mirror of
https://github.com/facebookresearch/fairseq.git
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9831634946
Summary: Pull Request resolved: https://github.com/pytorch/fairseq/pull/2448 Reviewed By: ngoyal2707 Differential Revision: D23011193 Pulled By: myleott fbshipit-source-id: 1a29481707108e4465aca78ec1581fb79f05efba
495 lines
16 KiB
Python
495 lines
16 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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import argparse
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import os
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import random
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import sys
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import torch
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import torch.nn.functional as F
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from io import StringIO
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from fairseq import options, utils
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from fairseq.data import Dictionary
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from fairseq.data.language_pair_dataset import collate
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from fairseq.models import (
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FairseqEncoder,
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FairseqEncoderDecoderModel,
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FairseqIncrementalDecoder,
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)
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from fairseq.models.fairseq_encoder import EncoderOut
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from fairseq.tasks import FairseqTask
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from fairseq_cli import (
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generate,
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interactive,
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preprocess,
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train,
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validate,
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)
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def dummy_dictionary(vocab_size, prefix='token_'):
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d = Dictionary()
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for i in range(vocab_size):
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token = prefix + str(i)
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d.add_symbol(token)
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d.finalize(padding_factor=1) # don't add extra padding symbols
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return d
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def dummy_dataloader(
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samples,
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padding_idx=1,
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eos_idx=2,
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batch_size=None,
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):
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if batch_size is None:
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batch_size = len(samples)
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# add any missing data to samples
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for i, sample in enumerate(samples):
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if 'id' not in sample:
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sample['id'] = i
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# create dataloader
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dataset = TestDataset(samples)
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dataloader = torch.utils.data.DataLoader(
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dataset,
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batch_size=batch_size,
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collate_fn=(lambda samples: collate(samples, padding_idx, eos_idx)),
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)
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return iter(dataloader)
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def sequence_generator_setup():
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# construct dummy dictionary
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d = dummy_dictionary(vocab_size=2)
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eos = d.eos()
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w1 = 4
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w2 = 5
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# construct source data
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src_tokens = torch.LongTensor([[w1, w2, eos], [w1, w2, eos]])
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src_lengths = torch.LongTensor([2, 2])
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args = argparse.Namespace()
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unk = 0.
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args.beam_probs = [
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# step 0:
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torch.FloatTensor([
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# eos w1 w2
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# sentence 1:
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[0.0, unk, 0.9, 0.1], # beam 1
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[0.0, unk, 0.9, 0.1], # beam 2
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# sentence 2:
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[0.0, unk, 0.7, 0.3],
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[0.0, unk, 0.7, 0.3],
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]),
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# step 1:
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torch.FloatTensor([
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# eos w1 w2 prefix
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# sentence 1:
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[1.0, unk, 0.0, 0.0], # w1: 0.9 (emit: w1 <eos>: 0.9*1.0)
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[0.0, unk, 0.9, 0.1], # w2: 0.1
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# sentence 2:
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[0.25, unk, 0.35, 0.4], # w1: 0.7 (don't emit: w1 <eos>: 0.7*0.25)
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[0.00, unk, 0.10, 0.9], # w2: 0.3
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]),
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# step 2:
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torch.FloatTensor([
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# eos w1 w2 prefix
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# sentence 1:
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[0.0, unk, 0.1, 0.9], # w2 w1: 0.1*0.9
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[0.6, unk, 0.2, 0.2], # w2 w2: 0.1*0.1 (emit: w2 w2 <eos>: 0.1*0.1*0.6)
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# sentence 2:
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[0.60, unk, 0.4, 0.00], # w1 w2: 0.7*0.4 (emit: w1 w2 <eos>: 0.7*0.4*0.6)
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[0.01, unk, 0.0, 0.99], # w2 w2: 0.3*0.9
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]),
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# step 3:
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torch.FloatTensor([
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# eos w1 w2 prefix
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# sentence 1:
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[1.0, unk, 0.0, 0.0], # w2 w1 w2: 0.1*0.9*0.9 (emit: w2 w1 w2 <eos>: 0.1*0.9*0.9*1.0)
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[1.0, unk, 0.0, 0.0], # w2 w1 w1: 0.1*0.9*0.1 (emit: w2 w1 w1 <eos>: 0.1*0.9*0.1*1.0)
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# sentence 2:
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[0.1, unk, 0.5, 0.4], # w2 w2 w2: 0.3*0.9*0.99 (emit: w2 w2 w2 <eos>: 0.3*0.9*0.99*0.1)
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[1.0, unk, 0.0, 0.0], # w1 w2 w1: 0.7*0.4*0.4 (emit: w1 w2 w1 <eos>: 0.7*0.4*0.4*1.0)
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]),
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]
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task = TestTranslationTask.setup_task(args, d, d)
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model = task.build_model(args)
