การอนุมานด้วย Gemma โดยใช้ JAX และ Flax

ดูใน ai.google.dev เรียกใช้ใน Google Colab เปิดใน Vertex AI ดูแหล่งที่มาใน GitHub

ภาพรวม

Gemma เป็นตระกูลโมเดลภาษาขนาดใหญ่แบบเปิดที่ทันสมัยและเรียบง่าย อิงจากงานวิจัยและเทคโนโลยีของ Google DeepMind Gemini บทแนะนำนี้สาธิตวิธีการสุ่มตัวอย่าง/การอนุมานพื้นฐานด้วยโมเดล Gemma 2B Instruct โดยใช้ไลบรารี gemma ของ Google DeepMind ที่เขียนด้วย JAX (ไลบรารีการประมวลผลตัวเลขประสิทธิภาพสูง), Flax (ไลบรารีโครงข่ายระบบประสาทเทียมแบบ JAX), Orbax (ไลบรารีของ JAX สำหรับการฝึกใช้งาน เช่น checkpointing) และ SentencePiece แม้ว่าไม่ได้ใช้ Flax ในสมุดบันทึกนี้โดยตรง แต่ใช้ Flax เพื่อสร้าง Gemma

สมุดบันทึกนี้ทำงานบน Google Colab ได้ด้วย GPU รุ่น T4 ฟรี (ไปที่แก้ไข > การตั้งค่าสมุดบันทึก > ใต้ตัวเร่งฮาร์ดแวร์ เลือก T4 GPU)

ตั้งค่า

1. ตั้งค่าการเข้าถึง Kaggle สำหรับ Gemma

หากต้องการจบบทแนะนำนี้ ก่อนอื่นคุณต้องทำตามวิธีการตั้งค่าที่การตั้งค่า Gemma ซึ่งแสดงวิธีดำเนินการต่อไปนี้

  • รับสิทธิ์เข้าถึง Gemma ใน kaggle.com
  • เลือกรันไทม์ของ Colab ที่มีทรัพยากรเพียงพอที่จะเรียกใช้โมเดล Gemma
  • สร้างและกำหนดค่าชื่อผู้ใช้และคีย์ API ของ Kaggle

หลังจากตั้งค่า Gemma เสร็จแล้ว ให้ไปยังส่วนถัดไปซึ่งจะตั้งค่าตัวแปรสภาพแวดล้อมสำหรับสภาพแวดล้อม Colab

2. ตั้งค่าตัวแปรสภาพแวดล้อม

ตั้งค่าตัวแปรสภาพแวดล้อมสำหรับ KAGGLE_USERNAME และ KAGGLE_KEY เมื่อมีข้อความแจ้งด้วยข้อความ "ให้สิทธิ์เข้าถึงไหม" ยอมรับการเข้าถึงข้อมูลลับ

import os
from google.colab import userdata # `userdata` is a Colab API.

os.environ["KAGGLE_USERNAME"] = userdata.get('KAGGLE_USERNAME')
os.environ["KAGGLE_KEY"] = userdata.get('KAGGLE_KEY')

3. ติดตั้งไลบรารี gemma

สมุดบันทึกนี้มุ่งเน้นที่การใช้ Colab GPU ฟรี หากต้องการเปิดใช้การเร่งฮาร์ดแวร์ ให้คลิกแก้ไข > การตั้งค่าสมุดบันทึก > เลือก T4 GPU > บันทึก

ถัดไป คุณต้องติดตั้งไลบรารี Google DeepMind gemma จาก github.com/google-deepmind/gemma หากคุณได้รับข้อผิดพลาดเกี่ยวกับ "รีโซลเวอร์ทรัพยากร Dependency ของ PIP" โดยปกติแล้วคุณไม่ต้องสนใจ

pip install -q git+https://github.com/google-deepmind/gemma.git

โหลดและเตรียมโมเดล Gemma

  1. โหลดโมเดล Gemma ด้วย kagglehub.model_download ซึ่งมีอาร์กิวเมนต์ 3 อย่าง ดังนี้
  • handle: แฮนเดิลโมเดลจาก Kaggle
  • path: (สตริงที่ไม่บังคับ) เส้นทางในเครื่อง
  • force_download: (บูลีนที่ไม่บังคับ) บังคับให้ดาวน์โหลดโมเดลอีกครั้ง
GEMMA_VARIANT = 'gemma2-2b-it' # @param ['gemma2-2b', 'gemma2-2b-it'] {type:"string"}

import kagglehub

GEMMA_PATH = kagglehub.model_download(f'google/gemma-2/flax/{GEMMA_VARIANT}')

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print('GEMMA_PATH:', GEMMA_PATH)

