Source code for cerebras.modelzoo.data_preparation.data_preprocessing.nlg_token_generator

# Copyright 2022 Cerebras Systems.
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# Licensed under the Apache License, Version 2.0 (the "License");
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import numpy as np


[docs]class NLGTokenGenerator: """Token Generator for NLG data sets such as E2E, DART, and WebNLG. Assumes the dataset has already been tokenized. Expect .jsonl input files that contains a "context" and a "completion" key. Used with GptHDF5DataProcessor. """ def __init__(self, max_seq_length): self.max_seq_length = max_seq_length self.sample_features = ["input_ids", "attention_mask", "labels"] def encode(self, semantic_data_array): context, completion = self.parse_semantic_data_array( semantic_data_array ) raw_chars_count = 0 ## As the dataset is already tokenized into tokens. Raw dataset is not available. raw_bytes_count = 0 ## As the dataset is already tokenized into tokens. Raw dataset is not available. files_processed = 0 discarded_files = 0 normalized_chars_count = raw_chars_count normalized_bytes_count = raw_bytes_count input_ids = np.concatenate((context, completion[:-1])) labels = np.concatenate((context[1:], completion)) num_pad_tokens = self.max_seq_length - len(input_ids) num_masked_tokens = self.max_seq_length - len(completion) input_ids = np.pad(input_ids, (0, self.max_seq_length - len(input_ids))) labels = np.pad(labels, (0, self.max_seq_length - len(labels))) indices = np.arange(self.max_seq_length) attention_mask = np.where(indices < len(context) - 1, 0, indices) attention_mask = np.where( attention_mask >= len(context) - 1 + len(completion), 0, attention_mask, ) attention_mask = np.where(attention_mask != 0, 1, 0) sample = np.stack([input_ids, attention_mask, labels]).reshape( 1, 3, self.max_seq_length ) loss_valid_tokens = int(attention_mask.sum()) num_tokens = int(input_ids.shape[0]) if sample.size == 0: discarded_files += 1 files_processed += 1 data_stats = { "discarded": discarded_files, "processed": files_processed, "successful": files_processed - discarded_files, "raw_chars_count": raw_chars_count, "raw_bytes_count": raw_bytes_count, "num_pad_tokens": num_pad_tokens, "num_masked_tokens": num_masked_tokens, "loss_valid_tokens": loss_valid_tokens, "num_tokens": num_tokens, "normalized_chars_count": normalized_chars_count, "normalized_bytes_count": normalized_bytes_count, } data = {"data": sample} return data, data_stats def parse_semantic_data_array(self, semantic_data_array): context = semantic_data_array[0]['content'][0]['text'] completion = semantic_data_array[1]['content'][0]['text'] return context, completion