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tgt_dict = task.target_dictionary
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return tgt_dict, w1, w2, src_tokens, src_lengths, model
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def create_dummy_data(data_dir, num_examples=100, maxlen=20, alignment=False):
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def _create_dummy_data(filename):
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data = torch.rand(num_examples * maxlen)
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data = 97 + torch.floor(26 * data).int()
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with open(os.path.join(data_dir, filename), 'w') as h:
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offset = 0
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for _ in range(num_examples):
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ex_len = random.randint(1, maxlen)
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ex_str = ' '.join(map(chr, data[offset:offset+ex_len]))
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print(ex_str, file=h)
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offset += ex_len
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def _create_dummy_alignment_data(filename_src, filename_tgt, filename):
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with open(os.path.join(data_dir, filename_src), 'r') as src_f, \
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open(os.path.join(data_dir, filename_tgt), 'r') as tgt_f, \
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open(os.path.join(data_dir, filename), 'w') as h:
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for src, tgt in zip(src_f, tgt_f):
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src_len = len(src.split())
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tgt_len = len(tgt.split())
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avg_len = (src_len + tgt_len) // 2
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num_alignments = random.randint(avg_len // 2, 2 * avg_len)
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src_indices = torch.floor(torch.rand(num_alignments) * src_len).int()
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tgt_indices = torch.floor(torch.rand(num_alignments) * tgt_len).int()
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ex_str = ' '.join(["{}-{}".format(src, tgt) for src, tgt in zip(src_indices, tgt_indices)])
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print(ex_str, file=h)
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_create_dummy_data('train.in')
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_create_dummy_data('train.out')
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_create_dummy_data('valid.in')
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_create_dummy_data('valid.out')
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_create_dummy_data('test.in')
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_create_dummy_data('test.out')
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if alignment:
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_create_dummy_alignment_data('train.in', 'train.out', 'train.align')
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_create_dummy_alignment_data('valid.in', 'valid.out', 'valid.align')
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_create_dummy_alignment_data('test.in', 'test.out', 'test.align')
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def preprocess_lm_data(data_dir):
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preprocess_parser = options.get_preprocessing_parser()
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preprocess_args = preprocess_parser.parse_args([
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'--only-source',
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'--trainpref', os.path.join(data_dir, 'train.out'),
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'--validpref', os.path.join(data_dir, 'valid.out'),
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'--testpref', os.path.join(data_dir, 'test.out'),
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'--destdir', data_dir,
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])
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preprocess.main(preprocess_args)
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def preprocess_translation_data(data_dir, extra_flags=None):
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preprocess_parser = options.get_preprocessing_parser()
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preprocess_args = preprocess_parser.parse_args(
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[
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'--source-lang', 'in',
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'--target-lang', 'out',
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'--trainpref', os.path.join(data_dir, 'train'),
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'--validpref', os.path.join(data_dir, 'valid'),
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'--testpref', os.path.join(data_dir, 'test'),
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'--thresholdtgt', '0',
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'--thresholdsrc', '0',
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'--destdir', data_dir,
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] + (extra_flags or []),
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)
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preprocess.main(preprocess_args)
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def train_translation_model(data_dir, arch, extra_flags=None, task='translation', run_validation=False,
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lang_flags=None, extra_valid_flags=None):
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if lang_flags is None:
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lang_flags = [
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'--source-lang', 'in',
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'--target-lang', 'out',
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]
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train_parser = options.get_training_parser()
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train_args = options.parse_args_and_arch(
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train_parser,
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[
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'--task', task,
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data_dir,
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'--save-dir', data_dir,
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'--arch', arch,
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'--optimizer', 'nag',
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'--lr', '0.05',
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'--max-tokens', '500',
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'--max-epoch', '1',
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'--no-progress-bar',
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'--distributed-world-size', '1',
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'--num-workers', 0,
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] + lang_flags + (extra_flags or []),
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)
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train.main(train_args)
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if run_validation:
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# test validation
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validate_parser = options.get_validation_parser()
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validate_args = options.parse_args_and_arch(
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validate_parser,
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[
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'--task', task,
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data_dir,
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'--path', os.path.join(data_dir, 'checkpoint_last.pt'),
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'--valid-subset', 'valid',
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'--max-tokens', '500',
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'--no-progress-bar',
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] + lang_flags + (extra_valid_flags or [])
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)
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validate.main(validate_args)
