bridge.recipes.nemotronh.nemotron_nano_9b_v2#

Module Contents#

Functions#

model_config

Configure the Nemotron Nano 9B v2 model.

pretrain_config

Create a pre-training configuration for Nemotron Nano 9B v2 model.

API#

bridge.recipes.nemotronh.nemotron_nano_9b_v2.model_config(
tensor_parallelism: int = 2,
pipeline_parallelism: int = 1,
pipeline_parallelism_dtype: Optional[torch.dtype] = torch.bfloat16,
virtual_pipeline_parallelism: Optional[int] = None,
context_parallelism: int = 1,
sequence_parallelism: bool = True,
) → megatron.bridge.models.nemotronh.NemotronNano9Bv2Provider#

Configure the Nemotron Nano 9B v2 model.

Parameters:

  • tensor_parallelism – Degree of tensor model parallelism.

  • pipeline_parallelism – Degree of pipeline model parallelism.

  • pipeline_parallelism_dtype – Data type for pipeline parallelism.

  • virtual_pipeline_parallelism – Size of virtual pipeline parallelism.

  • context_parallelism – Degree of context parallelism.

  • sequence_parallelism – Whether to use sequence parallelism.

Returns:

Configuration for the Nemotron Nano 9B v2 model.

Return type:

NemotronNano9Bv2Provider

bridge.recipes.nemotronh.nemotron_nano_9b_v2.pretrain_config(
dir: Optional[str] = None,
name: str = 'default',
data_paths: Optional[list[str]] = None,
data_args_path: Optional[str] = None,
train_data_path: Optional[list[str]] = None,
valid_data_path: Optional[list[str]] = None,
test_data_path: Optional[list[str]] = None,
per_split_data_args_path: Optional[str] = None,
mock: bool = False,
tensor_parallelism: int = 2,
pipeline_parallelism: int = 1,
pipeline_parallelism_dtype: Optional[torch.dtype] = torch.bfloat16,
virtual_pipeline_parallelism: Optional[int] = None,
context_parallelism: int = 1,
sequence_parallelism: bool = True,
train_iters: int = 1168251,
global_batch_size: int = 768,
micro_batch_size: int = 1,
seq_length: int = 8192,
lr: float = 0.0003,
min_lr: float = 3e-05,
lr_warmup_iters: int = 2000,
lr_decay_iters: Optional[int] = None,
precision_config: Optional[Union[megatron.bridge.training.mixed_precision.MixedPrecisionConfig, str]] = 'bf16_mixed',
comm_overlap_config: Optional[megatron.bridge.training.comm_overlap.CommOverlapConfig] = None,
) → megatron.bridge.training.config.ConfigContainer#

Create a pre-training configuration for Nemotron Nano 9B v2 model.

Parameters:

  • dir (Optional[str]) – Base directory for saving logs and checkpoints.

  • name (str) – Name of the pre-training run.

  • data_paths (Optional[List[str]]) – List of paths to dataset files. If None, mock data will be used.

  • data_args_path (Optional[str]) – Path to file containing data arguments.

  • train_data_path (Optional[List[str]]) – List of training data paths.

  • valid_data_path (Optional[List[str]]) – List of validation data paths.

  • test_data_path (Optional[List[str]]) – List of test data paths.

  • per_split_data_args_path (Optional[str]) – Path to JSON file with per-split data configuration.

  • mock (bool) – Whether to use mock data. If True, ignores data_paths.

  • tensor_parallelism (int) – Degree of tensor model parallelism.

  • pipeline_parallelism (int) – Degree of pipeline model parallelism.

  • pipeline_parallelism_dtype (Optional[torch.dtype]) – Data type for pipeline parallelism.

  • virtual_pipeline_parallelism (Optional[int]) – Size of virtual pipeline parallelism.

  • context_parallelism (int) – Degree of context parallelism to be passed to model_config.

  • sequence_parallelism (bool) – Whether to use sequence parallelism.

  • train_iters (int) – Total number of training iterations.

  • global_batch_size (int) – Global batch size for training.

  • micro_batch_size (int) – Micro batch size for training.

  • seq_length (int) – Sequence length for training data.

  • lr (float) – Learning rate.

  • min_lr (float) – Minimum learning rate for cosine decay.

  • lr_warmup_iters (int) – Number of warmup iterations for the learning rate.

  • lr_decay_iters (Optional[int]) – Number of iterations for learning rate decay.

  • precision_config (Optional[Union[MixedPrecisionConfig, str]]) – Precision configuration for the model.

  • comm_overlap_config (Optional[CommOverlapConfig]) – Communication overlap configuration for the model.

Returns:

Configuration for pre-training.

Return type:

ConfigContainer