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Megatron-LM utilities

MegatronLMPlugin

class accelerate.utils.MegatronLMPlugin

< source >

( tp_degree: int = None pp_degree: int = None num_micro_batches: int = None gradient_clipping: float = None sequence_parallelism: bool = None recompute_activations: bool = None use_distributed_optimizer: bool = None pipeline_model_parallel_split_rank: int = None num_layers_per_virtual_pipeline_stage: int = None is_train_batch_min: str = True train_iters: int = None train_samples: int = None weight_decay_incr_style: str = 'constant' start_weight_decay: float = None end_weight_decay: float = None lr_decay_style: str = 'linear' lr_decay_iters: int = None lr_decay_samples: int = None lr_warmup_iters: int = None lr_warmup_samples: int = None lr_warmup_fraction: float = None min_lr: float = 0 consumed_samples: list = None no_wd_decay_cond: typing.Optional[typing.Callable] = None scale_lr_cond: typing.Optional[typing.Callable] = None lr_mult: float = 1.0 megatron_dataset_flag: bool = False seq_length: int = None encoder_seq_length: int = None decoder_seq_length: int = None tensorboard_dir: str = None set_all_logging_options: bool = False eval_iters: int = 100 eval_interval: int = 1000 return_logits: bool = False custom_train_step_class: typing.Optional[typing.Any] = None custom_train_step_kwargs: typing.Optional[dict[str, typing.Any]] = None custom_model_provider_function: typing.Optional[typing.Callable] = None custom_prepare_model_function: typing.Optional[typing.Callable] = None custom_megatron_datasets_provider_function: typing.Optional[typing.Callable] = None custom_get_batch_function: typing.Optional[typing.Callable] = None custom_loss_function: typing.Optional[typing.Callable] = None other_megatron_args: typing.Optional[dict[str, typing.Any]] = None )

Parameters

tp_degree (int, defaults to None) — Tensor parallelism degree.
pp_degree (int, defaults to None) — Pipeline parallelism degree.
num_micro_batches (int, defaults to None) — Number of micro-batches.
gradient_clipping (float, defaults to None) — Gradient clipping value based on global L2 Norm (0 to disable).
sequence_parallelism (bool, defaults to None) — Enable sequence parallelism.
recompute_activations (bool, defaults to None) — Enable selective activation recomputation.
use_distributed_optimizr (bool, defaults to None) — Enable distributed optimizer.
pipeline_model_parallel_split_rank (int, defaults to None) — Rank where encoder and decoder should be split.
num_layers_per_virtual_pipeline_stage (int, defaults to None) — Number of layers per virtual pipeline stage.
is_train_batch_min (str, defaults to True) — If both tran & eval dataloaders are specified, this will decide the micro_batch_size.
train_iters (int, defaults to None) — Total number of samples to train over all training runs. Note that either train-iters or train-samples should be provided when using MegatronLMDummyScheduler.
train_samples (int, defaults to None) — Total number of samples to train over all training runs. Note that either train-iters or train-samples should be provided when using MegatronLMDummyScheduler.
weight_decay_incr_style (str, defaults to 'constant') — Weight decay increment function. choices=[“constant”, “linear”, “cosine”].
start_weight_decay (float, defaults to None) — Initial weight decay coefficient for L2 regularization.
end_weight_decay (float, defaults to None) — End of run weight decay coefficient for L2 regularization.
lr_decay_style (str, defaults to 'linear') — Learning rate decay function. choices=[‘constant’, ‘linear’, ‘cosine’].
lr_decay_iters (int, defaults to None) — Number of iterations for learning rate decay. If None defaults to train_iters.
lr_decay_samples (int, defaults to None) — Number of samples for learning rate decay. If None defaults to train_samples.
lr_warmup_iters (int, defaults to None) — Number of iterations to linearly warmup learning rate over.
lr_warmup_samples (int, defaults to None) — Number of samples to linearly warmup learning rate over.
lr_warmup_fraction (float, defaults to None) — Fraction of lr-warmup-(iters/samples) to linearly warmup learning rate over.
min_lr (float, defaults to 0) — Minimum value for learning rate. The scheduler clip values below this threshold.
consumed_samples (List, defaults to None) — Number of samples consumed in the same order as the dataloaders to accelerator.prepare call.
no_wd_decay_cond (Optional, defaults to None) — Condition to disable weight decay.
scale_lr_cond (Optional, defaults to None) — Condition to scale learning rate.
lr_mult (float, defaults to 1.0) — Learning rate multiplier.
megatron_dataset_flag (bool, defaults to False) — Whether the format of dataset follows Megatron-LM Indexed/Cached/MemoryMapped format.
seq_length (int, defaults to None) — Maximum sequence length to process.
encoder_seq_length (int, defaults to None) — Maximum sequence length to process for the encoder.
decoder_seq_length (int, defaults to None) — Maximum sequence length to process for the decoder.
tensorboard_dir (str, defaults to None) — Path to save tensorboard logs.
set_all_logging_options (bool, defaults to False) — Whether to set all logging options.
eval_iters (int, defaults to 100) — Number of iterations to run for evaluation validation/test for.
eval_interval (int, defaults to 1000) — Interval between running evaluation on validation set.
return_logits (bool, defaults to False) — Whether to return logits from the model.
custom_train_step_class (Optional, defaults to None) — Custom train step class.
custom_train_step_kwargs (Optional, defaults to None) — Custom train step kwargs.
custom_model_provider_function (Optional, defaults to None) — Custom model provider function.
custom_prepare_model_function (Optional, defaults to None) — Custom prepare model function.
custom_megatron_datasets_provider_function (Optional, defaults to None) — Custom megatron train_valid_test datasets provider function.
custom_get_batch_function (Optional, defaults to None) — Custom get batch function.
custom_loss_function (Optional, defaults to None) — Custom loss function.
other_megatron_args (Optional, defaults to None) — Other Megatron-LM arguments. Please refer Megatron-LM.

Plugin for Megatron-LM to enable tensor, pipeline, sequence and data parallelism. Also to enable selective activation recomputation and optimized fused kernels.

MegatronLMDummyScheduler

class accelerate.utils.MegatronLMDummyScheduler

< source >

( optimizer total_num_steps = None warmup_num_steps = 0 **kwargs )

Parameters

optimizer (torch.optim.optimizer.Optimizer) — The optimizer to wrap.
total_num_steps (int) — Total number of steps.
warmup_num_steps (int) — Number of steps for warmup.
**kwargs (additional keyword arguments, optional) — Other arguments.

Dummy scheduler presents model parameters or param groups, this is primarily used to follow conventional training loop when scheduler config is specified in the deepspeed config file.