Why PiPPy? | Install guide | PiPPy Quickstart | Future Work | References | License | Citing PiPPy
One of the most important techniques for advancing the state of the art in deep learning is scaling. Common techniques for scaling neural networks include data parallelism, tensor/model parallelism, and pipeline parallelism. In many cases, pipeline parallelism in particular can be an effective technique for scaling, however it is often difficult to implement, requiring intrusive code changes to model code and difficult-to-implement runtime orchestration code. PiPPy aims to provide a toolkit that does said things automatically to allow high-productivity scaling of models.
The PiPPy project consists of a compiler and runtime stack for automated parallelism and scaling of PyTorch models. Currently, PiPPy focuses on pipeline parallelism, a technique in which the code of the model is partitioned and multiple micro-batches execute different parts of the model code concurrently. To learn more about pipeline parallelism, see this article.
PiPPy provides the following features that make pipeline parallelism easier:
- Automatic splitting of model code via
torch.fx. The goal is for the user to provide model code as-is to the system for parallelization, without having to make heavyweight modifications to make parallelism work. - Related to the last point, PiPPy supports non-trivial topologies, including skip connections and tied weights/layers. PiPPy provides configurable behavior for tied weights, allowing for transmission across pipeline stages or replication and gradient synchronization.
- First-class support for cross-host pipeline parallelism, as this is where PP is typically used (over slower interconnects). This is currently missing from the torchgpipe-based
torch.distributed.pipeline.sync.Pipe. - Composability with other parallelism schemes such as data parallelism or tensor splitting model parallelism (overall, known as "3d parallelism"). Currently, pipelining and data parallelism can be composed. Other compositions will be available in the future.
- Support for pipeline scheduling paradigms, including static schedules like fill-drain (GPipe), 1f1b, interleaved 1f1b and dynamic schedules like lookahead or registers/back-pressure.
For in-depth technical architecture, see ARCHITECTURE.md.
PiPPy requires PyTorch version newer than 1.12 to work. To quickly install, for example, PyTorch nightly, run the following command from the same directory as this README:
pip install -r requirements.txt --find-links https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
You can also select the CUDA build of PyTorch if your system has NVIDIA GPUs, for example:
pip install -r requirements.txt --find-links https://download.pytorch.org/whl/nightly/cu116/torch_nightly.html
To install PiPPy from source, run the following command in the same directory as this README:
python setup.py install
To expose PiPPy for development such that changes to this repo are reflected in the imported package, run:
python setup.py develop
PiPPy consists of two parts: a compiler and a runtime. The compiler takes your model code, splits it up, and transforms it into a Pipe, which is a wrapper that describes how to execute the model in pipeline parallelism. The runtime executes the Pipe in parallel, handling things like micro-batch splitting and gradient propagation/syncing. We will cover the APIs for these concepts in this section.
To see how we can split a model into a pipeline, let's first take an example trivial neural network:
import torch
class MyNetworkBlock(torch.nn.Module):
def __init__(self, in_dim, out_dim):
super().__init__()
self.lin = torch.nn.Linear(in_dim, out_dim)
def forward(self, x):
x = self.lin(x)
x = torch.relu(x)
return x
class MyNetwork(torch.nn.Module):
def __init__(self, in_dim, layer_dims):
super().__init__()
prev_dim = in_dim
for i, dim in enumerate(layer_dims):
setattr(self, f'layer{i}', MyNetworkBlock(prev_dim, dim))
prev_dim = dim
self.num_layers = len(layer_dims)
# 10 output classes
self.output_proj = torch.nn.Linear(layer_dims[-1], 10)
def forward(self, x):
for i in range(self.num_layers):
x = getattr(self, f'layer{i}')(x)
return self.output_proj(x)
mn = MyNetwork(512, [512, 1024, 256])This network is written as free-form Python code; it has not been modified for any specific parallelism technique.
