Pipeline Parallelism


torch.distributed.pipelining is currently in alpha state and under development. API changes may be possible. It was migrated from the PiPPy project.

Why Pipeline Parallel?

Pipeline Parallelism is one of the primitive parallelism for deep learning. It allows the execution of a model to be partitioned such that multiple micro-batches can execute different parts of the model code concurrently. Pipeline parallelism can be an effective technique for:

  • large-scale training

  • bandwidth-limited clusters

  • large model inference

The above scenarios share a commonality that the computation per device cannot hide the communication of conventional parallelism, for example, the weight all-gather of FSDP.

What is torch.distributed.pipelining?

While promising for scaling, pipelining is often difficult to implement because it needs to partition the execution of a model in addition to model weights. The partitioning of execution often requires intrusive code changes to your model. Another aspect of complexity comes from scheduling micro-batches in a distributed environment, with data flow dependency considered.

The pipelining package provides a toolkit that does said things automatically which allows easy implementation of pipeline parallelism on general models.

It consists of two parts: a splitting frontend and a distributed runtime. The splitting frontend takes your model code as-is, splits it up into “model partitions”, and captures the data-flow relationship. The distributed runtime executes the pipeline stages on different devices in parallel, handling things like micro-batch splitting, scheduling, communication, and gradient propagation, etc.

Overall, the pipelining package provides the following features:

  • Splitting of model code based on simple specification.

  • Rich support for pipeline schedules, including GPipe, 1F1B, Interleaved 1F1B and Looped BFS, and providing the infrastruture for writing customized schedules.

  • First-class support for cross-host pipeline parallelism, as this is where PP is typically used (over slower interconnects).

  • Composability with other PyTorch parallel techniques such as data parallel (DDP, FSDP) or tensor parallel. The TorchTitan project demonstrates a “3D parallel” application on the Llama model.

Step 1: build PipelineStage

Before we can use a PipelineSchedule, we need to create PipelineStage objects that wrap the part of the model running in that stage. The PipelineStage is responsible for allocating communication buffers and creating send/recv ops to communicate with its peers. It manages intermediate buffers e.g. for the outputs of forward that have not been consumed yet, and it provides a utility for running the backwards for the stage model.

A PipelineStage needs to know the input and output shapes for the stage model, so that it can correctly allocate communication buffers. The shapes must be static, e.g. at runtime the shapes can not change from step to step. A class PipeliningShapeError will be raised if runtime shapes do not match the expected shapes. When composing with other paralleisms or applying mixed precision, these techniques must be taken into account so the PipelineStage knows the correct shape (and dtype) for the output of the stage module at runtime.

Users may construct a PipelineStage instance directly, by passing in an nn.Module representing the portion of the model that should run on the stage. This may require changes to the original model code. See the example in Option 1: splitting a model manually.

Alternatively, the splitting frontend can use graph partitioning to split your model into a series of nn.Module automatically. This technique requires the model is traceable with torch.Export. Composability of the resulting nn.Module with other parallelism techniques is experimental, and may require some workarounds. Usage of this frontend may be more appealing if the user cannot easily change the model code. See Option 2: splitting a model automatically for more information.

Step 2: use PipelineSchedule for execution

We can now attach the PipelineStage to a pipeline schedule, and run the schedule with input data. Here is a GPipe example:

from torch.distributed.pipelining import ScheduleGPipe

# Create a schedule
schedule = ScheduleGPipe(stage, n_microbatches)

# Input data (whole batch)
x = torch.randn(batch_size, in_dim, device=device)

# Run the pipeline with input `x`
# `x` will be divided into microbatches automatically
if rank == 0:
    output = schedule.step()

Note that the above code needs to be launched for each worker, thus we use a launcher service to launch multiple processes:

torchrun --nproc_per_node=2

Options for Splitting a Model

Option 1: splitting a model manually

To directly construct a PipelineStage, the user is responsible for providing a single nn.Module instance that owns the relevant nn.Parameters and nn.Buffers, and defines a forward() method that executes the operations relevant for that stage. For example, a condensed version of the Transformer class defined in Torchtitan shows a pattern of building an easily partitionable model.

class Transformer(nn.Module):
    def __init__(self, model_args: ModelArgs):

        self.tok_embeddings = nn.Embedding(...)

