Source code for onmt.translate.translation_server

#!/usr/bin/env python
"""REST Translation server."""
import codecs
import sys
import os
import time
import json
import threading
import re
import traceback
import importlib
import torch
import onmt.opts

from itertools import islice, zip_longest
from copy import deepcopy

from onmt.constants import DefaultTokens
from onmt.utils.logging import init_logger
from onmt.utils.misc import set_random_seed
from onmt.utils.misc import check_model_config
from onmt.utils.alignment import to_word_align
from onmt.utils.parse import ArgumentParser
from onmt.translate.translator import build_translator

def critical(func):
    """Decorator for critical section (mutually exclusive code)"""
    def wrapper(server_model, *args, **kwargs):
        if sys.version_info[0] == 3:
            if not server_model.running_lock.acquire(True, 120):
                raise ServerModelError("Model %d running lock timeout"
                                       % server_model.model_id)
            # semaphore doesn't have a timeout arg in Python 2.7
            o = func(server_model, *args, **kwargs)
        except (Exception, RuntimeError):
        return o
    return wrapper

[docs]class Timer: def __init__(self, start=False): self.stime = -1 self.prev = -1 self.times = {} if start: self.start() def start(self): self.stime = time.time() self.prev = self.stime self.times = {} def tick(self, name=None, tot=False): t = time.time() if not tot: elapsed = t - self.prev else: elapsed = t - self.stime self.prev = t if name is not None: self.times[name] = elapsed return elapsed
[docs]class ServerModelError(Exception): pass
class CTranslate2Translator(object): """ This class wraps the ctranslate2.Translator object to reproduce the onmt.translate.translator API. """ def __init__(self, model_path, device, device_index, batch_size, beam_size, n_best, target_prefix=False, preload=False): import ctranslate2 self.translator = ctranslate2.Translator( model_path, device=device, device_index=device_index, inter_threads=1, intra_threads=1, compute_type="default") self.batch_size = batch_size self.beam_size = beam_size self.n_best = n_best self.target_prefix = target_prefix if preload: # perform a first request to initialize everything dummy_translation = self.translate(["a"]) print("Performed a dummy translation to initialize the model", dummy_translation) time.sleep(1) self.translator.unload_model(to_cpu=True) def translate(self, texts_to_translate, batch_size=8, tgt=None): batch = [item.split(" ") for item in texts_to_translate] if tgt is not None: tgt = [item.split(" ") for item in tgt] preds = self.translator.translate_batch( batch, target_prefix=tgt if self.target_prefix else None, max_batch_size=self.batch_size, beam_size=self.beam_size, num_hypotheses=self.n_best ) scores = [[item["score"] for item in ex] for ex in preds] predictions = [[" ".join(item["tokens"]) for item in ex] for ex in preds] return scores, predictions def to_cpu(self): self.translator.unload_model(to_cpu=True) def to_gpu(self): self.translator.load_model()
[docs]class TranslationServer(object): def __init__(self): self.models = {} self.next_id = 0
[docs] def start(self, config_file): """Read the config file and pre-/load the models.""" self.config_file = config_file with open(self.config_file) as f: self.confs = json.load(f) self.models_root = self.confs.get('models_root', './available_models') for i, conf in enumerate(self.confs["models"]): if "models" not in conf: if "model" in conf: # backwards compatibility for confs conf["models"] = [conf["model"]] else: raise ValueError("""Incorrect config file: missing 'models' parameter for model #%d""" % i) check_model_config(conf, self.models_root) kwargs = {'timeout': conf.get('timeout', None), 'load': conf.get('load', None), 'preprocess_opt': conf.get('preprocess', None), 'tokenizer_opt': conf.get('tokenizer', None), 'postprocess_opt': conf.get('postprocess', None), 'custom_opt': conf.get('custom_opt', None), 'on_timeout': conf.get('on_timeout', None), 'model_root': conf.get('model_root', self.models_root), 'ct2_model': conf.get('ct2_model', None) } kwargs = {k: v for (k, v) in kwargs.items() if v is not None} model_id = conf.get("id", None) opt = conf["opt"] opt["models"] = conf["models"] self.preload_model(opt, model_id=model_id, **kwargs)