Let us see our first usage of the pippy.IR.Pipe interface:
from pippy.IR import annotate_split_points, PipeSplitWrapper
annotate_split_points(mn, {'layer0': PipeSplitWrapper.SplitPoint.END,
'layer1': PipeSplitWrapper.SplitPoint.END})
pipe = Pipe.from_tracing(mn)
print(pipe)
"""
************************************* pipe *************************************
GraphModule(
(submod_0): GraphModule(
(layer0_mod_lin): Linear(in_features=512, out_features=512, bias=True)
)
(submod_1): GraphModule(
(layer1_mod_lin): Linear(in_features=512, out_features=1024, bias=True)
)
(submod_2): GraphModule(
(layer2_lin): Linear(in_features=1024, out_features=256, bias=True)
(output_proj): Linear(in_features=256, out_features=10, bias=True)
)
)
def forward(self, x):
submod_0 = self.submod_0(x); x = None
submod_1 = self.submod_1(submod_0); submod_0 = None
submod_2 = self.submod_2(submod_1); submod_1 = None
return submod_2
"""
print(pipe.split_gm.submod_0)
"""
*********************************** submod0 ************************************
GraphModule(
(layer0_mod_lin): Linear(in_features=512, out_features=512, bias=True)
)
def forward(self, x):
layer0_mod_lin = self.layer0_mod_lin(x); x = None
relu = torch.relu(layer0_mod_lin); layer0_mod_lin = None
return relu
"""
print(pipe.split_gm.submod_1)
"""
*********************************** submod1 ************************************
GraphModule(
(layer1_mod_lin): Linear(in_features=512, out_features=1024, bias=True)
)
def forward(self, relu):
layer1_mod_lin = self.layer1_mod_lin(relu); relu = None
relu_1 = torch.relu(layer1_mod_lin); layer1_mod_lin = None
return relu_1
"""
print(pipe.split_gm.submod_2)
"""
*********************************** submod2 ************************************
GraphModule(
(layer2_lin): Linear(in_features=1024, out_features=256, bias=True)
(output_proj): Linear(in_features=256, out_features=10, bias=True)
)
def forward(self, relu_1):
layer2_lin = self.layer2_lin(relu_1); relu_1 = None
relu = torch.relu(layer2_lin); layer2_lin = None
output_proj = self.output_proj(relu); relu = None
return output_proj
"""So what's going on here? First, Pipe.from_tracing uses torch.fx symbolic tracing to turn our model into a directed acyclic graph (DAG) representation. Then, it groups together the operations and parameters into pipeline stages. Stages are represented as submod_N submodules, where N is a natural number.
Our code has now been split into three pipeline stages. We used annotate_split_points to specify that the code should be split and the end of layer0 and layer1.
In addition to custom splitting policy, PiPPy also provides automatic splitting policies. For example:
split_on_size_threshold(numel): create a new pipeline stage upon reaching a given number of parameters;split_into_equal_size(num_stages): split the model in to specified number of equal-size stages.
We can pass the splitting policy to the from_tracing API:
from pippy import split_into_equal_size
split_policy = split_into_equal_size(world_size)
pipe = Pipe.from_tracing(mn, split_policy=split_policy)This covers the basic usage of the Pipe API. For more information, see the documentation.
Given the above Pipe object, we can use one of the PipelineDriver classes to execute our model in a pipelined fashion. First off, let us instantiate a PipelineDriverFillDrain instance:
# To run a distributed training job, we must launch the script in multiple
# different processes. We are using `torchrun` to do so in this example.
# `torchrun` defines two environment variables: `LOCAL_RANK` and `WORLD_SIZE`,
# which represent the index of this process within the set of processes and
# the total number of processes, respectively.
#
# To learn more about `torchrun`, see
# https://pytorch.org/docs/stable/elastic/run.html
import os
local_rank = int(os.environ["LOCAL_RANK"])
world_size = int(os.environ['WORLD_SIZE'])
# PiPPy uses the PyTorch RPC interface. To use RPC, we must call `init_rpc`
# and inform the RPC framework of this process's rank and the total world
# size. We can directly pass values `torchrun` provided.`
#
# To learn more about the PyTorch RPC framework, see
# https://pytorch.org/docs/stable/rpc.html
import torch.distributed.rpc as rpc
rpc.init_rpc(f'worker{local_rank}', rank=local_rank, world_size=world_size)
# PiPPy relies on the concept of a "driver" process. The driver process
# should be a single process within the RPC group that instantiates the
# PipelineDriver and issues commands on that object. The other processes
# in the RPC group will receive commands from this process and execute
# the pipeline stages
if local_rank == 0:
# We are going to use the PipelineDriverFillDrain class. This class
# provides an interface for executing the `Pipe` in a style similar
# to the GPipe fill-drain schedule. To learn more about GPipe and
# the fill-drain schedule, see https://arxiv.org/abs/1811.06965
from pippy.PipelineDriver import PipelineDriverFillDrain
from pippy.microbatch import TensorChunkSpec
# Pipelining relies on _micro-batching_--that is--the process of
# dividing the program's input data into smaller chunks and
# feeding those chunks through the pipeline sequentially. Doing
# this requires that the data and operations be _separable_, i.e.
# there should be at least one dimension along which data can be
# split such that the program does not have interactions across
# this dimension. PiPPy provides `chunk_spec` arguments for this
# purpose, to specify the batch dimension for tensors in each of
# the args, kwargs, and outputs. The structure of the `chunk_spec`s
# should mirror that of the data type. Here, the program has a
# single tensor input and single tensor output, so we specify
# a single `TensorChunkSpec` instance indicating dimension 0
# for args[0] and the output value.
args_chunk_spec = (TensorChunkSpec(0),)
kwargs_chunk_spec = {}
output_chunk_spec = TensorChunkSpec(0)
# Finally, we instantiate the PipelineDriver. We pass in the pipe,
# chunk specs, and world size, and the constructor will distribute
# our code to the processes in the RPC group. `driver` is an object
# we can invoke to run the pipeline.
driver = PipelineDriverFillDrain(
pipe, args_chunk_spec=args_chunk_spec, kwargs_chunk_spec=kwargs_chunk_spec,
output_chunk_spec=output_chunk_spec, world_size=world_size)
# <following code goes here>
rpc.shutdown()Note that our script must now be replicated across multiple workers. For this example, we will use torchrun to run multiple processes within a single machine for demonstration purposes. We can collect up all of the code blocks above into a file named example.py and then run it with torchrun like so:
torchrun --nproc_per_node=3 example.py
Note that we have launched 3 processes, as we have 3 pipeline stages.
We can now run the pipeline by passing input to the PipelineDriver because PipelineDriver is also a nn.module:
# Instantiate a random input for testing purposes.
x = torch.randn(512, 512)
# Run the pipeline with input `x`. Divide the batch into 64 micro-batches
# and run them in parallel on the pipeline
driver.chunks = 64
output = driver(x)
# Run the original code and get the output for comparison
reference_output = mn(x)
# Compare numerics of pipeline and original model
torch.testing.assert_close(output, reference_output)
print(' Pipeline parallel model ran successfully! '.center(80, '*'))We can see that we can now execute our model in a pipelined fashion and get the same numeric outputs.