        # Using a ModuleDict lets us delete layers witout affecting names,
        # ensuring checkpoints will correctly save and load.
        self.layers = torch.nn.ModuleDict()
        for layer_id in range(model_args.n_layers):
            self.layers[str(layer_id)] = TransformerBlock(...)

        self.output = nn.Linear(...)

    def forward(self, tokens: torch.Tensor):
        # Handling layers being 'None' at runtime enables easy pipeline splitting
        h = self.tok_embeddings(tokens) if self.tok_embeddings else tokens

        for layer in self.layers.values():
            h = layer(h, self.freqs_cis)

        h = self.norm(h) if self.norm else h
        output = self.output(h).float() if self.output else h
        return output

A model defined in this manner can be easily configured per stage by first initializing the whole model (using meta-device to avoid OOM errors), deleting undesired layers for that stage, and then creating a PipelineStage that wraps the model. For example:

with torch.device("meta"):
    assert num_stages == 2, "This is a simple 2-stage example"

    # we construct the entire model, then delete the parts we do not need for this stage
    # in practice, this can be done using a helper function that automatically divides up layers across stages.
    model = Transformer()

    if stage_index == 0:
        # prepare the first stage model
        del model.layers["1"]
        model.norm = None
        model.output = None

    elif stage_index == 1:
        # prepare the second stage model
        model.tok_embeddings = None
        del model.layers["0"]

    from torch.distributed.pipelining import PipelineStage
    stage = PipelineStage(

The PipelineStage requires an example argument input_args representing the runtime input to the stage, which would be one microbatch worth of input data. This argument is passed through the forward method of the stage module to determine the input and output shapes required for communication.

When composing with other Data or Model parallelism techniques, output_args may also be required, if the output shape/dtype of the model chunk will be affected.

Option 2: splitting a model automatically

If you have a full model and do not want to spend time on modifying it into a sequence of “model partitions”, the pipeline API is here to help. Here is a brief example:

class Model(torch.nn.Module):
    def __init__(self) -> None:
        self.emb = torch.nn.Embedding(10, 3)
        self.layers = torch.nn.ModuleList(
            Layer() for _ in range(2)
        self.lm = LMHead()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.emb(x)
        for layer in self.layers:
            x = layer(x)
        x = self.lm(x)
        return x

If we print the model, we can see multiple hierarchies, which makes it hard to split by hand:

  (emb): Embedding(10, 3)
  (layers): ModuleList(
    (0-1): 2 x Layer(
      (lin): Linear(in_features=3, out_features=3, bias=True)
  (lm): LMHead(
    (proj): Linear(in_features=3, out_features=3, bias=True)

Let us see how the pipeline API works:

from torch.distributed.pipelining import pipeline, SplitPoint

# An example micro-batch input
x = torch.LongTensor([1, 2, 4, 5])

pipe = pipeline(
        "layers.1": SplitPoint.BEGINNING,

The pipeline API splits your model given a split_spec, where SplitPoint.BEGINNING stands for adding a split point before execution of certain submodule in the forward function, and similarly, SplitPoint.END for split point after such.

If we print(pipe), we can see:

  (submod_0): GraphModule(
    (emb): InterpreterModule()
    (layers): Module(
      (0): InterpreterModule(
        (lin): InterpreterModule()
  (submod_1): GraphModule(
    (layers): Module(
      (1): InterpreterModule(
        (lin): InterpreterModule()
    (lm): InterpreterModule(
      (proj): InterpreterModule()

def forward(self, x):
    submod_0 = self.submod_0(x);  x = None
    submod_1 = self.submod_1(submod_0);  submod_0 = None
    return (submod_1,)

The “model partitions” are represented by submodules (submod_0, submod_1), each of which is reconstructed with original model operations, weights and hierarchies. In addition, a “root-level” forward function is reconstructed to capture the data flow between those partitions. Such data flow will be replayed by the pipeline runtime later, in a distributed fashion.

The Pipe object provides a method for retrieving the “model partitions”:

stage_mod : nn.Module = pipe.get_stage_module(stage_idx)

The returned stage_mod is a nn.Module, with which you can create an optimizer, save or load checkpoints, or apply other parallelisms.

Pipe also allows you to create a distributed stage runtime on a device given a ProcessGroup:

stage = pipe.build_stage(stage_idx, device, group)

Alternatively, if you would like to build the stage runtime later after some modification to the stage_mod, you can use a functional version of the build_stage API. For example:

from torch.distributed.pipelining import build_stage
from torch.nn.parallel import DistributedDataParallel

dp_mod = DistributedDataParallel(stage_mod)
info =
stage = build_stage(dp_mod, stage_idx, info, device, group)


The pipeline frontend uses a tracer (torch.export) to capture your model into a single graph. If your model is not full-graph’able, you can use our manual frontend below.

Hugging Face Examples

In the PiPPy repo where this package was original created, we kept examples based on unmodified Hugging Face models. See the examples/huggingface directory.

Examples include:

Technical Deep Dive

How does the pipeline API split a model?

First, the pipeline API turns our model into a directed acyclic graph (DAG) by tracing the model. It traces the model using torch.export – a PyTorch 2 full-graph capturing tool.

