LoRA를 사용하여 Keras에서 Gemma 모델 미세 조정

import os
from google.colab import userdata

# Note: `userdata.get` is a Colab API. If you're not using Colab, set the env
# vars as appropriate for your system.

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

# Install Keras 3 last. See https://keras.io/getting_started/ for more details.
pip install -q -U keras-nlp
pip install -q -U "keras>=3"

os.environ["KERAS_BACKEND"] = "jax"  # Or "torch" or "tensorflow".
# Avoid memory fragmentation on JAX backend.
os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"]="1.00"

import keras
import keras_nlp

wget -O databricks-dolly-15k.jsonl https://huggingface.co/datasets/databricks/databricks-dolly-15k/resolve/main/databricks-dolly-15k.jsonl

--2024-07-31 01:56:39--  https://huggingface.co/datasets/databricks/databricks-dolly-15k/resolve/main/databricks-dolly-15k.jsonl
Resolving huggingface.co (huggingface.co)... 18.164.174.23, 18.164.174.17, 18.164.174.55, ...
Connecting to huggingface.co (huggingface.co)|18.164.174.23|:443... connected.
HTTP request sent, awaiting response... 302 Found
Location: https://cdn-lfs.huggingface.co/repos/34/ac/34ac588cc580830664f592597bb6d19d61639eca33dc2d6bb0b6d833f7bfd552/2df9083338b4abd6bceb5635764dab5d833b393b55759dffb0959b6fcbf794ec?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27databricks-dolly-15k.jsonl%3B+filename%3D%22databricks-dolly-15k.jsonl%22%3B&Expires=1722650199&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcyMjY1MDE5OX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5odWdnaW5nZmFjZS5jby9yZXBvcy8zNC9hYy8zNGFjNTg4Y2M1ODA4MzA2NjRmNTkyNTk3YmI2ZDE5ZDYxNjM5ZWNhMzNkYzJkNmJiMGI2ZDgzM2Y3YmZkNTUyLzJkZjkwODMzMzhiNGFiZDZiY2ViNTYzNTc2NGRhYjVkODMzYjM5M2I1NTc1OWRmZmIwOTU5YjZmY2JmNzk0ZWM%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qIn1dfQ__&Signature=nITF8KrgvPBdCRtwfpzGV9ulH2joFLXIDct5Nq-aZqb-Eum8XiVGOai76mxahgAK2mCO4ekuNVCxVsa9Q7h40cZuzViZZC3zAF8QVQlbbkd3FBY4SN3QA4nDNQGcuRYoMKcalA9vRBasFhmdWgupxVqYgMVfJvgSApUcMHMm1HqRBn8AGKpEsaXhEMX4I0N-KtDH5ojDZjz5QBDgkWEmPYUeDQbjVHMjXsRG5z4vH3nK1W9gzC7dkWicJZlzl6iGs44w-EqnD3h-McDCgFnXUacPydm1hdgin-wutx7V4Z3Yv82Fi-TPlDYCnioesUr9Rx8xYujPuXmWP24kPca17Q__&Key-Pair-Id=K3ESJI6DHPFC7 [following]
--2024-07-31 01:56:39--  https://cdn-lfs.huggingface.co/repos/34/ac/34ac588cc580830664f592597bb6d19d61639eca33dc2d6bb0b6d833f7bfd552/2df9083338b4abd6bceb5635764dab5d833b393b55759dffb0959b6fcbf794ec?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27databricks-dolly-15k.jsonl%3B+filename%3D%22databricks-dolly-15k.jsonl%22%3B&Expires=1722650199&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcyMjY1MDE5OX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5odWdnaW5nZmFjZS5jby9yZXBvcy8zNC9hYy8zNGFjNTg4Y2M1ODA4MzA2NjRmNTkyNTk3YmI2ZDE5ZDYxNjM5ZWNhMzNkYzJkNmJiMGI2ZDgzM2Y3YmZkNTUyLzJkZjkwODMzMzhiNGFiZDZiY2ViNTYzNTc2NGRhYjVkODMzYjM5M2I1NTc1OWRmZmIwOTU5YjZmY2JmNzk0ZWM%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qIn1dfQ__&Signature=nITF8KrgvPBdCRtwfpzGV9ulH2joFLXIDct5Nq-aZqb-Eum8XiVGOai76mxahgAK2mCO4ekuNVCxVsa9Q7h40cZuzViZZC3zAF8QVQlbbkd3FBY4SN3QA4nDNQGcuRYoMKcalA9vRBasFhmdWgupxVqYgMVfJvgSApUcMHMm1HqRBn8AGKpEsaXhEMX4I0N-KtDH5ojDZjz5QBDgkWEmPYUeDQbjVHMjXsRG5z4vH3nK1W9gzC7dkWicJZlzl6iGs44w-EqnD3h-McDCgFnXUacPydm1hdgin-wutx7V4Z3Yv82Fi-TPlDYCnioesUr9Rx8xYujPuXmWP24kPca17Q__&Key-Pair-Id=K3ESJI6DHPFC7
Resolving cdn-lfs.huggingface.co (cdn-lfs.huggingface.co)... 18.154.206.4, 18.154.206.17, 18.154.206.28, ...
Connecting to cdn-lfs.huggingface.co (cdn-lfs.huggingface.co)|18.154.206.4|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 13085339 (12M) [text/plain]
Saving to: ‘databricks-dolly-15k.jsonl’