[docs] def clone_model(self, model_id, opt, timeout=-1): """Clone a model `model_id`. Different options may be passed. If `opt` is None, it will use the same set of options """ if model_id in self.models: if opt is None: opt = self.models[model_id].user_opt opt["models"] = self.models[model_id].opt.models return self.load_model(opt, timeout) else: raise ServerModelError("No such model '%s'" % str(model_id))
[docs] def load_model(self, opt, model_id=None, **model_kwargs): """Load a model given a set of options """ model_id = self.preload_model(opt, model_id=model_id, **model_kwargs) load_time = self.models[model_id].load_time return model_id, load_time
[docs] def preload_model(self, opt, model_id=None, **model_kwargs): """Preloading the model: updating internal datastructure It will effectively load the model if `load` is set """ if model_id is not None: if model_id in self.models.keys(): raise ValueError("Model ID %d already exists" % model_id) else: model_id = self.next_id while model_id in self.models.keys(): model_id += 1 self.next_id = model_id + 1 print("Pre-loading model %d" % model_id) model = ServerModel(opt, model_id, **model_kwargs) self.models[model_id] = model return model_id
[docs] def run(self, inputs): """Translate `inputs` We keep the same format as the Lua version i.e. ``[{"id": model_id, "src": "sequence to translate"},{ ...}]`` We use inputs[0]["id"] as the model id """ model_id = inputs[0].get("id", 0) if model_id in self.models and self.models[model_id] is not None: return self.models[model_id].run(inputs) else: print("Error No such model '%s'" % str(model_id)) raise ServerModelError("No such model '%s'" % str(model_id))
[docs] def unload_model(self, model_id): """Manually unload a model. It will free the memory and cancel the timer """ if model_id in self.models and self.models[model_id] is not None: self.models[model_id].unload() else: raise ServerModelError("No such model '%s'" % str(model_id))
[docs] def list_models(self): """Return the list of available models """ models = [] for _, model in self.models.items(): models += [model.to_dict()] return models
[docs]class ServerModel(object): """Wrap a model with server functionality. Args: opt (dict): Options for the Translator model_id (int): Model ID preprocess_opt (list): Options for preprocess processus or None tokenizer_opt (dict): Options for the tokenizer or None postprocess_opt (list): Options for postprocess processus or None custom_opt (dict): Custom options, can be used within preprocess or postprocess, default None load (bool): whether to load the model during :func:`__init__()` timeout (int): Seconds before running :func:`do_timeout()` Negative values means no timeout on_timeout (str): Options are ["to_cpu", "unload"]. Set what to do on timeout (see :func:`do_timeout()`.) model_root (str): Path to the model directory it must contain the model and tokenizer file """ def __init__(self, opt, model_id, preprocess_opt=None, tokenizer_opt=None, postprocess_opt=None, custom_opt=None, load=False, timeout=-1, on_timeout="to_cpu", model_root="./", ct2_model=None): self.model_root = model_root self.opt = self.parse_opt(opt) self.custom_opt = custom_opt self.model_id = model_id self.preprocess_opt = preprocess_opt self.tokenizers_opt = tokenizer_opt self.postprocess_opt = postprocess_opt