Then, it groups together the operations and parameters needed by a stage into a reconstructed submodule: submod_0, submod_1, …

Different from conventional submodule access methods like Module.children(), the pipeline API does not only cut the module structure of your model, but also the forward function of your model.

This is necessary because model structure like Module.children() merely captures information during Module.__init__(), and does not capture any information about Module.forward(). Said differently, Module.children() lacks information about the following aspects key to pipelininig:

  • Execution order of child modules in forward

  • Activation flows between child modules

  • Whether there are any functional operators between child modules (for example, relu or add operations will not be captured by Module.children()).

The pipeline API, on the contrary, makes sure that the forward behavior is truly preserved. It also captures the activation flow between the partitions, helping the distributed runtime to make correct send/receive calls without human intervention.

Another flexibility of the pipeline API is that split points can be at arbitrary levels within your model hierarchy. In the split partitions, the original model hierarchy related to that partition will be reconstructed at no cost to you. At a result, fully-qualified names (FQNs) pointing to a submodule or parameter would be still valid, and services that relies on FQNs (such as FSDP, TP or checkpointing) can still run with your partitioned modules with almost zero code change.

Implementing Your Own Schedule

You can implement your own pipeline schedule by extending one of the following two class:

  • PipelineScheduleSingle

  • PipelineScheduleMulti

PipelineScheduleSingle is for schedules that assigns only one stage per rank. PipelineScheduleMulti is for schedules that assigns multiple stages per rank.

For example, ScheduleGPipe and Schedule1F1B are subclasses of PipelineScheduleSingle. Whereas, ScheduleFlexibleInterleaved1F1B, ScheduleInterleaved1F1B and ScheduleLoopedBFS are subclasses of PipelineScheduleMulti.


You can turn on additional logging using the TORCH_LOGS environment variable from [torch._logging](

  • TORCH_LOGS=+pp will display logging.DEBUG messages and all levels above it.

  • TORCH_LOGS=pp will display logging.INFO messages and above.

  • TORCH_LOGS=-pp will display logging.WARNING messages and above.

API Reference

Model Split APIs

The following set of APIs transform your model into a pipeline representation.

class torch.distributed.pipelining.SplitPoint(value)[source]

An enumeration.

torch.distributed.pipelining.pipeline(module, mb_args, mb_kwargs=None, split_spec=None, split_policy=None)[source]

Split a module based on a specification.

See Pipe for more details.

Return type

A pipeline representation of class Pipe.

class torch.distributed.pipelining.Pipe(split_gm, num_stages, has_loss_and_backward, loss_spec)[source]

pipe_split is a special operator that is used to mark the boundary between stages in a module. It is used to split the module into stages. It is a no-op if your annotated module is run eagerly.


>>> def forward(self, x):
>>>     x =, self.mm_param)
>>>     x = torch.relu(x)
>>>     pipe_split()
>>>     x = self.lin(x)
>>>     return x

The above example will be split into two stages.

Microbatch Utilities

class torch.distributed.pipelining.microbatch.TensorChunkSpec(split_dim)[source]

Class used to specify chunking of inputs

torch.distributed.pipelining.microbatch.split_args_kwargs_into_chunks(args, kwargs, chunks, args_chunk_spec=None, kwargs_chunk_spec=None)[source]

Given a sequence of args and kwargs, split them into a number of chunks according to their respective chunking specs.


List of sharded args kwargs_split: List of sharded kwargs

Return type


torch.distributed.pipelining.microbatch.merge_chunks(chunks, chunk_spec)[source]

Given a list of chunks, merge them into a single value according to the chunk spec.

  • chunks (List[Any]) – list of chunks

  • chunk_spec – Chunking spec for the chunks


Merged value

Return type


Pipeline Stages

class torch.distributed.pipelining.stage.PipelineStage(submodule, stage_index, num_stages, device, input_args, output_args=None, group=None, dw_builder=None)[source]

A class representing a pipeline stage in a pipeline parallelism setup. This class is created manually by providing a example input (and optionally output) as opposed to the PipelineStage class that is outputed from pipeline(). This class extends the _PipelineStageBase class and can similarly be used in PipelineScheule.

  • submodule (nn.Module) – The PyTorch module wrapped by this stage.

  • stage_index (int) – The ID of this stage.

  • num_stages (int) – The total number of stages.

  • device (torch.device) – The device where this stage is located.

  • input_args (Union[torch.Tensor, Tuple[torch.tensor]], optional) – The input arguments for the submodule.

  • output_args (Union[torch.Tensor, Tuple[torch.tensor]], optional) – The output arguments for the submodule.

  • group (dist.ProcessGroup, optional) – The process group for distributed training. If None, default group.

