示例#1
0
    def test_load_adapter_from_hub(self):
        for config in ["pfeiffer", "houlsby"]:
            with self.subTest(config=config):
                model = BertForSequenceClassification.from_pretrained(
                    "bert-base-uncased")

                loading_info = {}
                adapter_name = model.load_adapter("sts/mrpc@ukp",
                                                  config=config,
                                                  version="1",
                                                  loading_info=loading_info)

                self.assertEqual(0, len(loading_info["missing_keys"]))

                # hotfix for unnecessary weights in old adapters
                unexpected_keys = [
                    k for k in loading_info["unexpected_keys"]
                    if "adapter_attention" not in k
                ]
                self.assertEqual(0, len(unexpected_keys))

                self.assertIn(adapter_name, model.config.adapters.adapters)
                # check if config is valid
                expected_hash = get_adapter_config_hash(
                    AdapterConfig.load(config))
                real_hash = get_adapter_config_hash(
                    model.config.adapters.get(adapter_name))
                self.assertEqual(expected_hash, real_hash)

                # check size of output
                in_data = ids_tensor((1, 128), 1000)
                output = model(in_data)
                self.assertEqual([1, 2], list(output[0].size()))
 def test_config_load(self):
     download_kwargs = {"force_download": True}
     for config_name in self.config_names:
         with self.subTest(config_name=config_name):
             config = AdapterConfig.load(config_name, download_kwargs=download_kwargs, non_linearity="leakyrelu")
             self.assertTrue(isinstance(config, AdapterConfig))
             self.assertEqual(config.non_linearity, "leakyrelu")
    def test_add_adapter_fusion(self):
        config_name = "pfeiffer"
        adapter_config = AdapterConfig.load(config_name)

        for adater_fusion_config_name, adapter_fusion_config in ADAPTERFUSION_CONFIG_MAP.items():
            model = AutoModel.from_config(self.config())
            model.eval()

            with self.subTest(model_class=model.__class__.__name__, config=config_name):
                name1 = f"{config_name}-1"
                name2 = f"{config_name}-2"
                model.add_adapter(name1, config=config_name)
                model.add_adapter(name2, config=config_name)

                # adapter is correctly added to config
                self.assertTrue(name1 in model.config.adapters)
                self.assertTrue(name2 in model.config.adapters)
                self.assertEqual(asdict(adapter_config), asdict(model.config.adapters.get(name1)))
                self.assertEqual(asdict(adapter_config), asdict(model.config.adapters.get(name2)))

                model.add_fusion([name1, name2], adater_fusion_config_name)

                # check forward pass
                input_ids = self.get_input_samples((1, 128), config=model.config)
                input_data = {"input_ids": input_ids}
                model.set_active_adapters([[name1, name2]])
                adapter_output = model(**input_data)
                model.set_active_adapters(None)
                base_output = model(**input_data)
                self.assertEqual(len(adapter_output), len(base_output))
                self.assertFalse(torch.equal(adapter_output[0], base_output[0]))
    def test_load_lang_adapter_from_hub(self):
        for config in ["pfeiffer+inv", "houlsby+inv"]:
            with self.subTest(config=config):
                model = AutoModel.from_pretrained(
                    "bert-base-multilingual-cased")
                config = AdapterConfig.load(config,
                                            non_linearity="gelu",
                                            reduction_factor=2)

                loading_info = {}
                adapter_name = model.load_adapter("fi/wiki@ukp",
                                                  config=config,
                                                  set_active=True,
                                                  loading_info=loading_info)

                self.assertEqual(0, len(loading_info["missing_keys"]))
                self.assertEqual(0, len(loading_info["unexpected_keys"]))

                # check if adapter & invertible adapter were added
                self.assertIn(adapter_name, model.config.adapters.adapters)
                self.assertIn(adapter_name, model.invertible_adapters)

                # check if config is valid
                # TODO-AH hashes are not guaranteed to be equal because of legacy keys in lang adapter config
                # expected_hash = get_adapter_config_hash(config)
                # real_hash = get_adapter_config_hash(model.config.adapters.get(adapter_name))
                # self.assertEqual(expected_hash, real_hash)

                # check size of output
                in_data = ids_tensor((1, 128), 1000)
                output = model(in_data)
                self.assertEqual([1, 128, 768], list(output[0].size()))
    def test_load_task_adapter_from_hub(self):
        """This test checks if an adapter is loaded from the Hub correctly by evaluating it on some MRPC samples
        and comparing with the expected result.
        """
        for config in ["pfeiffer", "houlsby"]:
            with self.subTest(config=config):
                tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
                model = BertForSequenceClassification.from_pretrained(
                    "bert-base-uncased")

                loading_info = {}
                adapter_name = model.load_adapter("sts/mrpc@ukp",
                                                  config=config,
                                                  version="1",
                                                  loading_info=loading_info)
                model.train_adapter(adapter_name)

                self.assertEqual(0, len(loading_info["missing_keys"]))
                self.assertEqual(0, len(loading_info["unexpected_keys"]))

                self.assertIn(adapter_name, model.config.adapters.adapters)
                self.assertNotIn(adapter_name,
                                 model.base_model.invertible_adapters)

                # check if config is valid
                expected_hash = get_adapter_config_hash(
                    AdapterConfig.load(config))
                real_hash = get_adapter_config_hash(
                    model.config.adapters.get(adapter_name))
                self.assertEqual(expected_hash, real_hash)

                # setup dataset
                data_args = GlueDataTrainingArguments(
                    task_name="mrpc",
                    data_dir="./tests/fixtures/tests_samples/MRPC",
                    overwrite_cache=True)
                eval_dataset = GlueDataset(data_args,
                                           tokenizer=tokenizer,
                                           mode="dev")
                training_args = TrainingArguments(output_dir="./examples",
                                                  no_cuda=True)

                # evaluate
                trainer = Trainer(
                    model=model,
                    args=training_args,
                    eval_dataset=eval_dataset,
                    compute_metrics=self._compute_glue_metrics("mrpc"),
                    adapter_names=["mrpc"],
                )
                result = trainer.evaluate()
                self.assertGreater(result["eval_acc"], 0.9)
示例#6
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    def test_load_adapter_with_head_from_hub(self):
        model = BertModelWithHeads.from_pretrained("bert-base-uncased")

        loading_info = {}
        adapter_name = model.load_adapter("qa/squad1@ukp", config="houlsby", version="1", loading_info=loading_info)

        self.assertEqual(0, len(loading_info["missing_keys"]))
        self.assertEqual(0, len(loading_info["unexpected_keys"]))

        self.assertIn(adapter_name, model.config.adapters.adapters)
        # check if config is valid
        expected_hash = get_adapter_config_hash(AdapterConfig.load("houlsby"))
        real_hash = get_adapter_config_hash(model.config.adapters.get(adapter_name))
        self.assertEqual(expected_hash, real_hash)

        # check size of output
        in_data = ids_tensor((1, 128), 1000)
        output = model(in_data)
        self.assertEqual([1, 128], list(output[0].size()))
示例#7
0
 def __init__(self, config, task_name):
     super().__init__()
     self.config = config
     self.task_name = task_name
     # xlmr encoder
     self.xlmr_dim = 768
     self.xlmr = XLMRobertaModel.from_pretrained(config.xlmr_model_name,
                                                 cache_dir=os.path.join(
                                                     config._cache_dir,
                                                     'xlmr'),
                                                 output_hidden_states=True)
     self.xlmr_dropout = nn.Dropout(p=config.xlmr_dropout)
     # add task adapters
     task_config = AdapterConfig.load("pfeiffer", reduction_factor=6)
     self.xlmr.add_adapter(task_name,
                           AdapterType.text_task,
                           config=task_config)
     self.xlmr.train_adapter([task_name])
     self.xlmr.set_active_adapters([task_name])
示例#8
0
def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments, MultiLingAdapterArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args, training_args, adapter_args = parser.parse_json_file(
            json_file=os.path.abspath(sys.argv[1])
        )
    else:
        model_args, data_args, training_args, adapter_args = parser.parse_args_into_dataclasses()

    if (
        os.path.exists(training_args.output_dir)
        and os.listdir(training_args.output_dir)
        and training_args.do_train
        and not training_args.overwrite_output_dir
    ):
        raise ValueError(
            f"Output directory ({training_args.output_dir}) already exists and is not empty. "
            f"Use --overwrite_output_dir to overcome."
        )

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,
    )
    logger.warning(
        "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
        training_args.local_rank,
        training_args.device,
        training_args.n_gpu,
        bool(training_args.local_rank != -1),
        training_args.fp16,
    )
    logger.info("Training/evaluation parameters %s", training_args)

    # Set seed
    set_seed(training_args.seed)

    # Prepare CONLL-2003 task
    labels = get_labels(data_args.labels)
    label_map: Dict[int, str] = {i: label for i, label in enumerate(labels)}
    num_labels = len(labels)

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    config = AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
        num_labels=num_labels,
        id2label=label_map,
        label2id={label: i for i, label in enumerate(labels)},
        cache_dir=model_args.cache_dir,
    )
    tokenizer = AutoTokenizer.from_pretrained(
        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        use_fast=model_args.use_fast,
    )
    model = AutoModelForTokenClassification.from_pretrained(
        model_args.model_name_or_path,
        from_tf=bool(".ckpt" in model_args.model_name_or_path),
        config=config,
        cache_dir=model_args.cache_dir,
    )

    # Setup adapters
    if adapter_args.train_adapter:
        task_name = "ner"
        # check if adapter already exists, otherwise add it
        if task_name not in model.config.adapters.adapter_list(AdapterType.text_task):
            # resolve the adapter config
            adapter_config = AdapterConfig.load(
                adapter_args.adapter_config,
                non_linearity=adapter_args.adapter_non_linearity,
                reduction_factor=adapter_args.adapter_reduction_factor,
            )
            # load a pre-trained from Hub if specified
            if adapter_args.load_adapter:
                model.load_adapter(
                    adapter_args.load_adapter, AdapterType.text_task, config=adapter_config, load_as=task_name,
                )
            # otherwise, add a fresh adapter
            else:
                model.add_adapter(task_name, AdapterType.text_task, config=adapter_config)
        # optionally load a pre-trained language adapter
        if adapter_args.load_lang_adapter:
            # resolve the language adapter config
            lang_adapter_config = AdapterConfig.load(
                adapter_args.lang_adapter_config,
                non_linearity=adapter_args.lang_adapter_non_linearity,
                reduction_factor=adapter_args.lang_adapter_reduction_factor,
            )
            # load the language adapter from Hub
            lang_adapter_name = model.load_adapter(
                adapter_args.load_lang_adapter,
                AdapterType.text_lang,
                config=lang_adapter_config,
                load_as=adapter_args.language,
            )
        else:
            lang_adapter_name = None
        # Freeze all model weights except of those of this adapter
        model.train_adapter([task_name])
        # Set the adapters to be used in every forward pass
        if lang_adapter_name:
            model.set_active_adapters([lang_adapter_name, task_name])
        else:
            model.set_active_adapters([task_name])

    # Get datasets
    train_dataset = (
        NerDataset(
            data_dir=data_args.data_dir,
            tokenizer=tokenizer,
            labels=labels,
            model_type=config.model_type,
            max_seq_length=data_args.max_seq_length,
            overwrite_cache=data_args.overwrite_cache,
            mode=Split.train,
        )
        if training_args.do_train
        else None
    )
    eval_dataset = (
        NerDataset(
            data_dir=data_args.data_dir,
            tokenizer=tokenizer,
            labels=labels,
            model_type=config.model_type,
            max_seq_length=data_args.max_seq_length,
            overwrite_cache=data_args.overwrite_cache,
            mode=Split.dev,
        )
        if training_args.do_eval
        else None
    )

    def align_predictions(predictions: np.ndarray, label_ids: np.ndarray) -> Tuple[List[int], List[int]]:
        preds = np.argmax(predictions, axis=2)

        batch_size, seq_len = preds.shape

        out_label_list = [[] for _ in range(batch_size)]
        preds_list = [[] for _ in range(batch_size)]

        for i in range(batch_size):
            for j in range(seq_len):
                if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
                    out_label_list[i].append(label_map[label_ids[i][j]])
                    preds_list[i].append(label_map[preds[i][j]])

        return preds_list, out_label_list

    def compute_metrics(p: EvalPrediction) -> Dict:
        preds_list, out_label_list = align_predictions(p.predictions, p.label_ids)
        return {
            "precision": precision_score(out_label_list, preds_list),
            "recall": recall_score(out_label_list, preds_list),
            "f1": f1_score(out_label_list, preds_list),
        }

    # Initialize our Trainer
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset,
        eval_dataset=eval_dataset,
        compute_metrics=compute_metrics,
        do_save_full_model=not adapter_args.train_adapter,
        do_save_adapters=adapter_args.train_adapter,
    )

    # Training
    if training_args.do_train:
        trainer.train(
            model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None
        )
        trainer.save_model()
        # For convenience, we also re-save the tokenizer to the same directory,
        # so that you can share your model easily on huggingface.co/models =)
        if trainer.is_world_master():
            tokenizer.save_pretrained(training_args.output_dir)

    # Evaluation
    results = {}
    if training_args.do_eval:
        logger.info("*** Evaluate ***")

        result = trainer.evaluate()

        output_eval_file = os.path.join(training_args.output_dir, "eval_results.txt")
        if trainer.is_world_master():
            with open(output_eval_file, "w") as writer:
                logger.info("***** Eval results *****")
                for key, value in result.items():
                    logger.info("  %s = %s", key, value)
                    writer.write("%s = %s\n" % (key, value))

            results.update(result)

    # Predict
    if training_args.do_predict:
        test_dataset = NerDataset(
            data_dir=data_args.data_dir,
            tokenizer=tokenizer,
            labels=labels,
            model_type=config.model_type,
            max_seq_length=data_args.max_seq_length,
            overwrite_cache=data_args.overwrite_cache,
            mode=Split.test,
        )

        predictions, label_ids, metrics = trainer.predict(test_dataset)
        preds_list, _ = align_predictions(predictions, label_ids)

        output_test_results_file = os.path.join(training_args.output_dir, "test_results.txt")
        if trainer.is_world_master():
            with open(output_test_results_file, "w") as writer:
                for key, value in metrics.items():
                    logger.info("  %s = %s", key, value)
                    writer.write("%s = %s\n" % (key, value))

        # Save predictions
        output_test_predictions_file = os.path.join(training_args.output_dir, "test_predictions.txt")
        if trainer.is_world_master():
            with open(output_test_predictions_file, "w") as writer:
                with open(os.path.join(data_args.data_dir, "test.txt"), "r") as f:
                    example_id = 0
                    for line in f:
                        if line.startswith("-DOCSTART-") or line == "" or line == "\n":
                            writer.write(line)
                            if not preds_list[example_id]:
                                example_id += 1
                        elif preds_list[example_id]:
                            output_line = line.split()[0] + " " + preds_list[example_id].pop(0) + "\n"
                            writer.write(output_line)
                        else:
                            logger.warning(
                                "Maximum sequence length exceeded: No prediction for '%s'.", line.split()[0]
                            )

    return results
示例#9
0
def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments,
                               TrainingArguments, AdapterArguments))

    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args, training_args, adapter_args = parser.parse_json_file(
            json_file=os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args, training_args, adapter_args = parser.parse_args_into_dataclasses(
        )

    if (os.path.exists(training_args.output_dir)
            and os.listdir(training_args.output_dir) and training_args.do_train
            and not training_args.overwrite_output_dir):
        raise ValueError(
            f"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome."
        )

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO
        if training_args.local_rank in [-1, 0] else logging.WARN,
    )
    logger.warning(
        "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
        training_args.local_rank,
        training_args.device,
        training_args.n_gpu,
        bool(training_args.local_rank != -1),
        training_args.fp16,
    )
    logger.info("Training/evaluation parameters %s", training_args)

    # Set seed
    set_seed(training_args.seed)

    try:
        num_labels = glue_tasks_num_labels[data_args.task_name]
        output_mode = glue_output_modes[data_args.task_name]
    except KeyError:
        raise ValueError("Task not found: %s" % (data_args.task_name))

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    # config = AutoConfig.from_pretrained(
    #     model_args.config_name if model_args.config_name else model_args.model_name_or_path,
    #     num_labels=num_labels,
    #     finetuning_task=data_args.task_name,
    #     cache_dir=model_args.cache_dir,
    # )
    tokenizer = BertTokenizer.from_pretrained(
        model_args.tokenizer_name
        if model_args.tokenizer_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
    )
    model = BertForSequenceClassification.from_pretrained(
        "bert-base-uncased",
        cache_dir=model_args.cache_dir,
        num_labels=num_labels)
    # model = AutoModelWithHeads.from_pretrained(
    #     model_args.model_name_or_path,
    #     from_tf=bool(".ckpt" in model_args.model_name_or_path),
    #     config=config,
    #     cache_dir=model_args.cache_dir,
    # )
    # model.add_classification_head(data_args.task_name, num_labels=num_labels)

    config = AdapterConfig.load("pfeiffer")
    model.load_adapter("nli/multinli@ukp", "text_task", config=config)

    # Setup adapters
    # setup_task_adapter_training(model, "mnli", adapter_args)
    # model.train_adapter(["mnli"])
    # model.set_active_adapters(["mnli"])

    # Get datasets
    train_dataset = GlueDataset(
        data_args, tokenizer=tokenizer) if training_args.do_train else None
    eval_dataset = GlueDataset(data_args, tokenizer=tokenizer,
                               mode="dev") if training_args.do_eval else None
    test_dataset = GlueDataset(
        data_args, tokenizer=tokenizer,
        mode="test") if training_args.do_predict else None

    def compute_metrics(p: EvalPrediction) -> Dict:
        if output_mode == "classification":
            preds = np.argmax(p.predictions, axis=1)
        elif output_mode == "regression":
            preds = np.squeeze(p.predictions)
        return glue_compute_metrics(data_args.task_name, preds, p.label_ids)

    # Initialize our Trainer
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset,
        eval_dataset=eval_dataset,
        compute_metrics=compute_metrics,
        do_save_full_model=not adapter_args.train_adapter,
        do_save_adapters=adapter_args.train_adapter,
    )

    # Training
    if training_args.do_train:
        trainer.train(model_path=model_args.model_name_or_path if os.path.
                      isdir(model_args.model_name_or_path) else None)
        trainer.save_model()
        # For convenience, we also re-save the tokenizer to the same directory,
        # so that you can share your model easily on huggingface.co/models =)
        if trainer.is_world_master():
            tokenizer.save_pretrained(training_args.output_dir)

    # Evaluation
    eval_results = {}
    if training_args.do_eval:
        logger.info("*** Evaluate ***")

        # Loop to handle MNLI double evaluation (matched, mis-matched)
        eval_datasets = [eval_dataset]
        if data_args.task_name == "mnli":
            mnli_mm_data_args = dataclasses.replace(data_args,
                                                    task_name="mnli-mm")
            eval_datasets.append(
                GlueDataset(mnli_mm_data_args, tokenizer=tokenizer,
                            mode="dev"))

        for eval_dataset in eval_datasets:
            eval_result = trainer.evaluate(eval_dataset=eval_dataset)

            output_eval_file = os.path.join(
                training_args.output_dir,
                f"eval_results_{eval_dataset.args.task_name}.txt")
            if trainer.is_world_master():
                with open(output_eval_file, "w") as writer:
                    logger.info("***** Eval results {} *****".format(
                        eval_dataset.args.task_name))
                    for key, value in eval_result.items():
                        logger.info("  %s = %s", key, value)
                        writer.write("%s = %s\n" % (key, value))

            eval_results.update(eval_result)

    if training_args.do_predict:
        logging.info("*** Test ***")
        test_datasets = [test_dataset]
        if data_args.task_name == "mnli":
            mnli_mm_data_args = dataclasses.replace(data_args,
                                                    task_name="mnli-mm")
            test_datasets.append(
                GlueDataset(mnli_mm_data_args,
                            tokenizer=tokenizer,
                            mode="test"))

        for test_dataset in test_datasets:
            predictions = trainer.predict(
                test_dataset=test_dataset).predictions
            if output_mode == "classification":
                predictions = np.argmax(predictions, axis=1)

            output_test_file = os.path.join(
                training_args.output_dir,
                f"test_results_{test_dataset.args.task_name}.txt")
            if trainer.is_world_master():
                with open(output_test_file, "w") as writer:
                    logger.info("***** Test results {} *****".format(
                        test_dataset.args.task_name))
                    writer.write("index\tprediction\n")
                    for index, item in enumerate(predictions):
                        if output_mode == "regression":
                            writer.write("%d\t%3.3f\n" % (index, item))
                        else:
                            item = test_dataset.get_labels()[item]
                            writer.write("%d\t%s\n" % (index, item))
    return eval_results
示例#10
0
def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments,
                               TrainingArguments, MultiLingAdapterArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args, training_args, adapter_args = parser.parse_json_file(
            json_file=os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args, training_args, adapter_args = parser.parse_args_into_dataclasses(
        )

    # Detecting last checkpoint.
    last_checkpoint = None
    if os.path.isdir(
            training_args.output_dir
    ) and training_args.do_train and not training_args.overwrite_output_dir:
        last_checkpoint = get_last_checkpoint(training_args.output_dir)
        if last_checkpoint is None and len(os.listdir(
                training_args.output_dir)) > 0:
            raise ValueError(
                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                "Use --overwrite_output_dir to overcome.")
        elif last_checkpoint is not None:
            logger.info(
                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
            )

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )
    logger.setLevel(logging.INFO if is_main_process(training_args.local_rank
                                                    ) else logging.WARN)

    # Log on each process the small summary:
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
        +
        f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
    )
    # Set the verbosity to info of the Transformers logger (on main process only):
    if is_main_process(training_args.local_rank):
        transformers.utils.logging.set_verbosity_info()
        transformers.utils.logging.enable_default_handler()
        transformers.utils.logging.enable_explicit_format()
    logger.info(f"Training/evaluation parameters {training_args}")

    # Set seed before initializing model.
    set_seed(training_args.seed)

    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
    # (the dataset will be downloaded automatically from the datasets Hub).
    #
    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
    # 'text' is found. You can easily tweak this behavior (see below).
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if data_args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        datasets = load_dataset(data_args.dataset_name,
                                data_args.dataset_config_name)
    else:
        data_files = {}
        if data_args.train_file is not None:
            data_files["train"] = data_args.train_file
            extension = data_args.train_file.split(".")[-1]

        if data_args.validation_file is not None:
            data_files["validation"] = data_args.validation_file
            extension = data_args.validation_file.split(".")[-1]
        if data_args.test_file is not None:
            data_files["test"] = data_args.test_file
            extension = data_args.test_file.split(".")[-1]
        datasets = load_dataset(extension, data_files=data_files, field="data")
    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
    # https://huggingface.co/docs/datasets/loading_datasets.html.

