# # Copyright (c) 2018, Salesforce, Inc. # The Board of Trustees of the Leland Stanford Junior University # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # * Neither the name of the copyright holder nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. import time import os import torch import random import numpy as np import ujson as json import logging from .utils import generic_dataset logger = logging.getLogger(__name__) def tokenizer(s): return s.split() def get_context_question(ex, context, question, field): return ex.context_special + ex.context + ex.question_special + ex.question def preprocess_examples(args, tasks, splits, field, logger=None, train=True): min_length = 1 max_context_length = args.max_train_context_length if train else args.max_val_context_length is_too_long = lambda ex: (len(ex.answer) > args.max_answer_length or len(ex.context)>max_context_length) is_too_short = lambda ex: (len(ex.answer) < min_length or len(ex.context) {len(s.examples)}') l = len(s.examples) s.examples = [ex for ex in s.examples if not is_too_short(ex)] if len(s.examples) < l: if logger is not None: logger.info(f'Filtering out short {task.name} examples: {l} -> {len(s.examples)}') if logger is not None: context_lengths = [len(ex.context) for ex in s.examples] question_lengths = [len(ex.question) for ex in s.examples] answer_lengths = [len(ex.answer) for ex in s.examples] logger.info(f'{task.name} context lengths (min, mean, max): {np.min(context_lengths)}, {int(np.mean(context_lengths))}, {np.max(context_lengths)}') logger.info(f'{task.name} question lengths (min, mean, max): {np.min(question_lengths)}, {int(np.mean(question_lengths))}, {np.max(question_lengths)}') logger.info(f'{task.name} answer lengths (min, mean, max): {np.min(answer_lengths)}, {int(np.mean(answer_lengths))}, {np.max(answer_lengths)}') for x in s.examples: x.context_question = get_context_question(x, x.context, x.question, field) if logger is not None: logger.info('Tokenized examples:') for ex in s.examples[:10]: logger.info('Context: ' + ' '.join([token.strip() for token in ex.context])) logger.info('Question: ' + ' '.join([token.strip() for token in ex.question])) logger.info(' '.join([token.strip() for token in ex.context_question])) logger.info('Answer: ' + ' '.join([token.strip() for token in ex.answer])) def set_seed(args, rank=None): if not torch.cuda.is_available(): ordinal = -1 elif rank is None and len(args.devices) > 0: ordinal = args.devices[0] else: ordinal = args.devices[rank] device = torch.device(f'cuda:{ordinal}' if ordinal > -1 else 'cpu') # device = torch.device(f'cuda:{ordinal}' if ordinal > -1 else 'cpu') logger.debug(f'device: {device}') np.random.seed(args.seed) random.seed(args.seed) torch.manual_seed(args.seed) with torch.cuda.device(ordinal): torch.cuda.manual_seed(args.seed) return device def count_params(params): def mult(ps): r = 0 for p in ps: this_r = 1 for s in p.size(): this_r *= s r += this_r return r return mult(params) def get_trainable_params(model): return list(filter(lambda p: p.requires_grad, model.parameters())) def elapsed_time(log): t = time.time() - log.start day = int(t // (24 * 3600)) t = t % (24 * 3600) hour = int(t // 3600) t %= 3600 minutes = int(t // 60) t %= 60 seconds = int(t) return f'{day:02}:{hour:02}:{minutes:02}:{seconds:02}' def batch_fn(new, i, sofar): prev_max_len = sofar / (i - 1) if i > 1 else 0 return max(len(new.context), 5*len(new.answer), prev_max_len) * i def pad(x, new_channel, dim, val=None): if x.size(dim) > new_channel: x = x.narrow(dim, 0, new_channel) channels = x.size() assert (new_channel >= channels[dim]) if new_channel == channels[dim]: return x size = list(channels) size[dim] = new_channel - size[dim] padding = x.new(*size).fill_(val) return torch.cat([x, padding], dim) def load_config_json(args): args.almond_type_embeddings = False with open(os.path.join(args.path, 'config.json')) as config_file: config = json.load(config_file) retrieve = ['model', 'transformer_layers', 'rnn_layers', 'transformer_hidden', 'dimension', 'load', 'max_val_context_length', 'val_batch_size', 'transformer_heads', 'max_output_length', 'max_generative_vocab', 'lower', 'cove', 'intermediate_cove', 'elmo', 'glove_and_char', 'use_maxmargin_loss', 'small_glove', 'almond_type_embeddings'] for r in retrieve: if r in config: setattr(args, r, config[r]) elif 'cove' in r: setattr(args, r, False) elif 'elmo' in r: setattr(args, r, [-1]) elif 'glove_and_char' in r: setattr(args, r, True) else: setattr(args, r, None) args.dropout_ratio = 0.0 args.best_checkpoint = os.path.join(args.path, args.checkpoint_name)