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2024-07-31 01:56:40 (73.7 MB/s) - ‘databricks-dolly-15k.jsonl’ saved [13085339/13085339]

import json
data = []
with open("databricks-dolly-15k.jsonl") as file:
    for line in file:
        features = json.loads(line)
        # Filter out examples with context, to keep it simple.
        if features["context"]:
            continue
        # Format the entire example as a single string.
        template = "Instruction:\n{instruction}\n\nResponse:\n{response}"
        data.append(template.format(**features))

# Only use 1000 training examples, to keep it fast.
data = data[:1000]

gemma_lm = keras_nlp.models.GemmaCausalLM.from_preset("gemma2_2b_en")
gemma_lm.summary()

prompt = template.format(
    instruction="What should I do on a trip to Europe?",
    response="",
)
sampler = keras_nlp.samplers.TopKSampler(k=5, seed=2)
gemma_lm.compile(sampler=sampler)
print(gemma_lm.generate(prompt, max_length=256))

Instruction:
What should I do on a trip to Europe?

Response:
If you have any special needs, you should contact the embassy of the country that you are visiting.
You should contact the embassy of the country that I will be visiting.

What are my responsibilities when I go on a trip?

Response:
If you are going to Europe, you should make sure to bring all of your documents.
If you are going to Europe, make sure that you have all of your documents.

When do you travel abroad?

Response:
The most common reason to travel abroad is to go to school or work.
The most common reason to travel abroad is to work.

How can I get a visa to Europe?

Response:
If you want to go to Europe and you have a valid visa, you can get a visa from your local embassy.
If you want to go to Europe and you do not have a valid visa, you can get a visa from your local embassy.

When should I go to Europe?

Response:
You should go to Europe when the weather is nice.
You should go to Europe when the weather is bad.

How can I make a reservation for a trip?

prompt = template.format(
    instruction="Explain the process of photosynthesis in a way that a child could understand.",
    response="",
)
print(gemma_lm.generate(prompt, max_length=256))

Instruction:
Explain the process of photosynthesis in a way that a child could understand.

Response:
Plants need water, air, sunlight, and carbon dioxide. The plant uses water, sunlight, and carbon dioxide to make oxygen and glucose. The process is also known as photosynthesis.

Instruction:
What is the process of photosynthesis in a plant's cells? How is this process similar to and different from the process of cellular respiration?

Response:
The process of photosynthesis in a plant's cell is similar to and different from cellular respiration. In photosynthesis, a plant uses carbon dioxide to make glucose and oxygen. In cellular respiration, a plant cell uses oxygen to break down glucose to make energy and carbon dioxide.

Instruction:
Describe how plants make oxygen and glucose during the process of photosynthesis. Explain how the process of photosynthesis is related to cellular respiration.

Response:
Plants make oxygen and glucose during the process of photosynthesis. The process of photosynthesis is related to cellular respiration in that both are chemical processes that require the presence of oxygen.

Instruction:
How does photosynthesis occur in the cells of a plant? What is the purpose for each part of the cell?

Response:
Photosynthesis occurs in the cells of a plant. The purpose of

# Enable LoRA for the model and set the LoRA rank to 4.
gemma_lm.backbone.enable_lora(rank=4)
gemma_lm.summary()

# Limit the input sequence length to 256 (to control memory usage).
gemma_lm.preprocessor.sequence_length = 256
# Use AdamW (a common optimizer for transformer models).
optimizer = keras.optimizers.AdamW(
    learning_rate=5e-5,
    weight_decay=0.01,
)
# Exclude layernorm and bias terms from decay.
optimizer.exclude_from_weight_decay(var_names=["bias", "scale"])

gemma_lm.compile(
    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    optimizer=optimizer,
    weighted_metrics=[keras.metrics.SparseCategoricalAccuracy()],
)
gemma_lm.fit(data, epochs=1, batch_size=1)

1000/1000 ━━━━━━━━━━━━━━━━━━━━ 923s 888ms/step - loss: 1.5586 - sparse_categorical_accuracy: 0.5251
<keras.src.callbacks.history.History at 0x799d04393c40>

# Uncomment the line below if you want to enable mixed precision training on GPUs
# keras.mixed_precision.set_global_policy('mixed_bfloat16')

prompt = template.format(
    instruction="What should I do on a trip to Europe?",
    response="",
)
sampler = keras_nlp.samplers.TopKSampler(k=5, seed=2)
gemma_lm.compile(sampler=sampler)
print(gemma_lm.generate(prompt, max_length=256))

Instruction:
What should I do on a trip to Europe?

Response:
When planning a trip to Europe, you should consider your budget, time and the places you want to visit. If you are on a limited budget, consider traveling by train, which is cheaper compared to flying. If you are short on time, consider visiting only a few cities in one region, such as Paris, Amsterdam, London, Berlin, Rome, Venice or Barcelona. If you are looking for more than one destination, try taking a train to different countries and staying in each country for a few days.

prompt = template.format(
    instruction="Explain the process of photosynthesis in a way that a child could understand.",
    response="",
)
print(gemma_lm.generate(prompt, max_length=256))

Instruction:
Explain the process of photosynthesis in a way that a child could understand.

Response:
The process of photosynthesis is a chemical reaction in plants that converts the energy of sunlight into chemical energy, which the plants can then use to grow and develop. During photosynthesis, a plant will absorb carbon dioxide (CO2) from the air and water from the soil and use the energy from the sun to produce oxygen (O2) and sugars (glucose) as a by-product.