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    config = AutoConfig.from_pretrained(
        model_args.config_name
        if model_args.config_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        use_auth_token=True if model_args.use_auth_token else None,
    )
    tokenizer = AutoTokenizer.from_pretrained(
        model_args.tokenizer_name
        if model_args.tokenizer_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        use_fast=True,
        revision=model_args.model_revision,
        use_auth_token=True if model_args.use_auth_token else None,
    )
    model = AutoModelForQuestionAnswering.from_pretrained(
        model_args.model_name_or_path,
        from_tf=bool(".ckpt" in model_args.model_name_or_path),
        config=config,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        use_auth_token=True if model_args.use_auth_token else None,
    )

    # Tokenizer check: this script requires a fast tokenizer.
    if not isinstance(tokenizer, PreTrainedTokenizerFast):
        raise ValueError(
            "This example script only works for models that have a fast tokenizer. Checkout the big table of models "
            "at https://huggingface.co/transformers/index.html#bigtable to find the model types that meet this "
            "requirement")

    # Setup adapters
    if adapter_args.train_adapter:

        # new
        if data_args.madx2:
            # do not add adapter in the last transformer layers
            leave_out = [len(model.bert.encoder.layer) - 1]
        else:
            leave_out = []

        # new
        # task_name = data_args.dataset_name or "squad"
        task_name = "qa"

        # optionally load  a pretrained language adapter
        if adapter_args.load_lang_adapter:

            # resolve language adapter config
            lang_adapter_config = AdapterConfig.load(
                adapter_args.lang_adapter_config,
                non_linearity=adapter_args.lang_adapter_non_linearity,
                reduction_factor=adapter_args.lang_adapter_reduction_factor,
                leave_out=leave_out)
            #             # load language adapter from Hub
            #             lang_adapter_name = model.load_adapter(
            #                 adapter_args.load_lang_adapter,
            #                 config=lang_adapter_config,
            #                 load_as=adapter_args.language,
            #             )
            # new
            # load language adapter from path in load_lang_adapter
            task_mlm_load_as = 'mlm'
            lang_adapter_name = model.load_adapter(
                adapter_args.load_lang_adapter,
                config=lang_adapter_config,
                load_as=task_mlm_load_as,
                with_head=False)
        else:
            lang_adapter_name = None

        # check if adapter already exists otherwise add it
        if task_name not in model.config.adapters:

            #             # resolve adapter config
            #             adapter_config = AdapterConfig.load(
            #                 adapter_args.adapter_config,
            #                 non_linearity=adapter_args.adapter_non_linearity,
            #                 reduction_factor=adapter_args.adapter_reduction_factor,
            #             )

            # new
            # resolve adapter config with (eventually) the MAD-X 2.0 option
            if adapter_args.adapter_config == "pfeiffer":
                from transformers.adapters.configuration import PfeifferConfig
                adapter_config = PfeifferConfig(
                    non_linearity=adapter_args.adapter_non_linearity,
                    reduction_factor=adapter_args.adapter_reduction_factor,
                    leave_out=leave_out)
            elif adapter_args.adapter_config == "pfeiffer+inv":
                from transformers.adapters.configuration import PfeifferInvConfig
                adapter_config = PfeifferInvConfig(
                    non_linearity=adapter_args.adapter_non_linearity,
                    reduction_factor=adapter_args.adapter_reduction_factor,
                    leave_out=leave_out)
            elif adapter_args.adapter_config == "houlsby":
                from transformers.adapters.configuration import HoulsbyConfig
                adapter_config = HoulsbyConfig(
                    non_linearity=adapter_args.adapter_non_linearity,
                    reduction_factor=adapter_args.adapter_reduction_factor,
                    leave_out=leave_out)
            elif adapter_args.adapter_config == "houlsby+inv":
                from transformers.adapters.configuration import HoulsbyInvConfig
                adapter_config = HoulsbyInvConfig(
                    non_linearity=adapter_args.adapter_non_linearity,
                    reduction_factor=adapter_args.adapter_reduction_factor,
                    leave_out=leave_out)

            # load adapter from hub if specified
            if adapter_args.load_adapter:
                model.load_adapter(adapter_args.load_adapter,
                                   config=adapter_config,
                                   load_as=task_name)
            else:
                model.add_adapter(task_name, config=adapter_config)

        # Set the adapters to be used in every forward pass
        if lang_adapter_name:
            model.active_adapters = Stack(task_mlm_load_as, task_name)
        else:
            model.set_active_adapters(task_name)

        # Freeze all model weights except of those in this adapter
        model.train_adapter(task_name)

    else:
        if adapter_args.load_adapter or adapter_args.load_lang_adapter:
            raise ValueError(
                "Adapters can only be loaded in adapters training mode."
                "Use --train_adapter to enable adapter_training")

    # new
    # Put only the adapter after the MHA but not after the FF in the last layer
    if data_args.houlsby_MHA_lastlayer and adapter_args.train_adapter and not data_args.madx2 and task_name in model.config.adapters and (
            adapter_args.adapter_config == "houlsby"
            or adapter_args.adapter_config == "houlsby+inv"):
        import torch
        from torch.nn import ModuleDict
        model.bert.encoder.layer[len(model.bert.encoder.layer) -
                                 1].output.adapters = ModuleDict()

    # Preprocessing the datasets.
    # Preprocessing is slighlty different for training and evaluation.
    if training_args.do_train:
        column_names = datasets["train"].column_names
    elif training_args.do_eval:
        column_names = datasets["validation"].column_names
    else:
        column_names = datasets["test"].column_names
    question_column_name = "question" if "question" in column_names else column_names[
        0]
    context_column_name = "context" if "context" in column_names else column_names[
        1]
    answer_column_name = "answers" if "answers" in column_names else column_names[
        2]

    # Padding side determines if we do (question|context) or (context|question).
    pad_on_right = tokenizer.padding_side == "right"

    if data_args.max_seq_length > tokenizer.model_max_length:
        logger.warn(
            f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
            f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
        )
    max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)

    # Training preprocessing
    def prepare_train_features(examples):
        # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
        # in one example possible giving several features when a context is long, each of those features having a
        # context that overlaps a bit the context of the previous feature.
        tokenized_examples = tokenizer(
            examples[
                question_column_name if pad_on_right else context_column_name],
            examples[
                context_column_name if pad_on_right else question_column_name],
            truncation="only_second" if pad_on_right else "only_first",
            max_length=max_seq_length,
            stride=data_args.doc_stride,
            return_overflowing_tokens=True,
            return_offsets_mapping=True,
            padding="max_length" if data_args.pad_to_max_length else False,
        )

        # Since one example might give us several features if it has a long context, we need a map from a feature to
        # its corresponding example. This key gives us just that.
        sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
        # The offset mappings will give us a map from token to character position in the original context. This will
        # help us compute the start_positions and end_positions.
        offset_mapping = tokenized_examples.pop("offset_mapping")

        # Let's label those examples!
        tokenized_examples["start_positions"] = []
        tokenized_examples["end_positions"] = []

        for i, offsets in enumerate(offset_mapping):
            # We will label impossible answers with the index of the CLS token.
            input_ids = tokenized_examples["input_ids"][i]
            cls_index = input_ids.index(tokenizer.cls_token_id)

            # Grab the sequence corresponding to that example (to know what is the context and what is the question).
            sequence_ids = tokenized_examples.sequence_ids(i)

            # One example can give several spans, this is the index of the example containing this span of text.
            sample_index = sample_mapping[i]
            answers = examples[answer_column_name][sample_index]
            # If no answers are given, set the cls_index as answer.
            if len(answers["answer_start"]) == 0:
                tokenized_examples["start_positions"].append(cls_index)
                tokenized_examples["end_positions"].append(cls_index)
            else:
                # Start/end character index of the answer in the text.
                start_char = answers["answer_start"][0]
                end_char = start_char + len(answers["text"][0])

                # Start token index of the current span in the text.
                token_start_index = 0
                while sequence_ids[token_start_index] != (1 if pad_on_right
                                                          else 0):
                    token_start_index += 1

                # End token index of the current span in the text.
                token_end_index = len(input_ids) - 1
                while sequence_ids[token_end_index] != (1 if pad_on_right else
                                                        0):
                    token_end_index -= 1

                # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).
                if not (offsets[token_start_index][0] <= start_char
                        and offsets[token_end_index][1] >= end_char):
                    tokenized_examples["start_positions"].append(cls_index)
                    tokenized_examples["end_positions"].append(cls_index)
                else:
                    # Otherwise move the token_start_index and token_end_index to the two ends of the answer.
                    # Note: we could go after the last offset if the answer is the last word (edge case).
                    while token_start_index < len(offsets) and offsets[
                            token_start_index][0] <= start_char:
                        token_start_index += 1
                    tokenized_examples["start_positions"].append(
                        token_start_index - 1)
                    while offsets[token_end_index][1] >= end_char:
                        token_end_index -= 1
                    tokenized_examples["end_positions"].append(
                        token_end_index + 1)

        return tokenized_examples

    if training_args.do_train:
        if "train" not in datasets:
            raise ValueError("--do_train requires a train dataset")
        train_dataset = datasets["train"]
        if data_args.max_train_samples is not None:
            # We will select sample from whole data if agument is specified
            train_dataset = train_dataset.select(
                range(data_args.max_train_samples))
        # Create train feature from dataset
        train_dataset = train_dataset.map(
            prepare_train_features,
            batched=True,
            num_proc=data_args.preprocessing_num_workers,
            remove_columns=column_names,
            load_from_cache_file=not data_args.overwrite_cache,
        )
        if data_args.max_train_samples is not None:
            # Number of samples might increase during Feature Creation, We select only specified max samples
            train_dataset = train_dataset.select(
                range(data_args.max_train_samples))

    # Validation preprocessing
    def prepare_validation_features(examples):
        # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
        # in one example possible giving several features when a context is long, each of those features having a
        # context that overlaps a bit the context of the previous feature.
        tokenized_examples = tokenizer(
            examples[
                question_column_name if pad_on_right else context_column_name],
            examples[
                context_column_name if pad_on_right else question_column_name],
            truncation="only_second" if pad_on_right else "only_first",
            max_length=max_seq_length,
            stride=data_args.doc_stride,
            return_overflowing_tokens=True,
            return_offsets_mapping=True,
            padding="max_length" if data_args.pad_to_max_length else False,
        )

        # Since one example might give us several features if it has a long context, we need a map from a feature to
        # its corresponding example. This key gives us just that.
        sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")

        # For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
        # corresponding example_id and we will store the offset mappings.
        tokenized_examples["example_id"] = []

        for i in range(len(tokenized_examples["input_ids"])):
            # Grab the sequence corresponding to that example (to know what is the context and what is the question).
            sequence_ids = tokenized_examples.sequence_ids(i)
            context_index = 1 if pad_on_right else 0

            # One example can give several spans, this is the index of the example containing this span of text.
            sample_index = sample_mapping[i]
            tokenized_examples["example_id"].append(
                examples["id"][sample_index])

            # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
            # position is part of the context or not.
            tokenized_examples["offset_mapping"][i] = [
                (o if sequence_ids[k] == context_index else None)
                for k, o in enumerate(tokenized_examples["offset_mapping"][i])
            ]

        return tokenized_examples

    if training_args.do_eval:
        if "validation" not in datasets:
            raise ValueError("--do_eval requires a validation dataset")
        eval_examples = datasets["validation"]
        if data_args.max_val_samples is not None:
            # We will select sample from whole data
            eval_examples = eval_examples.select(
                range(data_args.max_val_samples))
        # Validation Feature Creation
        eval_dataset = eval_examples.map(
            prepare_validation_features,
            batched=True,
            num_proc=data_args.preprocessing_num_workers,
            remove_columns=column_names,
            load_from_cache_file=not data_args.overwrite_cache,
        )
        if data_args.max_val_samples is not None:
            # During Feature creation dataset samples might increase, we will select required samples again
            eval_dataset = eval_dataset.select(range(
                data_args.max_val_samples))

    if training_args.do_predict:
        if "test" not in datasets:
            raise ValueError("--do_predict requires a test dataset")
        test_examples = datasets["test"]
        if data_args.max_test_samples is not None:
            # We will select sample from whole data
            test_examples = test_examples.select(
                range(data_args.max_test_samples))
        # Test Feature Creation
        test_dataset = test_examples.map(
            prepare_validation_features,
            batched=True,
            num_proc=data_args.preprocessing_num_workers,
            remove_columns=column_names,
            load_from_cache_file=not data_args.overwrite_cache,
        )
        if data_args.max_test_samples is not None:
            # During Feature creation dataset samples might increase, we will select required samples again
            test_dataset = test_dataset.select(
                range(data_args.max_test_samples))

    # Data collator
    # We have already padded to max length if the corresponding flag is True, otherwise we need to pad in the data
    # collator.
    data_collator = (default_data_collator if data_args.pad_to_max_length else
                     DataCollatorWithPadding(
                         tokenizer,
                         pad_to_multiple_of=8 if training_args.fp16 else None))

    # Post-processing:
    def post_processing_function(examples,
                                 features,
                                 predictions,
                                 stage="eval"):
        # Post-processing: we match the start logits and end logits to answers in the original context.
        predictions = postprocess_qa_predictions(
            examples=examples,
            features=features,
            predictions=predictions,
            version_2_with_negative=data_args.version_2_with_negative,
            n_best_size=data_args.n_best_size,
            max_answer_length=data_args.max_answer_length,
            null_score_diff_threshold=data_args.null_score_diff_threshold,
            output_dir=training_args.output_dir,
            is_world_process_zero=trainer.is_world_process_zero(),
            prefix=stage,
        )
        # Format the result to the format the metric expects.
        if data_args.version_2_with_negative:
            formatted_predictions = [{
                "id": k,
                "prediction_text": v,
                "no_answer_probability": 0.0
            } for k, v in predictions.items()]
        else:
            formatted_predictions = [{
                "id": k,
                "prediction_text": v
            } for k, v in predictions.items()]

        references = [{
            "id": ex["id"],
            "answers": ex[answer_column_name]
        } for ex in examples]
        return EvalPrediction(predictions=formatted_predictions,
                              label_ids=references)

    metric = load_metric(
        "squad_v2" if data_args.version_2_with_negative else "squad")

    def compute_metrics(p: EvalPrediction):
        results = metric.compute(predictions=p.predictions,
                                 references=p.label_ids)

        # new (change the metric name f1 to eval_f1 in order to avoid a bug at the evaluation time
        results['eval_f1'] = results['f1']
        results.pop('f1')
        return results

    # Initialize our Trainer
    trainer = QuestionAnsweringTrainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset if training_args.do_train else None,
        eval_dataset=eval_dataset if training_args.do_eval else None,
        eval_examples=eval_examples if training_args.do_eval else None,
        tokenizer=tokenizer,
        data_collator=data_collator,
        post_process_function=post_processing_function,
        compute_metrics=compute_metrics,
        do_save_full_model=not adapter_args.train_adapter,
        do_save_adapters=adapter_args.train_adapter,
        callbacks=[
            EarlyStoppingCallback(
                early_stopping_patience=data_args.early_stopping_patience)
        ] if training_args.load_best_model_at_end else None,
    )

    # Training
    if training_args.do_train:
        if last_checkpoint is not None:
            checkpoint = last_checkpoint
        elif os.path.isdir(model_args.model_name_or_path):
            checkpoint = model_args.model_name_or_path
        else:
            checkpoint = None
        train_result = trainer.train(resume_from_checkpoint=checkpoint)
        trainer.save_model()  # Saves the tokenizer too for easy upload

        metrics = train_result.metrics
        max_train_samples = (data_args.max_train_samples
                             if data_args.max_train_samples is not None else
                             len(train_dataset))
        metrics["train_samples"] = min(max_train_samples, len(train_dataset))

        trainer.log_metrics("train", metrics)
        trainer.save_metrics("train", metrics)
        trainer.save_state()

    # Evaluation
    if training_args.do_eval:
        logger.info("*** Evaluate ***")
        metrics = trainer.evaluate()

        max_val_samples = data_args.max_val_samples if data_args.max_val_samples is not None else len(
            eval_dataset)
        metrics["eval_samples"] = min(max_val_samples, len(eval_dataset))

        trainer.log_metrics("eval", metrics)
        trainer.save_metrics("eval", metrics)
        return metrics

    # Prediction
    if training_args.do_predict:
        logger.info("*** Predict ***")
        results = trainer.predict(test_dataset, test_examples)
        metrics = results.metrics

        max_test_samples = data_args.max_test_samples if data_args.max_test_samples is not None else len(
            test_dataset)
        metrics["test_samples"] = min(max_test_samples, len(test_dataset))

        trainer.log_metrics("test", metrics)
        trainer.save_metrics("test", metrics)
示例#11
0
def main():
    parser = argparse.ArgumentParser()

    # Required parameters
    parser.add_argument(
        "--model_type",
        default=None,
        type=str,
        required=True,
        help="Model type selected in the list: " + ", ".join(MODEL_TYPES),
    )
    parser.add_argument(
        "--model_name_or_path",
        default=None,
        type=str,
        required=True,
        help="Path to pretrained model or model identifier from huggingface.co/models",
    )
    parser.add_argument(
        "--output_dir",
        default=None,
        type=str,
        required=True,
        help="The output directory where the model checkpoints and predictions will be written.",
    )

    # Other parameters
    parser.add_argument(
        "--data_dir",
        default=None,
        type=str,
        help="The input data dir. Should contain the .json files for the task."
        + "If no data dir or train/predict files are specified, will run with tensorflow_datasets.",
    )
    parser.add_argument(
        "--train_file",
        default=None,
        type=str,
        help="The input training file. If a data dir is specified, will look for the file there"
        + "If no data dir or train/predict files are specified, will run with tensorflow_datasets.",
    )
    parser.add_argument(
        "--predict_file",
        default=None,
        type=str,
        help="The input evaluation file. If a data dir is specified, will look for the file there"
        + "If no data dir or train/predict files are specified, will run with tensorflow_datasets.",
    )
    parser.add_argument(
        "--config_name",
        default="",
        type=str,
        help="Pretrained config name or path if not the same as model_name",
    )
    parser.add_argument(
        "--tokenizer_name",
        default="",
        type=str,
        help="Pretrained tokenizer name or path if not the same as model_name",
    )
    parser.add_argument(
        "--cache_dir",
        default="",
        type=str,
        help="Where do you want to store the pre-trained models downloaded from s3",
    )

    parser.add_argument(
        "--version_2_with_negative",
        action="store_true",
        help="If true, the SQuAD examples contain some that do not have an answer.",
    )
    parser.add_argument(
        "--null_score_diff_threshold",
        type=float,
        default=0.0,
        help="If null_score - best_non_null is greater than the threshold predict null.",
    )

    parser.add_argument(
        "--max_seq_length",
        default=384,
        type=int,
        help="The maximum total input sequence length after WordPiece tokenization. Sequences "
        "longer than this will be truncated, and sequences shorter than this will be padded.",
    )
    parser.add_argument(
        "--doc_stride",
        default=128,
        type=int,
        help="When splitting up a long document into chunks, how much stride to take between chunks.",
    )
    parser.add_argument(
        "--max_query_length",
        default=64,
        type=int,
        help="The maximum number of tokens for the question. Questions longer than this will "
        "be truncated to this length.",
    )
    parser.add_argument("--do_train", action="store_true", help="Whether to run training.")
    parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.")
    parser.add_argument(
        "--evaluate_during_training",
        action="store_true",
        help="Run evaluation during training at each logging step.",
    )
    parser.add_argument(
        "--do_lower_case",
        action="store_true",
        help="Set this flag if you are using an uncased model.",
    )

    parser.add_argument(
        "--per_gpu_train_batch_size",
        default=8,
        type=int,
        help="Batch size per GPU/CPU for training.",
    )
    parser.add_argument(
        "--per_gpu_eval_batch_size",
        default=8,
        type=int,
        help="Batch size per GPU/CPU for evaluation.",
    )
    parser.add_argument(
        "--learning_rate",
        default=5e-5,
        type=float,
        help="The initial learning rate for Adam.",
    )
    parser.add_argument(
        "--gradient_accumulation_steps",
        type=int,
        default=1,
        help="Number of updates steps to accumulate before performing a backward/update pass.",
    )
    parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.")
    parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
    parser.add_argument(
        "--num_train_epochs",
        default=3.0,
        type=float,
        help="Total number of training epochs to perform.",
    )
    parser.add_argument(
        "--max_steps",
        default=-1,
        type=int,
        help="If > 0: set total number of training steps to perform. Override num_train_epochs.",
    )
    parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
    parser.add_argument(
        "--n_best_size",
        default=20,
        type=int,
        help="The total number of n-best predictions to generate in the nbest_predictions.json output file.",
    )
    parser.add_argument(
        "--max_answer_length",
        default=30,
        type=int,
        help="The maximum length of an answer that can be generated. This is needed because the start "
        "and end predictions are not conditioned on one another.",
    )
    parser.add_argument(
        "--verbose_logging",
        action="store_true",
        help="If true, all of the warnings related to data processing will be printed. "
        "A number of warnings are expected for a normal SQuAD evaluation.",
    )
    parser.add_argument(
        "--lang_id",
        default=0,
        type=int,
        help="language id of input for language-specific xlm models "
        "(see tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)",
    )

    parser.add_argument("--logging_steps", type=int, default=500, help="Log every X updates steps.")
    parser.add_argument(
        "--save_steps",
        type=int,
        default=500,
        help="Save checkpoint every X updates steps.",
    )
    parser.add_argument(
        "--eval_all_checkpoints",
        action="store_true",
        help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number",
    )
    parser.add_argument("--no_cuda", action="store_true", help="Whether not to use CUDA when available")
    parser.add_argument(
        "--overwrite_output_dir",
        action="store_true",
        help="Overwrite the content of the output directory",
    )
    parser.add_argument(
        "--overwrite_cache",
        action="store_true",
        help="Overwrite the cached training and evaluation sets",
    )
    parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")

    parser.add_argument(
        "--local_rank",
        type=int,
        default=-1,
        help="local_rank for distributed training on gpus",
    )
    parser.add_argument(
        "--fp16",
        action="store_true",
        help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
    )
    parser.add_argument(
        "--fp16_opt_level",
        type=str,
        default="O1",
        help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
        "See details at https://nvidia.github.io/apex/amp.html",
    )
    parser.add_argument("--server_ip", type=str, default="", help="Can be used for distant debugging.")
    parser.add_argument("--server_port", type=str, default="", help="Can be used for distant debugging.")

    parser.add_argument(
        "--threads",
        type=int,
        default=1,
        help="multiple threads for converting example to features",
    )

    parser.add_argument(
        "--train_adapter",
        action="store_true",
        default=False,
        help="Train a text task adapter instead of the full model",
    )
    parser.add_argument(
        "--load_adapter",
        type=str,
        default="",
        help="Pre-trained adapter module to be loaded from Hub.",
    )
    parser.add_argument(
        "--load_lang_adapter",
        type=str,
        default=None,
        help="Pre-trained language adapter module to be loaded from Hub.",
    )
    parser.add_argument(
        "--language",
        type=str,
        default=None,
        help="The training language, e.g. 'en' for English.",
    )
    parser.add_argument(
        "--adapter_config",
        type=str,
        default="pfeiffer",
        help="Adapter configuration. Either an identifier or a path to a file.",
    )
    parser.add_argument(
        "--adapter_non_linearity",
        type=str,
        default=None,
        help="Override the non-linearity of the adapter configuration.",
    )
    parser.add_argument(
        "--adapter_reduction_factor",
        type=int,
        default=None,
        help="Override the reduction factor of the adapter configuration.",
    )
    parser.add_argument(
        "--lang_adapter_config",
        type=str,
        default=None,
        help="Language adapter configuration. Either an identifier or a path to a file.",
    )
    parser.add_argument(
        "--lang_adapter_non_linearity",
        type=str,
        default=None,
        help="Override the non-linearity of the language adapter configuration.",
    )
    parser.add_argument(
        "--lang_adapter_reduction_factor",
        type=int,
        default=None,
        help="Override the reduction factor of the language adapter configuration.",
    )
    args = parser.parse_args()

    if args.doc_stride >= args.max_seq_length - args.max_query_length:
        logger.warning(
            "WARNING - You've set a doc stride which may be superior to the document length in some "
            "examples. This could result in errors when building features from the examples. Please reduce the doc "
            "stride or increase the maximum length to ensure the features are correctly built."
        )

    if (
        os.path.exists(args.output_dir)
        and os.listdir(args.output_dir)
        and args.do_train
        and not args.overwrite_output_dir
    ):
        raise ValueError(
            "Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(
                args.output_dir
            )
        )

    # Setup distant debugging if needed
    if args.server_ip and args.server_port:
        # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
        import ptvsd

        print("Waiting for debugger attach")
        ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
        ptvsd.wait_for_attach()

