@tensorflow/tfjs-layers/dist/layers/nlp/models/gpt2/gpt2_causal_lm_preprocessor.d.ts

TensorFlow layers API in JavaScript

/**
 * @license
 * Copyright 2023 Google LLC.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/// <amd-module name="@tensorflow/tfjs-layers/dist/layers/nlp/models/gpt2/gpt2_causal_lm_preprocessor" />
/**
 * GPT2 Causal LM preprocessor layer.
 */
import { NamedTensorMap, Tensor } from '@tensorflow/tfjs-core';
import { GPT2Preprocessor, GPT2PreprocessorOptions } from './gpt2_preprocessor';
/**
 * GPT2 Causal LM preprocessor.
 *
 * This preprocessing layer is meant for use with
 * `GPT2CausalLM`. By default, it will take in batches of
 * strings, and return outputs in a `[x, y, sampleWeight]` format, where the
 * `y` label is the next token id in the `x` sequence.
 *
 * For use with generation, the layer also exposes two methods
 * generatePreprocess()` and `generatePostprocess()`. When this preprocessor
 * is attached to a `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).
 *
 * Examples:
 * ```js
 * // Load the preprocessor from a preset.
 * const preprocessor = GPT2CausalLMPreprocessor.from_preset('gpt2_base_en');
 *
 * // Tokenize and pack a single sentence.
 * const sentence = tf.scalar('League of legends');
 * preprocessor.apply(sentence);
 * // Same output.
 * preprocessor('League of legends');
 *
 * // Tokenize a batch of sentences.
 * const sentences = tf.constant(['Taco tuesday', 'Fish taco please!']);
 * preprocessor.apply(sentences);
 * // Same output.
 * preprocessor.apply(['Taco tuesday', 'Fish taco please!']);
 * ```
 */
export declare class GPT2CausalLMPreprocessor extends GPT2Preprocessor {
    /** @nocollapse */
    static className: string;
    call(inputs: Tensor | Tensor[], kwargs: GPT2PreprocessorOptions): Tensor | Tensor[];
    /**
     * Calls the layer and returns extra information like the paddingMask used to
     * pack the sequence, the label data, and the sample weights used.
     */
    callAndPackArgs(inputs: Tensor | Tensor[], kwargs: GPT2PreprocessorOptions): NamedTensorMap | [NamedTensorMap, Tensor] | [NamedTensorMap, Tensor, Tensor];
    /**
     * Covert 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 the layer for training, this method does not compute
     * labels and will never append a `tokenizer.endTokenId` to the end of
     * the sequence (as generation is expected to continue at the end of the
     * inputted prompt).
     */
    generatePreprocess(x: Tensor, sequenceLength?: number): NamedTensorMap;
    /**
     * Covert integer token output to strings for generation.
     *
     * This method reverses `generatePreprocess()`, by first removing all
     * padding and start/end tokens, and then converting the integer sequence
     * back to a string.
     */
    generatePostprocess(x: NamedTensorMap): Tensor;
}