self.timeout = timeout self.on_timeout = on_timeout self.ct2_model = os.path.join(model_root, ct2_model) \ if ct2_model is not None else None self.unload_timer = None self.user_opt = opt self.tokenizers = None if len(self.opt.log_file) > 0: log_file = os.path.join(model_root, self.opt.log_file) else: log_file = None self.logger = init_logger(log_file=log_file, log_file_level=self.opt.log_file_level, rotate=True) self.loading_lock = threading.Event() self.loading_lock.set() self.running_lock = threading.Semaphore(value=1) set_random_seed(self.opt.seed, self.opt.cuda) if self.preprocess_opt is not None:"Loading preprocessor") self.preprocessor = [] for function_path in self.preprocess_opt: function = get_function_by_path(function_path) self.preprocessor.append(function) if self.tokenizers_opt is not None: if "src" in self.tokenizers_opt and "tgt" in self.tokenizers_opt:"Loading src & tgt tokenizer") self.tokenizers = { 'src': self.build_tokenizer(tokenizer_opt['src']), 'tgt': self.build_tokenizer(tokenizer_opt['tgt']) } else:"Loading tokenizer") self.tokenizers_opt = { 'src': tokenizer_opt, 'tgt': tokenizer_opt } tokenizer = self.build_tokenizer(tokenizer_opt) self.tokenizers = { 'src': tokenizer, 'tgt': tokenizer } if self.postprocess_opt is not None:"Loading postprocessor") self.postprocessor = [] for function_path in self.postprocess_opt: function = get_function_by_path(function_path) self.postprocessor.append(function) if load: self.load(preload=True) self.stop_unload_timer()
[docs] def parse_opt(self, opt): """Parse the option set passed by the user using `onmt.opts` Args: opt (dict): Options passed by the user Returns: opt (argparse.Namespace): full set of options for the Translator """ prec_argv = sys.argv sys.argv = sys.argv[:1] parser = ArgumentParser() onmt.opts.translate_opts(parser) models = opt['models'] if not isinstance(models, (list, tuple)): models = [models] opt['models'] = [os.path.join(self.model_root, model) for model in models] opt['src'] = "dummy_src" for (k, v) in opt.items(): if k == 'models': sys.argv += ['-model'] sys.argv += [str(model) for model in v] elif type(v) == bool: sys.argv += ['-%s' % k] else: sys.argv += ['-%s' % k, str(v)] opt = parser.parse_args() ArgumentParser.validate_translate_opts(opt) opt.cuda = opt.gpu > -1 sys.argv = prec_argv return opt
@property def loaded(self): return hasattr(self, 'translator') def load(self, preload=False): self.loading_lock.clear() timer = Timer()"Loading model %d" % self.model_id) timer.start() try: if self.ct2_model is not None: self.translator = CTranslate2Translator( self.ct2_model, device="cuda" if self.opt.cuda else "cpu", device_index=self.opt.gpu if self.opt.cuda else 0, batch_size=self.opt.batch_size, beam_size=self.opt.beam_size, n_best=self.opt.n_best, target_prefix=self.opt.tgt_prefix, preload=preload) else: self.translator = build_translator( self.opt, report_score=False,, "w", "utf-8")) except RuntimeError as e: raise ServerModelError("Runtime Error: %s" % str(e)) timer.tick("model_loading") self.load_time = timer.tick() self.reset_unload_timer() self.loading_lock.set() @critical def run(self, inputs): """Translate `inputs` using this model Args: inputs (List[dict[str, str]]): [{"src": "..."},{"src": ...