    # Setup CUDA, GPU & distributed training
    if args.local_rank == -1 or args.no_cuda:
        device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
        args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count()
    else:  # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
        torch.cuda.set_device(args.local_rank)
        device = torch.device("cuda", args.local_rank)
        torch.distributed.init_process_group(backend="nccl")
        args.n_gpu = 1
    args.device = device

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN,
    )
    logger.warning(
        "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
        args.local_rank,
        device,
        args.n_gpu,
        bool(args.local_rank != -1),
        args.fp16,
    )

    # Set seed
    set_seed(args)

    # Load pretrained model and tokenizer
    if args.local_rank not in [-1, 0]:
        # Make sure only the first process in distributed training will download model & vocab
        torch.distributed.barrier()

    args.model_type = args.model_type.lower()
    config = AutoConfig.from_pretrained(
        args.config_name if args.config_name else args.model_name_or_path,
        cache_dir=args.cache_dir if args.cache_dir else None,
    )
    tokenizer = AutoTokenizer.from_pretrained(
        args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,
        do_lower_case=args.do_lower_case,
        cache_dir=args.cache_dir if args.cache_dir else None,
    )
    model = XLMRobertaForQuestionAnswering.from_pretrained(
        args.model_name_or_path,
        from_tf=bool(".ckpt" in args.model_name_or_path),
        config=config,
        cache_dir=args.cache_dir if args.cache_dir else None,
    )

    # Setup adapters
    if args.train_adapter:
        task_name = "squad2" if args.version_2_with_negative else "squad1"
        # check if adapter already exists, otherwise add it
        if task_name not in model.config.adapters.adapter_list(AdapterType.text_task):
            # resolve the adapter config
            adapter_config = AdapterConfig.load(
                args.adapter_config,
                non_linearity=args.adapter_non_linearity,
                reduction_factor=args.adapter_reduction_factor,
            )
            # load a pre-trained from Hub if specified
            if args.load_adapter:
                model.load_adapter(
                    args.load_adapter,
                    AdapterType.text_task,
                    config=adapter_config,
                    load_as=task_name,
                )
            # otherwise, add a fresh adapter
            else:
                model.add_adapter(task_name, AdapterType.text_task, config=adapter_config)
        # optionally load a pre-trained language adapter
        if args.load_lang_adapter:
            # resolve the language adapter config
            lang_adapter_config = AdapterConfig.load(
                args.lang_adapter_config,
                non_linearity=args.lang_adapter_non_linearity,
                reduction_factor=args.lang_adapter_reduction_factor,
            )
            # load the language adapter from Hub
            lang_adapter_name = model.load_adapter(
                args.load_lang_adapter,
                AdapterType.text_lang,
                config=lang_adapter_config,
                load_as=args.language,
            )
        else:
            lang_adapter_name = None
        # Freeze all model weights except of those of this adapter
        model.train_adapter([task_name])
        # Set the adapters to be used in every forward pass
        if lang_adapter_name:
            model.set_active_adapters([lang_adapter_name, task_name])
        else:
            model.set_active_adapters([task_name])
    else:
        if args.load_adapter or args.load_lang_adapter:
            raise ValueError(
                "Adapters can only be loaded in adapters training mode."
                "Use --train_adapter to enable adapter training"
            )

    if args.local_rank == 0:
        # Make sure only the first process in distributed training will download model & vocab
        torch.distributed.barrier()

    model.to(args.device)

    logger.info("Training/evaluation parameters %s", args)

    # Before we do anything with models, we want to ensure that we get fp16 execution of torch.einsum if
    # args.fp16 is set.Otherwise it'll default to "promote" mode, and we'll get fp32 operations.
    # Note that running `--fp16_opt_level="O2"` will remove the need for this code, but it is still valid.
    if args.fp16:
        try:
            import apex

            apex.amp.register_half_function(torch, "einsum")
        except ImportError:
            raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")

    # Training
    if args.do_train:
        train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False)
        global_step, tr_loss = train(args, train_dataset, model, tokenizer)
        logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)

    # Save the trained model and the tokenizer
    if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
        logger.info("Saving model checkpoint to %s", args.output_dir)
        # Save a trained model, configuration and tokenizer using `save_pretrained()`.
        # They can then be reloaded using `from_pretrained()`
        # Take care of distributed/parallel training
        model_to_save = model.module if hasattr(model, "module") else model
        if args.train_adapter:
            model_to_save.save_all_adapters(args.output_dir)
        else:
            model_to_save.save_pretrained(args.output_dir)
        tokenizer.save_pretrained(args.output_dir)

        # Good practice: save your training arguments together with the trained model
        torch.save(args, os.path.join(args.output_dir, "training_args.bin"))

        # Load a trained model and vocabulary that you have fine-tuned
        if not args.train_adapter:
            model = XLMRobertaForQuestionAnswering.from_pretrained(args.output_dir)  # , force_download=True)
            model.to(args.device)

    # Evaluation - we can ask to evaluate all the checkpoints (sub-directories) in a directory
    results = {}
    if args.do_eval and args.local_rank in [-1, 0]:
        if args.do_train:
            logger.info("Loading checkpoints saved during training for evaluation")
            checkpoints = [args.output_dir]
            if args.eval_all_checkpoints:
                if args.train_adapter:
                    checkpoints = set(
                        os.path.dirname(os.path.dirname(c))
                        for c in sorted(
                            glob.glob(
                                args.output_dir + "/**/" + "pytorch_adapter.bin",
                                recursive=True,
                            )
                        )
                    )
                else:
                    checkpoints = list(
                        os.path.dirname(c)
                        for c in sorted(glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True))
                    )
                logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN)  # Reduce model loading logs
        else:
            logger.info("Loading checkpoint %s for evaluation", args.model_name_or_path)
            checkpoints = [args.model_name_or_path]

        logger.info("Evaluate the following checkpoints: %s", checkpoints)

        for checkpoint in checkpoints:
            # Reload the adapters / model
            global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 and "-" in checkpoint else ""
            if args.train_adapter:
                model.load_adapter(
                    os.path.join(checkpoint, task_name) if args.do_train else args.load_task_adapter,
                    AdapterType.text_task,
                    load_as=task_name,
                )
                if args.language:
                    lang_adapter_name = model.load_adapter(
                        os.path.join(checkpoint, args.language) if args.do_train else args.load_lang_adapter,
                        AdapterType.text_lang,
                        load_as=args.language,
                    )
                else:
                    lang_adapter_name = None
                if lang_adapter_name:
                    model.set_active_adapters([lang_adapter_name, task_name])
                else:
                    model.set_active_adapters([task_name])
            else:
                model = XLMRobertaForQuestionAnswering.from_pretrained(checkpoint)  # , force_download=True)
                model.to(args.device)

            # Evaluate
            result = evaluate(args, model, tokenizer, prefix=global_step)

            result = dict((k + ("_{}".format(global_step) if global_step else ""), v) for k, v in result.items())
            results.update(result)

    logger.info("Results: {}".format(results))
    with open(os.path.join(args.output_dir, "results.txt"), "w") as f:
        for key, value in results.items():
            f.write("%s = %s\n" % (key, value))
    return results
示例#12
0
def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.
    parser = HfArgumentParser((ModelArguments, DataTrainingArguments,
                               UDTrainingArguments, MultiLingAdapterArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args, training_args, adapter_args = parser.parse_json_file(
            json_file=os.path.abspath(sys.argv[1]))
    else:
        (
            model_args,
            data_args,
            training_args,
            adapter_args,
        ) = parser.parse_args_into_dataclasses()

    if (os.path.exists(training_args.output_dir)
            and os.listdir(training_args.output_dir) and training_args.do_train
            and not training_args.overwrite_output_dir):
        raise ValueError(
            f"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome."
        )

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO
        if training_args.local_rank in [-1, 0] else logging.WARN,
    )
    logger.warning(
        "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
        training_args.local_rank,
        training_args.device,
        training_args.n_gpu,
        bool(training_args.local_rank != -1),
        training_args.fp16,
    )
    logger.info("Training/evaluation parameters %s", training_args)

    # Set seed
    set_seed(training_args.seed)

    # Prepare for UD dependency parsing task
    labels = UD_HEAD_LABELS
    label_map: Dict[int, str] = {i: label for i, label in enumerate(labels)}
    num_labels = len(labels)

    config = AutoConfig.from_pretrained(
        model_args.config_name
        if model_args.config_name else model_args.model_name_or_path,
        num_labels=num_labels,
        id2label=label_map,
        label2id={label: i
                  for i, label in enumerate(labels)},
        cache_dir=model_args.cache_dir,
        pad_token_id=-1,
    )

    if model_args.is_japanese:
        assert model_args.mecab_dir is not None
        assert model_args.mecab_dic_dir is not None

    tokenizer = AutoTokenizer.from_pretrained(
        model_args.tokenizer_name
        if model_args.tokenizer_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        use_fast=model_args.use_fast,
        do_lower_case=model_args.do_lower_case,
        add_prefix_space=True,  # Used e.g. for RoBERTa
        mecab_kwargs={
            "mecab_option":
            f"-r {model_args.mecab_dir} -d {model_args.mecab_dic_dir}"
        } if model_args.is_japanese else None,
    )

    # The task name (with prefix)
    task_name = "ud_" + data_args.task_name
    language = adapter_args.language

    model = AutoModelWithHeads.from_pretrained(
        model_args.model_name_or_path,
        config=config,
        cache_dir=model_args.cache_dir,
    )
    model.add_dependency_parsing_head(
        task_name,
        num_labels=num_labels,
        id2label=label_map,
    )

    if model_args.leave_out_twelvth:
        logger.info("Leaving out 12")
        leave_out = [11]
    else:
        leave_out = []

    # Setup adapters
    if adapter_args.train_adapter:
        # check if adapter already exists, otherwise add it
        if task_name not in model.config.adapters:
            # resolve the adapter config
            adapter_config = AdapterConfig.load(
                adapter_args.adapter_config,
                non_linearity=adapter_args.adapter_non_linearity,
                reduction_factor=adapter_args.adapter_reduction_factor,
                leave_out=leave_out,
            )
            # load a pre-trained from Hub if specified
            if adapter_args.load_adapter:
                model.load_adapter(
                    adapter_args.load_adapter,
                    config=adapter_config,
                    load_as=task_name,
                    leave_out=leave_out,
                )
            # otherwise, add a fresh adapter
            else:
                model.add_adapter(task_name, config=adapter_config)
        # optionally load a pre-trained language adapter
        if adapter_args.load_lang_adapter:
            # resolve the language adapter config
            lang_adapter_config = AdapterConfig.load(
                adapter_args.lang_adapter_config,
                non_linearity=adapter_args.lang_adapter_non_linearity,
                reduction_factor=adapter_args.lang_adapter_reduction_factor,
                leave_out=leave_out,
            )
            # load the language adapter from Hub
            lang_adapter_name = model.load_adapter(
                adapter_args.load_lang_adapter,
                config=lang_adapter_config,
                load_as=adapter_args.language,
                leave_out=leave_out,
            )
        else:
            lang_adapter_name = None
        # Freeze all model weights except of those of this adapter
        model.train_adapter([task_name])
        # Set the adapters to be used in every forward pass
        if lang_adapter_name:
            model.set_active_adapters(ac.Stack(lang_adapter_name, task_name))
        else:
            model.set_active_adapters(task_name)
    else:
        if adapter_args.load_adapter or adapter_args.load_lang_adapter:
            raise ValueError(
                "Adapters can only be loaded in adapters training mode."
                "Use --train_adapter to enable adapter training")

    # Load and preprocess dataset
    dataset = load_dataset("universal_dependencies", data_args.task_name)
    dataset = preprocess_dataset(dataset,
                                 tokenizer,
                                 labels,
                                 data_args,
                                 pad_token_id=-1)

    # Initialize our Trainer
    # HACK: Set this attribute to False to prevent label columns from being deleted
    training_args.remove_unused_columns = False
    trainer_class = DependencyParsingAdapterTrainer if adapter_args.train_adapter else DependencyParsingTrainer
    trainer = trainer_class(
        model=model,
        args=training_args,
        train_dataset=dataset["train"],
        eval_dataset=dataset["validation"],
    )

    # Training
    if training_args.do_train:
        trainer.train(model_path=model_args.model_name_or_path if os.path.
                      isdir(model_args.model_name_or_path) else None)
        trainer.save_model()
        # For convenience, we also re-save the tokenizer to the same directory,
        # so that you can share your model easily on huggingface.co/models =)
        if trainer.is_world_process_zero():
            tokenizer.save_pretrained(training_args.output_dir)

    # Evaluation
    results = {}
    if training_args.do_eval:
        logger.info("*** Evaluate ***")

        result = trainer.evaluate()

        output_eval_file = os.path.join(training_args.output_dir,
                                        "eval_results.txt")
        if trainer.is_world_process_zero():
            with open(output_eval_file, "w") as writer:
                logger.info("***** Eval results *****")
                for key, value in result.items():
                    logger.info("  %s = %s", key, value)
                    writer.write("%s = %s\n" % (key, value))

            results.update(result)

    # Predict
    if training_args.do_predict:
        logging.info("*** Test ***")

        if training_args.store_best_model:
            logger.info("Loading best model for predictions.")

            if adapter_args.train_adapter:
                if language:
                    lang_adapter_config = AdapterConfig.load(
                        config="pfeiffer",
                        non_linearity="gelu",
                        reduction_factor=2,
                        leave_out=leave_out)
                    model.load_adapter(
                        os.path.join(training_args.output_dir, "best_model",
                                     language) if training_args.do_train else
                        adapter_args.load_lang_adapter,
                        config=lang_adapter_config,
                        load_as=language,
                        leave_out=leave_out,
                    )
                task_adapter_config = AdapterConfig.load(config="pfeiffer",
                                                         non_linearity="gelu",
                                                         reduction_factor=16,
                                                         leave_out=leave_out)
                model.load_adapter(
                    os.path.join(training_args.output_dir, "best_model",
                                 task_name)
                    if training_args.do_train else adapter_args.load_adapter,
                    config=task_adapter_config,
                    load_as=task_name,
                    leave_out=leave_out,
                )
                if language:
                    model.set_active_adapters(
                        ac.Stack(lang_adapter_name, task_name))
                else:
                    model.set_active_adapters(task_name)
                model.to(training_args.device)
            else:
                trainer.model = AutoModelWithHeads.from_pretrained(
                    os.path.join(training_args.output_dir, "best_model"),
                    from_tf=bool(".ckpt" in model_args.model_name_or_path),
                    config=config,
                    cache_dir=model_args.cache_dir,
                ).to(training_args.device)

        predictions, _, metrics = trainer.predict(dataset["test"])

        output_test_results_file = os.path.join(training_args.output_dir,
                                                "test_results.txt")
        if trainer.is_world_process_zero():
            with open(output_test_results_file, "w") as writer:
                for key, value in metrics.items():
                    logger.info("  %s = %s", key, value)
                    writer.write("%s = %s\n" % (key, value))

    return results
示例#13
0
def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments,
                               TrainingArguments, MultiLingAdapterArguments))

    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args, training_args, adapter_args = parser.parse_json_file(
            json_file=os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args, training_args, adapter_args = parser.parse_args_into_dataclasses(
        )

    if (os.path.exists(training_args.output_dir)
            and os.listdir(training_args.output_dir) and training_args.do_train
            and not training_args.overwrite_output_dir):
        raise ValueError(
            f"Output directory ({training_args.output_dir}) already exists and is not empty. "
            f"Use --overwrite_output_dir to overcome.")

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO
        if training_args.local_rank in [-1, 0] else logging.WARN,
    )
    logger.warning(
        "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
        training_args.local_rank,
        training_args.device,
        training_args.n_gpu,
        bool(training_args.local_rank != -1),
        training_args.fp16,
    )
    logger.info("Training/evaluation parameters %s", training_args)

    # Set seed
    set_seed(training_args.seed)

    try:
        num_labels = glue_tasks_num_labels[data_args.task_name]
        output_mode = glue_output_modes[data_args.task_name]
    except KeyError:
        raise ValueError("Task not found: %s" % (data_args.task_name))

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    config = AutoConfig.from_pretrained(
        model_args.config_name
        if model_args.config_name else model_args.model_name_or_path,
        num_labels=num_labels,
        finetuning_task=data_args.task_name,
        cache_dir=model_args.cache_dir,
    )
    tokenizer = AutoTokenizer.from_pretrained(
        model_args.tokenizer_name
        if model_args.tokenizer_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
    )
    model = AutoModelForSequenceClassification.from_pretrained(
        model_args.model_name_or_path,
        from_tf=bool(".ckpt" in model_args.model_name_or_path),
        config=config,
        cache_dir=model_args.cache_dir,
    )

    # Setup adapters
    if adapter_args.train_adapter:
        task_name = data_args.task_name
        # check if adapter already exists, otherwise add it
        if task_name not in model.config.adapters.adapter_list(
                AdapterType.text_task):
            # resolve the adapter config
            adapter_config = AdapterConfig.load(
                adapter_args.adapter_config,
                non_linearity=adapter_args.adapter_non_linearity,
                reduction_factor=adapter_args.adapter_reduction_factor,
            )
            # load a pre-trained from Hub if specified
            if adapter_args.load_adapter:
                model.load_adapter(
                    adapter_args.load_adapter,
                    AdapterType.text_task,
                    config=adapter_config,
                    load_as=task_name,
                )
            # otherwise, add a fresh adapter
            else:
                model.add_adapter(task_name,
                                  AdapterType.text_task,
                                  config=adapter_config)
        # optionally load a pre-trained language adapter
        if adapter_args.load_lang_adapter:
            # resolve the language adapter config
            lang_adapter_config = AdapterConfig.load(
                adapter_args.lang_adapter_config,
                non_linearity=adapter_args.lang_adapter_non_linearity,
                reduction_factor=adapter_args.lang_adapter_reduction_factor,
            )
            # load the language adapter from Hub
            lang_adapter_name = model.load_adapter(
                adapter_args.load_lang_adapter,
                AdapterType.text_lang,
                config=lang_adapter_config,
                load_as=adapter_args.language,
            )
        else:
            lang_adapter_name = None
        # Freeze all model weights except of those of this adapter
        model.train_adapter([task_name])
        # Set the adapters to be used in every forward pass
        if lang_adapter_name:
            model.set_active_adapters([lang_adapter_name, task_name])
        else:
            model.set_active_adapters([task_name])

    # Get datasets
    train_dataset = (GlueDataset(
        data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir)
                     if training_args.do_train else None)
    eval_dataset = (GlueDataset(data_args,
                                tokenizer=tokenizer,
                                mode="dev",
                                cache_dir=model_args.cache_dir)
                    if training_args.do_eval else None)
    test_dataset = (GlueDataset(data_args,
                                tokenizer=tokenizer,
                                mode="test",
                                cache_dir=model_args.cache_dir)
                    if training_args.do_predict else None)

    def build_compute_metrics_fn(
            task_name: str) -> Callable[[EvalPrediction], Dict]:
        def compute_metrics_fn(p: EvalPrediction):
            preds = p.predictions[0] if isinstance(p.predictions,
                                                   tuple) else p.predictions
            if output_mode == "classification":
                preds = np.argmax(preds, axis=1)
            else:  # regression
                preds = np.squeeze(preds)
            return glue_compute_metrics(task_name, preds, p.label_ids)

        return compute_metrics_fn

    # Initialize our Trainer
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset,
        eval_dataset=eval_dataset,
        compute_metrics=build_compute_metrics_fn(data_args.task_name),
        do_save_full_model=not adapter_args.train_adapter,
        do_save_adapters=adapter_args.train_adapter,
    )

    # Training
    if training_args.do_train:
        trainer.train(model_path=model_args.model_name_or_path if os.path.
                      isdir(model_args.model_name_or_path) else None)
        trainer.save_model()
        # For convenience, we also re-save the tokenizer to the same directory,
        # so that you can share your model easily on huggingface.co/models =)
        if trainer.is_world_master():
            tokenizer.save_pretrained(training_args.output_dir)

    # Evaluation
    eval_results = {}
    if training_args.do_eval:
        logger.info("*** Evaluate ***")

        # Loop to handle MNLI double evaluation (matched, mis-matched)
        eval_datasets = [eval_dataset]
        if data_args.task_name == "mnli":
            mnli_mm_data_args = dataclasses.replace(data_args,
                                                    task_name="mnli-mm")
            eval_datasets.append(
                GlueDataset(mnli_mm_data_args,
                            tokenizer=tokenizer,
                            mode="dev",
                            cache_dir=model_args.cache_dir))

        for eval_dataset in eval_datasets:
            trainer.compute_metrics = build_compute_metrics_fn(
                eval_dataset.args.task_name)
            eval_result = trainer.evaluate(eval_dataset=eval_dataset)

            output_eval_file = os.path.join(
                training_args.output_dir,
                f"eval_results_{eval_dataset.args.task_name}.txt")
            if trainer.is_world_master():
                with open(output_eval_file, "w") as writer:
                    logger.info("***** Eval results {} *****".format(
                        eval_dataset.args.task_name))
                    for key, value in eval_result.items():
                        logger.info("  %s = %s", key, value)
                        writer.write("%s = %s\n" % (key, value))

            eval_results.update(eval_result)

    if training_args.do_predict:
        logging.info("*** Test ***")
        test_datasets = [test_dataset]
        if data_args.task_name == "mnli":
            mnli_mm_data_args = dataclasses.replace(data_args,
                                                    task_name="mnli-mm")
            test_datasets.append(
                GlueDataset(mnli_mm_data_args,
                            tokenizer=tokenizer,
                            mode="test",
                            cache_dir=model_args.cache_dir))

        for test_dataset in test_datasets:
            predictions = trainer.predict(
                test_dataset=test_dataset).predictions
            if output_mode == "classification":
                predictions = np.argmax(predictions, axis=1)

            output_test_file = os.path.join(
                training_args.output_dir,
                f"test_results_{test_dataset.args.task_name}.txt")
            if trainer.is_world_master():
                with open(output_test_file, "w") as writer:
                    logger.info("***** Test results {} *****".format(
                        test_dataset.args.task_name))
                    writer.write("index\tprediction\n")
                    for index, item in enumerate(predictions):
                        if output_mode == "regression":
                            writer.write("%d\t%3.3f\n" % (index, item))
                        else:
                            item = test_dataset.get_labels()[item]
                            writer.write("%d\t%s\n" % (index, item))
    return eval_results
示例#14
0
def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments, MultiLingAdapterArguments))
    model_args, data_args, training_args, adapter_args = parser.parse_args_into_dataclasses()

    if (
        os.path.exists(training_args.output_dir)
        and os.listdir(training_args.output_dir)
        and training_args.do_train
        and not training_args.overwrite_output_dir
    ):
        raise ValueError(
            f"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome."
        )

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,
    )
    logger.warning(
        "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
        training_args.local_rank,
        training_args.device,
        training_args.n_gpu,
        bool(training_args.local_rank != -1),
        training_args.fp16,
    )
    logger.info("Training/evaluation parameters %s", training_args)

    # Set seed
    set_seed(training_args.seed)

    try:
        processor = processors[data_args.task_name]()
        label_list = processor.get_labels()
        num_labels = len(label_list)
    except KeyError:
        raise ValueError("Task not found: %s" % (data_args.task_name))

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    config = AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
        num_labels=num_labels,
        finetuning_task=data_args.task_name,
        cache_dir=model_args.cache_dir,
    )
    tokenizer = AutoTokenizer.from_pretrained(
        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
    )
    model = AutoModelForMultipleChoice.from_pretrained(
        model_args.model_name_or_path,
        from_tf=bool(".ckpt" in model_args.model_name_or_path),
        config=config,
        cache_dir=model_args.cache_dir,
    )