GEMMA_PATH: /root/.cache/kagglehub/models/google/gemma-2-2b/flax/gemma2-2b-it/1
  1. ตรวจสอบตำแหน่งของน้ำหนักโมเดลและเครื่องมือแปลงข้อมูลเป็นโทเค็น จากนั้นตั้งค่าตัวแปรเส้นทาง ไดเรกทอรีโทเคนไลซ์จะอยู่ในไดเรกทอรีหลักที่คุณดาวน์โหลดโมเดลไป ขณะที่น้ำหนักโมเดลจะอยู่ในไดเรกทอรีย่อย เช่น
  • ไฟล์ tokenizer.model จะอยู่ใน /LOCAL/PATH/TO/gemma/flax/2b-it/2)
  • จุดตรวจสอบโมเดลจะอยู่ใน /LOCAL/PATH/TO/gemma/flax/2b-it/2/2b-it)
CKPT_PATH = os.path.join(GEMMA_PATH, GEMMA_VARIANT)
TOKENIZER_PATH = os.path.join(GEMMA_PATH, 'tokenizer.model')
print('CKPT_PATH:', CKPT_PATH)
print('TOKENIZER_PATH:', TOKENIZER_PATH)

CKPT_PATH: /root/.cache/kagglehub/models/google/gemma-2-2b/flax/gemma2-2b-it/1/gemma2-2b-it
TOKENIZER_PATH: /root/.cache/kagglehub/models/google/gemma-2-2b/flax/gemma2-2b-it/1/tokenizer.model

ทำการสุ่มตัวอย่าง/การอนุมาน

  1. โหลดและจัดรูปแบบจุดตรวจสอบโมเดล Gemma ด้วยเมธอด gemma.params.load_and_format_params
from gemma import params as params_lib

params = params_lib.load_and_format_params(CKPT_PATH)
  1. โหลดเครื่องมือแปลงข้อมูลเป็นโทเค็นของ Gemma ซึ่งสร้างขึ้นโดยใช้ sentencepiece.SentencePieceProcessor:
import sentencepiece as spm

vocab = spm.SentencePieceProcessor()
vocab.Load(TOKENIZER_PATH)

True
  1. หากต้องการโหลดการกำหนดค่าที่ถูกต้องจากจุดตรวจสอบโมเดล Gemma โดยอัตโนมัติ ให้ใช้ gemma.transformer.TransformerConfig อาร์กิวเมนต์ cache_size คือจำนวนขั้นตอนเวลาในแคช Gemma Transformer หลังจากนั้น ให้สร้างอินสแตนซ์โมเดล Gemma เป็น transformer ด้วย gemma.transformer.Transformer (ซึ่งรับค่าจาก flax.linen.Module)
from gemma import transformer as transformer_lib

transformer_config = transformer_lib.TransformerConfig.from_params(
    params=params,
    cache_size=1024
)

transformer = transformer_lib.Transformer(transformer_config)
  1. สร้าง sampler ด้วย gemma.sampler.Sampler บนจุดตรวจสอบ/น้ำหนักของโมเดล Gemma และเครื่องมือแปลงข้อมูลเป็นโทเค็นโดยทำดังนี้
from gemma import sampler as sampler_lib

sampler = sampler_lib.Sampler(
    transformer=transformer,
    vocab=vocab,
    params=params['transformer'],
)
  1. เขียนพรอมต์ใน input_batch และดำเนินการอนุมาน คุณสามารถปรับแต่ง total_generation_steps (จำนวนขั้นตอนที่ดำเนินการเมื่อสร้างคำตอบ โดยตัวอย่างนี้ใช้ 100 เพื่อเก็บรักษาหน่วยความจำของโฮสต์)
prompt = [
    "what is JAX in 3 bullet points?",
]

reply = sampler(input_strings=prompt,
                total_generation_steps=128,
                )

for input_string, out_string in zip(prompt, reply.text):
    print(f"Prompt:\n{input_string}\nOutput:\n{out_string}")

Prompt:
what is JAX in 3 bullet points?
Output:


* **High-performance numerical computation:** JAX leverages the power of GPUs and TPUs to accelerate complex mathematical operations, making it ideal for scientific computing, machine learning, and data analysis.
* **Automatic differentiation:** JAX provides automatic differentiation capabilities, allowing you to compute gradients and optimize models efficiently. This simplifies the process of training deep learning models.
* **Functional programming:** JAX embraces functional programming principles, promoting code readability and maintainability. It offers a flexible and expressive syntax for defining and manipulating data. 


<end_of_turn>
  1. (ไม่บังคับ) เรียกใช้เซลล์นี้เพื่อเพิ่มหน่วยความจำหากคุณสร้างสมุดบันทึกเสร็จแล้วและต้องการลองใช้พรอมต์อื่น หลังจากนั้น คุณจะสร้างอินสแตนซ์ sampler อีกครั้งในขั้นตอนที่ 3 แล้วปรับแต่งและเรียกใช้ข้อความแจ้งในขั้นตอนที่ 4 ได้
del sampler

ดูข้อมูลเพิ่มเติม