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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,237 @@ | ||
| """Annif backend using the transformer variant of pecos.""" | ||
|
|
||
| import logging | ||
| import os.path as osp | ||
| from sys import stdout | ||
|
|
||
| import numpy as np | ||
| import scipy.sparse as sp | ||
| from pecos.utils.featurization.text.preprocess import Preprocessor | ||
| from pecos.xmc.xtransformer import matcher | ||
| from pecos.xmc.xtransformer.model import XTransformer | ||
| from pecos.xmc.xtransformer.module import MLProblemWithText | ||
|
|
||
| from annif.exception import NotInitializedException, NotSupportedException | ||
| from annif.suggestion import ListSuggestionResult, SubjectSuggestion | ||
| from annif.util import ( | ||
| apply_param_parse_config, | ||
| atomic_save, | ||
| atomic_save_folder, | ||
| boolean, | ||
| ) | ||
|
|
||
| from . import backend, mixins | ||
|
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|
|
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| class XTransformerBackend(mixins.TfidfVectorizerMixin, backend.AnnifBackend): | ||
| """XTransformer based backend for Annif""" | ||
|
|
||
| name = "xtransformer" | ||
| needs_subject_index = True | ||
|
|
||
| _model = None | ||
|
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||
| train_X_file = "xtransformer-train-X.npz" | ||
| train_y_file = "xtransformer-train-y.npz" | ||
| train_txt_file = "xtransformer-train-raw.txt" | ||
| model_folder = "xtransformer-model" | ||
|
|
||
| PARAM_CONFIG = { | ||
| "min_df": int, | ||
| "ngram": int, | ||
| "fix_clustering": boolean, | ||
| "nr_splits": int, | ||
| "min_codes": int, | ||
| "max_leaf_size": int, | ||
| "imbalanced_ratio": float, | ||
| "imbalanced_depth": int, | ||
| "max_match_clusters": int, | ||
| "do_fine_tune": boolean, | ||
| "model_shortcut": str, | ||
| "beam_size": int, | ||
| "limit": int, | ||
| "post_processor": str, | ||
| "negative_sampling": str, | ||
| "ensemble_method": str, | ||
| "threshold": float, | ||
| "loss_function": str, | ||
| "truncate_length": int, | ||
| "hidden_droput_prob": float, | ||
| "batch_size": int, | ||
| "gradient_accumulation_steps": int, | ||
| "learning_rate": float, | ||
| "weight_decay": float, | ||
| "adam_epsilon": float, | ||
| "num_train_epochs": int, | ||
| "max_steps": int, | ||
| "lr_schedule": str, | ||
| "warmup_steps": int, | ||
| "logging_steps": int, | ||
| "save_steps": int, | ||
| "max_active_matching_labels": int, | ||
| "max_num_labels_in_gpu": int, | ||
| "use_gpu": boolean, | ||
| "bootstrap_model": str, | ||
| } | ||
|
|
||
| DEFAULT_PARAMETERS = { | ||
| "min_df": 1, | ||
| "ngram": 1, | ||
| "fix_clustering": False, | ||
| "nr_splits": 16, | ||
| "min_codes": None, | ||
| "max_leaf_size": 100, | ||
| "imbalanced_ratio": 0.0, | ||
| "imbalanced_depth": 100, | ||
| "max_match_clusters": 32768, | ||
| "do_fine_tune": True, | ||
| "model_shortcut": "distilbert-base-multilingual-cased", | ||
| "beam_size": 20, | ||
| "limit": 100, | ||
| "post_processor": "sigmoid", | ||
| "negative_sampling": "tfn", | ||
| "ensemble_method": "transformer-only", | ||
| "threshold": 0.1, | ||
| "loss_function": "squared-hinge", | ||
| "truncate_length": 128, | ||
| "hidden_droput_prob": 0.1, | ||
| "batch_size": 32, | ||
| "gradient_accumulation_steps": 1, | ||
| "learning_rate": 1e-4, | ||
| "weight_decay": 0.0, | ||
| "adam_epsilon": 1e-8, | ||
| "num_train_epochs": 1, | ||
| "max_steps": 0, | ||
| "lr_schedule": "linear", | ||
| "warmup_steps": 0, | ||
| "logging_steps": 100, | ||
| "save_steps": 1000, | ||
| "max_active_matching_labels": None, | ||
| "max_num_labels_in_gpu": 65536, | ||
| "use_gpu": True, | ||
| "bootstrap_model": "linear", | ||
| } | ||
|
|
||
| def _initialize_model(self): | ||
| if self._model is None: | ||
| path = osp.join(self.datadir, self.model_folder) | ||
| self.debug("loading model from {}".format(path)) | ||
| if osp.exists(path): | ||
| self._model = XTransformer.load(path) | ||
| else: | ||
| raise NotInitializedException( | ||
| "model {} not found".format(path), backend_id=self.backend_id | ||
| ) | ||
|
|
||
| def initialize(self, parallel=False): | ||