    # Setup adapters
    if adapter_args.train_adapter:
        task_name = data_args.task_name
        # check if adapter already exists, otherwise add it
        if task_name not in model.config.adapters.adapter_list(AdapterType.text_task):
            # resolve the adapter config
            adapter_config = AdapterConfig.load(
                adapter_args.adapter_config,
                non_linearity=adapter_args.adapter_non_linearity,
                reduction_factor=adapter_args.adapter_reduction_factor,
            )
            # load a pre-trained from Hub if specified
            if adapter_args.load_adapter:
                model.load_adapter(
                    adapter_args.load_adapter, AdapterType.text_task, config=adapter_config, load_as=task_name,
                )
            # otherwise, add a fresh adapter
            else:
                model.add_adapter(task_name, AdapterType.text_task, config=adapter_config)
        # optionally load a pre-trained language adapter
        if adapter_args.load_lang_adapter:
            # resolve the language adapter config
            lang_adapter_config = AdapterConfig.load(
                adapter_args.lang_adapter_config,
                non_linearity=adapter_args.lang_adapter_non_linearity,
                reduction_factor=adapter_args.lang_adapter_reduction_factor,
            )
            # load the language adapter from Hub
            lang_adapter_name = model.load_adapter(
                adapter_args.load_lang_adapter,
                AdapterType.text_lang,
                config=lang_adapter_config,
                load_as=adapter_args.language,
            )
        else:
            lang_adapter_name = None
        # Freeze all model weights except of those of this adapter
        model.train_adapter([task_name])
        # Set the adapters to be used in every forward pass
        if lang_adapter_name:
            model.set_active_adapters([lang_adapter_name, task_name])
        else:
            model.set_active_adapters([task_name])

    # Get datasets
    train_dataset = (
        MultipleChoiceDataset(
            data_dir=data_args.data_dir,
            tokenizer=tokenizer,
            task=data_args.task_name,
            max_seq_length=data_args.max_seq_length,
            overwrite_cache=data_args.overwrite_cache,
            mode=Split.train,
        )
        if training_args.do_train
        else None
    )
    eval_dataset = (
        MultipleChoiceDataset(
            data_dir=data_args.data_dir,
            tokenizer=tokenizer,
            task=data_args.task_name,
            max_seq_length=data_args.max_seq_length,
            overwrite_cache=data_args.overwrite_cache,
            mode=Split.dev,
        )
        if training_args.do_eval
        else None
    )

    def compute_metrics(p: EvalPrediction) -> Dict:
        preds = np.argmax(p.predictions, axis=1)
        return {"acc": simple_accuracy(preds, p.label_ids)}

    # Initialize our Trainer
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset,
        eval_dataset=eval_dataset,
        compute_metrics=compute_metrics,
        do_save_full_model=not adapter_args.train_adapter,
        do_save_adapters=adapter_args.train_adapter,
    )

    # Training
    if training_args.do_train:
        trainer.train(
            model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None
        )
        trainer.save_model()
        # For convenience, we also re-save the tokenizer to the same directory,
        # so that you can share your model easily on huggingface.co/models =)
        if trainer.is_world_master():
            tokenizer.save_pretrained(training_args.output_dir)

    # Evaluation
    results = {}
    if training_args.do_eval:
        logger.info("*** Evaluate ***")

        result = trainer.evaluate()

        output_eval_file = os.path.join(training_args.output_dir, "eval_results.txt")
        if trainer.is_world_master():
            with open(output_eval_file, "w") as writer:
                logger.info("***** Eval results *****")
                for key, value in result.items():
                    logger.info("  %s = %s", key, value)
                    writer.write("%s = %s\n" % (key, value))

                results.update(result)

    return results
示例#15
0
def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments,
                               TrainingArguments, MultiLingAdapterArguments))

    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args, training_args, adapter_args = parser.parse_json_file(
            json_file=os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args, training_args, adapter_args = parser.parse_args_into_dataclasses(
        )

    if (os.path.exists(training_args.output_dir)
            and os.listdir(training_args.output_dir) and training_args.do_train
            and not training_args.overwrite_output_dir):
        raise ValueError(
            f"Output directory ({training_args.output_dir}) already exists and is not empty. "
            "Use --overwrite_output_dir to overcome.")

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO
        if is_main_process(training_args.local_rank) else logging.WARN,
    )

    # Log on each process the small summary:
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
        +
        f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
    )
    # Set the verbosity to info of the Transformers logger (on main process only):
    if is_main_process(training_args.local_rank):
        transformers.utils.logging.set_verbosity_info()
    logger.info(f"Training/evaluation parameters {training_args}")

    # Set seed before initializing model.
    set_seed(training_args.seed)

    # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
    # or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
    #
    # For CSV/JSON files, this script will use as labels the column called 'label' and as pair of sentences the
    # sentences in columns called 'sentence1' and 'sentence2' if such column exists or the first two columns not named
    # label if at least two columns are provided.
    #
    # If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
    # single column. You can easily tweak this behavior (see below)
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if data_args.task_name is not None:
        # Downloading and loading a dataset from the hub.
        datasets = load_dataset("glue", data_args.task_name)
    elif data_args.train_file.endswith(".csv"):
        # Loading a dataset from local csv files
        datasets = load_dataset("csv",
                                data_files={
                                    "train": data_args.train_file,
                                    "validation": data_args.validation_file
                                })
    else:
        # Loading a dataset from local json files
        datasets = load_dataset("json",
                                data_files={
                                    "train": data_args.train_file,
                                    "validation": data_args.validation_file
                                })
    # See more about loading any type of standard or custom dataset at
    # https://huggingface.co/docs/datasets/loading_datasets.html.

    # Labels
    if data_args.task_name is not None:
        is_regression = data_args.task_name == "stsb"
        if not is_regression:
            label_list = datasets["train"].features["label"].names
            num_labels = len(label_list)
        else:
            num_labels = 1
    else:
        # Trying to have good defaults here, don't hesitate to tweak to your needs.
        is_regression = datasets["train"].features["label"].dtype in [
            "float32", "float64"
        ]
        if is_regression:
            num_labels = 1
        else:
            # A useful fast method:
            # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.unique
            label_list = datasets["train"].unique("label")
            label_list.sort()  # Let's sort it for determinism
            num_labels = len(label_list)

    # Load pretrained model and tokenizer
    #
    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    config = AutoConfig.from_pretrained(
        model_args.config_name
        if model_args.config_name else model_args.model_name_or_path,
        num_labels=num_labels,
        finetuning_task=data_args.task_name,
        cache_dir=model_args.cache_dir,
    )
    tokenizer = AutoTokenizer.from_pretrained(
        model_args.tokenizer_name
        if model_args.tokenizer_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        use_fast=model_args.use_fast_tokenizer,
    )
    model = AutoModelForSequenceClassification.from_pretrained(
        model_args.model_name_or_path,
        from_tf=bool(".ckpt" in model_args.model_name_or_path),
        config=config,
        cache_dir=model_args.cache_dir,
    )

    # Setup adapters
    if adapter_args.train_adapter:
        task_name = data_args.task_name
        # check if adapter already exists, otherwise add it
        if task_name not in model.config.adapters.adapter_list(
                AdapterType.text_task):
            # resolve the adapter config
            adapter_config = AdapterConfig.load(
                adapter_args.adapter_config,
                non_linearity=adapter_args.adapter_non_linearity,
                reduction_factor=adapter_args.adapter_reduction_factor,
            )
            # load a pre-trained from Hub if specified
            if adapter_args.load_adapter:
                model.load_adapter(
                    adapter_args.load_adapter,
                    AdapterType.text_task,
                    config=adapter_config,
                    load_as=task_name,
                )
            # otherwise, add a fresh adapter
            else:
                model.add_adapter(task_name,
                                  AdapterType.text_task,
                                  config=adapter_config)
        # optionally load a pre-trained language adapter
        if adapter_args.load_lang_adapter:
            # resolve the language adapter config
            lang_adapter_config = AdapterConfig.load(
                adapter_args.lang_adapter_config,
                non_linearity=adapter_args.lang_adapter_non_linearity,
                reduction_factor=adapter_args.lang_adapter_reduction_factor,
            )
            # load the language adapter from Hub
            lang_adapter_name = model.load_adapter(
                adapter_args.load_lang_adapter,
                AdapterType.text_lang,
                config=lang_adapter_config,
                load_as=adapter_args.language,
            )
        else:
            lang_adapter_name = None
        # Freeze all model weights except of those of this adapter
        model.train_adapter([task_name])
        # Set the adapters to be used in every forward pass
        if lang_adapter_name:
            model.set_active_adapters([lang_adapter_name, task_name])
        else:
            model.set_active_adapters([task_name])

    # Preprocessing the datasets
    if data_args.task_name is not None:
        sentence1_key, sentence2_key = task_to_keys[data_args.task_name]
    else:
        # Again, we try to have some nice defaults but don't hesitate to tweak to your use case.
        non_label_column_names = [
            name for name in datasets["train"].column_names if name != "label"
        ]
        if "sentence1" in non_label_column_names and "sentence2" in non_label_column_names:
            sentence1_key, sentence2_key = "sentence1", "sentence2"
        else:
            if len(non_label_column_names) >= 2:
                sentence1_key, sentence2_key = non_label_column_names[:2]
            else:
                sentence1_key, sentence2_key = non_label_column_names[0], None

    # Padding strategy
    if data_args.pad_to_max_length:
        padding = "max_length"
        max_length = data_args.max_seq_length
    else:
        # We will pad later, dynamically at batch creation, to the max sequence length in each batch
        padding = False
        max_length = None

    # Some models have set the order of the labels to use, so let's make sure we do use it.
    label_to_id = None
    if (model.config.label2id !=
            PretrainedConfig(num_labels=num_labels).label2id
            and data_args.task_name is not None and is_regression):
        # Some have all caps in their config, some don't.
        label_name_to_id = {
            k.lower(): v
            for k, v in model.config.label2id.items()
        }
        if list(sorted(label_name_to_id.keys())) == list(sorted(label_list)):
            label_to_id = {
                i: label_name_to_id[label_list[i]]
                for i in range(num_labels)
            }
        else:
            logger.warn(
                "Your model seems to have been trained with labels, but they don't match the dataset: ",
                f"model labels: {list(sorted(label_name_to_id.keys()))}, dataset labels: {list(sorted(label_list))}."
                "\nIgnoring the model labels as a result.",
            )
    elif data_args.task_name is None:
        label_to_id = {v: i for i, v in enumerate(label_list)}

    def preprocess_function(examples):
        # Tokenize the texts
        args = ((examples[sentence1_key], ) if sentence2_key is None else
                (examples[sentence1_key], examples[sentence2_key]))
        result = tokenizer(*args,
                           padding=padding,
                           max_length=max_length,
                           truncation=True)

        # Map labels to IDs (not necessary for GLUE tasks)
        if label_to_id is not None and "label" in examples:
            result["label"] = [label_to_id[l] for l in examples["label"]]
        return result

    datasets = datasets.map(preprocess_function,
                            batched=True,
                            load_from_cache_file=not data_args.overwrite_cache)

    train_dataset = datasets["train"]
    eval_dataset = datasets["validation_matched" if data_args.task_name ==
                            "mnli" else "validation"]
    if data_args.task_name is not None:
        test_dataset = datasets["test_matched" if data_args.task_name ==
                                "mnli" else "test"]

    # Log a few random samples from the training set:
    for index in random.sample(range(len(train_dataset)), 3):
        logger.info(
            f"Sample {index} of the training set: {train_dataset[index]}.")

    # Get the metric function
    if data_args.task_name is not None:
        metric = load_metric("glue", data_args.task_name)
    # TODO: When datasets metrics include regular accuracy, make an else here and remove special branch from
    # compute_metrics

    # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
    # predictions and label_ids field) and has to return a dictionary string to float.
    def compute_metrics(p: EvalPrediction):
        preds = p.predictions[0] if isinstance(p.predictions,
                                               tuple) else p.predictions
        preds = np.squeeze(preds) if is_regression else np.argmax(preds,
                                                                  axis=1)
        if data_args.task_name is not None:
            result = metric.compute(predictions=preds, references=p.label_ids)
            if len(result) > 1:
                result["combined_score"] = np.mean(list(
                    result.values())).item()
            return result
        elif is_regression:
            return {"mse": ((preds - p.label_ids)**2).mean().item()}
        else:
            return {
                "accuracy":
                (preds == p.label_ids).astype(np.float32).mean().item()
            }

    # Initialize our Trainer
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset,
        eval_dataset=eval_dataset if training_args.do_eval else None,
        compute_metrics=compute_metrics,
        tokenizer=tokenizer,
        # Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
        data_collator=default_data_collator
        if data_args.pad_to_max_length else None,
        do_save_full_model=not adapter_args.train_adapter,
        do_save_adapters=adapter_args.train_adapter,
    )

    # Training
    if training_args.do_train:
        trainer.train(model_path=model_args.model_name_or_path if os.path.
                      isdir(model_args.model_name_or_path) else None)
        trainer.save_model()  # Saves the tokenizer too for easy upload

    # Evaluation
    eval_results = {}
    if training_args.do_eval:
        logger.info("*** Evaluate ***")

        # Loop to handle MNLI double evaluation (matched, mis-matched)
        tasks = [data_args.task_name]
        eval_datasets = [eval_dataset]
        if data_args.task_name == "mnli":
            tasks.append("mnli-mm")
            eval_datasets.append(datasets["validation_mismatched"])

        for eval_dataset, task in zip(eval_datasets, tasks):
            eval_result = trainer.evaluate(eval_dataset=eval_dataset)

            output_eval_file = os.path.join(training_args.output_dir,
                                            f"eval_results_{task}.txt")
            if trainer.is_world_process_zero():
                with open(output_eval_file, "w") as writer:
                    logger.info(f"***** Eval results {task} *****")
                    for key, value in eval_result.items():
                        logger.info(f"  {key} = {value}")
                        writer.write(f"{key} = {value}\n")

            eval_results.update(eval_result)

    if training_args.do_predict:
        logger.info("*** Test ***")

        # Loop to handle MNLI double evaluation (matched, mis-matched)
        tasks = [data_args.task_name]
        test_datasets = [test_dataset]
        if data_args.task_name == "mnli":
            tasks.append("mnli-mm")
            test_datasets.append(datasets["test_mismatched"])

        for test_dataset, task in zip(test_datasets, tasks):
            # Removing the `label` columns because it contains -1 and Trainer won't like that.
            test_dataset.remove_columns_("label")
            predictions = trainer.predict(
                test_dataset=test_dataset).predictions
            predictions = np.squeeze(
                predictions) if is_regression else np.argmax(predictions,
                                                             axis=1)

            output_test_file = os.path.join(training_args.output_dir,
                                            f"test_results_{task}.txt")
            if trainer.is_world_process_zero():
                with open(output_test_file, "w") as writer:
                    logger.info(f"***** Test results {task} *****")
                    writer.write("index\tprediction\n")
                    for index, item in enumerate(predictions):
                        if is_regression:
                            writer.write(f"{index}\t{item:3.3f}\n")
                        else:
                            item = label_list[item]
                            writer.write(f"{index}\t{item}\n")
    return eval_results
示例#16
0
def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments, MultiLingAdapterArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args, training_args, adapter_args = parser.parse_json_file(
            json_file=os.path.abspath(sys.argv[1])
        )
    else:
        model_args, data_args, training_args, adapter_args = parser.parse_args_into_dataclasses()

    # Detecting last checkpoint.
    last_checkpoint = None
    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
        last_checkpoint = get_last_checkpoint(training_args.output_dir)
        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
            raise ValueError(
                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                "Use --overwrite_output_dir to overcome."
            )
        elif last_checkpoint is not None:
            logger.info(
                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
            )

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )
    logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)

    # Log on each process the small summary:
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
        + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
    )
    # Set the verbosity to info of the Transformers logger (on main process only):
    if is_main_process(training_args.local_rank):
        transformers.utils.logging.set_verbosity_info()
        transformers.utils.logging.enable_default_handler()
        transformers.utils.logging.enable_explicit_format()
    logger.info(f"Training/evaluation parameters {training_args}")

    # Set seed before initializing model.
    set_seed(training_args.seed)

    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
    # (the dataset will be downloaded automatically from the datasets Hub).

    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
    # 'text' is found. You can easily tweak this behavior (see below).

    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if data_args.train_file is not None or data_args.validation_file is not None:
        data_files = {}
        if data_args.train_file is not None:
            data_files["train"] = data_args.train_file
        if data_args.validation_file is not None:
            data_files["validation"] = data_args.validation_file
        extension = data_args.train_file.split(".")[-1]
        datasets = load_dataset(extension, data_files=data_files)
    else:
        # Downloading and loading the swag dataset from the hub.
        datasets = load_dataset("swag", "regular")
    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
    # https://huggingface.co/docs/datasets/loading_datasets.html.

    # Load pretrained model and tokenizer

    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    config = AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        use_auth_token=True if model_args.use_auth_token else None,
    )
    tokenizer = AutoTokenizer.from_pretrained(
        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        use_fast=model_args.use_fast_tokenizer,
        revision=model_args.model_revision,
        use_auth_token=True if model_args.use_auth_token else None,
    )
    model = AutoModelForMultipleChoice.from_pretrained(
        model_args.model_name_or_path,
        from_tf=bool(".ckpt" in model_args.model_name_or_path),
        config=config,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        use_auth_token=True if model_args.use_auth_token else None,
    )

    # Setup adapters
    if adapter_args.train_adapter:
        task_name = "swag"
        # check if adapter already exists otherwise add it
        if task_name not in model.config.adapters:
            # resolve adapter config
            adapter_config = AdapterConfig.load(
                adapter_args.adapter_config,
                non_linearity=adapter_args.adapter_non_linearity,
                reduction_factor=adapter_args.adapter_reduction_factor,
            )
            # load adapter from hub if specified
            if adapter_args.load_adapter:
                model.load_adapter(adapter_args.load_adapter, config=adapter_config, load_as=task_name)
            else:
                model.add_adapter(task_name, config=adapter_config)
        # optionally load  a pretrained language adapter
        if adapter_args.load_lang_adapter:
            # resolve language adapter config
            lang_adapter_config = AdapterConfig.load(
                adapter_args.lang_adapter_config,
                non_linearity=adapter_args.lang_adapter_non_linearity,
                reduction_factor=adapter_args.lang_adapter_reduction_factor,
            )
            # load language adapter from Hub
            lang_adapter_name = model.load_adapter(
                adapter_args.load_lang_adapter,
                config=lang_adapter_config,
                load_as=adapter_args.language,
            )
        else:
            lang_adapter_name = None
        # Freeze all model weights except of those in this adapter
        model.train_adapter(task_name)
        # Set the adapters to be used in every forward pass
        if lang_adapter_name:
            model.set_active_adapters(Fuse(lang_adapter_name, task_name))
        else:
            model.set_active_adapters(task_name)
    else:
        if adapter_args.load_adapter or adapter_args.load_lang_adapter:
            raise ValueError(
                "Adapters can only be loaded in adapters training mode."
                "Use --train_adapter to enable adapter_training"
            )

    # When using your own dataset or a different dataset from swag, you will probably need to change this.
    ending_names = [f"ending{i}" for i in range(4)]
    context_name = "sent1"
    question_header_name = "sent2"

    if data_args.max_seq_length is None:
        max_seq_length = tokenizer.model_max_length
        if max_seq_length > 1024:
            logger.warn(
                f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). "
                "Picking 1024 instead. You can change that default value by passing --max_seq_length xxx."
            )
            max_seq_length = 1024
    else:
        if data_args.max_seq_length > tokenizer.model_max_length:
            logger.warn(
                f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
                f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
            )
        max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)

    # Preprocessing the datasets.
    def preprocess_function(examples):
        first_sentences = [[context] * 4 for context in examples[context_name]]
        question_headers = examples[question_header_name]
        second_sentences = [
            [f"{header} {examples[end][i]}" for end in ending_names] for i, header in enumerate(question_headers)
        ]

        # Flatten out
        first_sentences = sum(first_sentences, [])
        second_sentences = sum(second_sentences, [])

        # Tokenize
        tokenized_examples = tokenizer(
            first_sentences,
            second_sentences,
            truncation=True,
            max_length=max_seq_length,
            padding="max_length" if data_args.pad_to_max_length else False,
        )
        # Un-flatten
        return {k: [v[i : i + 4] for i in range(0, len(v), 4)] for k, v in tokenized_examples.items()}

    if training_args.do_train:
        train_dataset = datasets["train"]
        if "train" not in datasets:
            raise ValueError("--do_train requires a train dataset")
        if data_args.max_train_samples is not None:
            train_dataset = train_dataset.select(range(data_args.max_train_samples))
        train_dataset = train_dataset.map(
            preprocess_function,
            batched=True,
            num_proc=data_args.preprocessing_num_workers,
            load_from_cache_file=not data_args.overwrite_cache,
        )

    if training_args.do_eval:
        if "validation" not in datasets:
            raise ValueError("--do_eval requires a validation dataset")
        eval_dataset = datasets["validation"]
        if data_args.max_val_samples is not None:
            eval_dataset = eval_dataset.select(range(data_args.max_val_samples))
        eval_dataset = eval_dataset.map(
            preprocess_function,
            batched=True,
            num_proc=data_args.preprocessing_num_workers,
            load_from_cache_file=not data_args.overwrite_cache,
        )

    # Data collator
    data_collator = (
        default_data_collator
        if data_args.pad_to_max_length
        else DataCollatorForMultipleChoice(tokenizer=tokenizer, pad_to_multiple_of=8 if training_args.fp16 else None)
    )

    # Metric
    def compute_metrics(eval_predictions):
        predictions, label_ids = eval_predictions
        preds = np.argmax(predictions, axis=1)
        return {"accuracy": (preds == label_ids).astype(np.float32).mean().item()}

    # Initialize our Trainer
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset if training_args.do_train else None,
        eval_dataset=eval_dataset if training_args.do_eval else None,
        tokenizer=tokenizer,
        data_collator=data_collator,
        compute_metrics=compute_metrics,
        do_save_full_model=not adapter_args.train_adapter,
        do_save_adapters=adapter_args.train_adapter,
    )

    # Training
    if training_args.do_train:
        if last_checkpoint is not None:
            checkpoint = last_checkpoint
        elif os.path.isdir(model_args.model_name_or_path):
            checkpoint = model_args.model_name_or_path
        else:
            checkpoint = None
        train_result = trainer.train(resume_from_checkpoint=checkpoint)
        trainer.save_model()  # Saves the tokenizer too for easy upload
        metrics = train_result.metrics

        max_train_samples = (
            data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
        )
        metrics["train_samples"] = min(max_train_samples, len(train_dataset))

        trainer.log_metrics("train", metrics)
        trainer.save_metrics("train", metrics)
        trainer.save_state()

    # Evaluation
    if training_args.do_eval:
        logger.info("*** Evaluate ***")

        metrics = trainer.evaluate()
        max_val_samples = data_args.max_val_samples if data_args.max_val_samples is not None else len(eval_dataset)
        metrics["eval_samples"] = min(max_val_samples, len(eval_dataset))

        trainer.log_metrics("eval", metrics)
        trainer.save_metrics("eval", metrics)
def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    parser = HfArgumentParser(
        (ModelArguments, DataTrainingArguments, TrainingArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args, training_args = parser.parse_json_file(
            json_file=os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args, training_args = parser.parse_args_into_dataclasses(
        )

    if (os.path.exists(training_args.output_dir)
            and os.listdir(training_args.output_dir) and training_args.do_train
            and not training_args.overwrite_output_dir):
        raise ValueError(
            f"Output directory ({training_args.output_dir}) already exists and is not empty."
            "Use --overwrite_output_dir to overcome.")