Most users do not need to use the pipe object generated by Pipe.from_tracing. For convenience, PiPPy provides a compile API that directly generates a PipelineDriver from user's model.
import pippy
if rank == 0:
# Create pipeline driver
driver = pippy.compile(
mn,
num_ranks=world_size,
num_chunks=world_size,
schedule="FillDrain",
split_policy=split_poicy,
)
output = driver(x)All examples above assume that the driver process has enough memory to materialize the model (before splitting). In case that the model is so large that the driver process cannot materialize it on a single device, it would be necessary to first split the model and then let each process materialize its pipeline stage on its own device. pippy.all_compile provides such functionality. Different from pippy.compile, pippy.all_compile requires all ranks to call into it so that they all know which part of the model they should materialize. For example:
import pippy
# All ranks call into it
driver, stage_mod = pippy.all_compile(
mn,
num_ranks=world_size,
num_chunks=world_size,
schedule="FillDrain",
split_policy=split_poicy,
)
if rank == 0:
output = driver(x)Only rank 0 will have the pipeline driver returned, but all ranks will be returned a handle to their local stage module (stage_mod).
The above example demonstrated only pipelining the forward() computation, for example for the purposes of model evaluation. We can extend the example to include the loss and back-propagation computation for the purposes of model training. Let us replace the code under the if local_rank == 0: block in the example:
if local_rank == 0:
from pippy.PipelineDriver import PipelineDriverFillDrain
from pippy.microbatch import TensorChunkSpec
# LossWrapper is a convenient base class you can use to compose your model
# with the desired loss function for the purpose of pipeline parallel training.
# Since the loss is executed as part of the pipeline, it cannot reside in the
# training loop, so you must embed it like this
from pippy.IR import LossWrapper
class ModelLossWrapper(LossWrapper):
def forward(self, x, target):
return self.loss_fn(self.module(x), target)
# TODO: mean reduction
loss_wrapper = ModelLossWrapper(module=mn, loss_fn=torch.nn.MSELoss(reduction='sum'))
# Instantiate the `Pipe` similarly to before, but with two differences:
# 1) We pass in the `loss_wrapper` module to include the loss in the
# computation
# 2) We specify `output_loss_value_spec`. This is a data structure
# that should mimic the structure of the output of LossWrapper
# and has a True value in the position where the loss value will
# be. Since LossWrapper returns just the loss, we just pass True
pipe = Pipe.from_tracing(loss_wrapper, output_loss_value_spec=True)
# We now have two args: `x` and `target`, so specify batch dimension
# for both.
args_chunk_spec = (TensorChunkSpec(0), TensorChunkSpec(0))
kwargs_chunk_spec = {}
# The output is now a `loss` value, which is a scalar tensor.
# PiPPy's default is to concatenate outputs, but that will not
# work with a scalar tensor. So we use a LossReducer instead
# to merge together the partial loss values.
from pippy.microbatch import LossReducer
output_chunk_spec = LossReducer(0.0, lambda a, b: a + b)
# Instantiate the driver as usual.
driver = PipelineDriverFillDrain(
pipe, args_chunk_spec=args_chunk_spec, kwargs_chunk_spec=kwargs_chunk_spec,
output_chunk_spec=output_chunk_spec, world_size=world_size)The comments describe the new components that have been added to enable training. We can print out the new pipe to see the loss and backward stages:
print(pipe)
"""
def forward(self, x, target):
submod_0 = self.submod_0(x)
submod_1 = self.submod_1(submod_0)
submod_2 = self.submod_2(submod_1, target)
stage_backward = pippy_IR_stage_backward(stage_output = (submod_2,), output_grads = (None,), input_values = [submod_1, target], outputs_with_grads_idxs = [0], stage_info = 'stage_backward for stage %submod_2 : [#users=2] = call_module[target=submod_2](args = (%submod_1, %target), kwargs = {})'); target = None
getitem = stage_backward[0]
getitem_1 = stage_backward[1]; stage_backward = None
getitem_2 = getitem[0]
getitem_3 = getitem[1]; getitem = None
stage_backward_1 = pippy_IR_stage_backward(stage_output = (submod_1,), output_grads = (getitem_2,), input_values = [submod_0], outputs_with_grads_idxs = [0], stage_info = 'stage_backward_1 for stage %submod_1 : [#users=3] = call_module[target=submod_1](args = (%submod_0,), kwargs = {})'); submod_1 = getitem_2 = None
getitem_4 = stage_backward_1[0]
