> This page is for version 0.5.0 · 26.06. > For other versions, use one of these documentation indexes: > - Nightly (default): https://docs.nvidia.com/nemo/automodel/nightly/llms.txt > - Latest: https://docs.nvidia.com/nemo/automodel/latest/llms.txt > - 0.5.0 · 26.06: https://docs.nvidia.com/nemo/automodel/v0.5/llms.txt > - 0.4.0 · 26.04: https://docs.nvidia.com/nemo/automodel/v0.4/llms.txt > For clean Markdown of any page, append .md to the page URL. > For a complete documentation index, see https://docs.nvidia.com/nemo/automodel/llms.txt. > For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemo/automodel/_mcp/server. # nemo_automodel.components.distributed.pipelining.config Pipeline parallel configuration class. Design principle: * Device mesh (world\_mesh, moe\_mesh) is passed separately to from\_pretrained/from\_config * PipelineConfig contains scheduling, splitting, and runtime options * loss\_fn is included here since it's only used for pipelining * Axis names are inferred automatically from device\_mesh in \_instantiate\_pipeline Usage: from nemo\_automodel.components.distributed.pipelining.config import PipelineConfig config = PipelineConfig( pp\_schedule="1f1b", pp\_microbatch\_size=2, pp\_batch\_size=8, loss\_fn=my\_loss\_fn, ) ## Module Contents ### Classes | Name | Description | | ------------------------------------------------------------------------------------------- | --------------------------------------------- | | [`PipelineConfig`](#nemo_automodel-components-distributed-pipelining-config-PipelineConfig) | Configuration for pipeline parallel training. | ### API ```python class nemo_automodel.components.distributed.pipelining.config.PipelineConfig( pp_schedule: str | None = '1f1b', pp_schedule_csv: str | None = None, pp_microbatch_size: int = 1, pp_batch_size: int = 1, layers_per_stage: int | None = None, round_virtual_stages_to_pp_multiple: typing.Literal['up', 'down'] | None = None, module_fqns_per_model_part: typing.List[typing.List[str]] | None = None, patch_inner_model: bool = True, patch_causal_lm_model: bool = True, patch_stage_backward_maybe_with_nosync: bool = False, dtype: torch.dtype | None = None, scale_grads_in_schedule: bool = False, loss_fn: typing.Callable | None = None, pp_seq_len: int | None = None, pp_recv_buffer_pool: bool = False, pp_recv_buffer_pool_slack: int = 2 ) ``` Dataclass Configuration for pipeline parallel training. Note: Device mesh (world\_mesh, moe\_mesh) is passed separately on the from\_pretrained/from\_config method signature. Pipeline parallelism is enabled when pp\_size > 1. Axis names are inferred automatically from the device mesh structure. **`dtype`** `dtype | None = None` Data type for pipeline computation. If None, uses the model's default dtype. --- **`layers_per_stage`** `int | None = None` Number of transformer layers per pipeline stage. If None, layers are split evenly across stages. --- **`loss_fn`** `Callable | None = None` Loss function used for pipeline training. Required when pipeline is enabled. The function signature should be compatible with the model's output format. --- **`module_fqns_per_model_part`** `List[List[str]] | None = None` Explicit specification of which module FQNs belong to each model part/stage. If provided, overrides automatic layer splitting. --- **`patch_causal_lm_model`** `bool = True` Apply pipeline patches to the CausalLM wrapper model. Defaults to True. --- **`patch_inner_model`** `bool = True` Apply pipeline patches to the inner model (e.g., the base transformer in a CausalLM wrapper). Defaults to True. --- **`patch_stage_backward_maybe_with_nosync`** `bool = False` Patch stage backward to use no\_sync context for gradient accumulation efficiency. Useful when combining PP with FSDP. --- **`pp_batch_size`** `int = 1` Total batch size per pipeline stage. Must be divisible by pp\_microbatch\_size. --- **`pp_microbatch_size`** `int = 1` Size of each microbatch for pipeline execution. pp\_batch\_size must be divisible by pp\_microbatch\_size. --- **`pp_recv_buffer_pool`** `bool = False` Pool the per-microbatch P2P recv buffers into a ring sized to the 1F1B in-flight depth instead of pre-allocating one buffer set per microbatch and direction. Cuts pipeline-stage buffer memory roughly by num\_microbatches / (num\_stages - stage\_index + slack) on early/middle stages. For native interleaved 1F1B, receives are pooled using the lowered schedule's actual lifetimes. Inputs are copied into independent leaves before forward, and gradients are copied before backward to protect checkpoint recomputation and escaping aliases. Other schedules are ignored with a warning. Defaults to False. --- **`pp_recv_buffer_pool_slack`** `int = 2` Extra buffer sets beyond the 1F1B in-flight depth when pp\_recv\_buffer\_pool is enabled. Defaults to 2. --- **`pp_schedule`** `str | None = '1f1b'` Pipeline schedule type. Supported values: "1f1b" (one-forward-one-backward), "gpipe", "interleaved\_1f1b", "looped\_bfs", "dfs", "v\_schedule", "zero\_bubble". Defaults to "1f1b". --- **`pp_schedule_csv`** `str | None = None` Path to a CSV file defining a custom pipeline schedule. If provided, overrides pp\_schedule. --- **`pp_seq_len`** `int | None = None` Sequence length hint for pipeline parallel shape inference. If None, it will be inferred from the dataset config. --- **`round_virtual_stages_to_pp_multiple`** `Literal['up', 'down'] | None = None` When using virtual stages (interleaved schedules), round the number of virtual stages to a multiple of pp\_size. "up" rounds up, "down" rounds down. If None, no rounding is applied. --- **`scale_grads_in_schedule`** `bool = False` Scale gradients within the pipeline schedule (by 1/n\_microbatches). If False, gradients must be scaled externally. Defaults to False. --- ```python nemo_automodel.components.distributed.pipelining.config.PipelineConfig.__post_init__() -> None ``` Validate the recv-buffer-pool knobs. ```python nemo_automodel.components.distributed.pipelining.config.PipelineConfig.to_dict() -> typing.Dict[str, typing.Any] ``` Convert config to dictionary.