Parameters

• checkpoint_dir (str) — The directory containing the FSDP checkpoints (can be either the model or optimizer).
• output_path (str) — The path to save the merged checkpoint.
• safe_serialization (bool, optional, defaults to True) — Whether to save the merged weights with safetensors (recommended).
• remove_checkpoint_dir (bool, optional, defaults to False) — Whether to remove the checkpoint directory after merging.

Parameters

• fsdp_version (int, defaults to 1) — The version of FSDP to use. Defaults to 1. If set to 2, launcher expects the config to be converted to FSDP2 format.
• sharding_strategy (Union[str, torch.distributed.fsdp.ShardingStrategy], defaults to 'FULL_SHARD') — Sharding strategy to use. Should be either a str or an instance of torch.distributed.fsdp.fully_sharded_data_parallel.ShardingStrategy. Is deprecated in favor of reshard_after_forward.
• reshard_after_forward (Union[str, torch.distributed.fsdp.ShardingStrategy, bool], defaults to 'FULL_SHARD' for fsdp_version=1 and True for fsdp_version=2) — Sharding strategy to use. Should be a bool if fsdp_version is set to 2 else a str or an instance of torch.distributed.fsdp.fully_sharded_data_parallel.ShardingStrategy.
• backward_prefetch (Union[str, torch.distributed.fsdp.BackwardPrefetch], defaults to 'NO_PREFETCH') — Backward prefetch strategy to use. Should be either a str or an instance of torch.distributed.fsdp.fully_sharded_data_parallel.BackwardPrefetch.
• mixed_precision_policy (Optional[Union[dict, torch.distributed.fsdp.MixedPrecision, torch.distributed.fsdp.MixedPrecisionPolicy]], defaults to None) — A config to enable mixed precision training with FullyShardedDataParallel. If passing in a dict, it should have the following keys: param_dtype, reduce_dtype, and buffer_dtype, can be an instance of torch.distributed.fsdp.MixedPrecisionPolicy if fsdp_version is set to 2.
• auto_wrap_policy (Optional(Union[Callable, Literal["transformer_based_wrap", "size_based_wrap", "no_wrap"]]), defaults to NO_WRAP) -- A callable or string specifying a policy to recursively wrap layers with FSDP. If a string, it must be one of transformer_based_wrap, size_based_wrap, or no_wrap. See torch.distributed.fsdp.wrap.size_based_wrap_policy` for a direction on what it should look like.
• cpu_offload (Union[bool, torch.distributed.fsdp.CPUOffload, torch.distributed.fsdp.CPUOffloadPolicy], defaults to False) — Whether to offload parameters to CPU. Should be either a bool or an instance of torch.distributed.fsdp.fully_sharded_data_parallel.CPUOffload or torch.distributed.fsdp.fully_sharded_data_parallel.CPUOffloadPolicy if fsdp_version is set to 2.
• ignored_modules (Optional[Iterable[torch.nn.Module]], defaults to None) — A list of modules to ignore when wrapping with FSDP.
• state_dict_type (Union[str, torch.distributed.fsdp.StateDictType], defaults to 'FULL_STATE_DICT') — State dict type to use. If a string, it must be one of full_state_dict, local_state_dict, or sharded_state_dict.
• state_dict_config (Optional[Union[torch.distributed.fsdp.FullStateDictConfig, torch.distributed.fsdp.ShardedStateDictConfig], defaults to None) — State dict config to use. Is determined based on the state_dict_type if not passed in.
• optim_state_dict_config (Optional[Union[torch.distributed.fsdp.FullOptimStateDictConfig, torch.distributed.fsdp.ShardedOptimStateDictConfig], defaults to None) — Optim state dict config to use. Is determined based on the state_dict_type if not passed in.
• limit_all_gathers (bool, defaults to True) — Whether to have FSDP explicitly synchronizes the CPU thread to prevent too many in-flight all-gathers. This bool only affects the sharded strategies that schedule all-gathers. Enabling this can help lower the number of CUDA malloc retries.
• use_orig_params (bool, defaults to False) — Whether to use the original parameters for the optimizer.
• param_init_fn (Optional[Callable[[torch.nn.Module], None], defaults to None) — A Callable[torch.nn.Module] -> None that specifies how modules that are currently on the meta device should be initialized onto an actual device. Only applicable when sync_module_states is True. By default is a lambda which calls to_empty on the module.
• sync_module_states (bool, defaults to False) — Whether each individually wrapped FSDP unit should broadcast module parameters from rank 0 to ensure they are the same across all ranks after initialization. Defaults to False unless cpu_ram_efficient_loading is True, then will be forcibly enabled.
• forward_prefetch (bool, defaults to False) — Whether to have FSDP explicitly prefetches the next upcoming all-gather while executing in the forward pass. only use with Static graphs.
• activation_checkpointing (bool, defaults to False) — A technique to reduce memory usage by clearing activations of certain layers and recomputing them during a backward pass. Effectively, this trades extra computation time for reduced memory usage.
• cpu_ram_efficient_loading (bool, defaults to None) — If True, only the first process loads the pretrained model checkoint while all other processes have empty weights. Only applicable for Transformers. When using this, sync_module_states needs to be True.
• transformer_cls_names_to_wrap (Optional[List[str]], defaults to None) — A list of transformer layer class names to wrap. Only applicable when auto_wrap_policy is transformer_based_wrap.
• min_num_params (Optional[int], defaults to None) — The minimum number of parameters a module must have to be wrapped. Only applicable when auto_wrap_policy is size_based_wrap.

Parameters

• accelerator (Accelerator) — The accelerator instance
• model (torch.nn.Module) — The model to load the state dict into
• full_sd (dict) — The full state dict to load, can only be on rank 0

Parameters

• optimizer (torch.optim.Optimizer) — Optimizer instance which contains the original model parameters
• mapping (dict) — Mapping from the original parameter (specified by data_ptr) to the sharded parameter

• KeyError — If a parameter in the optimizer couldn’t be switched to its sharded version. This should never happen and indicates a bug. If we kept the original params instead of raising, the training wouldn’t be numerically correct and weights wouldn’t get updated.

Parameters

• accelerator (Accelerator) — The accelerator instance
• model (torch.nn.Module) — The model to prepare

Prepared model