Picture by Editor | ChatGPT# Introduction
Hugging Face has turn into the usual for a lot of AI builders and knowledge scientists as a result of it drastically lowers the barrier to working with superior AI. Quite than working with AI fashions from scratch, builders can entry a variety of pretrained fashions with out problem. Customers may adapt these fashions with customized datasets and deploy them rapidly.
One of many Hugging Face framework API wrappers is the Transformers Pipelines, a collection of packages that consists of the pretrained mannequin, its tokenizer, pre- and post-processing, and associated elements to make an AI use case work. These pipelines summary advanced code and supply a easy, seamless API.
Nevertheless, working with Transformers Pipelines can get messy and will not yield an optimum pipeline. That’s the reason we’ll discover 5 alternative ways you possibly can optimize your Transformers Pipelines.
Let’s get into it.
# 1. Batch Inference Requests
Typically, when utilizing Transformers Pipelines, we don’t absolutely make the most of the graphics processing unit (GPU). Batch processing of a number of inputs can considerably enhance GPU utilization and improve inference effectivity.
As a substitute of processing one pattern at a time, you should utilize the pipeline’s batch_size parameter or go a listing of inputs so the mannequin processes a number of inputs in a single ahead go. Here’s a code instance:
from transformers import pipeline
pipe = pipeline(
activity="text-classification",
mannequin="distilbert-base-uncased-finetuned-sst-2-english",
device_map="auto"
)
texts = [
"Great product and fast delivery!",
"The UI is confusing and slow.",
"Support resolved my issue quickly.",
"Not worth the price."
]
outcomes = pipe(texts, batch_size=16, truncation=True, padding=True)
for r in outcomes:
print(r)
By batching requests, you possibly can obtain greater throughput with solely a minimal affect on latency.
# 2. Use Decrease Precision And Quantization
Many pretrained fashions fail at inference as a result of improvement and manufacturing environments would not have sufficient reminiscence. Decrease numerical precision helps scale back reminiscence utilization and hastens inference with out sacrificing a lot accuracy.
For instance, right here is how one can use half precision on the GPU in a Transformers Pipeline:
import torch
from transformers import AutoModelForSequenceClassification
mannequin = AutoModelForSequenceClassification.from_pretrained(
model_id,
torch_dtype=torch.float16
)
Equally, quantization methods can compress mannequin weights with out noticeably degrading efficiency:
# Requires bitsandbytes for 8-bit quantization
from transformers import AutoModelForCausalLM
mannequin = AutoModelForCausalLM.from_pretrained(
model_id,
load_in_8bit=True,
device_map="auto"
)
Utilizing decrease precision and quantization in manufacturing often hastens pipelines and reduces reminiscence use with out considerably impacting mannequin accuracy.
# 3. Choose Environment friendly Mannequin Architectures
In lots of functions, you don’t want the biggest mannequin to unravel the duty. Choosing a lighter transformer structure, comparable to a distilled mannequin, usually yields higher latency and throughput with an appropriate accuracy trade-off.
Compact fashions or distilled variations, comparable to DistilBERT, retain a lot of the authentic mannequin’s accuracy however with far fewer parameters, leading to sooner inference.
Select a mannequin whose structure is optimized for inference and fits your activity’s accuracy necessities.
# 4. Leverage Caching
Many techniques waste compute by repeating costly work. Caching can considerably improve efficiency by reusing the outcomes of pricey computations.
with torch.inference_mode():
output_ids = mannequin.generate(
**inputs,
max_new_tokens=120,
do_sample=False,
use_cache=True
)
Environment friendly caching reduces computation time and improves response instances, decreasing latency in manufacturing techniques.
# 5. Use An Accelerated Runtime By way of Optimum (ONNX Runtime)
Many pipelines run in a PyTorch not-so-optimal mode, which provides Python overhead and further reminiscence copies. Utilizing Optimum with Open Neural Community Alternate (ONNX) Runtime — through ONNX Runtime — converts the mannequin to a static graph and fuses operations, so the runtime can use sooner kernels on a central processing unit (CPU) or GPU with much less overhead. The result’s often sooner inference, particularly on CPU or blended {hardware}, with out altering the way you name the pipeline.
Set up the required packages with:
pip set up -U transformers optimum[onnxruntime] onnxruntime
Then, convert the mannequin with code like this:
from optimum.onnxruntime import ORTModelForSequenceClassification
ort_model = ORTModelForSequenceClassification.from_pretrained(
model_id,
from_transformers=True
)
By changing the pipeline to ONNX Runtime via Optimum, you possibly can hold your present pipeline code whereas getting decrease latency and extra environment friendly inference.
# Wrapping Up
Transformers Pipelines is an API wrapper within the Hugging Face framework that facilitates AI software improvement by condensing advanced code into easier interfaces. On this article, we explored 5 tricks to optimize Hugging Face Transformers Pipelines, from batch inference requests, to choosing environment friendly mannequin architectures, to leveraging caching and past.
I hope this has helped!
Cornellius Yudha Wijaya is a knowledge science assistant supervisor and knowledge author. Whereas working full-time at Allianz Indonesia, he likes to share Python and knowledge ideas through social media and writing media. Cornellius writes on quite a lot of AI and machine studying matters.