`bridge.recipes.nemotronh.nemotron_nano_v2`#

Module Contents#

Classes#

NemotronNanoV2CommonKwargs

Typed options accepted by Nemotron Nano v2 recipe helper functions.

Functions#

`nemotron_nano_9b_v2_pretrain_config`	Return a pre-training config for Nemotron Nano 9B v2.
`nemotron_nano_12b_v2_pretrain_config`	Return a pre-training config for Nemotron Nano 12B v2.
`_nemotron_nano_v2_common`	Create a pre-training configuration for Nemotron Nano v2 models.

API#

class bridge.recipes.nemotronh.nemotron_nano_v2.NemotronNanoV2CommonKwargs#

Bases: typing_extensions.TypedDict

Typed options accepted by Nemotron Nano v2 recipe helper functions.

Initialization

Initialize self. See help(type(self)) for accurate signature.

model_provider: megatron.bridge.models.nemotronh.NemotronNano9Bv2Provider | megatron.bridge.models.nemotronh.NemotronNano12Bv2Provider#: None

tokenizer_model: str | None#: None

dir: str | None#: None

name: str#: None

data_paths: list[str] | None#: None

data_args_path: str | None#: None

train_data_path: list[str] | None#: None

valid_data_path: list[str] | None#: None

test_data_path: list[str] | None#: None

per_split_data_args_path: str | None#: None

mock: bool#: None

tensor_parallelism: int#: None

pipeline_parallelism: int#: None

pipeline_parallelism_dtype: torch.dtype | None#: None

virtual_pipeline_parallelism: int | None#: None

context_parallelism: int#: None

sequence_parallelism: bool#: None

train_iters: int#: None

global_batch_size: int#: None

micro_batch_size: int#: None

seq_length: int#: None

lr: float#: None

min_lr: float#: None

lr_warmup_iters: int#: None

lr_decay_iters: int | None#: None

use_null_tokenizer: bool#: None

precision_config: megatron.bridge.training.mixed_precision.MixedPrecisionConfig | str | None#: None

comm_overlap_config: megatron.bridge.training.comm_overlap.CommOverlapConfig | None#: None

enable_default_comm_overlap: bool#: None

bridge.recipes.nemotronh.nemotron_nano_v2.nemotron_nano_9b_v2_pretrain_config(

**user_kwargs: typing_extensions.Unpack[bridge.recipes.nemotronh.nemotron_nano_v2.NemotronNanoV2CommonKwargs],

) → megatron.bridge.training.config.ConfigContainer#

Return a pre-training config for Nemotron Nano 9B v2.

This recipe is designed for single-node training (1 node). Default parallelism: TP=2, PP=1, SP=True.

See _nemotron_nano_v2_common for the full list of parameters.

bridge.recipes.nemotronh.nemotron_nano_v2.nemotron_nano_12b_v2_pretrain_config(

**user_kwargs: typing_extensions.Unpack[bridge.recipes.nemotronh.nemotron_nano_v2.NemotronNanoV2CommonKwargs],

) → megatron.bridge.training.config.ConfigContainer#

Return a pre-training config for Nemotron Nano 12B v2.

This recipe is designed for single-node training (1 node). Default parallelism: TP=4, PP=1, SP=True.

Note: Uses FP8 precision by default. Communication overlap is disabled by default.

See _nemotron_nano_v2_common for the full list of parameters.

bridge.recipes.nemotronh.nemotron_nano_v2._nemotron_nano_v2_common( model_provider: type[megatron.bridge.models.nemotronh.NemotronNano9Bv2Provider] | type[megatron.bridge.models.nemotronh.NemotronNano12Bv2Provider], tokenizer_model: str | None = None, dir: str | None = None, name: str = 'default', data_paths: list[str] | None = None, data_args_path: str | None = None, train_data_path: list[str] | None = None, valid_data_path: list[str] | None = None, test_data_path: list[str] | None = None, per_split_data_args_path: str | None = None, mock: bool = False, tensor_parallelism: int = 2, pipeline_parallelism: int = 1, pipeline_parallelism_dtype: torch.dtype | None = torch.bfloat16, virtual_pipeline_parallelism: int | None = 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: int | None = None, use_null_tokenizer: bool = True, precision_config: megatron.bridge.training.mixed_precision.MixedPrecisionConfig | str | None = 'bf16_mixed', comm_overlap_config: megatron.bridge.training.comm_overlap.CommOverlapConfig | None = None, enable_default_comm_overlap: bool = True, ) → megatron.bridge.training.config.ConfigContainer#

Create a pre-training configuration for Nemotron Nano v2 models.

Parameters:

model_provider – The model provider class for the specific Nemotron Nano v2 variant.
tokenizer_model – HuggingFace tokenizer model name (only used when use_null_tokenizer=False).
dir – Base directory for saving logs and checkpoints.
name – Name of the pre-training run.
data_paths – List of paths to dataset files. If None, mock data will be used.
data_args_path – Path to file containing data arguments.
train_data_path – List of training data paths.
valid_data_path – List of validation data paths.
test_data_path – List of test data paths.
per_split_data_args_path – Path to JSON file with per-split data configuration.
mock – Whether to use mock data. If True, ignores data_paths.
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 to be passed to model_config.
sequence_parallelism – Whether to use sequence parallelism.
train_iters – Total number of training iterations.
global_batch_size – Global batch size for training.
micro_batch_size – Micro batch size for training.
seq_length – Sequence length for training data.
lr – Learning rate.
min_lr – Minimum learning rate for cosine decay.
lr_warmup_iters – Number of warmup iterations for the learning rate.
lr_decay_iters – Number of iterations for learning rate decay.
use_null_tokenizer – Whether to use NullTokenizer instead of HuggingFaceTokenizer.
precision_config – Precision configuration for the model.
comm_overlap_config – Communication overlap configuration for the model.
enable_default_comm_overlap – Whether to enable default comm overlap config if none is provided.

Returns:

Configuration for pre-training.

Return type:

ConfigContainer

bridge.recipes.nemotronh.nemotron_nano_v2#

Module Contents#

Classes#

Functions#

API#

`bridge.recipes.nemotronh.nemotron_nano_v2`#