  • dw_builder (Optional[Callable[[], Callable[[], None]]]) – TODO clean up comments

torch.distributed.pipelining.stage.build_stage(stage_module, stage_index, pipe_info, device, group=None)[source]

Create a pipeline stage given a stage_module to be wrapped by this stage and pipeline information.

  • stage_module (torch.nn.Module) – the module to be wrapped by this stage

  • stage_index (int) – the index of this stage in the pipeline

  • pipe_info (PipeInfo) – information about the pipeline, can be retrieved by

  • device (torch.device) – the device to be used by this stage

  • group (Optional[dist.ProcessGroup]) – the process group to be used by this stage


a pipeline stage that can run with PipelineSchedules.

Return type


Pipeline Schedules

class torch.distributed.pipelining.schedules.ScheduleGPipe(stage, n_microbatches, loss_fn=None, args_chunk_spec=None, kwargs_chunk_spec=None, output_merge_spec=None)[source]

The GPipe schedule. Will go through all the microbatches in a fill-drain manner.

class torch.distributed.pipelining.schedules.Schedule1F1B(stage, n_microbatches, loss_fn=None, args_chunk_spec=None, kwargs_chunk_spec=None, output_merge_spec=None)[source]

The 1F1B schedule. Will perform one forward and one backward on the microbatches in steady state.

class torch.distributed.pipelining.schedules.ScheduleFlexibleInterleaved1F1B(stages, n_microbatches, loss_fn=None, args_chunk_spec=None, kwargs_chunk_spec=None, output_merge_spec=None, zero_bubble_algorithm=None)[source]

The Flexible Interleaved 1F1B schedule.

This schedule is mostly similar to the interleaved 1F1B schedule. It differs by being relaxing the requirement of num_microbatch % pp_size == 0. Using the flex_pp schedule, we will have num_rounds = max(1, n_microbatches // pp_group_size) and it works as long as n_microbatches % num_rounds is 0. As a few examples, support

  1. pp_group_size = 4, n_microbatches = 10. We will have num_rounds = 2 and n_microbatches % 2 is 0.

  2. pp_group_size = 4, n_microbatches = 3. We will have num_rounds = 1 and n_microbatches % 1 is 0.

When zero_bubble_algorithm is passed in, we will use the corresponding schedule in

class torch.distributed.pipelining.schedules.ScheduleInterleaved1F1B(stages, n_microbatches, loss_fn=None, args_chunk_spec=None, kwargs_chunk_spec=None, output_merge_spec=None)[source]

The Interleaved 1F1B schedule. See for details. Will perform one forward and one backward on the microbatches in steady state and supports multiple stages per rank. When microbatches are ready for multiple local stages, Interleaved 1F1B prioritizes the earlier microbatch (also called “depth first”).

class torch.distributed.pipelining.schedules.ScheduleLoopedBFS(stages, n_microbatches, loss_fn=None, output_merge_spec=None)[source]

Breadth-First Pipeline Parallelism. See for details. Simliar to Interleaved 1F1B, Looped BFS supports multiple stages per rank. What is different is that when microbatches are ready for multiple local stages, Loops BFS will prioritizes the earlier stage, running all available microbatches at once.

class torch.distributed.pipelining.schedules.ZeroBubbleAlgorithm(value)[source]

An enumeration.

class torch.distributed.pipelining.schedules.PipelineScheduleSingle(stage, n_microbatches, loss_fn=None, args_chunk_spec=None, kwargs_chunk_spec=None, output_merge_spec=None)[source]

Base class for single-stage schedules. Implements the step method. Derived classes should implement _step_microbatches.

step(*args, target=None, losses=None, **kwargs)[source]

Run one iteration of the pipeline schedule with whole-batch input. Will chunk the input into microbatches automatically, and go through the microbatches according to the schedule implementation.

args: positional arguments to the model (as in non-pipeline case). kwargs: keyword arguments to the model (as in non-pipeline case). target: target for the loss function. losses: a list to store the losses for each microbatch.

class torch.distributed.pipelining.schedules.PipelineScheduleMulti(stages, n_microbatches, loss_fn=None, args_chunk_spec=None, kwargs_chunk_spec=None, output_merge_spec=None, stage_index_to_group_rank=None, use_full_backward=True)[source]

Base class for multi-stage schedules. Implements the step method.

step(*args, target=None, losses=None, **kwargs)[source]

Run one iteration of the pipeline schedule with whole-batch input. Will chunk the input into microbatches automatically, and go through the microbatches according to the schedule implementation.

args: positional arguments to the model (as in non-pipeline case). kwargs: keyword arguments to the model (as in non-pipeline case). target: target for the loss function. losses: a list to store the losses for each microbatch.


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