الاستنتاج من "جيما" باستخدام JAX وFlax

العرض على ai.google.dev التنفيذ في Google Colab الفتح في Vertex AI عرض المصدر على GitHub

نظرة عامة

"جيما" هي عائلة تتألّف من نماذج لغوية كبيرة وحديثة وبسيطة ومفتوحة استنادًا إلى أبحاث وتقنيات Google DeepMind Gemini. يشرح هذا البرنامج التعليمي كيفية إجراء عملية أخذ العينات/الاستنتاج الأساسي باستخدام نموذج Gemma 2B Instruct (مكتبة Google DeepMind) في gemma التي تمت كتابتها باستخدام SentencePiece على الرغم من عدم استخدام Flax مباشرةً في هذا الدفتر، فقد تم استخدامه لإنشاء Gemma.

يمكن تشغيل ورقة الملاحظات هذه على Google Colab باستخدام وحدة معالجة رسومات مجانية من T4 (انتقِل إلى تعديل > إعدادات ورقة الملاحظات > ضِمن مسرِّع الأجهزة، اختَر وحدة معالجة الرسومات T4).

ضبط إعدادات الجهاز

1. إعداد وصول Kaggle لـ Gemma

لإكمال هذا البرنامج التعليمي، عليك أولاً اتّباع تعليمات الإعداد في إعداد Gemma، والتي توضِّح لك كيفية إجراء ما يلي:

  • يمكنك الوصول إلى Gemma على kaggle.com.
  • اختَر بيئة تشغيل Colab بها موارد كافية لتشغيل نموذج Gemma.
  • إنشاء وتكوين اسم مستخدم ومفتاح واجهة برمجة تطبيقات Kaggle.

بعد الانتهاء من إعداد Gemma، انتقِل إلى القسم التالي، حيث يمكنك ضبط متغيّرات البيئة لبيئة Colab.

2. ضبط متغيرات البيئة

ضبط متغيّرات البيئة لكل من KAGGLE_USERNAME وKAGGLE_KEY عندما يُطلب منك عرض رسالة "هل تريد منح إمكانية الوصول؟" أو الموافقة على توفير الوصول السري.

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- تثبيت مكتبة gemma

تركّز ورقة الملاحظات هذه على استخدام وحدة معالجة رسومات مجانية في Colab. لتفعيل ميزة "تسريع الأجهزة"، انقر على تعديل >. إعدادات ورقة الملاحظات > اختَر وحدة معالجة الرسومات T4 > انقر على حفظ.

بعد ذلك، عليك تثبيت مكتبة Google DeepMind gemma من github.com/google-deepmind/gemma. إذا ظهرت لك رسالة خطأ بشأن "أداة حل التبعية في pip"، يمكنك عادةً تجاهلها.

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

تحميل نموذج Gemma وإعداده

  1. حمِّل نموذج Gemma باستخدام kagglehub.model_download الذي يأخذ ثلاث وسيطات:
  • handle: مؤشر النموذج من Kaggle
  • path: (سلسلة اختيارية) المسار المحلي
  • force_download: (قيمة منطقية اختيارية) تفرض إعادة تنزيل النموذج
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. تحقَّق من الموقع الجغرافي لقيم ترجيح النموذج وأداة إنشاء الرموز المميّزة، ثم اضبط متغيّرات المسار. سيكون دليل أداة إنشاء الرموز المميّزة في الدليل الرئيسي الذي نزّلت فيه النموذج، بينما ستكون القيم التقديرية للنموذج في دليل فرعي. على سبيل المثال:
  • سيكون الملف tokenizer.model باللغة /LOCAL/PATH/TO/gemma/flax/2b-it/2).
  • ستكون النقطة المرجعية للنموذج في /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

إجراء أخذ العينات/الاستنتاج

  1. تحميل نقطة مراجعة نموذج Gemma وتنسيقها باستخدام الطريقة gemma.params.load_and_format_params:
from gemma import params as params_lib

params = params_lib.load_and_format_params(CKPT_PATH)
  1. تحميل برنامج ترميز Gemma المميز الذي تم إنشاؤه باستخدام sentencepiece.SentencePieceProcessor:
import sentencepiece as spm

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

True
  1. لتحميل الإعدادات الصحيحة تلقائيًا من نقطة مراجعة نموذج Gemma، استخدِم gemma.transformer.TransformerConfig. الوسيطة cache_size هي عدد الخطوات الزمنية في ذاكرة التخزين المؤقت لـ Gemma Transformer. بعد ذلك، يمكنك إنشاء مثيل لنموذج Gemma كـ transformer باستخدام gemma.transformer.Transformer (التي تكتسب من 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. أنشئ sampler باستخدام gemma.sampler.Sampler أعلى نقطة فحص أو معاملات نموذج Gemma وأداة إنشاء الرموز المميّزة:
from gemma import sampler as sampler_lib

sampler = sampler_lib.Sampler(
    transformer=transformer,
    vocab=vocab,
    params=params['transformer'],
)
  1. اكتب طلبًا باللغة input_batch ونفِّذ الاستنتاج. يمكنك تعديل total_generation_steps (عدد الخطوات التي يتم تنفيذها عند إنشاء ردّ، إذ يستخدم هذا المثال 100 للاحتفاظ بذاكرة المضيف).
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. (اختياري) قم بتشغيل هذه الخلية لتحرير الذاكرة إذا كنت قد أكملت دفتر الملاحظات وتريد تجربة مطالبة أخرى. بعد ذلك، يمكنك إنشاء مثيل من sampler مرة أخرى في الخطوة 3 وتخصيص الطلب في الخطوة 4 وتنفيذه.
del sampler

مزيد من المعلومات