokenizer
    vocab_file: './internlm-20b-chat/tokenizer.model'
  type: LlamaProcessor

# metric
metric:
  type: PerplexityMetric

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

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