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hub_utils.py
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hub_utils.py
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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.
import argparse
import copy
import logging
import os
from typing import List, Dict, Iterator, Tuple, Any
import torch
from torch import nn
from fairseq import utils
from fairseq.data import encoders
logger = logging.getLogger(__name__)
def from_pretrained(
model_name_or_path,
checkpoint_file='model.pt',
data_name_or_path='.',
archive_map=None,
**kwargs
):
from fairseq import checkpoint_utils, file_utils
if archive_map is not None:
if model_name_or_path in archive_map:
model_name_or_path = archive_map[model_name_or_path]
if data_name_or_path is not None and data_name_or_path in archive_map:
data_name_or_path = archive_map[data_name_or_path]
# allow archive_map to set default arg_overrides (e.g., tokenizer, bpe)
# for each model
if isinstance(model_name_or_path, dict):
for k, v in model_name_or_path.items():
if k == 'checkpoint_file':
checkpoint_file = v
elif (
k != 'path'
# only set kwargs that don't already have overrides
and k not in kwargs
):
kwargs[k] = v
model_name_or_path = model_name_or_path['path']
model_path = file_utils.load_archive_file(model_name_or_path)
# convenience hack for loading data and BPE codes from model archive
if data_name_or_path.startswith('.'):
kwargs['data'] = os.path.abspath(os.path.join(model_path, data_name_or_path))
else:
kwargs['data'] = file_utils.load_archive_file(data_name_or_path)
for file, arg in {
'code': 'bpe_codes',
'bpecodes': 'bpe_codes',
'sentencepiece.bpe.model': 'sentencepiece_vocab',
}.items():
path = os.path.join(model_path, file)
if os.path.exists(path):
kwargs[arg] = path
if 'user_dir' in kwargs:
utils.import_user_module(argparse.Namespace(user_dir=kwargs['user_dir']))
models, args, task = checkpoint_utils.load_model_ensemble_and_task(
[os.path.join(model_path, cpt) for cpt in checkpoint_file.split(os.pathsep)],
arg_overrides=kwargs,
)
return {
'args': args,
'task': task,
'models': models,
}
class GeneratorHubInterface(nn.Module):
"""
PyTorch Hub interface for generating sequences from a pre-trained
translation or language model.
"""
def __init__(self, args, task, models):
super().__init__()
self.args = args
self.task = task
self.models = nn.ModuleList(models)
self.src_dict = task.source_dictionary
self.tgt_dict = task.target_dictionary
# optimize model for generation
for model in self.models:
model.make_generation_fast_(
beamable_mm_beam_size=(
None if getattr(args, 'no_beamable_mm', False)
else getattr(args, 'beam', 5)
),
need_attn=getattr(args, 'print_alignment', False),
)
# Load alignment dictionary for unknown word replacement
# (None if no unknown word replacement, empty if no path to align dictionary)
self.align_dict = utils.load_align_dict(getattr(args, 'replace_unk', None))
self.tokenizer = encoders.build_tokenizer(args)
self.bpe = encoders.build_bpe(args)
self.max_positions = utils.resolve_max_positions(
self.task.max_positions(), *[model.max_positions() for model in models]
)
# this is useful for determining the device
self.register_buffer('_float_tensor', torch.tensor([0], dtype=torch.float))
@property
def device(self):
return self._float_tensor.device
def translate(self, sentences: List[str], beam: int = 5, verbose: bool = False, **kwargs) -> List[str]:
return self.sample(sentences, beam, verbose, **kwargs)
def sample(self, sentences: List[str], beam: int = 1, verbose: bool = False, **kwargs) -> List[str]:
if isinstance(sentences, str):
return self.sample([sentences], beam=beam, verbose=verbose, **kwargs)[0]
tokenized_sentences = [self.encode(sentence) for sentence in sentences]
batched_hypos = self.generate(tokenized_sentences, beam, verbose, **kwargs)
return [self.decode(hypos[0]['tokens']) for hypos in batched_hypos]
def score(self, sentences: List[str], **kwargs):
if isinstance(sentences, str):
return self.score([sentences], **kwargs)[0]
# NOTE: this doesn't support translation tasks currently
tokenized_sentences = [self.encode(sentence) for sentence in sentences]