getitem_5 = stage_backward_1[1]; stage_backward_1 = None
getitem_6 = getitem_4[0]; getitem_4 = None
stage_backward_2 = pippy_IR_stage_backward(stage_output = (submod_0,), output_grads = (getitem_6,), input_values = [x], outputs_with_grads_idxs = [0], stage_info = 'stage_backward_2 for stage %submod_0 : [#users=3] = call_module[target=submod_0](args = (%x,), kwargs = {})'); submod_0 = getitem_6 = x = None
getitem_7 = stage_backward_2[0]
getitem_8 = stage_backward_2[1]; stage_backward_2 = None
getitem_9 = getitem_7[0]; getitem_7 = None
sync_barrier = pippy_IR_sync_barrier(submod_2, [getitem_1, getitem_5, getitem_8]); submod_2 = getitem_1 = getitem_5 = getitem_8 = None
return sync_barrier
"""As before, we can now call the driver object to execute the pipeline; However this time, the forward, loss, and backward passes will all be executed:
x = torch.randn(512, 512)
target = torch.randn(512, 256)
# note the additional `target` argument, as the module we're running is
# ModelLossWrapper
driver.chunks = 64
output = driver(x, target)
# NOTE: Backpropagation is run implicitly by `driver.forward()` when supplied with
# a Pipe with loss computation. You should not run `output.backward()`; PiPPy's
# runtime has already done that. This divergence from the PyTorch API exists
# because of the distributed nature of pipeline parallelism.
reference_output = loss_wrapper(x, target)
# Compare numerics of pipeline and original model
torch.testing.assert_close(output, reference_output)
print(' Pipeline parallel model ran successfully! '.center(80, '*'))The above code has computed the gradients for the parameters of the model, but has not applied updates to the parameters. We use an Optimizer to do this by using the instantiate_optimizer() method on the pipeline driver:
# Instantiate remote Adam optimizers. `instantiate_optimizer` takes the
# optimizer class as the first argument, then additional arguments to that
# optimizer. Note that the `parameters` argument is omitted; PiPPy will
# populate that value for each pipeline stage for you.
optimizer = driver.instantiate_optimizer(torch.optim.Adam)
# Also instantiate a learning rate scheduler. Note that the `optimizer` argument is
# omitted; PiPPy will populate that argument for each pipeline stage
lr_scheduler = driver.instantiate_lr_scheduler(torch.optim.lr_scheduler.LinearLR, total_iters=100)
N_TRAINING_STEPS = 100
x = torch.randn(512, 512)
target = torch.randn(512, 10)
driver.chunks = 64
for i in range(N_TRAINING_STEPS):
optimizer.zero_grad()
pipe_loss = driver(x, target)
optimizer.step()
lr_scheduler.step()
log_info = f' Training step {i}, loss: {pipe_loss}, LR: {lr_scheduler.get_last_lr()} '
print(log_info.center(80, '*'))
print(' Pipeline parallel model ran successfully! '.center(80, '*'))Launching this file example_train.py with torchrun similarly as before:
torchrun --nproc_per_node=3 example_train.py
We see the model train, memorizing the 512 examples in our input batch:
***** Training step 0, loss: 5197.943359375, LR: [0.00033999999999999997] ******
***** Training step 1, loss: 5104.2080078125, LR: [0.0003466666666666666] ******
**** Training step 2, loss: 5025.17236328125, LR: [0.00035333333333333327] *****
****** Training step 3, loss: 4940.39453125, LR: [0.0003599999999999999] *******
***** Training step 4, loss: 4845.97998046875, LR: [0.0003666666666666666] *****
**** Training step 5, loss: 4742.01220703125, LR: [0.00037333333333333326] *****
<...>
**** Training step 94, loss: 16.55620765686035, LR: [0.0009666666666666657] ****
*** Training step 95, loss: 12.990996360778809, LR: [0.0009733333333333323] ****
**** Training step 96, loss: 8.712753295898438, LR: [0.000979999999999999] *****
*** Training step 97, loss: 3.0083038806915283, LR: [0.0009866666666666659] ****
*** Training step 98, loss: 6.2024617195129395, LR: [0.0009933333333333326] ****
*** Training step 99, loss: 12.104667663574219, LR: [0.0009999999999999994] ****
****************** Pipeline parallel model ran successfully! *******************
When using PiPPy on CUDA devices, the model can be on a CUDA device before being passed to PiPPy, for example:
model = MyNetwork()
# `dev_id` is the GPU index
model.to(f'cuda:{dev_id}')
pipe = Pipe.from_tracing(model)Note: in cases where a model's parameters do not fit into the memory of a single GPU, PiPPy also supports deferred
distributed initialization which only materializes a pipeline stage on its corresponding GPU worker. For details,
please see PiPPy's Pipe.defer_stage_init API.