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO
        if is_main_process(training_args.local_rank) else logging.WARN,
    )

    # Log on each process the small summary:
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
        +
        f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
    )
    # Set the verbosity to info of the Transformers logger (on main process only):
    if is_main_process(training_args.local_rank):
        transformers.utils.logging.set_verbosity_info()
    logger.info("Training/evaluation parameters %s", training_args)

    # Set seed before initializing model.
    set_seed(training_args.seed)

    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
    # (the dataset will be downloaded automatically from the datasets Hub
    #
    # For CSV/JSON files, this script will use the column called 'text' or the first column. You can easily tweak this
    # behavior (see below)
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if data_args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        datasets = load_dataset(data_args.dataset_name,
                                data_args.dataset_config_name)
    else:
        data_files = {}
        if data_args.train_file is not None:
            data_files["train"] = data_args.train_file
        if data_args.validation_file is not None:
            data_files["validation"] = data_args.validation_file
        extension = data_args.train_file.split(".")[-1]
        if extension == "txt":
            extension = "text"
        datasets = load_dataset(extension, data_files=data_files)
    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
    # https://huggingface.co/docs/datasets/loading_datasets.html.

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    if model_args.config_name:
        config = AutoConfig.from_pretrained(model_args.config_name,
                                            cache_dir=model_args.cache_dir)
    elif model_args.model_name_or_path:
        config = AutoConfig.from_pretrained(model_args.model_name_or_path,
                                            cache_dir=model_args.cache_dir)
    else:
        config = CONFIG_MAPPING[model_args.model_type]()
        logger.warning(
            "You are instantiating a new config instance from scratch.")

    if model_args.tokenizer_name:
        tokenizer = AutoTokenizer.from_pretrained(
            model_args.tokenizer_name,
            cache_dir=model_args.cache_dir,
            use_fast=model_args.use_fast_tokenizer)
    elif model_args.model_name_or_path:
        tokenizer = AutoTokenizer.from_pretrained(
            model_args.model_name_or_path,
            cache_dir=model_args.cache_dir,
            use_fast=model_args.use_fast_tokenizer)
    else:
        raise ValueError(
            "You are instantiating a new tokenizer from scratch. This is not supported by this script."
            "You can do it from another script, save it, and load it from here, using --tokenizer_name."
        )

    if model_args.model_name_or_path:
        #        model = AutoModelForMaskedLM.from_pretrained(
        #            '/home/projects/11001765/wenjuan/stable_pretrain/adapter-transformers/examples/my/temp_adpt_checkpoints/temp_adpt_checkpoints/CoLA_0/checkpoint-1/cola/pytorch_adapter.bin',
        #            from_tf=False,
        #            config=config,
        #            cache_dir=model_args.cache_dir,
        #        )
        model = AutoModelForMaskedLM.from_pretrained(
            model_args.model_name_or_path,
            from_tf=bool(".ckpt" in model_args.model_name_or_path),
            config=config,
            cache_dir=model_args.cache_dir,
        )
    else:
        logger.info("Training new model from scratch")
        model = AutoModelForMaskedLM.from_config(config)

##########################################################################TODO :hanw
# Setup adapters
    if True:  #adapter_args.train_adapter:
        task_name = 'task_checkpoint_file'  #'cola' #data_args.task_name
        # check if adapter already exists, otherwise add it
        if True:  #task_name not in model.config.adapters.adapter_list(AdapterType.text_task):
            # resolve the adapter config
            adapter_config = AdapterConfig.load(
                'houlsby',
                non_linearity=None,
                reduction_factor=None,
            )
            # load a pre-trained from Hub if specified
            if False:  #adapter_args.load_adapter:
                model.load_adapter(
                    adapter_args.load_adapter,
                    AdapterType.text_task,
                    config=adapter_config,
                    load_as=task_name,
                )
            # otherwise, add a fresh adapter
            else:
                model.add_adapter(task_name,
                                  AdapterType.text_task,
                                  config=adapter_config)
        # optionally load a pre-trained language adapter

        lang_adapter_name = None
        # Freeze all model weights except of those of this adapter
        model.train_adapter([task_name])
        # Set the adapters to be used in every forward pass
        if lang_adapter_name:
            model.set_active_adapters([lang_adapter_name, task_name])
        else:
            model.set_active_adapters([task_name])


##########################################################################
    model.resize_token_embeddings(len(tokenizer))
    # Preprocessing the datasets.
    # First we tokenize all the texts.
    if training_args.do_train:
        column_names = datasets["train"].column_names
    else:
        column_names = datasets["validation"].column_names
    text_column_name = "text" if "text" in column_names else column_names[0]

    if data_args.line_by_line:
        # When using line_by_line, we just tokenize each nonempty line.
        padding = "max_length" if data_args.pad_to_max_length else False

        def tokenize_function(examples):
            # Remove empty lines
            examples["text"] = [
                line for line in examples["text"]
                if len(line) > 0 and not line.isspace()
            ]
            return tokenizer(
                examples["text"],
                padding=padding,
                truncation=True,
                max_length=data_args.max_seq_length,
                # We use this option because DataCollatorForLanguageModeling (see below) is more efficient when it
                # receives the `special_tokens_mask`.
                return_special_tokens_mask=True,
            )

        tokenized_datasets = datasets.map(
            tokenize_function,
            batched=True,
            num_proc=data_args.preprocessing_num_workers,
            remove_columns=[text_column_name],
            load_from_cache_file=not data_args.overwrite_cache,
        )
    else:
        # Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts.
        # We use `return_special_tokens_mask=True` because DataCollatorForLanguageModeling (see below) is more
        # efficient when it receives the `special_tokens_mask`.
        def tokenize_function(examples):
            return tokenizer(examples[text_column_name],
                             return_special_tokens_mask=True)

        tokenized_datasets = datasets.map(
            tokenize_function,
            batched=True,
            num_proc=data_args.preprocessing_num_workers,
            remove_columns=[text_column_name],
            load_from_cache_file=not data_args.overwrite_cache,
        )

        if data_args.max_seq_length is None:
            max_seq_length = tokenizer.model_max_length
        else:
            if data_args.max_seq_length > tokenizer.model_max_length:
                logger.warn(
                    f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
                    f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
                )
            max_seq_length = min(data_args.max_seq_length,
                                 tokenizer.model_max_length)

        # Main data processing function that will concatenate all texts from our dataset and generate chunks of
        # max_seq_length.
        def group_texts(examples):
            # Concatenate all texts.
            concatenated_examples = {
                k: sum(examples[k], [])
                for k in examples.keys()
            }
            total_length = len(concatenated_examples[list(examples.keys())[0]])
            # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
            # customize this part to your needs.
            total_length = (total_length // max_seq_length) * max_seq_length
            # Split by chunks of max_len.
            result = {
                k: [
                    t[i:i + max_seq_length]
                    for i in range(0, total_length, max_seq_length)
                ]
                for k, t in concatenated_examples.items()
            }
            return result

        # Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a
        # remainder for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value
        # might be slower to preprocess.
        #
        # To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
        # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map
        tokenized_datasets = tokenized_datasets.map(
            group_texts,
            batched=True,
            num_proc=data_args.preprocessing_num_workers,
            load_from_cache_file=not data_args.overwrite_cache,
        )

    # Data collator
    # This one will take care of randomly masking the tokens.
    data_collator = DataCollatorForLanguageModeling(
        tokenizer=tokenizer, mlm_probability=data_args.mlm_probability)

    # Initialize our Trainer
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=tokenized_datasets["train"]
        if training_args.do_train else None,
        eval_dataset=tokenized_datasets["validation"]
        if training_args.do_eval else None,
        tokenizer=tokenizer,
        data_collator=data_collator,
        do_save_full_model=False,
        do_save_adapters=True,
    )

    # Training
    if training_args.do_train:
        trainer.train(model_path=model_args.model_name_or_path if os.path.
                      isdir(model_args.model_name_or_path) else None)
        trainer.save_model()  # Saves the tokenizer too for easy upload

    # Evaluation
    results = {}
    if training_args.do_eval:
        logger.info("*** Evaluate ***")

        eval_output = trainer.evaluate()

        perplexity = math.exp(eval_output["eval_loss"])
        results["perplexity"] = perplexity

        output_eval_file = os.path.join(training_args.output_dir,
                                        "eval_results_mlm.txt")
        if trainer.is_world_process_zero():
            with open(output_eval_file, "w") as writer:
                logger.info("***** Eval results *****")
                for key, value in results.items():
                    logger.info(f"  {key} = {value}")
                    writer.write(f"{key} = {value}\n")

    return results
示例#18
0
def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.
    parser = HfArgumentParser((ModelArguments, DataTrainingArguments,
                               TrainingArguments, AdapterArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args, training_args, adapter_args = parser.parse_json_file(
            json_file=os.path.abspath(sys.argv[1]))
    else:
        (
            model_args,
            data_args,
            training_args,
            adapter_args,
        ) = parser.parse_args_into_dataclasses()

    if (os.path.exists(training_args.output_dir)
            and os.listdir(training_args.output_dir) and training_args.do_train
            and not training_args.overwrite_output_dir):
        raise ValueError(
            f"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome."
        )

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO
        if training_args.local_rank in [-1, 0] else logging.WARN,
    )
    logger.warning(
        "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
        training_args.local_rank,
        training_args.device,
        training_args.n_gpu,
        bool(training_args.local_rank != -1),
        training_args.fp16,
    )
    logger.info("Training/evaluation parameters %s", training_args)

    # Set seed
    set_seed(training_args.seed)

    # Prepare for UD pos tagging task
    labels = UPOS_LABELS
    label_map: Dict[int, str] = {i: label for i, label in enumerate(labels)}
    num_labels = len(labels)

    config = AutoConfig.from_pretrained(
        model_args.config_name
        if model_args.config_name else model_args.model_name_or_path,
        num_labels=num_labels,
        id2label=label_map,
        label2id={label: i
                  for i, label in enumerate(labels)},
        cache_dir=model_args.cache_dir,
    )

    if model_args.is_japanese:
        assert model_args.mecab_dir is not None
        assert model_args.mecab_dic_dir is not None

    tokenizer = AutoTokenizer.from_pretrained(
        model_args.tokenizer_name
        if model_args.tokenizer_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        use_fast=model_args.use_fast,
        do_lower_case=model_args.do_lower_case,
        mecab_kwargs={
            "mecab_option":
            f"-r {model_args.mecab_dir} -d {model_args.mecab_dic_dir}"
        } if model_args.is_japanese else None,
    )

    model = AutoModelForTokenClassification.from_pretrained(
        model_args.model_name_or_path,
        config=config,
        cache_dir=model_args.cache_dir,
    )

    # Setup adapters
    task_name = "pos"
    language = adapter_args.language
    if model_args.replace_embeddings:
        model.resize_token_embeddings(len(tokenizer))

    if model_args.leave_out_twelvth:
        logger.info("Leaving out 12")
        leave_out = [11]
    else:
        leave_out = []

    setup_task_adapter_training(
        model,
        task_name,
        adapter_args,
        leave_out=leave_out,
        with_embeddings=model_args.replace_embeddings,
    )
    if model_args.leave_out_twelvth:
        if language in model.base_model.encoder.layer._modules[
                "11"].output.layer_text_lang_adapters:
            del model.base_model.encoder.layer._modules[
                "11"].output.layer_text_lang_adapters[language]
            logger.info("Deleted language adapter " + language +
                        " in layer 12")
        if language in model.base_model.encoder.layer._modules[
                "11"].attention.output.attention_text_lang_adapters:
            del model.base_model.encoder.layer._modules[
                "11"].attention.output.attention_text_lang_adapters[language]
            logger.info("Deleted language adapter " + language +
                        " in layer 12")

    if adapter_args.train_adapter:
        if language:
            adapter_names = [[language], [task_name]]
        else:
            adapter_names = [[task_name]]
    else:
        adapter_names = None

    train_dataset = (POSDataset(
        data_dir=data_args.data_dir,
        tokenizer=tokenizer,
        labels=labels,
        model_type=config.model_type,
        max_seq_length=data_args.max_seq_length,
        overwrite_cache=data_args.overwrite_cache,
        mode=Split.train,
    ) if training_args.do_train else None)

    eval_dataset = (POSDataset(
        data_dir=data_args.data_dir,
        tokenizer=tokenizer,
        labels=labels,
        model_type=config.model_type,
        max_seq_length=data_args.max_seq_length,
        overwrite_cache=data_args.overwrite_cache,
        mode=Split.dev,
    ) if training_args.do_eval else None)

    def align_predictions(
            predictions: np.ndarray,
            label_ids: np.ndarray) -> Tuple[List[int], List[int]]:
        preds = np.argmax(predictions, axis=2)

        batch_size, seq_len = preds.shape

        out_label_list = [[] for _ in range(batch_size)]
        preds_list = [[] for _ in range(batch_size)]

        for i in range(batch_size):
            for j in range(seq_len):
                if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
                    out_label_list[i].append(label_map[label_ids[i][j]])
                    preds_list[i].append(label_map[preds[i][j]])

        return preds_list, out_label_list

    def compute_metrics(p: EvalPrediction) -> Dict:
        preds_list, out_label_list = align_predictions(p.predictions,
                                                       p.label_ids)
        return {"acc": accuracy_score(out_label_list, preds_list)}

    # Initialize our Trainer
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset,
        eval_dataset=eval_dataset,
        compute_metrics=compute_metrics,
        do_save_full_model=not adapter_args.train_adapter,
        do_save_adapters=adapter_args.train_adapter,
        adapter_names=adapter_names,
    )

    # Training
    if training_args.do_train:
        trainer.train(model_path=model_args.model_name_or_path if os.path.
                      isdir(model_args.model_name_or_path) else None)
        trainer.save_model()
        # For convenience, we also re-save the tokenizer to the same directory,
        # so that you can share your model easily on huggingface.co/models =)
        if trainer.is_world_master():
            tokenizer.save_pretrained(training_args.output_dir)

    # Evaluation
    results = {}
    if training_args.do_eval:
        logger.info("*** Evaluate ***")

        result = trainer.evaluate()

        output_eval_file = os.path.join(training_args.output_dir,
                                        "eval_results.txt")
        if trainer.is_world_master():
            with open(output_eval_file, "w") as writer:
                logger.info("***** Eval results *****")
                for key, value in result.items():
                    logger.info("  %s = %s", key, value)
                    writer.write("%s = %s\n" % (key, value))

            results.update(result)

    # Predict
    if training_args.do_predict:
        test_dataset = POSDataset(
            data_dir=data_args.data_dir,
            tokenizer=tokenizer,
            labels=labels,
            model_type=config.model_type,
            max_seq_length=data_args.max_seq_length,
            overwrite_cache=data_args.overwrite_cache,
            mode=Split.test,
        )

        logging.info("*** Test ***")

        if training_args.store_best_model:
            logger.info("Loading best model for predictions.")

            if adapter_args.train_adapter:
                if language:
                    lang_adapter_config = AdapterConfig.load(
                        config="pfeiffer",
                        non_linearity="gelu",
                        reduction_factor=2,
                        leave_out=leave_out,
                    )
                    model.load_adapter(
                        os.path.join(training_args.output_dir, "best_model",
                                     language) if training_args.do_train else
                        adapter_args.load_lang_adapter,
                        AdapterType.text_lang,
                        config=lang_adapter_config,
                        load_as=language,
                    )
                task_adapter_config = AdapterConfig.load(
                    config="pfeiffer",
                    non_linearity="gelu",
                    reduction_factor=16,
                    leave_out=leave_out,
                )
                model.load_adapter(
                    os.path.join(training_args.output_dir, "best_model",
                                 task_name) if training_args.do_train else
                    adapter_args.load_task_adapter,
                    AdapterType.text_task,
                    config=task_adapter_config,
                    load_as=task_name,
                )
                if model_args.leave_out_twelvth:
                    if language in model.base_model.encoder.layer._modules[
                            "11"].output.layer_text_lang_adapters:
                        del model.base_model.encoder.layer._modules[
                            "11"].output.layer_text_lang_adapters[language]
                        logger.info("Deleted language adapter " + language +
                                    " in layer 12")
                    if (language
                            in model.base_model.encoder.layer._modules["11"].
                            attention.output.attention_text_lang_adapters):
                        del model.base_model.encoder.layer._modules[
                            "11"].attention.output.attention_text_lang_adapters[
                                language]
                        logger.info("Deleted language adapter " + language +
                                    " in layer 12")

                if language:
                    adapter_names = [[language], [task_name]]
                else:
                    adapter_names = [[task_name]]
            else:
                trainer.model = AutoModelForTokenClassification.from_pretrained(
                    os.path.join(training_args.output_dir, "best_model"),
                    from_tf=bool(".ckpt" in model_args.model_name_or_path),
                    config=config,
                    cache_dir=model_args.cache_dir,
                ).to(training_args.device)

        predictions, label_ids, metrics = trainer.predict(test_dataset)
        preds_list, _ = align_predictions(predictions, label_ids)

        output_test_results_file = os.path.join(training_args.output_dir,
                                                "test_results.txt")
        if trainer.is_world_master():
            with open(output_test_results_file, "w") as writer:
                for key, value in metrics.items():
                    logger.info("  %s = %s", key, value)
                    writer.write("%s = %s\n" % (key, value))

        # Save predictions
        output_test_predictions_file = os.path.join(training_args.output_dir,
                                                    "test_predictions.txt")
        if trainer.is_world_master():
            with open(output_test_predictions_file, "w",
                      encoding="utf-8") as writer:
                file_path = get_file(data_args.data_dir, Split.test)
                with open(file_path, "r", encoding="utf-8") as f:
                    example_id = 0
                    for line in f.readlines():
                        tok = line.strip().split("\t")
                        if len(tok) < 2 or line[0] == "#":
                            writer.write(line)
                            if not preds_list[example_id]:
                                example_id += 1
                        elif preds_list[example_id]:
                            if tok[0].isdigit():
                                output_line = tok[1] + " " + preds_list[
                                    example_id].pop(0) + "\n"
                                writer.write(output_line)
                        else:
                            logger.warning(
                                "Maximum sequence length exceeded: No prediction for '%s'",
                                tok[1],
                            )

    return results
    max_seq_length=max_seq_length,
    overwrite_cache=overwrite_tokenizer,
    mode=Split.test,
) if training_args.do_eval else None)

config = BertConfig.from_pretrained(
    model_name_or_path,
    num_labels=num_labels,
    finetuning_task=task_name,
    #cache_dir=cache_dir,
)

model = AutoModelWithHeads.from_pretrained(model_name_or_path, config=config)
model.add_multiple_choice_head("csqa", num_choices=5)

adapter_config = AdapterConfig.load(adapter_config_path)
model.load_adapter(adapter_name_or_path, "text_task", config=adapter_config)
#model.train_adapter([adapter_name_or_path])
model.set_active_adapters([[adapter_name_or_path]])


# Metric
def simple_accuracy(preds, labels):
    return (preds == labels).mean()


def compute_metrics(p: EvalPrediction):
    preds = np.argmax(p.predictions, axis=1)
    return {"acc": simple_accuracy(preds, p.label_ids)}

示例#20
0
def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    parser = HfArgumentParser(
        (ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments,
         MultiLingAdapterArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args, training_args, adapter_args = parser.parse_json_file(
            json_file=os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args, training_args, adapter_args = parser.parse_args_into_dataclasses(
        )

    if data_args.source_prefix is None and model_args.model_name_or_path in [
            "t5-small",
            "t5-base",
            "t5-large",
            "t5-3b",
            "t5-11b",
    ]:
        logger.warning(
            "You're running a t5 model but didn't provide a source prefix, which is the expected, e.g. with "
            "`--source_prefix 'summarize: ' `")

    # Detecting last checkpoint.
    last_checkpoint = None
    if os.path.isdir(
            training_args.output_dir
    ) and training_args.do_train and not training_args.overwrite_output_dir:
        last_checkpoint = get_last_checkpoint(training_args.output_dir)
        if last_checkpoint is None and len(os.listdir(
                training_args.output_dir)) > 0:
            raise ValueError(
                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                "Use --overwrite_output_dir to overcome.")
        elif last_checkpoint is not None:
            logger.info(
                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
            )

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )
    logger.setLevel(logging.INFO if is_main_process(training_args.local_rank
                                                    ) else logging.WARN)

    # Log on each process the small summary:
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
        +
        f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
    )
    # Set the verbosity to info of the Transformers logger (on main process only):
    if is_main_process(training_args.local_rank):
        transformers.utils.logging.set_verbosity_info()
    logger.info(f"Training/evaluation parameters {training_args}")

    # Set seed before initializing model.
    set_seed(training_args.seed)

    # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
    # (the dataset will be downloaded automatically from the datasets Hub).
    #
    # For CSV/JSON files this script will use the first column for the full texts and the second column for the
    # summaries (unless you specify column names for this with the `text_column` and `summary_column` arguments).
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if data_args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        datasets = load_dataset(data_args.dataset_name,
                                data_args.dataset_config_name)
    else:
        data_files = {}
        if data_args.train_file is not None:
            data_files["train"] = data_args.train_file
            extension = data_args.train_file.split(".")[-1]
        if data_args.validation_file is not None:
            data_files["validation"] = data_args.validation_file
            extension = data_args.validation_file.split(".")[-1]
        if data_args.test_file is not None:
            data_files["test"] = data_args.test_file
            extension = data_args.test_file.split(".")[-1]
        datasets = load_dataset(extension, data_files=data_files)
    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
    # https://huggingface.co/docs/datasets/loading_datasets.html.

