Add a "decanlp" script that calls out to the different subcommands
Usage: - decanlp train ... - decanlp predict ... - decanlp convert-to-logical-forms ...
This commit is contained in:
Giovanni Campagna
parent
a5a203b099
commit
5447d0c37c
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@ -20,11 +20,11 @@ def save_args(args):
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json.dump(vars(args), f, indent=2)
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def parse():
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def parse(argv):
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"""
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Returns the arguments from the command line.
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"""
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parser = ArgumentParser()
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parser = ArgumentParser(prog=argv[0])
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parser.add_argument('--root', default='./decaNLP', type=str, help='root directory for data, results, embeddings, code, etc.')
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parser.add_argument('--data', default='.data/', type=str, help='where to load data from.')
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parser.add_argument('--save', default='results', type=str, help='where to save results.')
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@ -96,7 +96,7 @@ def parse():
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parser.add_argument('--loss_switch', default=0.666, type=float, help='switch to BLEU loss after certain iterations controlled by this ratio')
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args = parser.parse_args()
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args = parser.parse_args(argv)
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if args.model is None:
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args.model = 'mcqa'
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if args.val_tasks is None:
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@ -3,6 +3,7 @@ from decanlp.text.torchtext.datasets.generic import Query
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from argparse import ArgumentParser
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import os
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import re
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import sys
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import ujson as json
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from decanlp.metrics import to_lf
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@ -64,14 +65,14 @@ def write_logical_forms(greedy, args):
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except Exception as e:
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f.write(json.dumps(correct_format({})) + '\n')
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if __name__ == '__main__':
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parser = ArgumentParser()
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def main(argv=sys.argv):
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parser = ArgumentParser(prog=argv[0])
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parser.add_argument('data', help='path to the directory containing data for WikiSQL')
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parser.add_argument('predictions', help='path to prediction file, containing one prediction per line')
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parser.add_argument('ids', help='path to file for indices, a list of integers indicating the index into the dev/test set of the predictions on the corresponding line in \'predicitons\'')
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parser.add_argument('output', help='path for logical forms output line by line')
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parser.add_argument('evaluate', help='running on the \'validation\' or \'test\' set')
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args = parser.parse_args()
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args = parser.parse_args(argv)
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with open(args.predictions) as f:
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greedy = [l for l in f]
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if args.ids is not None:
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@ -79,3 +80,6 @@ if __name__ == '__main__':
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ids = [int(l.strip()) for l in f]
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greedy = [x[1] for x in sorted([(i, g) for i, g in zip(ids, greedy)])]
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write_logical_forms(greedy, args)
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if __name__ == '__main__':
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main()
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@ -1,6 +1,6 @@
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from subprocess import Popen, PIPE, CalledProcessError
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import json
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from text.torchtext.datasets.generic import Query
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from .text.torchtext.datasets.generic import Query
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import logging
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import os
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import re
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@ -7,9 +7,10 @@ import ujson as json
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import torch
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import numpy as np
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import random
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import sys
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from pprint import pformat
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from .util import get_splits, set_seed, preprocess_examples, tokenizer
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from .util import get_splits, set_seed, preprocess_examples
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from .metrics import compute_metrics
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from . import models
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@ -207,8 +208,8 @@ def run(args, field, val_sets, model):
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print(f'\nSummary: | {sum(decaScore)} | {" | ".join([str(x) for x in decaScore])} |\n')
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def get_args():
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parser = ArgumentParser()
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def get_args(argv):
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parser = ArgumentParser(prog=argv[0])
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parser.add_argument('--path', required=True)
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parser.add_argument('--evaluate', type=str, required=True)
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parser.add_argument('--tasks', default=['almond', 'squad', 'iwslt.en.de', 'cnn_dailymail', 'multinli.in.out', 'sst', 'srl', 'zre', 'woz.en', 'wikisql', 'schema'], nargs='+')
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@ -227,7 +228,7 @@ def get_args():
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parser.add_argument('--eval_dir', type=str, default=None, help='use this directory to store eval results')
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parser.add_argument('--cached', default='', type=str, help='where to save cached files')
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args = parser.parse_args()
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args = parser.parse_args(argv)
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with open(os.path.join(args.path, 'config.json')) as config_file:
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config = json.load(config_file)
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@ -267,7 +268,7 @@ def get_args():
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return args
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if __name__ == '__main__':
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def main(argv=sys.argv):
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args = get_args()
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print(f'Arguments:\n{pformat(vars(args))}')
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@ -299,3 +300,6 @@ if __name__ == '__main__':
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model.set_embeddings(field.vocab.vectors)
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run(args, field, splits, model)
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if __name__ == '__main__':
