Samplers in Gemma model about keras-nlp

[tirthasheshpatel]

Hi @mostafamdy, it seems like the guide is outdated. Thanks for bringing this up! You can refer to the Sampler API docs or the "Example Use" section on the Kaggle model card. For your usecase, there's now a simpler API for plugging-in different samplers:

import keras_nlp

model = keras_nlp.models.GemmaCausalLM('gemma_2b_en')

# Tell KerasNLP to use a "greddy" sampler. Other options are "top_k", "top_p", etc.
# See https://keras.io/api/keras_nlp/samplers/ for more info
model.compile(sampler="greedy")
output = model.generate("What is Keras?", max_length=50)

# You can also initialize a sampler to configure it for your usecase
sampler = keras_nlp.samplers.TopKSampler(k=5, temperature=0.7)
causal_lm.compile(sampler=sampler)
causal_lm.generate(["What is Keras?"])

Does this answer your question?

[tirthasheshpatel]

Ah OK. Your code looks good to me. You seem to be printing out the next token predictions for each input sequence which is why I guess the outputs are different. Can you check if this code generates the right output:

import keras
from keras import ops
import keras_nlp


model = keras_nlp.models.GemmaCausalLM.from_preset('gemma_2b_en')
preprocessor = model.preprocessor
tokenizer = preprocessor.tokenizer
backbone = model.backbone


def loss_fn(y_true, y_pred, prompt=None, index=None):
    logits = y_pred
    temperature = 1.0
    logits = ops.cast(logits, "float32")

    # Compute probs and next token value
    probabilities = ops.softmax(logits[:, index, :], axis=-1)
    next_token = ops.argmax(probabilities, axis=-1)

    # Update the prompt
    prompt_tokens = tokenizer.tokenize(prompt)
    updated_prompt_tokens = ops.concatenate([prompt_tokens, next_token[..., None]], axis=-1)
    updated_prompt = tokenizer.detokenize(updated_prompt_tokens)

    # Print the updated prompt
    print(f"The updated prompt is: {updated_prompt}")


# Get the inputs
prompt = ["The quick brown"]
train_data = preprocessor(prompt, sequence_length=10)
index = ops.min(ops.sum(train_data[0]['padding_mask'], axis=-1)) - 2

# Evaluate the loss function
loss_fn(train_data[1], model(train_data[0]), prompt=prompt, index=index)
# The updated prompt is: [b'The quick brown fox']

# Check outputs match
model.generate(prompt, max_length=5)
# ['The quick brown fox']

[SuryanarayanaY]

Hi @mostafamdy ,

It seems the sampler expects the prompt in the form a List. The {{call_args}} in base Sampler class not defined properly IMO.

keras-nlp/keras_nlp/samplers/sampler.py

Lines 32 to 33 in 16d3ebb

	Call arguments:
	{{call_args}}

[mostafamdy]

Thanks @tirthasheshpatel
I want to use sampler in custom loss function is it possible? 😅

Is this code correct?

def custom_loss(y_true,y_pred):
    logits=y_pred
    temperature=1.0
    logits = ops.cast(logits, "float32")
    probabilities = keras.activations.softmax(logits / temperature)
    next_token=ops.argmax(probabilities, axis=-1)
    print(next_token)
    
    txt=tokenizer.detokenize(next_token)
    print(f"Greedy search generated text: \n{txt}\n")
 

[mostafamdy]

I tried to call gemma model like this

preprocessor=keras_nlp.models.GemmaPreprocessor(
    tokenizer, sequence_length=SEQ_LEN,
)
model_out = gemma_lm(preprocessor([data[0]]))

and then passed model_out to custom_loss function to generate text

def custom_loss(y_true,y_pred):
    logits=y_pred
    temperature=1.0
    logits = ops.cast(logits, "float32")
    probabilities = keras.activations.softmax(logits / temperature)
    next_token=ops.argmax(probabilities, axis=-1)    
    txt=tokenizer.detokenize(next_token)
    print(f"Greedy search generated text: \n{txt}\n")

custom_loss("y_true",model_out)

But the output is different from gemma_lm.generate()

[mostafamdy]

Thank you so much ❤️
I tried this code and it's working well, but I have a misunderstanding.
I tried to change the index of logits

for i in range(10):
    probabilities = ops.softmax(logits[:, i, :], axis=-1)
    next_token = ops.argmax(probabilities, axis=-1)
    updated_prompt = tokenizer.detokenize(next_token)
    print(updated_prompt)

the output was :
and fox fox,' the, the
When using generate with a sequence length of 10, I received:
The quick brown fox jumps over the sleeping dog