流水线并行（pipelining ）部署实施起来非常困难，因为这需要根据模型的weights把模型分块（通常涉及到对源码的修改），此外，分布式的调度和数据流的依赖也是要考虑的点；
pipelining 库可以让部署变得更加简单；
这个库包含两个部分：
splitting frontend：此部分用于把模型分块，并且捕捉到数据流之间的关系；
distributed runtime：并行地执行pipeline stage在不同的设备上，同时处理好batch的划分、调度、通信和梯度回传；
所以这个库支持以下操作：
1.对于模型的简单划分；
2.丰富的流水线调度策略，包括GPipe, 1F1B, Interleaved 1F1B and Looped BFS；
3.支持跨主机的并行；
4.支持一些常规的并行操作，比如data parallel (DDP, FSDP) or tensor parallel；
关于模型的splitting：
为了构建PipelineStage，需要提供包含了nn.Parameters and nn.Buffers的nn.Module，同时定义了能够执行对应stage的forward函数
python
class Transformer(nn.Module):
def __init__(self, model_args: ModelArgs):
super().__init__()
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用这种方式定义的模型可以很容易配置stage和初始化，（为了防止OMM error使用meta device),删除对应stage不需要的层，然后构造PipelineStage 来wrap model;
python
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(
model,
stage_index,
num_stages,
device,
input_args=example_input_microbatch,
)这里还提供自动切分模型的接口函数，这里不做细致赘述；
其中input_args 代表执行时候的input data samples,这个要拿去经过forward去确定输入输出的shapes;当同时使用其他并行trick的时候，output_args 也需要的，因为模型输出大小可能会受到影响；
第一步：构建一个执行的PipelineStage
PipelineStage用于分配通信内存，创造发送、接受操作去通信；它用来存储还未被consume的forward的缓存，同时为stage model执行backward;
PipelineStage需要知道输入输出的shape大小，方便创建通信缓存，shapes必须是固定大小的，也就是训练执行的时候它不能是变化的；
每一个stage model必须是nn.Module的格式；（所以第一步要做的事情就是手动分割模型）；
当然也有其他替代方式，可以用图分割去把你的模型自动分割为一系列的nn.Module,这个要求模型必须是torch.Export traceable ；所以能手动更改模型代码是最方便的；
第二步：用PipelineSchedule 去执行
以下是执行的示例代码：
python
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:
schedule.step(x)
else:
output = schedule.step()以上代码的rank应该指的是进程号，也就是在0进程中，执行stage1,在1进程中执行stage 2;
以下是官方给的关于llama流水线并行的示例代码，会更加清晰明了；
python
# $ torchrun --nproc-per-node 4 pippy_llama.py
import os
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from torch.distributed.pipelining import SplitPoint, pipeline, ScheduleGPipe
# Grab the model
llama = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-2-7b-chat-hf", low_cpu_mem_usage=True
)
print(llama)
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
tokenizer.pad_token = tokenizer.eos_token
mb_prompts = (
"How do you", "I like to",
) # microbatch size = 2
rank = int(os.environ["RANK"])
world_size = int(os.environ["WORLD_SIZE"])
device = torch.device(f"cuda:{rank % torch.cuda.device_count()}")
torch.distributed.init_process_group(rank=rank, world_size=world_size)
llama.to(device).eval()
# Cut model by equal number of layers per rank
layers_per_rank = llama.config.num_hidden_layers // world_size
print(f"layers_per_rank = {layers_per_rank}")
split_spec = {
f"model.layers.{i * layers_per_rank}": SplitPoint.BEGINNING
for i in range(1, world_size)
}
# Create a pipeline representation from the model
mb_inputs = tokenizer(mb_prompts, return_tensors="pt", padding=True).to(device)
pipe = pipeline(llama, mb_args=(mb_inputs["input_ids"],))
# Create pipeline stage for each rank
stage = pipe.build_stage(rank, device=device)
# Run time inputs
full_batch_prompts = (
"How do you", "I like to", "Can I help", "You need to",
"The weather is", "I found a", "What is your", "You are so",
) # full batch size = 8
inputs = tokenizer(full_batch_prompts, return_tensors="pt", padding=True).to(device)
# Attach to a schedule
# number of microbatches = 8 // 2 = 4
num_mbs = 4
schedule = ScheduleGPipe(stage, num_mbs)
# Run
if rank == 0:
args = inputs["input_ids"]
else:
args = None
output = schedule.step(args)
# Decode
if output is not None:
next_token_logits = output[0][:, -1, :]
next_token = torch.argmax(next_token_logits, dim=-1)
print(tokenizer.batch_decode(next_token))torchrun --nproc-per-node 8 pippy_llama.py