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main()
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@ -1,6 +1,5 @@
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#!/usr/bin/env python3
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import os
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from text import torchtext
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from argparse import ArgumentParser
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import ujson as json
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import torch
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@ -11,11 +10,12 @@ import logging
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from copy import deepcopy
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from pprint import pformat
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from util import set_seed
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import models
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from .util import set_seed
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from . import models
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from text.torchtext.data import Example
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from text.torchtext.datasets.generic import CONTEXT_SPECIAL, QUESTION_SPECIAL, get_context_question, CQA
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from .text import torchtext
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from .text.torchtext.data import Example
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from .text.torchtext.datasets.generic import CONTEXT_SPECIAL, QUESTION_SPECIAL, get_context_question, CQA
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logger = logging.getLogger(__name__)
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@ -37,14 +37,14 @@ class Server():
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def prepare_data(self):
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print(f'Vocabulary has {len(self.field.vocab)} tokens from training')
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char_vectors = torchtext.vocab.CharNGram(cache=args.embeddings)
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glove_vectors = torchtext.vocab.GloVe(cache=args.embeddings)
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char_vectors = torchtext.vocab.CharNGram(cache=self.args.embeddings)
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glove_vectors = torchtext.vocab.GloVe(cache=self.args.embeddings)
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vectors = [char_vectors, glove_vectors]
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self.field.vocab.load_vectors(vectors, True)
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self.field.decoder_to_vocab = {idx: self.field.vocab.stoi[word] for idx, word in enumerate(self.field.decoder_itos)}
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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}
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self._limited_idx_to_full_idx = deepcopy(field.decoder_to_vocab) # should avoid this with a conditional in map to full
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self._limited_idx_to_full_idx = deepcopy(self.field.decoder_to_vocab) # should avoid this with a conditional in map to full
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self._oov_to_limited_idx = {}
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assert self.field.include_lengths
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@ -55,8 +55,8 @@ class Server():
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for name in CQA.fields:
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# batch of size 1
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batch = [getattr(ex, name)]
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entry, lengths, limited_entry, raw = field.process(batch, device=self.device, train=True,
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limited=field.decoder_stoi, l2f=self._limited_idx_to_full_idx, oov2l=self._oov_to_limited_idx)
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entry, lengths, limited_entry, raw = self.field.process(batch, device=self.device, train=True,
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limited=self.field.decoder_stoi, l2f=self._limited_idx_to_full_idx, oov2l=self._oov_to_limited_idx)
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setattr(processed, name, entry)
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setattr(processed, f'{name}_lengths', lengths)
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setattr(processed, f'{name}_limited', limited_entry)
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@ -81,16 +81,16 @@ class Server():
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tokenize = None
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context_question = get_context_question(context, question)
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fields = [(x, field) for x in CQA.fields]
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fields = [(x, self.field) for x in CQA.fields]
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ex = Example.fromlist([context, question, answer, CONTEXT_SPECIAL, QUESTION_SPECIAL, context_question], fields, tokenize=tokenize)
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batch = self.numericalize_example(ex)
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_, prediction_batch = model(batch, iteration=0)
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_, prediction_batch = self.model(batch, iteration=0)
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if task == 'almond':
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predictions = field.reverse(prediction_batch, detokenize=lambda x: ' '.join(x))
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predictions = self.field.reverse(prediction_batch, detokenize=lambda x: ' '.join(x))
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else:
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predictions = field.reverse(prediction_batch)
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predictions = self.field.reverse(prediction_batch)
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client_writer.write((json.dumps(dict(id=request['id'], answer=predictions[0])) + '\n').encode('utf-8'))
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@ -110,15 +110,15 @@ class Server():
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this_r *= s
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r += this_r
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return r
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params = list(filter(lambda p: p.requires_grad, model.parameters()))
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params = list(filter(lambda p: p.requires_grad, self.model.parameters()))
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num_param = mult(params)
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print(f'{args.model} has {num_param:,} parameters')
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model.to(self.device)
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print(f'{self.args.model} has {num_param:,} parameters')
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self.model.to(self.device)
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model.eval()
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self.model.eval()
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with torch.no_grad():
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loop = asyncio.get_event_loop()
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server = loop.run_until_complete(asyncio.start_server(self.handle_client, port=args.port))
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server = loop.run_until_complete(asyncio.start_server(self.handle_client, port=self.args.port))
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try:
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loop.run_forever()
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except KeyboardInterrupt:
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@ -128,8 +128,8 @@ class Server():
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loop.close()
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def get_args():
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parser = ArgumentParser()
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def get_args(argv):
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parser = ArgumentParser(prog=argv[0])
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parser.add_argument('--path', required=True)
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parser.add_argument('--devices', default=[0], nargs='+', type=int, help='a list of devices that can be used (multi-gpu currently WIP)')
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parser.add_argument('--seed', default=123, type=int, help='Random seed.')
