JAX ve Keten kullanılarak Gemma ile çıkarım

ai.google.dev'de görüntüleyin Google Colab'de çalıştır Vertex AI'da aç Kaynağı GitHub'da görüntüle

Genel Bakış

Gemma, Google DeepMind Gemini araştırması ve teknolojisine dayanarak hazırlanan hafif ve son teknoloji ürünü açık büyük dil modelleri ailesidir. Bu eğiticide, Google DeepMind'ın gemma kitaplığı kullanılarak JAX (yüksek performanslı sayısal işlem kitaplığı), Flax (JAX tabanlı nöral ağ kitaplığı), Orbax (kontrol noktası/tokentoizer kitaplığı için JAX tabanlı bir jeton} veya SentencePiece Doğrudan bu not defterinde Flax kullanılmasa da Gemma'yı oluşturmak için Flax kullanılmıştır.

Bu not defteri, ücretsiz T4 GPU ile Google Colab'de çalışabilir (Düzenle > Not defteri ayarları > Donanım hızlandırıcı'nın altında T4 GPU'yu seçin).

Kurulum

1. Gemma için Kaggle erişimini ayarlama

Bu eğiticiyi tamamlamak için önce Gemma kurulumu'ndaki kurulum talimatlarını uygulamanız gerekir. Bu talimatlarda, aşağıdakileri nasıl yapacağınızı öğrenebilirsiniz:

  • kaggle.com adresinden Gemma'ya erişin.
  • Gemma modelini çalıştırmak için yeterli kaynağa sahip bir Colab çalışma zamanı seçin.
  • Kaggle kullanıcı adı ve API anahtarı oluşturup yapılandırın.

Gemma kurulumunu tamamladıktan sonra bir sonraki bölüme geçin. Burada, Colab ortamınız için ortam değişkenlerini ayarlayabilirsiniz.

2. Ortam değişkenlerini ayarlama

KAGGLE_USERNAME ve KAGGLE_KEY için ortam değişkenlerini ayarlayın. "Erişim izni verilsin mi?" sorusuyla karşılaştığınızda gizli erişim izni vermeyi kabul edin.

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. gemma kitaplığını yükle

Bu not defteri, ücretsiz Colab GPU kullanmaya odaklanmıştır. Donanım hızlandırmayı etkinleştirmek için Edit (Düzenle) > Not defteri ayarları > T4 GPU'yu seçin > Kaydet'i seçin.

Ardından, github.com/google-deepmind/gemma üzerinden Google DeepMind gemma kitaplığını yüklemeniz gerekir. "pip'in bağımlılık çözümleyicisi" hatası alırsanız genellikle bunu göz ardı edebilirsiniz.

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

Gemma modelini yükleme ve hazırlama

  1. Gemma modelini, üç bağımsız değişken alan kagglehub.model_download ile yükleyin:
  • handle: Kaggle'ın model tutma yeri
  • path: (İsteğe bağlı dize) Yerel yol
  • force_download: (İsteğe bağlı boole) Modeli yeniden indirmeye zorlar
import kagglehub

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

GEMMA_VARIANT = 'gemma2-2b-it' # @param ['gemma2-2b', 'gemma2-2b-it'] {type:"string"}

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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. Model ağırlıklarının ve belirteç oluşturucunun konumunu kontrol edin, ardından yol değişkenlerini ayarlayın. Jeton oluşturucu dizini, modeli indirdiğiniz ana dizinde, model ağırlıkları ise bir alt dizinde yer alır. Örneğin:
  • tokenizer.model dosyası /LOCAL/PATH/TO/gemma/flax/2b-it/2 klasöründe yer alır.)
  • Model kontrol noktası /LOCAL/PATH/TO/gemma/flax/2b-it/2/2b-it konumunda olacaktır.)
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

Örnekleme/çıkarım gerçekleştirme

  1. Gemma modeli kontrol noktasını gemma.params.load_and_format_params yöntemiyle yükleyin ve biçimlendirin:
from gemma import params as params_lib

params = params_lib.load_and_format_params(CKPT_PATH)
  1. sentencepiece.SentencePieceProcessor kullanılarak oluşturulan Gemma jeton oluşturucuyu yükleyin:
import sentencepiece as spm

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

True
  1. Gemma model kontrol noktasından doğru yapılandırmayı otomatik olarak yüklemek için gemma.transformer.TransformerConfig işlevini kullanın. cache_size bağımsız değişkeni, Gemma Transformer önbelleğindeki zaman adımlarının sayısıdır. Ardından, gemma.transformer.Transformer (flax.linen.Module öğesinden devralınır) kullanarak Gemma modelini transformer olarak örneklendirin.
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. Gemma model kontrol noktası/ağırlıkları ve jeton oluşturucunun üzerine gemma.sampler.Sampler ile bir sampler oluşturun:
from gemma import sampler as sampler_lib

sampler = sampler_lib.Sampler(
    transformer=transformer,
    vocab=vocab,
    params=params['transformer'],
)
  1. input_batch dilinde bir istem yazıp çıkarım yapın. total_generation_steps üzerinde değişiklik yapabilirsiniz (yanıt oluşturulurken gerçekleştirilen adım sayısı; bu örnekte ana makine belleğini korumak için 100 kullanılır).
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. (İsteğe bağlı) Not defterini tamamladıysanız ve başka bir istem denemek istiyorsanız bellekte yer açmak için bu hücreyi çalıştırın. Ardından, 3. adımda tekrar sampler örneğini oluşturup 4. adımda istemi özelleştirip çalıştırabilirsiniz.
del sampler

Daha fazla bilgi