In adition, some backend options need to be passed to RPC initialization. RPC by default uses the TensorPipe backend
that supports point-to-point communication in an asynchronous manner. Configurations for TensorPipe can be specified
with a TensorPipeRpcBackendOptions object. Here is an example:
# Create a backend option with 256 threads in the thread-pool used by
# TensorPipeAgent to execute requests
options = rpc.TensorPipeRpcBackendOptions(num_worker_threads=256)
# Number of GPUs per node
# (Assuming each node has the same number of GPUs)
n_devs = torch.cuda.device_count()
# Local GPU index of this worker within the node
dev_id = rank % n_devs
# Set device mappings from this worker to other RPC callees
for i in range(world_size):
options.set_device_map(f"worker{i}", {dev_id: i % n_devs})
# Initialize RPC
rpc.init_rpc(f'worker{rank}', rank=rank, world_size=world_size,
rpc_backend_options=options)The set_device_map call takes two arguments: the first one is the callee worker's name, the second one is a dictionary
that maps from this worker's device to the callee worker's device. For more details, please refer to the documentation
of TensorPipeRpcBackendOptions.
PiPPy supports combination with Distributed Data Parallel (DDP) via the init_data_parallel API. Users can create
multiple pipelines each targeting a distinct subset of ranks. For the same stages of the different pipelines, data
parallel training is possible as these stages are replicas of the same model chunk. The created pipelines can
collectively call the init_data_parallel API. PiPPy will then create a DDP group for each stage, across the pipelines.
In the backward pass of the training, gradients will be exchanged among the same stages of the different pipelines via
the DDP groups.
Here is an example of PiPPy + Distributed Data Parallel:
# Number of ranks in each pipeline
pp_group_size = 3
# Number of pipelines that coordinate in DDP fashion
dp_group_size = 4
# The total number of ranks
world_size = pp_group_size * dp_group_size
# In this example:
# DP ranks are contiguous rows of size `dp_group_size`
# PP ranks are non-contiguous columns of size `pp_group_size`
# PP ranks
# |
# v
# DP ranks -> 0 1 2 3
# 4 5 6 7
# 8 9 10 11
# The driver of each pipeline creates and runs the pipeline
def run_driver(pp_ranks):