}] Returns: result (list): translations times (dict): containing times """ self.stop_unload_timer() timer = Timer() timer.start()"Running translation using %d" % self.model_id) if not self.loading_lock.is_set(): "Model #%d is being loaded by another thread, waiting" % self.model_id) if not self.loading_lock.wait(timeout=30): raise ServerModelError("Model %d loading timeout" % self.model_id) else: if not self.loaded: self.load() timer.tick(name="load") elif self.opt.cuda: self.to_gpu() timer.tick(name="to_gpu") texts = [] head_spaces = [] tail_spaces = [] all_preprocessed = [] for i, inp in enumerate(inputs): src = inp['src'] whitespaces_before, whitespaces_after = "", "" match_before ='^\s+', src) match_after ='\s+$', src) if match_before is not None: whitespaces_before = if match_after is not None: whitespaces_after = head_spaces.append(whitespaces_before) # every segment becomes a dict for flexibility purposes seg_dict = self.maybe_preprocess(inp) all_preprocessed.append(seg_dict) for seg, ref in zip_longest(seg_dict["seg"], seg_dict["ref"]): tok = self.maybe_tokenize(seg) if ref is not None: ref = self.maybe_tokenize(ref, side='tgt') texts.append((tok, ref)) tail_spaces.append(whitespaces_after) empty_indices = [] texts_to_translate, texts_ref = [], [] for i, (tok, ref_tok) in enumerate(texts): if tok == "": empty_indices.append(i) else: texts_to_translate.append(tok) texts_ref.append(ref_tok) if any([item is None for item in texts_ref]): texts_ref = None scores = [] predictions = [] if len(texts_to_translate) > 0: try: scores, predictions = self.translator.translate( texts_to_translate, tgt=texts_ref, batch_size=len(texts_to_translate) if self.opt.batch_size == 0 else self.opt.batch_size) except (RuntimeError, Exception) as e: err = "Error: %s" % str(e) self.logger.error(err) self.logger.error("repr(text_to_translate): " + repr(texts_to_translate)) self.logger.error("model: #%s" % self.model_id) self.logger.error("model opt: " + str(self.opt.__dict__)) self.logger.error(traceback.format_exc()) raise ServerModelError(err) timer.tick(name="translation")"""Using model #%d\t%d inputs \ttranslation time: %f""" % (self.model_id, len(texts), timer.times['translation'])) self.reset_unload_timer() # NOTE: translator returns lists of `n_best` list def flatten_list(_list): return sum(_list, []) tiled_texts = [t for t in texts_to_translate for _ in range(self.opt.n_best)] results = flatten_list(predictions) def maybe_item(x): return x.item() if type(x) is torch.Tensor else x scores = [maybe_item(score_tensor) for score_tensor in flatten_list(scores)] results = [self.maybe_detokenize_with_align(result, src) for result, src in zip(results, tiled_texts)] aligns = [align for _, align in results] results = [tokens for tokens, _ in results] # build back results with empty texts for i in empty_indices: j = i * self.opt.n_best results = results[:j] + [""] * self.opt.n_best + results[j:] aligns = aligns[:j] + [None] * self.opt.n_best + aligns[j:] scores = scores[:j] + [0] * self.opt.n_best + scores[j:] rebuilt_segs, scores, aligns = self.rebuild_seg_packages( all_preprocessed, results, scores, aligns, self.opt.n_best) results = [self.maybe_postprocess(seg) for seg in rebuilt_segs] head_spaces = [h for h in head_spaces for i in range(self.opt.n_best)] tail_spaces = [h for h in tail_spaces for i in range(self.opt.n_best)] results = ["".join(items) for items in zip(head_spaces, results, tail_spaces)]"Translation Results: %d", len(results)) return results, scores, self.opt.n_best, timer.times, aligns
[docs] def rebuild_seg_packages(self, all_preprocessed, results, scores, aligns, n_best): """ Rebuild proper segment packages based on initial n_seg. """ offset = 0 rebuilt_segs = [] avg_scores = [] merged_aligns = [] for i, seg_dict in enumerate(all_preprocessed): n_seg = seg_dict["n_seg"] sub_results = results[n_best * offset: (offset + n_seg) * n_best] sub_scores = scores[n_best * offset: (offset + n_seg) * n_best] sub_aligns = aligns[n_best * offset: (offset + n_seg) * n_best] for j in range(n_best): _seg_dict = deepcopy(seg_dict) _seg_dict["seg"] = list(islice(sub_results, j, None, n_best)) rebuilt_segs.append(_seg_dict) sub_sub_scores = list(islice(sub_scores, j, None, n_best)) avg_score = sum(sub_sub_scores)/n_seg if n_seg != 0 else 0 avg_scores.append(avg_score) sub_sub_aligns = list(islice(sub_aligns, j, None, n_best)) merged_aligns.append(sub_sub_aligns) offset += n_seg return rebuilt_segs, avg_scores, merged_aligns
