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- # Copyright (c) 2017-present, Facebook, Inc.
- # All rights reserved.
- #
- # This source code is licensed under the license found in the LICENSE file in
- # the root directory of this source tree. An additional grant of patent rights
- # can be found in the PATENTS file in the same directory.
- import torch
- from torch.autograd import Variable
- from fairseq import data, dictionary, utils
- from fairseq.models import (
- FairseqEncoder,
- FairseqIncrementalDecoder,
- FairseqModel,
- )
- def dummy_dictionary(vocab_size, prefix='token_'):
- d = dictionary.Dictionary()
- for i in range(vocab_size):
- token = prefix + str(i)
- d.add_symbol(token)
- d.finalize(padding_factor=1) # don't add extra padding symbols
- return d
- def dummy_dataloader(
- samples,
- padding_idx=1,
- eos_idx=2,
- batch_size=None,
- ):
- if batch_size is None:
- batch_size = len(samples)
- # add any missing data to samples
- for i, sample in enumerate(samples):
- if 'id' not in sample:
- sample['id'] = i
- # create dataloader
- dataset = TestDataset(samples)
- dataloader = torch.utils.data.DataLoader(
- dataset,
- batch_size=batch_size,
- collate_fn=(
- lambda samples: data.LanguagePairDataset.collate(
- samples,
- padding_idx,
- eos_idx,
- )
- ),
- )
- return iter(dataloader)
- class TestDataset(torch.utils.data.Dataset):
- def __init__(self, data):
- super().__init__()
- self.data = data
- def __getitem__(self, index):
- return self.data[index]
- def __len__(self):
- return len(self.data)
- class TestModel(FairseqModel):
- def __init__(self, encoder, decoder):
- super().__init__(encoder, decoder)
- @classmethod
- def build_model(cls, args, src_dict, dst_dict):
- encoder = TestEncoder(args, src_dict)
- decoder = TestIncrementalDecoder(args, dst_dict)
- return cls(encoder, decoder)
- class TestEncoder(FairseqEncoder):
- def __init__(self, args, dictionary):
- super().__init__(dictionary)
- self.args = args
- def forward(self, src_tokens, src_lengths):
- return src_tokens
- class TestIncrementalDecoder(FairseqIncrementalDecoder):
- def __init__(self, args, dictionary):
- super().__init__(dictionary)
- assert hasattr(args, 'beam_probs') or hasattr(args, 'probs')
- args.max_decoder_positions = getattr(args, 'max_decoder_positions', 100)
- self.args = args
- def forward(self, prev_output_tokens, encoder_out, incremental_state=None):
- if incremental_state is not None:
- prev_output_tokens = prev_output_tokens[:, -1:]
- bbsz = prev_output_tokens.size(0)
- vocab = len(self.dictionary)
- src_len = encoder_out.size(1)
- tgt_len = prev_output_tokens.size(1)
- # determine number of steps
- if incremental_state is not None:
- # cache step number
- step = utils.get_incremental_state(self, incremental_state, 'step')
- if step is None:
- step = 0
- utils.set_incremental_state(self, incremental_state, 'step', step + 1)
- steps = [step]
- else:
- steps = list(range(tgt_len))
- # define output in terms of raw probs
- if hasattr(self.args, 'probs'):
- assert self.args.probs.dim() == 3, \
- 'expected probs to have size bsz*steps*vocab'
- probs = self.args.probs.index_select(1, torch.LongTensor(steps))
- else:
- probs = torch.FloatTensor(bbsz, len(steps), vocab).zero_()
- for i, step in enumerate(steps):
- # args.beam_probs gives the probability for every vocab element,
- # starting with eos, then unknown, and then the rest of the vocab
- if step < len(self.args.beam_probs):
- probs[:, i, self.dictionary.eos():] = self.args.beam_probs[step]
- else:
- probs[:, i, self.dictionary.eos()] = 1.0
- # random attention
- attn = torch.rand(bbsz, src_len, tgt_len)
- return Variable(probs), Variable(attn)
- def get_normalized_probs(self, net_output, log_probs):
- # the decoder returns probabilities directly
- probs = net_output[0]
- if log_probs:
- return probs.log()
- else:
- return probs
- def max_positions(self):
- return self.args.max_decoder_positions
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