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#!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Sample from a trained LM; hacked fairseq-interactive
"""
from collections import namedtuple
import os
import ast
import numpy as np
from fairseq import checkpoint_utils, options, tasks, utils
import tqdm
Batch = namedtuple('Batch', 'ids src_tokens src_lengths')
Translation = namedtuple('Translation', 'src_str hypos pos_scores alignments')
def make_batches(lines, args, task, max_positions):
tokens = [
task.source_dictionary.encode_line(
src_str, add_if_not_exist=False
).long()
for src_str in lines
]
lengths = [t.numel() for t in tokens]
itr = task.get_batch_iterator(
dataset=task.build_dataset_for_inference(tokens, lengths),
max_tokens=args.dataset.max_tokens,
max_sentences=args.dataset.batch_size,
max_positions=max_positions,
ignore_invalid_inputs=args.dataset.skip_invalid_size_inputs_valid_test
).next_epoch_itr(shuffle=False)
for batch in itr:
yield Batch(
ids=batch['id'],
src_tokens=batch['net_input']['src_tokens'], src_lengths=batch['net_input']['src_lengths'],
)
def main(args):
arg_prompts = args.prompts
arg_output = args.output
arg_debug = args.debug
arg_sample_size = args.samples_per_prompt
try:
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
args = convert_namespace_to_omegaconf(args)
except:
pass
# if args.max_tokens is None and args.max_sentences is None:
if args.common.seed is not None:
np.random.seed(args.common.seed)
utils.set_torch_seed(args.common.seed)
if args.generation.sampling:
args.generation.nbest = args.generation.beam = arg_sample_size
task = tasks.setup_task(args.task)
overrides = ast.literal_eval(args.common_eval.model_overrides)
models, _model_args = checkpoint_utils.load_model_ensemble(
args.common_eval.path.split(os.pathsep),
arg_overrides=overrides,
task=task,
suffix=getattr(args, "checkpoint_suffix", ""),
)
# Set dictionaries
src_dict = task.source_dictionary
tgt_dict = task.target_dictionary
# Optimize ensemble for generation
for model in models:
model.prepare_for_inference_(args)
model.cuda()
# Load alignment dictionary for unknown word replacement
# (None if no unknown word replacement, empty if no path to align dictionary)
align_dict = utils.load_align_dict(args.generation.replace_unk)
max_positions = utils.resolve_max_positions(
task.max_positions(),
*[model.max_positions() for model in models]
)
output_file = open(arg_output, 'w')
with open(arg_prompts, 'r') as fin:
lines = fin.readlines()
split = [x.split('|', 1) for x in lines]
seq_id = [x[0] for x in split]
prompts = [x[1] for x in split]
if args.generation.prefix_size >= 0:
prompts = [' '.join(l.split()[:args.generation.prefix_size])
for l in prompts]
if arg_debug:
prompts = prompts[:10]
generator = task.build_generator(models, args.generation)
start_id = 0
pbar = tqdm.tqdm(total=len(prompts))
for batch in make_batches(prompts, args, task, max_positions):
src_tokens = batch.src_tokens
src_lengths = batch.src_lengths
src_tokens = src_tokens.cuda()
src_lengths = src_lengths.cuda()
sample = {
'net_input': {
'src_tokens': src_tokens,
'src_lengths': src_lengths,
},
}
results = []
translations = task.inference_step(generator, models, sample)
for i, (id, hypos) in enumerate(zip(batch.ids.tolist(), translations)):
src_tokens_i = utils.strip_pad(src_tokens[i], tgt_dict.pad())
results.append((i + start_id, src_tokens_i, hypos))
# sort output to match input order
for id, src_tokens, hypos in sorted(results, key=lambda x: x[0]):
if src_dict is not None:
src_str = src_dict.string(
src_tokens, args.common_eval.post_process)
# Process top predictions
for hypo_id, hypo in enumerate(hypos):
_hypo_tokens, hypo_str, _alignment = utils.post_process_prediction(
hypo_tokens=hypo['tokens'].int().cpu(),
src_str=src_str,
alignment=hypo['alignment'],
align_dict=align_dict,
tgt_dict=tgt_dict,
remove_bpe=args.common_eval.post_process,
)
detok_hypo_str = hypo_str
utterance = detok_hypo_str
print(f'{seq_id[id]}__{hypo_id}|{utterance}', file=output_file)
pbar.update(1)
start_id += len(results)
# output_file.close()
def cli_main():
parser = options.get_interactive_generation_parser()
parser.add_argument('--prompts', type=str, default=None, required=True)
parser.add_argument('--output', type=str, default=None, required=True)
parser.add_argument('--debug', action='store_true')
parser.add_argument('--samples-per-prompt', type=int, default=1)
args = options.parse_args_and_arch(parser)
np.random.seed(args.seed)
utils.set_torch_seed(args.seed)
main(args)
if __name__ == '__main__':
cli_main()
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