GPT2CausalLMPreprocessor layer

• Original Link : https://keras.io/api/keras_nlp/models/gpt2/gpt2_causal_lm_preprocessor/
• Last Checked at : 2024-11-26

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GPT2CausalLMPreprocessor class

keras_nlp.models.GPT2CausalLMPreprocessor(
    tokenizer, sequence_length=1024, add_start_token=True, add_end_token=True, **kwargs
)

GPT2 Causal LM preprocessor.

This preprocessing layer is meant for use with keras_hub.models.GPT2CausalLM. By default, it will take in batches of strings, and return outputs in a (x, y, sample_weight) format, where the y label is the next token id in the x sequence.

For use with generation, the layer also exposes two methods generate_preprocess() and generate_postprocess(). When this preprocessor is attached to a keras_hub.models.GPT2CausalLM instance, these methods will be called implicitly in generate(). They can also be called standalone (e.g. to precompute preprocessing inputs for generation in a separate process).

Arguments

• tokenizer: A keras_hub.models.GPT2Tokenizer instance.
• sequence_length: The length of the packed inputs.
• add_start_token: If True, the preprocessor will prepend the tokenizer start token to each input sequence.
• add_end_token: If True, the preprocessor will append the tokenizer end token to each input sequence.

Call arguments

• x: A string, tf.Tensor or list of python strings.
• y: Label data. Should always be None as the layer generates labels.
• sample_weight: Label weights. Should always be None as the layer generates label weights.
• sequence_length: Pass to override the configured sequence_length of the layer.

Examples

# Load the preprocessor from a preset.
preprocessor = keras_hub.models.GPT2CausalLMPreprocessor.from_preset(
    "gpt2_base_en"
)
# Tokenize and pack a single sentence.
sentence = tf.constant("League of legends")
preprocessor(sentence)
# Same output.
preprocessor("League of legends")
# Tokenize a batch of sentences.
sentences = tf.constant(["Taco tuesday", "Fish taco please!"])
preprocessor(sentences)
# Same output.
preprocessor(["Taco tuesday", "Fish taco please!"])
# Map a dataset to preprocess a single sentence.
features = tf.constant(
    [
        "Avatar 2 is amazing!",
        "Well, I am not sure.",
    ]
)
labels = tf.constant([1, 0])
ds = tf.data.Dataset.from_tensor_slices((features, labels))
ds = ds.map(preprocessor, num_parallel_calls=tf.data.AUTOTUNE)
# Map a dataset to preprocess unlabled sentences.
ds = tf.data.Dataset.from_tensor_slices(features)
ds = ds.map(preprocessor, num_parallel_calls=tf.data.AUTOTUNE)

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from_preset method

GPT2CausalLMPreprocessor.from_preset(
    preset, config_file="preprocessor.json", **kwargs
)

Instantiate a keras_hub.models.Preprocessor from a model preset.

A preset is a directory of configs, weights and other file assets used to save and load a pre-trained model. The preset can be passed as one of:

1. a built-in preset identifier like 'bert_base_en'
2. a Kaggle Models handle like 'kaggle://user/bert/keras/bert_base_en'
3. a Hugging Face handle like 'hf://user/bert_base_en'
4. a path to a local preset directory like './bert_base_en'

For any Preprocessor subclass, you can run cls.presets.keys() to list all built-in presets available on the class.

As there are usually multiple preprocessing classes for a given model, this method should be called on a specific subclass like keras_hub.models.BertTextClassifierPreprocessor.from_preset().

Arguments

• preset: string. A built-in preset identifier, a Kaggle Models handle, a Hugging Face handle, or a path to a local directory.

Examples

# Load a preprocessor for Gemma generation.
preprocessor = keras_hub.models.GemmaCausalLMPreprocessor.from_preset(
    "gemma_2b_en",
)
# Load a preprocessor for Bert classification.
preprocessor = keras_hub.models.BertTextClassifierPreprocessor.from_preset(
    "bert_base_en",
)

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generate_preprocess method

GPT2CausalLMPreprocessor.generate_preprocess(x, sequence_length=None)

Convert strings to integer token input for generation.

Similar to calling the layer for training, this method takes in strings or tensor strings, tokenizes and packs the input, and computes a padding mask masking all inputs not filled in with a padded value.

Unlike calling the layer for training, this method does not compute labels and will never append a tokenizer.end_token_id to the end of the sequence (as generation is expected to continue at the end of the inputted prompt).

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generate_postprocess method

GPT2CausalLMPreprocessor.generate_postprocess(x)

Convert integer token output to strings for generation.

This method reverses generate_preprocess(), by first removing all padding and start/end tokens, and then converting the integer sequence back to a string.

tokenizer property

keras_nlp.models.GPT2CausalLMPreprocessor.tokenizer

The tokenizer used to tokenize strings.