[docs] def do_timeout(self): """Timeout function that frees GPU memory. Moves the model to CPU or unloads it; depending on attr`self.on_timemout` value """ if self.on_timeout == "unload":"Timeout: unloading model %d" % self.model_id) self.unload() if self.on_timeout == "to_cpu":"Timeout: sending model %d to CPU" % self.model_id) self.to_cpu()
@critical def unload(self):"Unloading model %d" % self.model_id) del self.translator if self.opt.cuda: torch.cuda.empty_cache() self.stop_unload_timer() self.unload_timer = None def stop_unload_timer(self): if self.unload_timer is not None: self.unload_timer.cancel() def reset_unload_timer(self): if self.timeout < 0: return self.stop_unload_timer() self.unload_timer = threading.Timer(self.timeout, self.do_timeout) self.unload_timer.start() def to_dict(self): hide_opt = ["models", "src"] d = {"model_id": self.model_id, "opt": {k: self.user_opt[k] for k in self.user_opt.keys() if k not in hide_opt}, "models": self.user_opt["models"], "loaded": self.loaded, "timeout": self.timeout, } if self.tokenizers_opt is not None: d["tokenizer"] = self.tokenizers_opt return d @critical def to_cpu(self): """Move the model to CPU and clear CUDA cache.""" if type(self.translator) == CTranslate2Translator: self.translator.to_cpu() else: self.translator.model.cpu() if self.opt.cuda: torch.cuda.empty_cache()
[docs] def to_gpu(self): """Move the model to GPU.""" if type(self.translator) == CTranslate2Translator: self.translator.to_gpu() else: torch.cuda.set_device(self.opt.gpu) self.translator.model.cuda()
[docs] def maybe_preprocess(self, sequence): """Preprocess the sequence (or not) """ if sequence.get("src", None) is not None: sequence = deepcopy(sequence) sequence["seg"] = [sequence["src"].strip()] sequence.pop("src") sequence["ref"] = [sequence.get('ref', None)] sequence["n_seg"] = 1 if self.preprocess_opt is not None: return self.preprocess(sequence) return sequence
[docs] def preprocess(self, sequence): """Preprocess a single sequence. Args: sequence (str): The sequence to preprocess. Returns: sequence (str): The preprocessed sequence. """ if self.preprocessor is None: raise ValueError("No preprocessor loaded") for function in self.preprocessor: sequence = function(sequence, self) return sequence
[docs] def build_tokenizer(self, tokenizer_opt): """Build tokenizer described by `tokenizer_opt`.""" if "type" not in tokenizer_opt: raise ValueError( "Missing mandatory tokenizer option 'type'") if tokenizer_opt['type'] == 'sentencepiece': if "model" not in tokenizer_opt: raise ValueError( "Missing mandatory tokenizer option 'model'") import sentencepiece as spm tokenizer = spm.SentencePieceProcessor() model_path = os.path.join(self.model_root, tokenizer_opt['model']) tokenizer.Load(model_path) elif tokenizer_opt['type'] == 'pyonmttok': if "params" not in tokenizer_opt: raise ValueError( "Missing mandatory tokenizer option 'params'") import pyonmttok if tokenizer_opt["mode"] is not None: mode = tokenizer_opt["mode"] else: mode = None # load can be called multiple times: modify copy tokenizer_params = dict(tokenizer_opt["params"]) for key, value in tokenizer_opt["params"].items(): if key.endswith("path"): tokenizer_params[key] = os.path.join( self.model_root, value) tokenizer = pyonmttok.Tokenizer(mode, **tokenizer_params) else: raise ValueError("Invalid value for tokenizer type") return tokenizer
[docs] def maybe_tokenize(self, sequence, side='src'): """Tokenize the sequence (or not). Same args/returns as `tokenize` """ if self.tokenizers_opt is not None: return self.tokenize(sequence, side) return sequence
