使用 JAX 和 Flax 透過 Gemma 進行推論

前往 ai.google.dev 查看 在 Google Colab 中執行 在 Vertex AI 中開啟 前往 GitHub 查看原始碼

總覽

Gemma 是一組最先進的開放式大型語言模型，以 Google DeepMind Gemini 的研究和技術為基礎。本教學課程示範如何使用 Google DeepMind 的 gemma 程式庫 (使用 JAX (高效能數值運算程式庫)、Flax (以 JAX 為基礎的類神經網路程式庫)、OrbaxSentencePiece雖然這個筆記本並未直接使用 Flax，但要使用 Flax 建立 Gemma。

這個筆記本可以在搭載免費 T4 GPU 的 Google Colab 上執行 (請依序前往「編輯」>「筆記本設定」>「硬體加速器」下方選取「T4 GPU」)。

設定

1. 設定 Gemma 的 Kaggle 存取權

為完成本教學課程，您必須先按照 Gemma 設定中的設定說明操作，其中將說明如何執行下列操作：

  • 前往 kaggle.com 存取 Gemma。
  • 選取具備足夠資源來執行 Gemma 模型的 Colab 執行階段。
  • 產生並設定 Kaggle 使用者名稱和 API 金鑰。

完成 Gemma 設定後，請繼續前往下一節，設定 Colab 環境的環境變數。

2. 設定環境變數

設定 KAGGLE_USERNAME 和 KAGGLE_KEY 的環境變數。系統顯示「授予存取權？」提示訊息時，訊息，同意提供密鑰存取權。

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. 安裝 gemma 程式庫

這個筆記本著重介紹免費的 Colab GPU，如要啟用硬體加速，請按一下「編輯」>筆記本設定 >依序選取「T4 GPU」>按一下「儲存」。

接下來，您需要從 github.com/google-deepmind/gemma 安裝 Google DeepMind gemma 程式庫。如果收到有關「pip 依附元件解析器」的錯誤，通常可以忽略。

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

載入並準備 Gemma 模型

  1. 使用 kagglehub.model_download 載入 Gemma 模型，該模型會使用三個引數：
  • handle：Kaggle 的模型控制代碼
  • path：(選用字串) 本機路徑
  • force_download：(選用布林值) 強制重新下載模型
，瞭解如何調查及移除這項存取權。 
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. 檢查模型權重的位置和符記化工具，然後設定路徑變數。符記化工具目錄會存放您下載模型的主要目錄，而模型權重則位於子目錄。例如：
  • tokenizer.model 檔案會在 /LOCAL/PATH/TO/gemma/flax/2b-it/2 中)。
  • 模型查核點位於 /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

執行取樣/推論

  1. 使用 gemma.params.load_and_format_params 方法載入 Gemma 模型查核點並設定格式：
from gemma import params as params_lib

params = params_lib.load_and_format_params(CKPT_PATH)
  1. 載入使用 sentencepiece.SentencePieceProcessor 建構的 Gemma 權杖化工具：
import sentencepiece as spm

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

True
  1. 如要自動從 Gemma 模型查核點載入正確的設定，請使用 gemma.transformer.TransformerConfig。cache_size 引數是 Gemma Transformer 快取中的時步數。之後，請使用 gemma.transformer.Transformer (繼承自 flax.linen.Module) 將 Gemma 模型例項化為 transformer。
，瞭解如何調查及移除這項存取權。 
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. 在 Gemma 模型查核點/權重和符記化工具之上，使用 gemma.sampler.Sampler 建立 sampler：
from gemma import sampler as sampler_lib

sampler = sampler_lib.Sampler(
    transformer=transformer,
    vocab=vocab,
    params=params['transformer'],
)
  1. 在 input_batch 中撰寫提示並執行推論。您可以調整 total_generation_steps (產生回應時執行的步驟數，此範例使用 100 來保留主機記憶體)。
，瞭解如何調查及移除這項存取權。 
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. (選用) 如果您已完成筆記本並想嘗試其他提示，請執行這個儲存格來釋出記憶體。之後，您可以在步驟 3 中再次將 sampler 例項化，並在步驟 4 中自訂及執行提示。
del sampler

瞭解詳情