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    config = AutoConfig.from_pretrained(
        model_args.config_name
        if model_args.config_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        use_auth_token=True if model_args.use_auth_token else None,
    )
    tokenizer = AutoTokenizer.from_pretrained(
        model_args.tokenizer_name
        if model_args.tokenizer_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        use_fast=model_args.use_fast_tokenizer,
        revision=model_args.model_revision,
        use_auth_token=True if model_args.use_auth_token else None,
    )
    model = AutoModelForSeq2SeqLM.from_pretrained(
        model_args.model_name_or_path,
        from_tf=bool(".ckpt" in model_args.model_name_or_path),
        config=config,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        use_auth_token=True if model_args.use_auth_token else None,
    )

    if model.config.decoder_start_token_id is None:
        raise ValueError(
            "Make sure that `config.decoder_start_token_id` is correctly defined"
        )

    # Setup adapters
    if adapter_args.train_adapter:
        task_name = data_args.dataset_name or "summarization"
        # check if adapter already exists, otherwise add it
        if task_name not in model.config.adapters:
            # resolve the adapter config
            adapter_config = AdapterConfig.load(
                adapter_args.adapter_config,
                non_linearity=adapter_args.adapter_non_linearity,
                reduction_factor=adapter_args.adapter_reduction_factor,
            )
            # load a pre-trained from Hub if specified
            if adapter_args.load_adapter:
                model.load_adapter(
                    adapter_args.load_adapter,
                    config=adapter_config,
                    load_as=task_name,
                )
            # otherwise, add a fresh adapter
            else:
                model.add_adapter(task_name, config=adapter_config)
        # optionally load a pre-trained language adapter
        if adapter_args.load_lang_adapter:
            # resolve the language adapter config
            lang_adapter_config = AdapterConfig.load(
                adapter_args.lang_adapter_config,
                non_linearity=adapter_args.lang_adapter_non_linearity,
                reduction_factor=adapter_args.lang_adapter_reduction_factor,
            )
            # load the language adapter from Hub
            lang_adapter_name = model.load_adapter(
                adapter_args.load_lang_adapter,
                config=lang_adapter_config,
                load_as=adapter_args.language,
            )
        else:
            lang_adapter_name = None
        # Freeze all model weights except of those of this adapter
        model.train_adapter([task_name])
        # Set the adapters to be used in every forward pass
        if lang_adapter_name:
            model.set_active_adapters([lang_adapter_name, task_name])
        else:
            model.set_active_adapters([task_name])
    else:
        if adapter_args.load_adapter or adapter_args.load_lang_adapter:
            raise ValueError(
                "Adapters can only be loaded in adapters training mode."
                "Use --train_adapter to enable adapter training")

    prefix = data_args.source_prefix if data_args.source_prefix is not None else ""

    # Preprocessing the datasets.
    # We need to tokenize inputs and targets.
    if training_args.do_train:
        column_names = datasets["train"].column_names
    elif training_args.do_eval:
        column_names = datasets["validation"].column_names
    elif training_args.do_predict:
        column_names = datasets["test"].column_names
    else:
        logger.info(
            "There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`."
        )
        return

    # Get the column names for input/target.
    dataset_columns = summarization_name_mapping.get(data_args.dataset_name,
                                                     None)
    if data_args.text_column is None:
        text_column = dataset_columns[
            0] if dataset_columns is not None else column_names[0]
    else:
        text_column = data_args.text_column
        if text_column not in column_names:
            raise ValueError(
                f"--text_column' value '{data_args.text_column}' needs to be one of: {', '.join(column_names)}"
            )
    if data_args.summary_column is None:
        summary_column = dataset_columns[
            1] if dataset_columns is not None else column_names[1]
    else:
        summary_column = data_args.summary_column
        if summary_column not in column_names:
            raise ValueError(
                f"--summary_column' value '{data_args.summary_column}' needs to be one of: {', '.join(column_names)}"
            )

    # Temporarily set max_target_length for training.
    max_target_length = data_args.max_target_length
    padding = "max_length" if data_args.pad_to_max_length else False

    if training_args.label_smoothing_factor > 0 and not hasattr(
            model, "prepare_decoder_input_ids_from_labels"):
        logger.warn(
            "label_smoothing is enabled but the `prepare_decoder_input_ids_from_labels` method is not defined for"
            f"`{model.__class__.__name__}`. This will lead to loss being calculated twice and will take up more memory"
        )

    def preprocess_function(examples):
        inputs = examples[text_column]
        targets = examples[summary_column]
        inputs = [prefix + inp for inp in inputs]
        model_inputs = tokenizer(inputs,
                                 max_length=data_args.max_source_length,
                                 padding=padding,
                                 truncation=True)

        # Setup the tokenizer for targets
        with tokenizer.as_target_tokenizer():
            labels = tokenizer(targets,
                               max_length=max_target_length,
                               padding=padding,
                               truncation=True)

        # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore
        # padding in the loss.
        if padding == "max_length" and data_args.ignore_pad_token_for_loss:
            labels["input_ids"] = [[
                (l if l != tokenizer.pad_token_id else -100) for l in label
            ] for label in labels["input_ids"]]

        model_inputs["labels"] = labels["input_ids"]
        return model_inputs

    if training_args.do_train:
        train_dataset = datasets["train"]
        if "train" not in datasets:
            raise ValueError("--do_train requires a train dataset")
        if data_args.max_train_samples is not None:
            train_dataset = train_dataset.select(
                range(data_args.max_train_samples))
        train_dataset = train_dataset.map(
            preprocess_function,
            batched=True,
            num_proc=data_args.preprocessing_num_workers,
            remove_columns=column_names,
            load_from_cache_file=not data_args.overwrite_cache,
        )

    if training_args.do_eval:
        max_target_length = data_args.val_max_target_length
        if "validation" not in datasets:
            raise ValueError("--do_eval requires a validation dataset")
        eval_dataset = datasets["validation"]
        if data_args.max_val_samples is not None:
            eval_dataset = eval_dataset.select(range(
                data_args.max_val_samples))
        eval_dataset = eval_dataset.map(
            preprocess_function,
            batched=True,
            num_proc=data_args.preprocessing_num_workers,
            remove_columns=column_names,
            load_from_cache_file=not data_args.overwrite_cache,
        )

    if training_args.do_predict:
        max_target_length = data_args.val_max_target_length
        if "test" not in datasets:
            raise ValueError("--do_predict requires a test dataset")
        test_dataset = datasets["test"]
        if data_args.max_test_samples is not None:
            test_dataset = test_dataset.select(
                range(data_args.max_test_samples))
        test_dataset = test_dataset.map(
            preprocess_function,
            batched=True,
            num_proc=data_args.preprocessing_num_workers,
            remove_columns=column_names,
            load_from_cache_file=not data_args.overwrite_cache,
        )

    # Data collator
    label_pad_token_id = -100 if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id
    data_collator = DataCollatorForSeq2Seq(
        tokenizer,
        model=model,
        label_pad_token_id=label_pad_token_id,
        pad_to_multiple_of=8 if training_args.fp16 else None,
    )

    # Metric
    metric = load_metric("rouge")

    def postprocess_text(preds, labels):
        preds = [pred.strip() for pred in preds]
        labels = [label.strip() for label in labels]

        # rougeLSum expects newline after each sentence
        preds = ["\n".join(nltk.sent_tokenize(pred)) for pred in preds]
        labels = ["\n".join(nltk.sent_tokenize(label)) for label in labels]

        return preds, labels

    def compute_metrics(eval_preds):
        preds, labels = eval_preds
        if isinstance(preds, tuple):
            preds = preds[0]
        decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
        if data_args.ignore_pad_token_for_loss:
            # Replace -100 in the labels as we can't decode them.
            labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
        decoded_labels = tokenizer.batch_decode(labels,
                                                skip_special_tokens=True)

        # Some simple post-processing
        decoded_preds, decoded_labels = postprocess_text(
            decoded_preds, decoded_labels)

        result = metric.compute(predictions=decoded_preds,
                                references=decoded_labels,
                                use_stemmer=True)
        # Extract a few results from ROUGE
        result = {
            key: value.mid.fmeasure * 100
            for key, value in result.items()
        }

        prediction_lens = [
            np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds
        ]
        result["gen_len"] = np.mean(prediction_lens)
        result = {k: round(v, 4) for k, v in result.items()}
        return result

    # Early stopping
    if data_args.patience and data_args.patience > 0:
        training_args.load_best_model_at_end = True

    # Initialize our Trainer
    trainer = Seq2SeqTrainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset if training_args.do_train else None,
        eval_dataset=eval_dataset if training_args.do_eval else None,
        tokenizer=tokenizer,
        data_collator=data_collator,
        compute_metrics=compute_metrics
        if training_args.predict_with_generate else None,
        do_save_full_model=not adapter_args.train_adapter,
        do_save_adapters=adapter_args.train_adapter,
    )
    if data_args.patience and data_args.patience > 0:
        callback = EarlyStoppingCallback(
            early_stopping_patience=data_args.patience)
        trainer.add_callback(callback)

    # Training
    if training_args.do_train:
        if last_checkpoint is not None:
            checkpoint = last_checkpoint
        elif os.path.isdir(model_args.model_name_or_path):
            checkpoint = model_args.model_name_or_path
        else:
            checkpoint = None
        train_result = trainer.train(resume_from_checkpoint=checkpoint)
        trainer.save_model()  # Saves the tokenizer too for easy upload

        metrics = train_result.metrics
        max_train_samples = (data_args.max_train_samples
                             if data_args.max_train_samples is not None else
                             len(train_dataset))
        metrics["train_samples"] = min(max_train_samples, len(train_dataset))

        trainer.log_metrics("train", metrics)
        trainer.save_metrics("train", metrics)
        trainer.save_state()

    # Evaluation
    results = {}
    if training_args.do_eval:
        logger.info("*** Evaluate ***")

        metrics = trainer.evaluate(max_length=data_args.val_max_target_length,
                                   num_beams=data_args.num_beams,
                                   metric_key_prefix="eval")
        max_val_samples = data_args.max_val_samples if data_args.max_val_samples is not None else len(
            eval_dataset)
        metrics["eval_samples"] = min(max_val_samples, len(eval_dataset))

        trainer.log_metrics("eval", metrics)
        trainer.save_metrics("eval", metrics)

    if training_args.do_predict:
        logger.info("*** Test ***")

        test_results = trainer.predict(
            test_dataset,
            metric_key_prefix="test",
            max_length=data_args.val_max_target_length,
            num_beams=data_args.num_beams,
        )
        metrics = test_results.metrics
        max_test_samples = data_args.max_test_samples if data_args.max_test_samples is not None else len(
            test_dataset)
        metrics["test_samples"] = min(max_test_samples, len(test_dataset))

        trainer.log_metrics("test", metrics)
        trainer.save_metrics("test", metrics)

        if trainer.is_world_process_zero():
            if training_args.predict_with_generate:
                test_preds = tokenizer.batch_decode(
                    test_results.predictions,
                    skip_special_tokens=True,
                    clean_up_tokenization_spaces=True)
                test_preds = [pred.strip() for pred in test_preds]
                output_test_preds_file = os.path.join(training_args.output_dir,
                                                      "test_generations.txt")
                with open(output_test_preds_file, "w") as writer:
                    writer.write("\n".join(test_preds))

    return results
示例#21
0
def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments,
                               TrainingArguments, MultiLingAdapterArguments))

    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args, training_args, adapter_args = parser.parse_json_file(
            json_file=os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args, training_args, adapter_args = parser.parse_args_into_dataclasses(
        )

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )

    log_level = training_args.get_process_log_level()
    logger.setLevel(log_level)
    datasets.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()

    # Log on each process the small summary:
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
        +
        f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
    )
    logger.info(f"Training/evaluation parameters {training_args}")

    # Detecting last checkpoint.
    last_checkpoint = None
    if os.path.isdir(
            training_args.output_dir
    ) and training_args.do_train and not training_args.overwrite_output_dir:
        last_checkpoint = get_last_checkpoint(training_args.output_dir)
        if last_checkpoint is None and len(os.listdir(
                training_args.output_dir)) > 0:
            raise ValueError(
                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                "Use --overwrite_output_dir to overcome.")
        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
            logger.info(
                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
            )

    # Set seed before initializing model.
    set_seed(training_args.seed)

    # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
    # or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
    #
    # For CSV/JSON files, this script will use as labels the column called 'label' and as pair of sentences the
    # sentences in columns called 'sentence1' and 'sentence2' if such column exists or the first two columns not named
    # label if at least two columns are provided.
    #
    # If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
    # single column. You can easily tweak this behavior (see below)
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if data_args.task_name is not None:
        # Downloading and loading a dataset from the hub.
        raw_datasets = load_dataset("glue",
                                    data_args.task_name,
                                    cache_dir=model_args.cache_dir)
    elif data_args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        raw_datasets = load_dataset(data_args.dataset_name,
                                    data_args.dataset_config_name,
                                    cache_dir=model_args.cache_dir)
    else:
        # Loading a dataset from your local files.
        # CSV/JSON training and evaluation files are needed.
        data_files = {
            "train": data_args.train_file,
            "validation": data_args.validation_file
        }

        # Get the test dataset: you can provide your own CSV/JSON test file (see below)
        # when you use `do_predict` without specifying a GLUE benchmark task.
        if training_args.do_predict:
            if data_args.test_file is not None:
                train_extension = data_args.train_file.split(".")[-1]
                test_extension = data_args.test_file.split(".")[-1]
                assert (
                    test_extension == train_extension
                ), "`test_file` should have the same extension (csv or json) as `train_file`."
                data_files["test"] = data_args.test_file
            else:
                raise ValueError(
                    "Need either a GLUE task or a test file for `do_predict`.")

        for key in data_files.keys():
            logger.info(f"load a local file for {key}: {data_files[key]}")

        if data_args.train_file.endswith(".csv"):
            # Loading a dataset from local csv files
            raw_datasets = load_dataset("csv",
                                        data_files=data_files,
                                        cache_dir=model_args.cache_dir)
        else:
            # Loading a dataset from local json files
            raw_datasets = load_dataset("json",
                                        data_files=data_files,
                                        cache_dir=model_args.cache_dir)
    # See more about loading any type of standard or custom dataset at
    # https://huggingface.co/docs/datasets/loading_datasets.html.

    # Labels
    if data_args.task_name is not None:
        is_regression = data_args.task_name == "stsb"
        if not is_regression:
            label_list = raw_datasets["train"].features["label"].names
            num_labels = len(label_list)
        else:
            num_labels = 1
    else:
        # Trying to have good defaults here, don't hesitate to tweak to your needs.
        is_regression = raw_datasets["train"].features["label"].dtype in [
            "float32", "float64"
        ]
        if is_regression:
            num_labels = 1
        else:
            # A useful fast method:
            # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.unique
            label_list = raw_datasets["train"].unique("label")
            label_list.sort()  # Let's sort it for determinism
            num_labels = len(label_list)

    # Load pretrained model and tokenizer
    #
    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    config = AutoConfig.from_pretrained(
        model_args.config_name
        if model_args.config_name else model_args.model_name_or_path,
        num_labels=num_labels,
        finetuning_task=data_args.task_name,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        use_auth_token=True if model_args.use_auth_token else None,
    )
    tokenizer = AutoTokenizer.from_pretrained(
        model_args.tokenizer_name
        if model_args.tokenizer_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        use_fast=model_args.use_fast_tokenizer,
        revision=model_args.model_revision,
        use_auth_token=True if model_args.use_auth_token else None,
    )
    model = AutoModelForSequenceClassification.from_pretrained(
        model_args.model_name_or_path,
        from_tf=bool(".ckpt" in model_args.model_name_or_path),
        config=config,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        use_auth_token=True if model_args.use_auth_token else None,
    )

    # Setup adapters
    if adapter_args.train_adapter:
        task_name = data_args.task_name or "glue"
        # check if adapter already exists, otherwise add it
        if task_name not in model.config.adapters:
            # resolve the adapter config
            adapter_config = AdapterConfig.load(
                adapter_args.adapter_config,
                non_linearity=adapter_args.adapter_non_linearity,
                reduction_factor=adapter_args.adapter_reduction_factor,
            )
            # load a pre-trained from Hub if specified
            if adapter_args.load_adapter:
                model.load_adapter(
                    adapter_args.load_adapter,
                    config=adapter_config,
                    load_as=task_name,
                )
            # otherwise, add a fresh adapter
            else:
                model.add_adapter(task_name, config=adapter_config)
        # optionally load a pre-trained language adapter
        if adapter_args.load_lang_adapter:
            # resolve the language adapter config
            lang_adapter_config = AdapterConfig.load(
                adapter_args.lang_adapter_config,
                non_linearity=adapter_args.lang_adapter_non_linearity,
                reduction_factor=adapter_args.lang_adapter_reduction_factor,
            )
            # load the language adapter from Hub
            lang_adapter_name = model.load_adapter(
                adapter_args.load_lang_adapter,
                config=lang_adapter_config,
                load_as=adapter_args.language,
            )
        else:
            lang_adapter_name = None
        # Freeze all model weights except of those of this adapter
        model.train_adapter([task_name])
        # Set the adapters to be used in every forward pass
        if lang_adapter_name:
            model.set_active_adapters(ac.Stack(lang_adapter_name, task_name))
        else:
            model.set_active_adapters([task_name])
    else:
        if adapter_args.load_adapter or adapter_args.load_lang_adapter:
            raise ValueError(
                "Adapters can only be loaded in adapters training mode."
                "Use --train_adapter to enable adapter training")

    # Preprocessing the datasets
    if data_args.task_name is not None:
        sentence1_key, sentence2_key = task_to_keys[data_args.task_name]
    else:
        # Again, we try to have some nice defaults but don't hesitate to tweak to your use case.
        non_label_column_names = [
            name for name in raw_datasets["train"].column_names
            if name != "label"
        ]
        if "sentence1" in non_label_column_names and "sentence2" in non_label_column_names:
            sentence1_key, sentence2_key = "sentence1", "sentence2"
        else:
            if len(non_label_column_names) >= 2:
                sentence1_key, sentence2_key = non_label_column_names[:2]
            else:
                sentence1_key, sentence2_key = non_label_column_names[0], None

    # Padding strategy
    if data_args.pad_to_max_length:
        padding = "max_length"
    else:
        # We will pad later, dynamically at batch creation, to the max sequence length in each batch
        padding = False

    # Some models have set the order of the labels to use, so let's make sure we do use it.
    label_to_id = None
    if (model.config.label2id !=
            PretrainedConfig(num_labels=num_labels).label2id
            and data_args.task_name is not None and not is_regression):
        # Some have all caps in their config, some don't.
        label_name_to_id = {
            k.lower(): v
            for k, v in model.config.label2id.items()
        }
        if list(sorted(label_name_to_id.keys())) == list(sorted(label_list)):
            label_to_id = {
                i: int(label_name_to_id[label_list[i]])
                for i in range(num_labels)
            }
        else:
            logger.warning(
                "Your model seems to have been trained with labels, but they don't match the dataset: ",
                f"model labels: {list(sorted(label_name_to_id.keys()))}, dataset labels: {list(sorted(label_list))}."
                "\nIgnoring the model labels as a result.",
            )
    elif data_args.task_name is None and not is_regression:
        label_to_id = {v: i for i, v in enumerate(label_list)}

    if label_to_id is not None:
        model.config.label2id = label_to_id
        model.config.id2label = {
            id: label
            for label, id in config.label2id.items()
        }
    elif data_args.task_name is not None and not is_regression:
        model.config.label2id = {l: i for i, l in enumerate(label_list)}
        model.config.id2label = {
            id: label
            for label, id in config.label2id.items()
        }

    if data_args.max_seq_length > tokenizer.model_max_length:
        logger.warning(
            f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
            f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
        )
    max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)

    def preprocess_function(examples):
        # Tokenize the texts
        args = ((examples[sentence1_key], ) if sentence2_key is None else
                (examples[sentence1_key], examples[sentence2_key]))
        result = tokenizer(*args,
                           padding=padding,
                           max_length=max_seq_length,
                           truncation=True)

        # Map labels to IDs (not necessary for GLUE tasks)
        if label_to_id is not None and "label" in examples:
            result["label"] = [(label_to_id[l] if l != -1 else -1)
                               for l in examples["label"]]
        return result

    with training_args.main_process_first(desc="dataset map pre-processing"):
        raw_datasets = raw_datasets.map(
            preprocess_function,
            batched=True,
            load_from_cache_file=not data_args.overwrite_cache,
            desc="Running tokenizer on dataset",
        )
    if training_args.do_train:
        if "train" not in raw_datasets:
            raise ValueError("--do_train requires a train dataset")
        train_dataset = raw_datasets["train"]
        if data_args.max_train_samples is not None:
            train_dataset = train_dataset.select(
                range(data_args.max_train_samples))

    if training_args.do_eval:
        if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
            raise ValueError("--do_eval requires a validation dataset")
        eval_dataset = raw_datasets["validation_matched" if data_args.
                                    task_name == "mnli" else "validation"]
        if data_args.max_eval_samples is not None:
            eval_dataset = eval_dataset.select(
                range(data_args.max_eval_samples))

    if training_args.do_predict or data_args.task_name is not None or data_args.test_file is not None:
        if "test" not in raw_datasets and "test_matched" not in raw_datasets:
            raise ValueError("--do_predict requires a test dataset")
        predict_dataset = raw_datasets["test_matched" if data_args.task_name ==
                                       "mnli" else "test"]
        if data_args.max_predict_samples is not None:
            predict_dataset = predict_dataset.select(
                range(data_args.max_predict_samples))

    # Log a few random samples from the training set:
    if training_args.do_train:
        for index in random.sample(range(len(train_dataset)), 3):
            logger.info(
                f"Sample {index} of the training set: {train_dataset[index]}.")

    # Get the metric function
    if data_args.task_name is not None:
        metric = load_metric("glue", data_args.task_name)
    else:
        metric = load_metric("accuracy")

    # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
    # predictions and label_ids field) and has to return a dictionary string to float.
    def compute_metrics(p: EvalPrediction):
        preds = p.predictions[0] if isinstance(p.predictions,
                                               tuple) else p.predictions
        preds = np.squeeze(preds) if is_regression else np.argmax(preds,
                                                                  axis=1)
        if data_args.task_name is not None:
            result = metric.compute(predictions=preds, references=p.label_ids)
            if len(result) > 1:
                result["combined_score"] = np.mean(list(
                    result.values())).item()
            return result
        elif is_regression:
            return {"mse": ((preds - p.label_ids)**2).mean().item()}
        else:
            return {
                "accuracy":
                (preds == p.label_ids).astype(np.float32).mean().item()
            }

    # Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
    if data_args.pad_to_max_length:
        data_collator = default_data_collator
    elif training_args.fp16:
        data_collator = DataCollatorWithPadding(tokenizer,
                                                pad_to_multiple_of=8)
    else:
        data_collator = None

    # Initialize our Trainer
    trainer_class = AdapterTrainer if adapter_args.train_adapter else Trainer
    trainer = trainer_class(
        model=model,
        args=training_args,
        train_dataset=train_dataset if training_args.do_train else None,
        eval_dataset=eval_dataset if training_args.do_eval else None,
        compute_metrics=compute_metrics,
        tokenizer=tokenizer,
        data_collator=data_collator,
    )

    # Training
    if training_args.do_train:
        checkpoint = None
        if training_args.resume_from_checkpoint is not None:
            checkpoint = training_args.resume_from_checkpoint
        elif last_checkpoint is not None:
            checkpoint = last_checkpoint
        train_result = trainer.train(resume_from_checkpoint=checkpoint)
        metrics = train_result.metrics
        max_train_samples = (data_args.max_train_samples
                             if data_args.max_train_samples is not None else
                             len(train_dataset))
        metrics["train_samples"] = min(max_train_samples, len(train_dataset))

        trainer.save_model()  # Saves the tokenizer too for easy upload

        trainer.log_metrics("train", metrics)
        trainer.save_metrics("train", metrics)
        trainer.save_state()

    # Evaluation
    if training_args.do_eval:
        logger.info("*** Evaluate ***")

        # Loop to handle MNLI double evaluation (matched, mis-matched)
        tasks = [data_args.task_name]
        eval_datasets = [eval_dataset]
        if data_args.task_name == "mnli":
            tasks.append("mnli-mm")
            eval_datasets.append(raw_datasets["validation_mismatched"])

        for eval_dataset, task in zip(eval_datasets, tasks):
            metrics = trainer.evaluate(eval_dataset=eval_dataset)

            max_eval_samples = (data_args.max_eval_samples
                                if data_args.max_eval_samples is not None else
                                len(eval_dataset))
            metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))

            trainer.log_metrics("eval", metrics)
            trainer.save_metrics("eval", metrics)

    if training_args.do_predict:
        logger.info("*** Predict ***")

        # Loop to handle MNLI double evaluation (matched, mis-matched)
        tasks = [data_args.task_name]
        predict_datasets = [predict_dataset]
        if data_args.task_name == "mnli":
            tasks.append("mnli-mm")
            predict_datasets.append(raw_datasets["test_mismatched"])

        for predict_dataset, task in zip(predict_datasets, tasks):
            # Removing the `label` columns because it contains -1 and Trainer won't like that.
            predict_dataset = predict_dataset.remove_columns("label")
            predictions = trainer.predict(
                predict_dataset, metric_key_prefix="predict").predictions
            predictions = np.squeeze(
                predictions) if is_regression else np.argmax(predictions,
                                                             axis=1)

            output_predict_file = os.path.join(training_args.output_dir,
                                               f"predict_results_{task}.txt")
            if trainer.is_world_process_zero():
                with open(output_predict_file, "w") as writer:
                    logger.info(f"***** Predict results {task} *****")
                    writer.write("index\tprediction\n")
                    for index, item in enumerate(predictions):
                        if is_regression:
                            writer.write(f"{index}\t{item:3.3f}\n")
                        else:
                            item = label_list[item]
                            writer.write(f"{index}\t{item}\n")

    kwargs = {
        "finetuned_from": model_args.model_name_or_path,
        "tasks": "text-classification"
    }
    if data_args.task_name is not None:
        kwargs["language"] = "en"
        kwargs["dataset_tags"] = "glue"
        kwargs["dataset_args"] = data_args.task_name
        kwargs["dataset"] = f"GLUE {data_args.task_name.upper()}"

    if training_args.push_to_hub:
        trainer.push_to_hub(**kwargs)
    else:
        trainer.create_model_card(**kwargs)
示例#22
0
def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments,
                               TrainingArguments, MultiLingAdapterArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args, training_args, adapter_args = parser.parse_json_file(
            json_file=os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args, training_args, adapter_args = parser.parse_args_into_dataclasses(
        )