[docs] def tokenize(self, sequence, side='src'): """Tokenize a single sequence. Args: sequence (str): The sequence to tokenize. Returns: tok (str): The tokenized sequence. """ if self.tokenizers is None: raise ValueError("No tokenizer loaded") if self.tokenizers_opt[side]["type"] == "sentencepiece": tok = self.tokenizers[side].EncodeAsPieces(sequence) tok = " ".join(tok) elif self.tokenizers_opt[side]["type"] == "pyonmttok": tok, _ = self.tokenizers[side].tokenize(sequence) tok = " ".join(tok) return tok
[docs] def tokenizer_marker(self, side='src'): """Return marker used in `side` tokenizer.""" marker = None if self.tokenizers_opt is not None: tokenizer_type = self.tokenizers_opt[side].get('type', None) if tokenizer_type == "pyonmttok": params = self.tokenizers_opt[side].get('params', None) if params is not None: if params.get("joiner_annotate", None) is not None: marker = 'joiner' elif params.get("spacer_annotate", None) is not None: marker = 'spacer' elif tokenizer_type == "sentencepiece": marker = 'spacer' return marker
[docs] def maybe_detokenize_with_align(self, sequence, src, side='tgt'): """De-tokenize (or not) the sequence (with alignment). Args: sequence (str): The sequence to detokenize, possible with alignment seperate by ` ||| `. Returns: sequence (str): The detokenized sequence. align (str): The alignment correspand to detokenized src/tgt sorted or None if no alignment in output. """ align = None if self.opt.report_align: # output contain alignment sequence, align = sequence.split(DefaultTokens.ALIGNMENT_SEPARATOR) if align != '': align = self.maybe_convert_align(src, sequence, align) sequence = self.maybe_detokenize(sequence, side) return (sequence, align)
[docs] def maybe_detokenize(self, sequence, side='tgt'): """De-tokenize the sequence (or not) Same args/returns as :func:`tokenize()` """ if self.tokenizers_opt is not None and ''.join(sequence.split()) != '': return self.detokenize(sequence, side) return sequence
[docs] def detokenize(self, sequence, side='tgt'): """Detokenize a single sequence Same args/returns as :func:`tokenize()` """ if self.tokenizers is None: raise ValueError("No tokenizer loaded") if self.tokenizers_opt[side]["type"] == "sentencepiece": detok = self.tokenizers[side].DecodePieces(sequence.split()) elif self.tokenizers_opt[side]["type"] == "pyonmttok": detok = self.tokenizers[side].detokenize(sequence.split()) return detok
[docs] def maybe_convert_align(self, src, tgt, align): """Convert alignment to match detokenized src/tgt (or not). Args: src (str): The tokenized source sequence. tgt (str): The tokenized target sequence. align (str): The alignment correspand to src/tgt pair. Returns: align (str): The alignment correspand to detokenized src/tgt. """ if self.tokenizers_opt is not None: src_marker = self.tokenizer_marker(side='src') tgt_marker = self.tokenizer_marker(side='tgt') if src_marker is None or tgt_marker is None: raise ValueError("To get decoded alignment, joiner/spacer " "should be used in both side's tokenizer.") elif ''.join(tgt.split()) != '': align = to_word_align(src, tgt, align, src_marker, tgt_marker) return align
[docs] def maybe_postprocess(self, sequence): """Postprocess the sequence (or not) """ if self.postprocess_opt is not None: return self.postprocess(sequence) else: return sequence["seg"][0]
[docs] def postprocess(self, sequence): """Preprocess a single sequence. Args: sequence (str): The sequence to process. Returns: sequence (str): The postprocessed sequence. """ if self.postprocessor is None: raise ValueError("No postprocessor loaded") for function in self.postprocessor: sequence = function(sequence, self) return sequence
def get_function_by_path(path, args=[], kwargs={}): module_name = ".".join(path.split(".")[:-1]) function_name = path.split(".")[-1] try: module = importlib.import_module(module_name) except ValueError as e: print("Cannot import module '%s'" % module_name) raise e function = getattr(module, function_name) return function