# # 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 os from argparse import ArgumentParser import ujson as json import torch import numpy as np import random import asyncio import logging import sys from copy import deepcopy from pprint import pformat from .util import set_seed from . import models from .text import torchtext from .text.torchtext.data import Example from .utils.generic_dataset import CONTEXT_SPECIAL, QUESTION_SPECIAL, get_context_question, CQA logger = logging.getLogger(__name__) class ProcessedExample(): pass def split_tokenize(x): return x.split() class Server(): def __init__(self, args, field, model): self.device = set_seed(args) self.args = args self.field = field self.model = model def prepare_data(self): print(f'Vocabulary has {len(self.field.vocab)} tokens from training') char_vectors = torchtext.vocab.CharNGram(cache=self.args.embeddings) glove_vectors = torchtext.vocab.GloVe(cache=self.args.embeddings) vectors = [char_vectors, glove_vectors] self.field.vocab.load_vectors(vectors, True) self.field.decoder_to_vocab = {idx: self.field.vocab.stoi[word] for idx, word in enumerate(self.field.decoder_itos)} self.field.vocab_to_decoder = {idx: self.field.decoder_stoi[word] for idx, word in enumerate(self.field.vocab.itos) if word in self.field.decoder_stoi} self._limited_idx_to_full_idx = deepcopy(self.field.decoder_to_vocab) # should avoid this with a conditional in map to full self._oov_to_limited_idx = {} assert self.field.include_lengths def numericalize_example(self, ex): processed = ProcessedExample() for name in CQA.fields: # batch of size 1 batch = [getattr(ex, name)] entry, lengths, limited_entry, raw = self.field.process(batch, device=self.device, train=True, limited=self.field.decoder_stoi, l2f=self._limited_idx_to_full_idx, oov2l=self._oov_to_limited_idx) setattr(processed, name, entry) setattr(processed, f'{name}_lengths', lengths) setattr(processed, f'{name}_limited', limited_entry) setattr(processed, f'{name}_elmo', [[s.strip() for s in l] for l in raw]) processed.oov_to_limited_idx = self._oov_to_limited_idx processed.limited_idx_to_full_idx = self._limited_idx_to_full_idx return processed async def handle_client(self, client_reader, client_writer): try: request = json.loads(await client_reader.readline()) task = request['task'] if 'task' in request else 'generic' context = request['context'] question = request['question'] answer = '' if task == 'almond': tokenize = split_tokenize else: tokenize = None context_question = get_context_question(context, question) fields = [(x, self.field) for x in CQA.fields] ex = Example.fromlist([context, question, answer, CONTEXT_SPECIAL, QUESTION_SPECIAL, context_question], fields, tokenize=tokenize) batch = self.numericalize_example(ex) _, prediction_batch = self.model(batch, iteration=0) if task == 'almond': predictions = self.field.reverse(prediction_batch, detokenize=lambda x: ' '.join(x)) else: predictions = self.field.reverse(prediction_batch) client_writer.write((json.dumps(dict(id=request['id'], answer=predictions[0])) + '\n').encode('utf-8')) except IOError: logger.info('Connection to client_reader closed') try: client_writer.close() except IOError: pass def run(self): 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 params = list(filter(lambda p: p.requires_grad, self.model.parameters())) num_param = mult(params) print(f'{self.args.model} has {num_param:,} parameters') self.model.to(self.device) self.model.eval() with torch.no_grad(): loop = asyncio.get_event_loop() server = loop.run_until_complete(asyncio.start_server(self.handle_client, port=self.args.port)) try: loop.run_forever() except KeyboardInterrupt: pass server.close() loop.run_until_complete(server.wait_closed()) loop.close() def get_args(argv): parser = ArgumentParser(prog=argv[0]) parser.add_argument('--path', required=True) parser.add_argument('--devices', default=[0], nargs='+', type=int, help='a list of devices that can be used (multi-gpu currently WIP)') parser.add_argument('--seed', default=123, type=int, help='Random seed.') parser.add_argument('--data', default='./decaNLP/.data/', type=str, help='where to load data from.') parser.add_argument('--embeddings', default='./decaNLP/.embeddings', type=str, help='where to save embeddings.') parser.add_argument('--checkpoint_name', default='best.pth', help='Checkpoint file to use (relative to --path, defaults to best.pth)') parser.add_argument('--port', default=8401, type=int, help='TCP port to listen on') args = parser.parse_args(argv) 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', 'reverse_task_bool'] 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.task_to_metric = { 'cnn_dailymail': 'avg_rouge', 'iwslt.en.de': 'bleu', 'multinli.in.out': 'em', 'squad': 'nf1', 'srl': 'nf1', 'almond': 'bleu' if args.reverse_task_bool else 'em', 'sst': 'em', 'wikisql': 'lfem', 'woz.en': 'joint_goal_em', 'zre': 'corpus_f1', 'schema': 'em' } args.best_checkpoint = os.path.join(args.path, args.checkpoint_name) return args def main(argv=sys.argv): args = get_args(argv) print(f'Arguments:\n{pformat(vars(args))}') np.random.seed(args.seed) random.seed(args.seed) torch.manual_seed(args.seed) torch.cuda.manual_seed(args.seed) print(f'Loading from {args.best_checkpoint}') if torch.cuda.is_available(): save_dict = torch.load(args.best_checkpoint) else: save_dict = torch.load(args.best_checkpoint, map_location='cpu') field = save_dict['field'] print(f'Initializing Model') Model = getattr(models, args.model) model = Model(field, args) model_dict = save_dict['model_state_dict'] backwards_compatible_cove_dict = {} for k, v in model_dict.items(): if 'cove.rnn.' in k: k = k.replace('cove.rnn.', 'cove.rnn1.') backwards_compatible_cove_dict[k] = v model_dict = backwards_compatible_cove_dict model.load_state_dict(model_dict) server = Server(args, field, model) server.prepare_data() model.set_embeddings(field.vocab.vectors) server.run()