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def generate_main(data_dir, extra_flags=None):
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if extra_flags is None:
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extra_flags = [
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'--print-alignment',
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]
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generate_parser = options.get_generation_parser()
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generate_args = options.parse_args_and_arch(
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generate_parser,
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[
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data_dir,
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'--path', os.path.join(data_dir, 'checkpoint_last.pt'),
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'--beam', '3',
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'--batch-size', '64',
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'--max-len-b', '5',
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'--gen-subset', 'valid',
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'--no-progress-bar',
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] + (extra_flags or []),
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)
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# evaluate model in batch mode
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generate.main(generate_args)
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# evaluate model interactively
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generate_args.buffer_size = 0
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generate_args.input = '-'
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generate_args.max_sentences = None
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orig_stdin = sys.stdin
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sys.stdin = StringIO('h e l l o\n')
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interactive.main(generate_args)
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sys.stdin = orig_stdin
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class TestDataset(torch.utils.data.Dataset):
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def __init__(self, data):
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super().__init__()
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self.data = data
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self.sizes = None
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def __getitem__(self, index):
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return self.data[index]
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def __len__(self):
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return len(self.data)
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class TestTranslationTask(FairseqTask):
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def __init__(self, args, src_dict, tgt_dict, model):
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super().__init__(args)
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self.src_dict = src_dict
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self.tgt_dict = tgt_dict
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self.model = model
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@classmethod
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def setup_task(cls, args, src_dict=None, tgt_dict=None, model=None):
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return cls(args, src_dict, tgt_dict, model)
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def build_model(self, args):
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return TestModel.build_model(args, self)
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@property
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def source_dictionary(self):
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return self.src_dict
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@property
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def target_dictionary(self):
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return self.tgt_dict
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class TestModel(FairseqEncoderDecoderModel):
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def __init__(self, encoder, decoder):
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super().__init__(encoder, decoder)
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@classmethod
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def build_model(cls, args, task):
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encoder = TestEncoder(args, task.source_dictionary)
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decoder = TestIncrementalDecoder(args, task.target_dictionary)
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return cls(encoder, decoder)
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class TestEncoder(FairseqEncoder):
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def __init__(self, args, dictionary):
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super().__init__(dictionary)
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self.args = args
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def forward(self, src_tokens, src_lengths=None, **kwargs):
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return EncoderOut(
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encoder_out=src_tokens,
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encoder_padding_mask=None,
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encoder_embedding=None,
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encoder_states=None,
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src_tokens=None,
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src_lengths=None,
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)
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def reorder_encoder_out(self, encoder_out, new_order):
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return EncoderOut(
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encoder_out=encoder_out.encoder_out.index_select(0, new_order),
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encoder_padding_mask=None,
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encoder_embedding=None,
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encoder_states=None,
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src_tokens=None,
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src_lengths=None,
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)
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class TestIncrementalDecoder(FairseqIncrementalDecoder):
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def __init__(self, args, dictionary):
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super().__init__(dictionary)
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assert hasattr(args, 'beam_probs') or hasattr(args, 'probs')
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args.max_decoder_positions = getattr(args, 'max_decoder_positions', 100)
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self.args = args
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def forward(self, prev_output_tokens, encoder_out=None, incremental_state=None):
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if incremental_state is not None:
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prev_output_tokens = prev_output_tokens[:, -1:]
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bbsz = prev_output_tokens.size(0)
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vocab = len(self.dictionary)
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src_len = encoder_out.encoder_out.size(1)
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tgt_len = prev_output_tokens.size(1)
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# determine number of steps
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if incremental_state is not None:
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# cache step number
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step = utils.get_incremental_state(self, incremental_state, 'step')
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if step is None:
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step = 0
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utils.set_incremental_state(self, incremental_state, 'step', step + 1)
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steps = [step]
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else:
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steps = list(range(tgt_len))
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# define output in terms of raw probs
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if hasattr(self.args, 'probs'):
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assert self.args.probs.dim() == 3, \
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'expected probs to have size bsz*steps*vocab'
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probs = self.args.probs.index_select(1, torch.LongTensor(steps))
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else:
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probs = torch.FloatTensor(bbsz, len(steps), vocab).zero_()
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for i, step in enumerate(steps):
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# args.beam_probs gives the probability for every vocab element,
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# starting with eos, then unknown, and then the rest of the vocab
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if step < len(self.args.beam_probs):
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probs[:, i, self.dictionary.eos():] = self.args.beam_probs[step]
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else:
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probs[:, i, self.dictionary.eos()] = 1.0
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# random attention
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attn = torch.rand(bbsz, tgt_len, src_len)
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dev = prev_output_tokens.device
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return probs.to(dev), {"attn": [attn.to(dev)]}
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def get_normalized_probs(self, net_output, log_probs, _):
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# the decoder returns probabilities directly
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probs = net_output[0]
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if log_probs:
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return probs.log()
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else:
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return probs
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def max_positions(self):
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return self.args.max_decoder_positions
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class TestReshapingEncoder(FairseqEncoder):
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def __init__(self, args, dictionary):
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super().__init__(dictionary)
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self.args = args
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def forward(self, src_tokens, src_lengths=None, **kwargs):
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b_sz, t_sz = src_tokens.shape
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padding_needed = t_sz % 2
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x = src_tokens
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if padding_needed > 0:
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padding_needed = 2 - padding_needed
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x = F.pad(x, (0, padding_needed))
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return EncoderOut(
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encoder_out=x.view(b_sz, -1, 2),
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encoder_padding_mask=None,
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encoder_embedding=None,
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encoder_states=None,
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src_tokens=None,
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src_lengths=None,
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)
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def reorder_encoder_out(self, encoder_out, new_order):
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return EncoderOut(
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encoder_out=encoder_out.encoder_out.index_select(0, new_order),
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encoder_padding_mask=None,
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encoder_embedding=None,
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encoder_states=None,
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src_tokens=None,
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src_lengths=None,
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)
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class TestReshapingModel(FairseqEncoderDecoderModel):
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def __init__(self, encoder, decoder):
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super().__init__(encoder, decoder)
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@classmethod
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def build_model(cls, args, task):
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encoder = TestReshapingEncoder(args, task.source_dictionary)
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decoder = TestIncrementalDecoder(args, task.target_dictionary)
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return cls(encoder, decoder)
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class TestAdditionalInputEncoder(FairseqEncoder):
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def __init__(self, args, dictionary):
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super().__init__(dictionary)
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self.args = args
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def forward(self, src_tokens, src_lengths=None, **kwargs):
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assert 'fancy_other_input' in kwargs
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assert kwargs['fancy_other_input'] is not None
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return EncoderOut(
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encoder_out=src_tokens,
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encoder_padding_mask=None,
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encoder_embedding=None,
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encoder_states=None,
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src_tokens=None,
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src_lengths=None,
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)
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def reorder_encoder_out(self, encoder_out, new_order):
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return EncoderOut(
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encoder_out=encoder_out.encoder_out.index_select(0, new_order),
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encoder_padding_mask=None,
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encoder_embedding=None,
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encoder_states=None,
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src_tokens=None,
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src_lengths=None,
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)
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class TestAdditionalInputModel(FairseqEncoderDecoderModel):
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def __init__(self, encoder, decoder):
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super().__init__(encoder, decoder)
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@classmethod
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def build_model(cls, args, task):
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encoder = TestAdditionalInputEncoder(args, task.source_dictionary)
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decoder = TestIncrementalDecoder(args, task.target_dictionary)
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return cls(encoder, decoder)
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|
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def forward(self, src_tokens, src_lengths, prev_output_tokens, **kwargs):
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encoder_out = self.encoder(
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src_tokens, src_lengths=src_lengths, **kwargs)
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decoder_out = self.decoder(
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prev_output_tokens, encoder_out=encoder_out, **kwargs)
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|
return decoder_out
|