    # Detecting last checkpoint.
    last_checkpoint = None
    if os.path.isdir(
            training_args.output_dir
    ) and training_args.do_train and not training_args.overwrite_output_dir:
        last_checkpoint = get_last_checkpoint(training_args.output_dir)
        if last_checkpoint is None and len(os.listdir(
                training_args.output_dir)) > 0:
            raise ValueError(
                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                "Use --overwrite_output_dir to overcome.")
        elif last_checkpoint is not None:
            logger.info(
                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
            )

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )
    logger.setLevel(logging.INFO if is_main_process(training_args.local_rank
                                                    ) else logging.WARN)

    # Log on each process the small summary:
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
        +
        f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
    )
    # Set the verbosity to info of the Transformers logger (on main process only):
    if is_main_process(training_args.local_rank):
        transformers.utils.logging.set_verbosity_info()
        transformers.utils.logging.enable_default_handler()
        transformers.utils.logging.enable_explicit_format()
    logger.info(f"Training/evaluation parameters {training_args}")

    # Set seed before initializing model.
    set_seed(training_args.seed)

    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
    # (the dataset will be downloaded automatically from the datasets Hub).
    #
    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
    # 'text' is found. You can easily tweak this behavior (see below).
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if data_args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        datasets = load_dataset(data_args.dataset_name,
                                data_args.dataset_config_name)
    else:
        data_files = {}
        if data_args.train_file is not None:
            data_files["train"] = data_args.train_file
        if data_args.validation_file is not None:
            data_files["validation"] = data_args.validation_file
        if data_args.test_file is not None:
            data_files["test"] = data_args.test_file
        extension = data_args.train_file.split(".")[-1]
        datasets = load_dataset(extension, data_files=data_files)
    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
    # https://huggingface.co/docs/datasets/loading_datasets.html.

    if training_args.do_train:
        column_names = datasets["train"].column_names
        features = datasets["train"].features
    else:
        column_names = datasets["validation"].column_names
        features = datasets["validation"].features
    text_column_name = "tokens" if "tokens" in column_names else column_names[0]
    label_column_name = (f"{data_args.task_name}_tags"
                         if f"{data_args.task_name}_tags" in column_names else
                         column_names[1])

    # In the event the labels are not a `Sequence[ClassLabel]`, we will need to go through the dataset to get the
    # unique labels.
    def get_label_list(labels):
        unique_labels = set()
        for label in labels:
            unique_labels = unique_labels | set(label)
        label_list = list(unique_labels)
        label_list.sort()
        return label_list

    if isinstance(features[label_column_name].feature, ClassLabel):
        label_list = features[label_column_name].feature.names
        # No need to convert the labels since they are already ints.
        label_to_id = {i: i for i in range(len(label_list))}
    else:
        label_list = get_label_list(datasets["train"][label_column_name])
        label_to_id = {l: i for i, l in enumerate(label_list)}
    num_labels = len(label_list)

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    config = AutoConfig.from_pretrained(
        model_args.config_name
        if model_args.config_name else model_args.model_name_or_path,
        num_labels=num_labels,
        finetuning_task=data_args.task_name,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        use_auth_token=True if model_args.use_auth_token else None,
    )
    tokenizer = AutoTokenizer.from_pretrained(
        model_args.tokenizer_name
        if model_args.tokenizer_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        use_fast=True,
        revision=model_args.model_revision,
        use_auth_token=True if model_args.use_auth_token else None,
    )
    model = AutoModelForTokenClassification.from_pretrained(
        model_args.model_name_or_path,
        from_tf=bool(".ckpt" in model_args.model_name_or_path),
        config=config,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        use_auth_token=True if model_args.use_auth_token else None,
    )

    # Setup adapters
    if adapter_args.train_adapter:
        task_name = data_args.dataset_name or "ner"
        # check if adapter already exists, otherwise add it
        if task_name not in model.config.adapters:
            # resolve the adapter config
            adapter_config = AdapterConfig.load(
                adapter_args.adapter_config,
                non_linearity=adapter_args.adapter_non_linearity,
                reduction_factor=adapter_args.adapter_reduction_factor,
            )
            # load a pre-trained from Hub if specified
            if adapter_args.load_adapter:
                model.load_adapter(
                    adapter_args.load_adapter,
                    config=adapter_config,
                    load_as=task_name,
                )
            # otherwise, add a fresh adapter
            else:
                model.add_adapter(task_name, config=adapter_config)
        # optionally load a pre-trained language adapter
        if adapter_args.load_lang_adapter:
            # resolve the language adapter config
            lang_adapter_config = AdapterConfig.load(
                adapter_args.lang_adapter_config,
                non_linearity=adapter_args.lang_adapter_non_linearity,
                reduction_factor=adapter_args.lang_adapter_reduction_factor,
            )
            # load the language adapter from Hub
            lang_adapter_name = model.load_adapter(
                adapter_args.load_lang_adapter,
                config=lang_adapter_config,
                load_as=adapter_args.language,
            )
        else:
            lang_adapter_name = None
        # Freeze all model weights except of those of this adapter
        model.train_adapter([task_name])
        # Set the adapters to be used in every forward pass
        if lang_adapter_name:
            model.set_active_adapters([lang_adapter_name, task_name])
        else:
            model.set_active_adapters([task_name])
    else:
        if adapter_args.load_adapter or adapter_args.load_lang_adapter:
            raise ValueError(
                "Adapters can only be loaded in adapters training mode."
                "Use --train_adapter to enable adapter training")

    # Tokenizer check: this script requires a fast tokenizer.
    if not isinstance(tokenizer, PreTrainedTokenizerFast):
        raise ValueError(
            "This example script only works for models that have a fast tokenizer. Checkout the big table of models "
            "at https://huggingface.co/transformers/index.html#bigtable to find the model types that meet this "
            "requirement")

    # Preprocessing the dataset
    # Padding strategy
    padding = "max_length" if data_args.pad_to_max_length else False

    # Tokenize all texts and align the labels with them.
    def tokenize_and_align_labels(examples):
        tokenized_inputs = tokenizer(
            examples[text_column_name],
            padding=padding,
            truncation=True,
            # We use this argument because the texts in our dataset are lists of words (with a label for each word).
            is_split_into_words=True,
        )
        labels = []
        for i, label in enumerate(examples[label_column_name]):
            word_ids = tokenized_inputs.word_ids(batch_index=i)
            previous_word_idx = None
            label_ids = []
            for word_idx in word_ids:
                # Special tokens have a word id that is None. We set the label to -100 so they are automatically
                # ignored in the loss function.
                if word_idx is None:
                    label_ids.append(-100)
                # We set the label for the first token of each word.
                elif word_idx != previous_word_idx:
                    label_ids.append(label_to_id[label[word_idx]])
                # For the other tokens in a word, we set the label to either the current label or -100, depending on
                # the label_all_tokens flag.
                else:
                    label_ids.append(label_to_id[label[word_idx]] if data_args.
                                     label_all_tokens else -100)
                previous_word_idx = word_idx

            labels.append(label_ids)
        tokenized_inputs["labels"] = labels
        return tokenized_inputs

    if training_args.do_train:
        if "train" not in datasets:
            raise ValueError("--do_train requires a train dataset")
        train_dataset = datasets["train"]
        if data_args.max_train_samples is not None:
            train_dataset = train_dataset.select(
                range(data_args.max_train_samples))
        train_dataset = train_dataset.map(
            tokenize_and_align_labels,
            batched=True,
            num_proc=data_args.preprocessing_num_workers,
            load_from_cache_file=not data_args.overwrite_cache,
        )

    if training_args.do_eval:
        if "validation" not in datasets:
            raise ValueError("--do_eval requires a validation dataset")
        eval_dataset = datasets["validation"]
        if data_args.max_val_samples is not None:
            eval_dataset = eval_dataset.select(range(
                data_args.max_val_samples))
        eval_dataset = eval_dataset.map(
            tokenize_and_align_labels,
            batched=True,
            num_proc=data_args.preprocessing_num_workers,
            load_from_cache_file=not data_args.overwrite_cache,
        )

    if training_args.do_predict:
        if "test" not in datasets:
            raise ValueError("--do_predict requires a test dataset")
        test_dataset = datasets["test"]
        if data_args.max_test_samples is not None:
            test_dataset = test_dataset.select(
                range(data_args.max_test_samples))
        test_dataset = test_dataset.map(
            tokenize_and_align_labels,
            batched=True,
            num_proc=data_args.preprocessing_num_workers,
            load_from_cache_file=not data_args.overwrite_cache,
        )

    # Data collator
    data_collator = DataCollatorForTokenClassification(
        tokenizer, pad_to_multiple_of=8 if training_args.fp16 else None)

    # Metrics
    metric = load_metric("seqeval")

    def compute_metrics(p):
        predictions, labels = p
        predictions = np.argmax(predictions, axis=2)

        # Remove ignored index (special tokens)
        true_predictions = [[
            label_list[p] for (p, l) in zip(prediction, label) if l != -100
        ] for prediction, label in zip(predictions, labels)]
        true_labels = [[
            label_list[l] for (p, l) in zip(prediction, label) if l != -100
        ] for prediction, label in zip(predictions, labels)]

        results = metric.compute(predictions=true_predictions,
                                 references=true_labels)
        if data_args.return_entity_level_metrics:
            # Unpack nested dictionaries
            final_results = {}
            for key, value in results.items():
                if isinstance(value, dict):
                    for n, v in value.items():
                        final_results[f"{key}_{n}"] = v
                else:
                    final_results[key] = value
            return final_results
        else:
            return {
                "precision": results["overall_precision"],
                "recall": results["overall_recall"],
                "f1": results["overall_f1"],
                "accuracy": results["overall_accuracy"],
            }

    # Initialize our Trainer
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset if training_args.do_train else None,
        eval_dataset=eval_dataset if training_args.do_eval else None,
        tokenizer=tokenizer,
        data_collator=data_collator,
        compute_metrics=compute_metrics,
        do_save_full_model=not adapter_args.train_adapter,
        do_save_adapters=adapter_args.train_adapter,
    )

    # Training
    if training_args.do_train:
        if last_checkpoint is not None:
            checkpoint = last_checkpoint
        elif os.path.isdir(model_args.model_name_or_path):
            checkpoint = model_args.model_name_or_path
        else:
            checkpoint = None
        train_result = trainer.train(resume_from_checkpoint=checkpoint)
        metrics = train_result.metrics
        trainer.save_model()  # Saves the tokenizer too for easy upload

        max_train_samples = (data_args.max_train_samples
                             if data_args.max_train_samples is not None else
                             len(train_dataset))
        metrics["train_samples"] = min(max_train_samples, len(train_dataset))

        trainer.log_metrics("train", metrics)
        trainer.save_metrics("train", metrics)
        trainer.save_state()

    # Evaluation
    if training_args.do_eval:
        logger.info("*** Evaluate ***")

        metrics = trainer.evaluate()

        max_val_samples = data_args.max_val_samples if data_args.max_val_samples is not None else len(
            eval_dataset)
        metrics["eval_samples"] = min(max_val_samples, len(eval_dataset))

        trainer.log_metrics("eval", metrics)
        trainer.save_metrics("eval", metrics)

    # Predict
    if training_args.do_predict:
        logger.info("*** Predict ***")

        predictions, labels, metrics = trainer.predict(test_dataset)
        predictions = np.argmax(predictions, axis=2)

        # Remove ignored index (special tokens)
        true_predictions = [[
            label_list[p] for (p, l) in zip(prediction, label) if l != -100
        ] for prediction, label in zip(predictions, labels)]

        trainer.log_metrics("test", metrics)
        trainer.save_metrics("test", metrics)

        # Save predictions
        output_test_predictions_file = os.path.join(training_args.output_dir,
                                                    "test_predictions.txt")
        if trainer.is_world_process_zero():
            with open(output_test_predictions_file, "w") as writer:
                for prediction in true_predictions:
                    writer.write(" ".join(prediction) + "\n")
示例#23
0
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification, AdapterType, BertForSequenceClassification, InputFeatures

model = BertForSequenceClassification.from_pretrained("bert-base-uncased",
                                                      num_labels=3)
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")

#model.load_adapter("sentiment/sst-2@ukp")
from transformers import AdapterConfig
config = AdapterConfig.load("pfeiffer")
#model.load_adapter("nli/multinli@ukp", "text_task", config=config)
model.load_adapter(
    "/home/theorist17/projects/adapter/adapters/MNLI/checkpoint-18000/mnli/",
    "text_task",
    config=config)

model.eval()


def predict(sentence, sentence2):
    bert_encoding = tokenizer.encode_plus(
        tokenizer.tokenize(sentence),
        text_pair=tokenizer.tokenize(sentence2),
        max_length=128,
        pad_to_max_length=True)
    # for key, value in bert_encoding.items():
    #     print("{}:\n\t{}".format(key, value))
    print(
        'Decoded!',
        tokenizer.convert_tokens_to_string(
            tokenizer.convert_ids_to_tokens(bert_encoding['input_ids'])))
示例#24
0
def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.
    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, UDTrainingArguments, AdapterArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args, training_args, adapter_args = parser.parse_json_file(
            json_file=os.path.abspath(sys.argv[1])
        )
    else:
        (model_args, data_args, training_args, adapter_args,) = parser.parse_args_into_dataclasses()

    if (
        os.path.exists(training_args.output_dir)
        and os.listdir(training_args.output_dir)
        and training_args.do_train
        and not training_args.overwrite_output_dir
    ):
        raise ValueError(
            f"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome."
        )

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,
    )
    logger.warning(
        "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
        training_args.local_rank,
        training_args.device,
        training_args.n_gpu,
        bool(training_args.local_rank != -1),
        training_args.fp16,
    )
    logger.info("Training/evaluation parameters %s", training_args)

    # Set seed
    set_seed(training_args.seed)

    # Prepare for UD dependency parsing task
    labels = UD_HEAD_LABELS
    label_map: Dict[int, str] = {i: label for i, label in enumerate(labels)}
    num_labels = len(labels)

    config = AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
        num_labels=num_labels,
        id2label=label_map,
        label2id={label: i for i, label in enumerate(labels)},
        cache_dir=model_args.cache_dir,
        pad_token_id=-1,
    )

    if model_args.is_japanese:
        assert model_args.mecab_dir is not None
        assert model_args.mecab_dic_dir is not None

    tokenizer = AutoTokenizer.from_pretrained(
        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        use_fast=model_args.use_fast,
        do_lower_case=model_args.do_lower_case,
        mecab_kwargs={"mecab_option": f"-r {model_args.mecab_dir} -d {model_args.mecab_dic_dir}"}
        if model_args.is_japanese
        else None,
    )

    model = BertForBiaffineParsing.from_pretrained(
        model_args.model_name_or_path, config=config, cache_dir=model_args.cache_dir,
    )

    # Setup adapters
    task_name = "udp"
    language = adapter_args.language
    if model_args.replace_embeddings:
        model.resize_token_embeddings(len(tokenizer))

    if model_args.leave_out_twelvth:
        logger.info("Leaving out 12")
        leave_out = [11]
    else:
        leave_out = []

    setup_task_adapter_training(
        model, task_name, adapter_args, leave_out=leave_out, with_embeddings=model_args.replace_embeddings
    )
    if model_args.leave_out_twelvth:
        if language in model.base_model.encoder.layer._modules["11"].output.layer_text_lang_adapters:
            del model.base_model.encoder.layer._modules["11"].output.layer_text_lang_adapters[language]
            logger.info("Deleted language adapter " + language + " in layer 12")
        if language in model.base_model.encoder.layer._modules["11"].attention.output.attention_text_lang_adapters:
            del model.base_model.encoder.layer._modules["11"].attention.output.attention_text_lang_adapters[language]
            logger.info("Deleted language adapter " + language + " in layer 12")

    if adapter_args.train_adapter:
        if language:
            adapter_names = [[language], [task_name]]
        else:
            adapter_names = [[task_name]]
    else:
        adapter_names = None

    train_dataset = (
        UDDataset(
            data_dir=data_args.data_dir,
            tokenizer=tokenizer,
            labels=labels,
            max_seq_length=data_args.max_seq_length,
            overwrite_cache=data_args.overwrite_cache,
            mode=Split.train,
        )
        if training_args.do_train
        else None
    )

    eval_dataset = (
        UDDataset(
            data_dir=data_args.data_dir,
            tokenizer=tokenizer,
            labels=labels,
            max_seq_length=data_args.max_seq_length,
            overwrite_cache=data_args.overwrite_cache,
            mode=Split.dev,
        )
        if training_args.do_eval
        else None
    )

    # Initialize our Trainer
    trainer = DependencyParsingTrainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset,
        eval_dataset=eval_dataset,
        do_save_full_model=not adapter_args.train_adapter,
        do_save_adapters=adapter_args.train_adapter,
        adapter_names=adapter_names,
    )

    # Training
    if training_args.do_train:
        trainer.train(
            model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None
        )
        trainer.save_model()
        # For convenience, we also re-save the tokenizer to the same directory,
        # so that you can share your model easily on huggingface.co/models =)
        if trainer.is_world_master():
            tokenizer.save_pretrained(training_args.output_dir)

    # Evaluation
    results = {}
    if training_args.do_eval:
        logger.info("*** Evaluate ***")

        result = trainer.evaluate()

        output_eval_file = os.path.join(training_args.output_dir, "eval_results.txt")
        if trainer.is_world_master():
            with open(output_eval_file, "w") as writer:
                logger.info("***** Eval results *****")
                for key, value in result.items():
                    logger.info("  %s = %s", key, value)
                    writer.write("%s = %s\n" % (key, value))

            results.update(result)

    # Predict
    if training_args.do_predict:
        test_dataset = UDDataset(
            data_dir=data_args.data_dir,
            tokenizer=tokenizer,
            labels=labels,
            max_seq_length=data_args.max_seq_length,
            overwrite_cache=data_args.overwrite_cache,
            mode=Split.test,
        )

        logging.info("*** Test ***")

        if training_args.store_best_model:
            logger.info("Loading best model for predictions.")

            if adapter_args.train_adapter:
                if language:
                    lang_adapter_config = AdapterConfig.load(
                        config="pfeiffer", non_linearity="gelu", reduction_factor=2, leave_out=leave_out
                    )
                    model.load_adapter(
                        os.path.join(training_args.output_dir, "best_model", language)
                        if training_args.do_train
                        else adapter_args.load_lang_adapter,
                        AdapterType.text_lang,
                        config=lang_adapter_config,
                        load_as=language,
                    )
                task_adapter_config = AdapterConfig.load(
                    config="pfeiffer", non_linearity="gelu", reduction_factor=16, leave_out=leave_out
                )
                model.load_adapter(
                    os.path.join(training_args.output_dir, "best_model", task_name)
                    if training_args.do_train
                    else adapter_args.load_task_adapter,
                    AdapterType.text_task,
                    config=task_adapter_config,
                    load_as=task_name,
                )
                if model_args.leave_out_twelvth:
                    if language in model.base_model.encoder.layer._modules["11"].output.layer_text_lang_adapters:
                        del model.base_model.encoder.layer._modules["11"].output.layer_text_lang_adapters[language]
                        logger.info("Deleted language adapter " + language + " in layer 12")
                    if (
                        language
                        in model.base_model.encoder.layer._modules["11"].attention.output.attention_text_lang_adapters
                    ):
                        del model.base_model.encoder.layer._modules[
                            "11"
                        ].attention.output.attention_text_lang_adapters[language]
                        logger.info("Deleted language adapter " + language + " in layer 12")

                if language:
                    adapter_names = [[language], [task_name]]
                else:
                    adapter_names = [[task_name]]
            else:
                trainer.model = BertForBiaffineParsing.from_pretrained(
                    os.path.join(training_args.output_dir, "best_model"),
                    from_tf=bool(".ckpt" in model_args.model_name_or_path),
                    config=config,
                    cache_dir=model_args.cache_dir,
                ).to(training_args.device)

        predictions, _, metrics = trainer.predict(test_dataset)

        output_test_results_file = os.path.join(training_args.output_dir, "test_results.txt")
        if trainer.is_world_master():
            with open(output_test_results_file, "w") as writer:
                for key, value in metrics.items():
                    logger.info("  %s = %s", key, value)
                    writer.write("%s = %s\n" % (key, value))

    return results
示例#25
0
def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    parser = HfArgumentParser(
        (ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments,
         MultiLingAdapterArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args, training_args, adapter_args = parser.parse_json_file(
            json_file=os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args, training_args, adapter_args = parser.parse_args_into_dataclasses(
        )

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )

    log_level = training_args.get_process_log_level()
    logger.setLevel(log_level)
    datasets.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()

    # Log on each process the small summary:
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
        +
        f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
    )
    logger.info(f"Training/evaluation parameters {training_args}")

    if data_args.source_prefix is None and model_args.model_name_or_path in [
            "t5-small",
            "t5-base",
            "t5-large",
            "t5-3b",
            "t5-11b",
    ]:
        logger.warning(
            "You're running a t5 model but didn't provide a source prefix, which is expected, e.g. with "
            "`--source_prefix 'translate English to German: ' `")

    # Detecting last checkpoint.
    last_checkpoint = None
    if os.path.isdir(
            training_args.output_dir
    ) and training_args.do_train and not training_args.overwrite_output_dir:
        last_checkpoint = get_last_checkpoint(training_args.output_dir)
        if last_checkpoint is None and len(os.listdir(
                training_args.output_dir)) > 0:
            raise ValueError(
                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                "Use --overwrite_output_dir to overcome.")
        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
            logger.info(
                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
            )

    # Set seed before initializing model.
    set_seed(training_args.seed)

    # Get the datasets: you can either provide your own JSON training and evaluation files (see below)
    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
    # (the dataset will be downloaded automatically from the datasets Hub).
    #
    # For translation, only JSON files are supported, with one field named "translation" containing two keys for the
    # source and target languages (unless you adapt what follows).
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if data_args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        raw_datasets = load_dataset(data_args.dataset_name,
                                    data_args.dataset_config_name,
                                    cache_dir=model_args.cache_dir)
    else:
        data_files = {}
        if data_args.train_file is not None:
            data_files["train"] = data_args.train_file
            extension = data_args.train_file.split(".")[-1]
        if data_args.validation_file is not None:
            data_files["validation"] = data_args.validation_file
            extension = data_args.validation_file.split(".")[-1]
        if data_args.test_file is not None:
            data_files["test"] = data_args.test_file
            extension = data_args.test_file.split(".")[-1]
        raw_datasets = load_dataset(extension,
                                    data_files=data_files,
                                    cache_dir=model_args.cache_dir)
    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
    # https://huggingface.co/docs/datasets/loading_datasets.html.

