ColossalAI/colossalai/legacy/inference/pipeline

🐳 Pipeline Inference

Table of Contents

• 💡 Introduction
• 🔗 Design
• 🔨 Usage
  • Example
  • Quick start
• 📊 Performance

Introduction

Pipeline Inference is a module designed to make inference on a pipeline way. In inference systems, although there is no need to store intermediate information such as activations during forward propagation for backward propagation, the weights of some larger models still cannot fit on a single GPU for inference. This requires us to use model parallelism and other methods to reduce the memory occupation on a single GPU. Pipeline parallelism, as one of the traditional model parallelism approaches, has been widely used due to its reduced all-reduce communication requirements and simple layout. The main issue with pipeline parallelism, known as bubbles, can be almost eliminated in inference because the backward propagation that causes bubbles no longer exists in inference. This makes pipeline parallelism almost bubble-free in the ideal scenario where the sequence length is the same across the pipeline.

Design

Pipeline Inference is composed of three parts: PPInferEngine, MicroBatchManager and generate schedule.

1. PPInderEngine is the High-Level API for users to use. It is responsible for the following tasks:

   • Initialize the pipeline inference environment with PipelineStageManager and model with ShardFormer.
   • Run the pipeline inference model.

2. MicroBatchManager is a structure to manage the micro-batch information. It is responsible for the following tasks:

   • Record each micro-batch information, like generated new tokens and kvcache.
   • Record each micro-batch inference state, like prefill, generate or done.
   • Update the micro-batch information.

3. generate schedule implements the simple pipeline inference layout. When pipeline size is 2, we use torch.distributed.P2Pop to implement the communication between stages, mainly to solve the race communication. When pipeline size is larger than 2, we use torch.distributed.broadcast which is faster than torch.distributed.P2Pop.

Usage

Example

from colossalai.inference import PPInferEngine
from colossalai.inference.pipeline.policies import LlamaModelInferPolicy
import colossalai
from transformers import LlamaForCausalLM, LlamaTokenizer

colossalai.launch_from_torch(config={})

model = LlamaForCausalLM.from_pretrained("/path/to/model")
tokenizer = LlamaTokenizer.from_pretrained("/path/to/model")

# assume the model is inferred with 2 pipeline stages
inferengine = PPInferEngine(pp_size=2, model=model, model_policy=LlamaModelInferPolicy(), new_length=32)

input = ["Introduce a landmark in London","Introduce a landmark in Singapore"]
data = tokenizer(input, return_tensors='pt')
output = inferengine.inference(data.to('cuda'))
print(tokenizer.batch_decode(output))

Performance

We conducted multiple benchmark tests to evaluate the performance. We compared the inference latency and throughputs between Pipeline Inference and hugging face pipeline. The test environment is 2 * A10, 20G / 2 * A800, 80G.

Llama Throughput (tokens/s) | input length=1024, output length=128

A10 7b, fp16

batch_size(micro_batch size)	2(1)	4(2)	8(4)	16(8)	32(8)	32(16)
Pipeline Inference	40.35	77.1	139.03	232.7	257.81	OOM
Hugging Face	41.43	65.30	91.93	114.62	OOM	OOM

A10 13b, fp16

batch_size(micro_batch size)	2(1)	4(2)	8(4)	16(4)
Pipeline Inference	25.39	47.09	83.7	89.46
Hugging Face	23.48	37.59	53.44	OOM

A800 7b, fp16

batch_size(micro_batch size)	2(1)	4(2)	8(4)	16(8)	32(16)
Pipeline Inference	57.97	110.13	213.33	389.86	670.12
Hugging Face	42.44	76.5	151.97	212.88	256.13

A800 13b, fp16

batch_size(micro_batch size)	2(1)	4(2)	8(4)	16(8)	32(16)
Pipeline Inference	41.78	94.18	172.67	310.75	470.15
Hugging Face	36.57	68.4	105.81	139.51	166.34