`bridge.recipes.llama.llama2`#

Module Contents#

Classes#

Llama2CommonKwargs

Typed options accepted by Llama2 recipe helper functions.

Functions#

`llama2_7b_pretrain_config`	Return a pre-training config for Llama-2 7B.
`_llama2_common`	Create a pre-training configuration for Llama2 models using a given HuggingFace path.

API#

class bridge.recipes.llama.llama2.Llama2CommonKwargs#

Bases: typing_extensions.TypedDict

Typed options accepted by Llama2 recipe helper functions.

Initialization

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

hf_path: str#: None

dir: Optional[str]#: None

name: str#: None

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#: None

tensor_parallelism: int#: None

pipeline_parallelism: int#: None

pipeline_parallelism_dtype: Optional[torch.dtype]#: None

virtual_pipeline_parallelism: Optional[int]#: None

context_parallelism: int#: None

sequence_parallelism: bool#: None

use_megatron_fsdp: 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: Optional[int]#: None

eval_interval: int#: None

save_interval: int#: None

use_null_tokenizer: bool#: None

precision_config: Optional[Union[megatron.bridge.training.mixed_precision.MixedPrecisionConfig, str]]#: None

comm_overlap_config: Optional[megatron.bridge.training.comm_overlap.CommOverlapConfig]#: None

bridge.recipes.llama.llama2.llama2_7b_pretrain_config(

**user_kwargs: typing_extensions.Unpack[bridge.recipes.llama.llama2.Llama2CommonKwargs],

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

Return a pre-training config for Llama-2 7B.

See _llama2_common for the full list of parameters.

bridge.recipes.llama.llama2._llama2_common( hf_path: str, 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] = None, virtual_pipeline_parallelism: Optional[int] = None, context_parallelism: int = 1, sequence_parallelism: bool = False, use_megatron_fsdp: bool = False, train_iters: int = 1168251, global_batch_size: int = 512, micro_batch_size: int = 1, seq_length: int = 4096, lr: float = 0.0003, min_lr: float = 3e-05, lr_warmup_iters: int = 2000, lr_decay_iters: Optional[int] = None, eval_interval: int = 2000, save_interval: int = 2000, use_null_tokenizer: bool = True, 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 Llama2 models using a given HuggingFace path.

Parameters:

hf_path (str) – HuggingFace model path (e.g., “meta-llama/Llama-2-7b-hf”).
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.
use_megatron_fsdp (bool) – Whether to use Megatron FSDP.
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 over which to decay the LR.
eval_interval (int) – Evaluation interval.
save_interval (int) – Save interval.
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

bridge.recipes.llama.llama2#

Module Contents#

Classes#

Functions#

API#

`bridge.recipes.llama.llama2`#