# Code to create the pipe object
# ...
# Create a PipelineDriver using the pipeline group size and the pipeline
# ranks given to this driver, e.g. [0, 4, 8] for driver 0
pipe_driver = PipelineDriverFillDrain(pipe, chunks, args_chunk_spec,
kwargs_chunk_spec, output_chunk_spec,
pp_group_size, pp_ranks)
# Create DDP groups for same pipeline stages, across pipelines
# `dp_group_size` specifies the number of pipelines expected to collectively
# make this call
pipe_driver.init_data_parallel(dp_group_size)
# Run training combining PiPPy and DDP
out = pipe_driver(input, target)
# Initialize the default distributed process group (involving all ranks)
# This is needed for DDP collectives
torch.distributed.init_process_group(backend=backend, rank=rank,
world_size=world_size)
# Initialize RPC (involving all ranks)
# This is needed by PiPPy
rpc.init_rpc(f'worker{rank}', rank=rank, world_size=world_size)
# Assuming each driver process is the first rank in its respective pipeline,
# then the driver processes are the first `dp_group_size` ranks of the world
if rank < dp_group_size:
# The list of ranks belonging to the pipeline of this driver
pp_ranks = [i * dp_group_size + rank for i in range(pp_group_size)]
run_driver(pp_ranks)
rpc.shutdown()Pipeline parallel training of deep neural networks is bidirectional since training requires running both forward- and back-propagation of the network. As a result, multiple items of work may be ready to run on a pipeline stage at a given time. The problem of selecting between these work items is known as scheduling, and a specific policy for selecting work-items is known as a pipeline schedule.
PiPPy provides both off-the-shelf pipeline schedules as described in the research literature as well as a programmable interface for creating new schedules. The schedules include:
- Fill-Drain. Fill-drain is a schedule that executes all forward microbatches before executing any backward microbatches. This is the "standard" schedule used in GPipe (Huang, 2018). Fill-drain scheduling can be used in PiPPy via the
PipelineDriverFillDraindriver class. A diagram illustrating the fill-drain schedule is below.
- 1F1B (one forward, one backward) is a schedule that provides good hardware utilization as well as limits the amount of memory needed on a stage. At steady-state, a pipeline stage will alternate between processing forward and backward micro-batches. 1F1B was introduced in its asynchronous form in (Harlap, 2018) and in its synchronous form in (Narayanan, 2021). 1F1B scheduling can be used in PiPPy via the
PipelineDriver1F1Bdriver class. A diagram illustrating the 1F1B schedule is below.
- Interleaved 1F1B. Interleaved 1F1B is a variant of 1F1B that divides the program into smaller chunks and assigns multiple chunks per stage in a wrap-around fashion. Interleaving improves pipeline throughput with similar memory characteristics to 1F1B. Interleaved 1F1B was introduced by (Narayanan, 2021). Interleaved 1F1B can be using in PiPPy via the
PipelineDriverInterleaved1F1Bdriver class.
Future work on PiPPy includes:
- Increasing automation. We aim to develop automated systems that can alleviate the burden of the user to specify things such as the batch dimension or pipeline split points. Automatic, optimal splitting of a program into balanced pipeline stages is an interesting research field with advances in the deep learning systems field (e.g. Zheng, 2022) and adjacent fields such as high-level synthesis for digital design (e.g. Zaretsky, 2007).
- Expanding to more forms of parallelism. PiPPy is our first foray into compiler-mediated distribution of PyTorch programs. We would like to explore expanding the analysis and partitioning capabilities enabled by a compiler stack to other forms of parallelism, including data parallelism, model parallelism, and MoE parallelism. Such automation is a rich area of research that we would like to contribute to.
- Chi-Chung Chen, Chia-Lin Yang, & Hsiang-Yun Cheng (2018). Efficient and Robust Parallel DNN Training through Model Parallelism on Multi-GPU Platform. CoRR, abs/1809.02839.
- Geng, J., Li, D., & Wang, S. (2019). ElasticPipe: An Efficient and Dynamic Model-Parallel Solution to DNN Training. In Proceedings of the 10th Workshop on Scientific Cloud Computing (pp. 5–9). Association for Computing Machinery.
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PiPPy is 3-clause BSD licensed, as found in the LICENSE file.
If you use PiPPy in your publication, please cite it by using the following BibTeX entry.
@Misc{pippy2022,
author = {James Reed, Pavel Belevich, Ke Wen},
title = {PiPPy: Pipeline Parallelism for PyTorch},
howpublished = {\url{https://github.com/pytorch/PiPPy}},
year = {2022}
}