| self.initialize_vectorizer() | ||
| self._initialize_model() | ||
|
|
||
| def default_params(self): | ||
| params = backend.AnnifBackend.DEFAULT_PARAMETERS.copy() | ||
| params.update(self.DEFAULT_PARAMETERS) | ||
| return params | ||
|
|
||
| def _create_train_files(self, veccorpus, corpus): | ||
| self.info("creating train file") | ||
| Xs = [] | ||
| ys = [] | ||
| txt_pth = osp.join(self.datadir, self.train_txt_file) | ||
| with open(txt_pth, "w", encoding="utf-8") as txt_file: | ||
| for doc, vector in zip(corpus.documents, veccorpus): | ||
| subject_set = doc.subject_set | ||
| if not (subject_set and doc.text): | ||
| continue # noqa | ||
| print(" ".join(doc.text.split()), file=txt_file) | ||
| Xs.append(sp.csr_matrix(vector, dtype=np.float32).sorted_indices()) | ||
| ys.append( | ||
| sp.csr_matrix( | ||
| ( | ||
| np.ones(len(subject_set)), | ||
| (np.zeros(len(subject_set)), [s for s in subject_set]), | ||
| ), | ||
| shape=(1, len(self.project.subjects)), | ||
| dtype=np.float32, | ||
| ).sorted_indices() | ||
| ) | ||
| atomic_save( | ||
| sp.vstack(Xs, format="csr"), | ||
| self.datadir, | ||
| self.train_X_file, | ||
| method=lambda mtrx, target: sp.save_npz(target, mtrx, compressed=True), | ||
| ) | ||
| atomic_save( | ||
| sp.vstack(ys, format="csr"), | ||
| self.datadir, | ||
| self.train_y_file, | ||
| method=lambda mtrx, target: sp.save_npz(target, mtrx, compressed=True), | ||
| ) | ||
|
|
||
| def _create_model(self, params, jobs): | ||
| train_txts = Preprocessor.load_data_from_file( | ||
| osp.join(self.datadir, self.train_txt_file), | ||
| label_text_path=None, | ||
| text_pos=0, | ||
| )["corpus"] | ||
| train_X = sp.load_npz(osp.join(self.datadir, self.train_X_file)) | ||
| train_y = sp.load_npz(osp.join(self.datadir, self.train_y_file)) | ||
| model_path = osp.join(self.datadir, self.model_folder) | ||
| new_params = apply_param_parse_config(self.PARAM_CONFIG, self.params) | ||
| new_params["only_topk"] = new_params.pop("limit") | ||
| train_params = XTransformer.TrainParams.from_dict( | ||
| new_params, recursive=True | ||
| ).to_dict() | ||
| pred_params = XTransformer.PredParams.from_dict( | ||
| new_params, recursive=True | ||
| ).to_dict() | ||
|
|
||
| self.info("Start training") | ||
| # enable progress | ||
| matcher.LOGGER.setLevel(logging.INFO) | ||
| matcher.LOGGER.addHandler(logging.StreamHandler(stream=stdout)) | ||
| self._model = XTransformer.train( | ||
| MLProblemWithText(train_txts, train_y, X_feat=train_X), | ||
| clustering=None, | ||
| val_prob=None, | ||
| train_params=train_params, | ||
| pred_params=pred_params, | ||
| beam_size=params["beam_size"], | ||
| steps_scale=None, | ||
| label_feat=None, | ||
| ) | ||
| atomic_save_folder(self._model, model_path) | ||
|
|
||
| def _train(self, corpus, params, jobs=0): | ||
| if corpus == "cached": | ||
| self.info("Reusing cached training data from previous run.") | ||
| else: | ||
| if corpus.is_empty(): | ||
| raise NotSupportedException("Cannot t project with no documents") | ||
| input = (doc.text for doc in corpus.documents) | ||
| vecparams = { | ||
| "min_df": int(params["min_df"]), | ||
| "tokenizer": self.project.analyzer.tokenize_words, | ||
| "ngram_range": (1, int(params["ngram"])), | ||
| } | ||
| veccorpus = self.create_vectorizer(input, vecparams) | ||
| self._create_train_files(veccorpus, corpus) | ||
| self._create_model(params, jobs) | ||
|
|
||
| def _suggest(self, text, params): | ||
| text = " ".join(text.split()) | ||
| vector = self.vectorizer.transform([text]) | ||
| if vector.nnz == 0: # All zero vector, empty result | ||
| return ListSuggestionResult([]) | ||
| new_params = apply_param_parse_config(self.PARAM_CONFIG, params) | ||
| prediction = self._model.predict( | ||
| [text], | ||
| X_feat=vector.sorted_indices(), | ||
| batch_size=new_params["batch_size"], | ||
| use_gpu=False, | ||
| only_top_k=new_params["limit"], | ||
| post_processor=new_params["post_processor"], | ||
| ) | ||
| results = [] | ||
| for idx, score in zip(prediction.indices, prediction.data): | ||
| results.append(SubjectSuggestion(subject_id=idx, score=score)) | ||
| return ListSuggestionResult(results) |
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