return [hypos[0] for hypos in self.generate(tokenized_sentences, score_reference=True, **kwargs)]
def generate(
self,
tokenized_sentences: List[torch.LongTensor],
beam: int = 5,
verbose: bool = False,
skip_invalid_size_inputs=False,
**kwargs
) -> List[List[Dict[str, torch.Tensor]]]:
if torch.is_tensor(tokenized_sentences) and tokenized_sentences.dim() == 1:
return self.generate(
tokenized_sentences.unsqueeze(0), beam=beam, verbose=verbose, **kwargs
)[0]
# build generator using current args as well as any kwargs
gen_args = copy.copy(self.args)
gen_args.beam = beam
for k, v in kwargs.items():
setattr(gen_args, k, v)
generator = self.task.build_generator(gen_args)
results = []
for batch in self._build_batches(tokenized_sentences, skip_invalid_size_inputs):
batch = utils.apply_to_sample(lambda t: t.to(self.device), batch)
translations = self.task.inference_step(generator, self.models, batch)
for id, hypos in zip(batch["id"].tolist(), translations):
results.append((id, hypos))
# sort output to match input order
outputs = [hypos for _, hypos in sorted(results, key=lambda x: x[0])]
if verbose:
def getarg(name, default):
return getattr(gen_args, name, getattr(self.args, name, default))
for source_tokens, target_hypotheses in zip(tokenized_sentences, outputs):
src_str_with_unk = self.string(source_tokens)
logger.info('S\t{}'.format(src_str_with_unk))
for hypo in target_hypotheses:
hypo_str = self.decode(hypo['tokens'])
logger.info('H\t{}\t{}'.format(hypo['score'], hypo_str))
logger.info('P\t{}'.format(
' '.join(map(lambda x: '{:.4f}'.format(x), hypo['positional_scores'].tolist()))
))
if hypo['alignment'] is not None and getarg('print_alignment', False):
logger.info('A\t{}'.format(
' '.join(map(lambda x: str(utils.item(x)), hypo['alignment'].int().cpu()))
))
return outputs
def encode(self, sentence: str) -> torch.LongTensor:
sentence = self.tokenize(sentence)
sentence = self.apply_bpe(sentence)
return self.binarize(sentence)
def decode(self, tokens: torch.LongTensor) -> str:
sentence = self.string(tokens)
sentence = self.remove_bpe(sentence)
return self.detokenize(sentence)
def tokenize(self, sentence: str) -> str:
if self.tokenizer is not None:
sentence = self.tokenizer.encode(sentence)
return sentence
def detokenize(self, sentence: str) -> str:
if self.tokenizer is not None:
sentence = self.tokenizer.decode(sentence)
return sentence
def apply_bpe(self, sentence: str) -> str:
if self.bpe is not None:
sentence = self.bpe.encode(sentence)
return sentence
def remove_bpe(self, sentence: str) -> str:
if self.bpe is not None:
sentence = self.bpe.decode(sentence)
return sentence
def binarize(self, sentence: str) -> torch.LongTensor:
return self.src_dict.encode_line(sentence, add_if_not_exist=False).long()
def string(self, tokens: torch.LongTensor) -> str:
return self.tgt_dict.string(tokens)
def _build_batches(
self, tokens: List[List[int]], skip_invalid_size_inputs: bool
) -> Iterator[Dict[str, Any]]:
lengths = torch.LongTensor([t.numel() for t in tokens])
batch_iterator = self.task.get_batch_iterator(
dataset=self.task.build_dataset_for_inference(tokens, lengths),
max_tokens=self.args.max_tokens,
max_sentences=self.args.max_sentences,
max_positions=self.max_positions,
ignore_invalid_inputs=skip_invalid_size_inputs,
).next_epoch_itr(shuffle=False)
return batch_iterator
class BPEHubInterface(object):
"""PyTorch Hub interface for Byte-Pair Encoding (BPE)."""
def __init__(self, bpe, **kwargs):
super().__init__()
args = argparse.Namespace(bpe=bpe, **kwargs)
self.bpe = encoders.build_bpe(args)
assert self.bpe is not None
def encode(self, sentence: str) -> str:
return self.bpe.encode(sentence)
def decode(self, sentence: str) -> str:
return self.bpe.decode(sentence)
class TokenizerHubInterface(object):
"""PyTorch Hub interface for tokenization."""
def __init__(self, tokenizer, **kwargs):
super().__init__()
args = argparse.Namespace(tokenizer=tokenizer, **kwargs)
self.tokenizer = encoders.build_tokenizer(args)
assert self.tokenizer is not None
def encode(self, sentence: str) -> str:
return self.tokenizer.encode(sentence)
def decode(self, sentence: str) -> str:
return self.tokenizer.decode(sentence)