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@ -138,7 +138,7 @@ def get_args():
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parser.add_argument('--checkpoint_name', default='best.pth', help='Checkpoint file to use (relative to --path, defaults to best.pth)')
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parser.add_argument('--port', default=8401, type=int, help='TCP port to listen on')
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args = parser.parse_args()
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args = parser.parse_args(argv)
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with open(os.path.join(args.path, 'config.json')) as config_file:
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config = json.load(config_file)
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@ -179,8 +179,8 @@ def get_args():
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return args
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if __name__ == '__main__':
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args = get_args()
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def main(argv=sys.argv):
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args = get_args(argv)
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print(f'Arguments:\n{pformat(vars(args))}')
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np.random.seed(args.seed)
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@ -0,0 +1,60 @@
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#!/usr/bin/env python3
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# Copyright 2019 The Board of Trustees of the Leland Stanford Junior University
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#
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# Author: Giovanni Campagna <gcampagn@cs.stanford.edu>
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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#
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# * Redistributions of source code must retain the above copyright notice, this
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# list of conditions and the following disclaimer.
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#
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# * Redistributions in binary form must reproduce the above copyright notice,
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# this list of conditions and the following disclaimer in the documentation
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# and/or other materials provided with the distribution.
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#
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# * Neither the name of the copyright holder nor the names of its
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# contributors may be used to endorse or promote products derived from
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# this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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import sys
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from decanlp import convert_to_logical_forms, train, predict, server
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subcommands = {
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'convert-to-logical-froms': ('Convert to logical forms (for SQL tasks)', convert_to_logical_forms.main),
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'train': ('Train a model', train.main),
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'predict': ('Evaluate a model, or compute predictions on a test dataset', predict.main),
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'server': ('Export RPC interface to predict', server.main)
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}
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def usage():
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print('Usage: %s SUBCOMMAND [OPTIONS]' % (sys.argv[0]))
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print()
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print('Available subcommands:')
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for subcommand,(help_text,_) in subcommands.items():
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print(' %s - %s' % (subcommand, help_text))
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sys.exit(1)
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def main():
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if len(sys.argv) < 2 or sys.argv[1] not in subcommands:
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usage()
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return
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main_fn = subcommands[sys.argv[1]][1]
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canned_argv = ['decanlp-' + sys.argv[1]] + sys.argv[2:]
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main_fn(canned_argv)
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if __name__ == '__main__':
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main()
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@ -5,6 +5,7 @@ import math
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import time
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import random
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import collections
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import sys
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from copy import deepcopy
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import logging
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@ -365,8 +366,8 @@ def init_opt(args, model):
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return opt
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def main():
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args = arguments.parse()
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def main(argv=sys.argv):
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args = arguments.parse(argv)
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if args is None:
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return
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set_seed(args)
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@ -1,4 +1,3 @@
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from text import torchtext
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import time
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import os
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import sys
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@ -6,7 +5,8 @@ import torch
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import random
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import numpy as np
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from text.torchtext.data.utils import get_tokenizer
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from .text import torchtext
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from .text.torchtext.data.utils import get_tokenizer
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def tokenizer(s):
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return s.split()
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@ -1,7 +1,7 @@
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import torch
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from .util import pad, tokenizer
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from .metrics import compute_metrics
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from text.torchtext.data.utils import get_tokenizer
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from .text.torchtext.data.utils import get_tokenizer
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def compute_validation_outputs(model, val_iter, field, iteration, optional_names=[]):
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