torchtune.training

Checkpointing

torchtune offers checkpointers to allow seamless transitioning between checkpoint formats for training and interoperability with the rest of the ecosystem. For a comprehensive overview of checkpointing, please see the checkpointing deep-dive.

FullModelHFCheckpointer	Checkpointer which reads and writes checkpoints in HF's format.
FullModelMetaCheckpointer	Checkpointer which reads and writes checkpoints in Meta's format.
FullModelTorchTuneCheckpointer	Checkpointer which reads and writes checkpoints in a format compatible with torchtune.
ModelType	ModelType is used by the checkpointer to distinguish between different model architectures.
update_state_dict_for_classifier	Validates the state dict for checkpoint loading for a classifier model.

Reduced Precision

Utilities for working in a reduced precision setting.

get_dtype	Get the torch.dtype corresponding to the given precision string.
set_default_dtype	Context manager to set torch's default dtype.
validate_expected_param_dtype	Validates that all input parameters have the expected dtype.
get_quantizer_mode	Given a quantizer object, returns a string that specifies the type of quantization.

Distributed

Utilities for enabling and working with distributed training.

FSDPPolicyType	A datatype for a function that can be used as an FSDP wrapping policy.
init_distributed	Initialize process group required for torch.distributed.
is_distributed	Check if all environment variables required to initialize torch.distributed are set and distributed is properly installed.
get_world_size_and_rank	Function that gets the current world size (aka total number of ranks) and rank number of the current process in the default process group.
get_full_finetune_fsdp_wrap_policy	Retrieves an FSDP wrapping policy based on the specified flags memory_efficient_fsdp_wrap and modules_to_wrap.
lora_fsdp_wrap_policy	A default policy for wrapping models trained with LoRA using FSDP.

Memory Management

Utilities to reduce memory consumption during training.

apply_selective_activation_checkpointing	Utility to setup activation checkpointing and wrap the model for checkpointing.
set_activation_checkpointing	Utility to apply activation checkpointing to the passed-in model.
OptimizerInBackwardWrapper	A bare-bones class meant for checkpoint save and load for optimizers running in backward.
create_optim_in_bwd_wrapper	Create a wrapper for optimizer step running in backward.
register_optim_in_bwd_hooks	Register hooks for optimizer step running in backward.

Metric Logging

Various logging utilities.

metric_logging.CometLogger	Logger for use w/ Comet (https://www.comet.com/site/).
metric_logging.WandBLogger	Logger for use w/ Weights and Biases application (https://wandb.ai/).
metric_logging.TensorBoardLogger	Logger for use w/ PyTorch's implementation of TensorBoard (https://pytorch.org/docs/stable/tensorboard.html).
metric_logging.StdoutLogger	Logger to standard output.
metric_logging.DiskLogger	Logger to disk.

Performance and Profiling

torchtune provides utilities to profile and debug the memory and performance of your finetuning job.

get_memory_stats	Computes a memory summary for the passed in device.
log_memory_stats	Logs a dict containing memory stats to the logger.
setup_torch_profiler	Sets up profile and returns the profiler config with post-setup updates.

Miscellaneous

get_unmasked_sequence_lengths	Returns the sequence lengths for each batch element, excluding masked tokens.
set_seed	Function that sets seed for pseudo-random number generators across commonly used libraries.