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    config = AutoConfig.from_pretrained(
        model_args.config_name
        if model_args.config_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        use_auth_token=True if model_args.use_auth_token else None,
    )
    tokenizer = AutoTokenizer.from_pretrained(
        model_args.tokenizer_name
        if model_args.tokenizer_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        use_fast=model_args.use_fast_tokenizer,
        revision=model_args.model_revision,
        use_auth_token=True if model_args.use_auth_token else None,
    )
    model = AutoModelForSeq2SeqLM.from_pretrained(
        model_args.model_name_or_path,
        from_tf=bool(".ckpt" in model_args.model_name_or_path),
        config=config,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        use_auth_token=True if model_args.use_auth_token else None,
    )

    model.resize_token_embeddings(len(tokenizer))

    # Set decoder_start_token_id
    if model.config.decoder_start_token_id is None and isinstance(
            tokenizer, (MBartTokenizer, MBartTokenizerFast)):
        if isinstance(tokenizer, MBartTokenizer):
            model.config.decoder_start_token_id = tokenizer.lang_code_to_id[
                data_args.target_lang]
        else:
            model.config.decoder_start_token_id = tokenizer.convert_tokens_to_ids(
                data_args.target_lang)

    if model.config.decoder_start_token_id is None:
        raise ValueError(
            "Make sure that `config.decoder_start_token_id` is correctly defined"
        )

    # Setup adapters
    if adapter_args.train_adapter:
        task_name = data_args.source_lang.split(
            "_")[0] + "_" + data_args.target_lang.split("_")[0]
        # check if adapter already exists, otherwise add it
        if task_name not in model.config.adapters:
            # resolve the adapter config
            adapter_config = AdapterConfig.load(
                adapter_args.adapter_config,
                non_linearity=adapter_args.adapter_non_linearity,
                reduction_factor=adapter_args.adapter_reduction_factor,
            )
            # load a pre-trained from Hub if specified
            if adapter_args.load_adapter:
                model.load_adapter(
                    adapter_args.load_adapter,
                    config=adapter_config,
                    load_as=task_name,
                )
            # otherwise, add a fresh adapter
            else:
                model.add_adapter(task_name, config=adapter_config)
        # optionally load a pre-trained language adapter
        if adapter_args.load_lang_adapter:
            # resolve the language adapter config
            lang_adapter_config = AdapterConfig.load(
                adapter_args.lang_adapter_config,
                non_linearity=adapter_args.lang_adapter_non_linearity,
                reduction_factor=adapter_args.lang_adapter_reduction_factor,
            )
            # load the language adapter from Hub
            lang_adapter_name = model.load_adapter(
                adapter_args.load_lang_adapter,
                config=lang_adapter_config,
                load_as=adapter_args.language,
            )
        else:
            lang_adapter_name = None
        # Freeze all model weights except of those of this adapter
        model.train_adapter([task_name])
        # Set the adapters to be used in every forward pass
        if lang_adapter_name:
            model.set_active_adapters(ac.Stack(lang_adapter_name, task_name))
        else:
            model.set_active_adapters([task_name])
    else:
        if adapter_args.load_adapter or adapter_args.load_lang_adapter:
            raise ValueError(
                "Adapters can only be loaded in adapters training mode."
                "Use --train_adapter to enable adapter training")

    prefix = data_args.source_prefix if data_args.source_prefix is not None else ""

    # Preprocessing the datasets.
    # We need to tokenize inputs and targets.
    if training_args.do_train:
        column_names = raw_datasets["train"].column_names
    elif training_args.do_eval:
        column_names = raw_datasets["validation"].column_names
    elif training_args.do_predict:
        column_names = raw_datasets["test"].column_names
    else:
        logger.info(
            "There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`."
        )
        return

    # For translation we set the codes of our source and target languages (only useful for mBART, the others will
    # ignore those attributes).
    if isinstance(tokenizer, tuple(MULTILINGUAL_TOKENIZERS)):
        assert data_args.target_lang is not None and data_args.source_lang is not None, (
            f"{tokenizer.__class__.__name__} is a multilingual tokenizer which requires --source_lang and "
            "--target_lang arguments.")

        tokenizer.src_lang = data_args.source_lang
        tokenizer.tgt_lang = data_args.target_lang

        # For multilingual translation models like mBART-50 and M2M100 we need to force the target language token
        # as the first generated token. We ask the user to explicitly provide this as --forced_bos_token argument.
        forced_bos_token_id = (
            tokenizer.lang_code_to_id[data_args.forced_bos_token]
            if data_args.forced_bos_token is not None else None)
        model.config.forced_bos_token_id = forced_bos_token_id

    # Get the language codes for input/target.
    source_lang = data_args.source_lang.split("_")[0]
    target_lang = data_args.target_lang.split("_")[0]

    # Temporarily set max_target_length for training.
    max_target_length = data_args.max_target_length
    padding = "max_length" if data_args.pad_to_max_length else False

    if training_args.label_smoothing_factor > 0 and not hasattr(
            model, "prepare_decoder_input_ids_from_labels"):
        logger.warning(
            "label_smoothing is enabled but the `prepare_decoder_input_ids_from_labels` method is not defined for"
            f"`{model.__class__.__name__}`. This will lead to loss being calculated twice and will take up more memory"
        )

    def preprocess_function(examples):
        inputs = [ex[source_lang] for ex in examples["translation"]]
        targets = [ex[target_lang] for ex in examples["translation"]]
        inputs = [prefix + inp for inp in inputs]
        model_inputs = tokenizer(inputs,
                                 max_length=data_args.max_source_length,
                                 padding=padding,
                                 truncation=True)

        # Setup the tokenizer for targets
        with tokenizer.as_target_tokenizer():
            labels = tokenizer(targets,
                               max_length=max_target_length,
                               padding=padding,
                               truncation=True)

        # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore
        # padding in the loss.
        if padding == "max_length" and data_args.ignore_pad_token_for_loss:
            labels["input_ids"] = [[
                (l if l != tokenizer.pad_token_id else -100) for l in label
            ] for label in labels["input_ids"]]

        model_inputs["labels"] = labels["input_ids"]
        return model_inputs

    if training_args.do_train:
        if "train" not in raw_datasets:
            raise ValueError("--do_train requires a train dataset")
        train_dataset = raw_datasets["train"]
        if data_args.max_train_samples is not None:
            train_dataset = train_dataset.select(
                range(data_args.max_train_samples))
        with training_args.main_process_first(
                desc="train dataset map pre-processing"):
            train_dataset = train_dataset.map(
                preprocess_function,
                batched=True,
                num_proc=data_args.preprocessing_num_workers,
                remove_columns=column_names,
                load_from_cache_file=not data_args.overwrite_cache,
                desc="Running tokenizer on train dataset",
            )

    if training_args.do_eval:
        max_target_length = data_args.val_max_target_length
        if "validation" not in raw_datasets:
            raise ValueError("--do_eval requires a validation dataset")
        eval_dataset = raw_datasets["validation"]
        if data_args.max_eval_samples is not None:
            eval_dataset = eval_dataset.select(
                range(data_args.max_eval_samples))
        with training_args.main_process_first(
                desc="validation dataset map pre-processing"):
            eval_dataset = eval_dataset.map(
                preprocess_function,
                batched=True,
                num_proc=data_args.preprocessing_num_workers,
                remove_columns=column_names,
                load_from_cache_file=not data_args.overwrite_cache,
                desc="Running tokenizer on validation dataset",
            )

    if training_args.do_predict:
        max_target_length = data_args.val_max_target_length
        if "test" not in raw_datasets:
            raise ValueError("--do_predict requires a test dataset")
        predict_dataset = raw_datasets["test"]
        if data_args.max_predict_samples is not None:
            predict_dataset = predict_dataset.select(
                range(data_args.max_predict_samples))
        with training_args.main_process_first(
                desc="prediction dataset map pre-processing"):
            predict_dataset = predict_dataset.map(
                preprocess_function,
                batched=True,
                num_proc=data_args.preprocessing_num_workers,
                remove_columns=column_names,
                load_from_cache_file=not data_args.overwrite_cache,
                desc="Running tokenizer on prediction dataset",
            )

    # Data collator
    label_pad_token_id = -100 if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id
    if data_args.pad_to_max_length:
        data_collator = default_data_collator
    else:
        data_collator = DataCollatorForSeq2Seq(
            tokenizer,
            model=model,
            label_pad_token_id=label_pad_token_id,
            pad_to_multiple_of=8 if training_args.fp16 else None,
        )

    # Metric
    metric = load_metric("sacrebleu")

    def postprocess_text(preds, labels):
        preds = [pred.strip() for pred in preds]
        labels = [[label.strip()] for label in labels]

        return preds, labels

    def compute_metrics(eval_preds):
        preds, labels = eval_preds
        if isinstance(preds, tuple):
            preds = preds[0]
        decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
        if data_args.ignore_pad_token_for_loss:
            # Replace -100 in the labels as we can't decode them.
            labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
        decoded_labels = tokenizer.batch_decode(labels,
                                                skip_special_tokens=True)

        # Some simple post-processing
        decoded_preds, decoded_labels = postprocess_text(
            decoded_preds, decoded_labels)

        result = metric.compute(predictions=decoded_preds,
                                references=decoded_labels)
        result = {"bleu": result["score"]}

        prediction_lens = [
            np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds
        ]
        result["gen_len"] = np.mean(prediction_lens)
        result = {k: round(v, 4) for k, v in result.items()}
        return result

    # Early stopping
    if data_args.patience and data_args.patience > 0:
        training_args.load_best_model_at_end = True

    # Initialize our Trainer
    trainer_class = Seq2SeqAdapterTrainer if adapter_args.train_adapter else Seq2SeqTrainer
    trainer = trainer_class(
        model=model,
        args=training_args,
        train_dataset=train_dataset if training_args.do_train else None,
        eval_dataset=eval_dataset if training_args.do_eval else None,
        tokenizer=tokenizer,
        data_collator=data_collator,
        compute_metrics=compute_metrics
        if training_args.predict_with_generate else None,
    )
    if data_args.patience and data_args.patience > 0:
        callback = EarlyStoppingCallback(
            early_stopping_patience=data_args.patience)
        trainer.add_callback(callback)

    # Training
    if training_args.do_train:
        checkpoint = None
        if training_args.resume_from_checkpoint is not None:
            checkpoint = training_args.resume_from_checkpoint
        elif last_checkpoint is not None:
            checkpoint = last_checkpoint
        train_result = trainer.train(resume_from_checkpoint=checkpoint)
        trainer.save_model()  # Saves the tokenizer too for easy upload

        metrics = train_result.metrics
        max_train_samples = (data_args.max_train_samples
                             if data_args.max_train_samples is not None else
                             len(train_dataset))
        metrics["train_samples"] = min(max_train_samples, len(train_dataset))

        trainer.log_metrics("train", metrics)
        trainer.save_metrics("train", metrics)
        trainer.save_state()

    # Evaluation
    results = {}
    max_length = (training_args.generation_max_length
                  if training_args.generation_max_length is not None else
                  data_args.val_max_target_length)
    num_beams = data_args.num_beams if data_args.num_beams is not None else training_args.generation_num_beams
    if training_args.do_eval:
        logger.info("*** Evaluate ***")

        metrics = trainer.evaluate(max_length=max_length,
                                   num_beams=num_beams,
                                   metric_key_prefix="eval")
        max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(
            eval_dataset)
        metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))

        trainer.log_metrics("eval", metrics)
        trainer.save_metrics("eval", metrics)

    if training_args.do_predict:
        logger.info("*** Predict ***")

        predict_results = trainer.predict(predict_dataset,
                                          metric_key_prefix="predict",
                                          max_length=max_length,
                                          num_beams=num_beams)
        metrics = predict_results.metrics
        max_predict_samples = (data_args.max_predict_samples
                               if data_args.max_predict_samples is not None
                               else len(predict_dataset))
        metrics["predict_samples"] = min(max_predict_samples,
                                         len(predict_dataset))

        trainer.log_metrics("predict", metrics)
        trainer.save_metrics("predict", metrics)

        if trainer.is_world_process_zero():
            if training_args.predict_with_generate:
                predictions = tokenizer.batch_decode(
                    predict_results.predictions,
                    skip_special_tokens=True,
                    clean_up_tokenization_spaces=True)
                predictions = [pred.strip() for pred in predictions]
                output_prediction_file = os.path.join(
                    training_args.output_dir, "generated_predictions.txt")
                with open(output_prediction_file, "w",
                          encoding="utf-8") as writer:
                    writer.write("\n".join(predictions))

    kwargs = {
        "finetuned_from": model_args.model_name_or_path,
        "tasks": "translation"
    }
    if data_args.dataset_name is not None:
        kwargs["dataset_tags"] = data_args.dataset_name
        if data_args.dataset_config_name is not None:
            kwargs["dataset_args"] = data_args.dataset_config_name
            kwargs[
                "dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
        else:
            kwargs["dataset"] = data_args.dataset_name

    languages = [
        l for l in [data_args.source_lang, data_args.target_lang]
        if l is not None
    ]
    if len(languages) > 0:
        kwargs["language"] = languages

    if training_args.push_to_hub:
        trainer.push_to_hub(**kwargs)
    else:
        trainer.create_model_card(**kwargs)

    return results
示例#26
0
def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments,
                               TrainingArguments, MultiLingAdapterArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args, training_args, adapter_args = parser.parse_json_file(
            json_file=os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args, training_args, adapter_args = parser.parse_args_into_dataclasses(
        )

    if (os.path.exists(training_args.output_dir)
            and os.listdir(training_args.output_dir) and training_args.do_train
            and not training_args.overwrite_output_dir):
        raise ValueError(
            f"Output directory ({training_args.output_dir}) already exists and is not empty."
            "Use --overwrite_output_dir to overcome.")

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO
        if is_main_process(training_args.local_rank) else logging.WARN,
    )

    # Log on each process the small summary:
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
        +
        f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
    )
    # Set the verbosity to info of the Transformers logger (on main process only):
    if is_main_process(training_args.local_rank):
        transformers.utils.logging.set_verbosity_info()
    logger.info("Training/evaluation parameters %s", training_args)

    # Set seed before initializing model.
    set_seed(training_args.seed)

    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
    # (the dataset will be downloaded automatically from the datasets Hub).
    #
    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
    # 'text' is found. You can easily tweak this behavior (see below).
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if data_args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        datasets = load_dataset(data_args.dataset_name,
                                data_args.dataset_config_name)
    else:
        data_files = {}
        if data_args.train_file is not None:
            data_files["train"] = data_args.train_file
        if data_args.validation_file is not None:
            data_files["validation"] = data_args.validation_file
        if data_args.test_file is not None:
            data_files["test"] = data_args.test_file
        extension = data_args.train_file.split(".")[-1]
        datasets = load_dataset(extension, data_files=data_files)
    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
    # https://huggingface.co/docs/datasets/loading_datasets.html.

    if training_args.do_train:
        column_names = datasets["train"].column_names
    else:
        column_names = datasets["validation"].column_names
    text_column_name = "words" if "words" in column_names else column_names[0]
    label_column_name = data_args.task_name if data_args.task_name in column_names else column_names[
        1]

    # Labeling (this part will be easier when https://github.com/huggingface/datasets/issues/797 is solved)
    def get_label_list(labels):
        unique_labels = set()
        for label in labels:
            unique_labels = unique_labels | set(label)
        label_list = list(unique_labels)
        label_list.sort()
        return label_list

    label_list = get_label_list(datasets["train"][label_column_name])
    label_to_id = {l: i for i, l in enumerate(label_list)}
    num_labels = len(label_list)

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    config = AutoConfig.from_pretrained(
        model_args.config_name
        if model_args.config_name else model_args.model_name_or_path,
        num_labels=num_labels,
        finetuning_task=data_args.task_name,
        cache_dir=model_args.cache_dir,
    )
    tokenizer = AutoTokenizer.from_pretrained(
        model_args.tokenizer_name
        if model_args.tokenizer_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        use_fast=True,
    )
    model = AutoModelForTokenClassification.from_pretrained(
        model_args.model_name_or_path,
        from_tf=bool(".ckpt" in model_args.model_name_or_path),
        config=config,
        cache_dir=model_args.cache_dir,
    )

    # Setup adapters
    if adapter_args.train_adapter:
        task_name = data_args.dataset_name or "ner"
        # check if adapter already exists, otherwise add it
        if task_name not in model.config.adapters.adapter_list(
                AdapterType.text_task):
            # resolve the adapter config
            adapter_config = AdapterConfig.load(
                adapter_args.adapter_config,
                non_linearity=adapter_args.adapter_non_linearity,
                reduction_factor=adapter_args.adapter_reduction_factor,
            )
            # load a pre-trained from Hub if specified
            if adapter_args.load_adapter:
                model.load_adapter(
                    adapter_args.load_adapter,
                    AdapterType.text_task,
                    config=adapter_config,
                    load_as=task_name,
                )
            # otherwise, add a fresh adapter
            else:
                model.add_adapter(task_name,
                                  AdapterType.text_task,
                                  config=adapter_config)
        # optionally load a pre-trained language adapter
        if adapter_args.load_lang_adapter:
            # resolve the language adapter config
            lang_adapter_config = AdapterConfig.load(
                adapter_args.lang_adapter_config,
                non_linearity=adapter_args.lang_adapter_non_linearity,
                reduction_factor=adapter_args.lang_adapter_reduction_factor,
            )
            # load the language adapter from Hub
            lang_adapter_name = model.load_adapter(
                adapter_args.load_lang_adapter,
                AdapterType.text_lang,
                config=lang_adapter_config,
                load_as=adapter_args.language,
            )
        else:
            lang_adapter_name = None
        # Freeze all model weights except of those of this adapter
        model.train_adapter([task_name])
        # Set the adapters to be used in every forward pass
        if lang_adapter_name:
            model.set_active_adapters([lang_adapter_name, task_name])
        else:
            model.set_active_adapters([task_name])
    else:
        if adapter_args.load_adapter or adapter_args.load_lang_adapter:
            raise ValueError(
                "Adapters can only be loaded in adapters training mode."
                "Use --train_adapter to enable adapter training")

    # Preprocessing the dataset
    # Padding strategy
    padding = "max_length" if data_args.pad_to_max_length else False

    # Tokenize all texts and align the labels with them.
    def tokenize_and_align_labels(examples):
        tokenized_inputs = tokenizer(
            examples[text_column_name],
            padding=padding,
            truncation=True,
            # We use this argument because the texts in our dataset are lists of words (with a label for each word).
            is_split_into_words=True,
            return_offsets_mapping=True,
        )
        offset_mappings = tokenized_inputs.pop("offset_mapping")
        labels = []
        for label, offset_mapping in zip(examples[label_column_name],
                                         offset_mappings):
            label_index = 0
            current_label = -100
            label_ids = []
            for offset in offset_mapping:
                # We set the label for the first token of each word. Special characters will have an offset of (0, 0)
                # so the test ignores them.
                if offset[0] == 0 and offset[1] != 0:
                    current_label = label_to_id[label[label_index]]
                    label_index += 1
                    label_ids.append(current_label)
                # For special tokens, we set the label to -100 so it's automatically ignored in the loss function.
                elif offset[0] == 0 and offset[1] == 0:
                    label_ids.append(-100)
                # For the other tokens in a word, we set the label to either the current label or -100, depending on
                # the label_all_tokens flag.
                else:
                    label_ids.append(
                        current_label if data_args.label_all_tokens else -100)

            labels.append(label_ids)
        tokenized_inputs["labels"] = labels
        return tokenized_inputs

    tokenized_datasets = datasets.map(
        tokenize_and_align_labels,
        batched=True,
        num_proc=data_args.preprocessing_num_workers,
        load_from_cache_file=not data_args.overwrite_cache,
    )

    # Data collator
    data_collator = DataCollatorForTokenClassification(tokenizer)

    # Metrics
    def compute_metrics(p):
        predictions, labels = p
        predictions = np.argmax(predictions, axis=2)

        # Remove ignored index (special tokens)
        true_predictions = [[
            label_list[p] for (p, l) in zip(prediction, label) if l != -100
        ] for prediction, label in zip(predictions, labels)]
        true_labels = [[
            label_list[l] for (p, l) in zip(prediction, label) if l != -100
        ] for prediction, label in zip(predictions, labels)]

        return {
            "accuracy_score": accuracy_score(true_labels, true_predictions),
            "precision": precision_score(true_labels, true_predictions),
            "recall": recall_score(true_labels, true_predictions),
            "f1": f1_score(true_labels, true_predictions),
        }

    # Initialize our Trainer
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=tokenized_datasets["train"]
        if training_args.do_train else None,
        eval_dataset=tokenized_datasets["validation"]
        if training_args.do_eval else None,
        tokenizer=tokenizer,
        data_collator=data_collator,
        compute_metrics=compute_metrics,
        do_save_full_model=not adapter_args.train_adapter,
        do_save_adapters=adapter_args.train_adapter,
    )

    # Training
    if training_args.do_train:
        trainer.train(model_path=model_args.model_name_or_path if os.path.
                      isdir(model_args.model_name_or_path) else None)
        trainer.save_model()  # Saves the tokenizer too for easy upload

    # Evaluation
    results = {}
    if training_args.do_eval:
        logger.info("*** Evaluate ***")

        results = trainer.evaluate()

        output_eval_file = os.path.join(training_args.output_dir,
                                        "eval_results_ner.txt")
        if trainer.is_world_process_zero():
            with open(output_eval_file, "w") as writer:
                logger.info("***** Eval results *****")
                for key, value in results.items():
                    logger.info(f"  {key} = {value}")
                    writer.write(f"{key} = {value}\n")

    # Predict
    if training_args.do_predict:
        logger.info("*** Predict ***")

        test_dataset = datasets["test"]
        predictions, labels, metrics = trainer.predict(test_dataset)
        predictions = np.argmax(predictions, axis=2)

        # Remove ignored index (special tokens)
        true_predictions = [[
            label_list[p] for (p, l) in zip(prediction, label) if l != -100
        ] for prediction, label in zip(predictions, labels)]

        output_test_results_file = os.path.join(training_args.output_dir,
                                                "test_results.txt")
        if trainer.is_world_master():
            with open(output_test_results_file, "w") as writer:
                for key, value in metrics.items():
                    logger.info(f"  {key} = {value}")
                    writer.write(f"{key} = {value}\n")

        # Save predictions
        output_test_predictions_file = os.path.join(training_args.output_dir,
                                                    "test_predictions.txt")
        if trainer.is_world_master():
            with open(output_test_predictions_file, "w") as writer:
                for prediction in true_predictions:
                    writer.write(" ".join(prediction) + "\n")

    return results
示例#27
0
 def set_attr(config: AdapterConfig):
     config.ln_before = True
示例#28
0
def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments,
                               TrainingArguments, AdapterArguments))
    model_args, data_args, training_args, adapter_args = parser.parse_args_into_dataclasses(
    )

    if data_args.eval_data_file is None and training_args.do_eval:
        raise ValueError(
            "Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file "
            "or remove the --do_eval argument.")

    if (os.path.exists(training_args.output_dir)
            and os.listdir(training_args.output_dir) and training_args.do_train
            and not training_args.overwrite_output_dir):
        raise ValueError(
            f"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome."
        )

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO
        if training_args.local_rank in [-1, 0] else logging.WARN,
    )
    logger.warning(
        "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
        training_args.local_rank,
        training_args.device,
        training_args.n_gpu,
        bool(training_args.local_rank != -1),
        training_args.fp16,
    )
    logger.info("Training/evaluation parameters %s", training_args)

    # Set seed
    set_seed(training_args.seed)

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    if model_args.config_name:
        config = AutoConfig.from_pretrained(model_args.config_name,
                                            cache_dir=model_args.cache_dir)
    elif model_args.model_name_or_path:
        config = AutoConfig.from_pretrained(model_args.model_name_or_path,
                                            cache_dir=model_args.cache_dir)
    else:
        config = CONFIG_MAPPING[model_args.model_type]()
        logger.warning(
            "You are instantiating a new config instance from scratch.")

    if model_args.tokenizer_name:
        tokenizer = AutoTokenizer.from_pretrained(
            model_args.tokenizer_name, cache_dir=model_args.cache_dir)
    elif model_args.model_name_or_path:
        tokenizer = AutoTokenizer.from_pretrained(
            model_args.model_name_or_path, cache_dir=model_args.cache_dir)
    else:
        raise ValueError(
            "You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another script, save it,"
            "and load it from here, using --tokenizer_name")

    if model_args.model_name_or_path:
        model = AutoModelWithLMHead.from_pretrained(
            model_args.model_name_or_path,
            from_tf=bool(".ckpt" in model_args.model_name_or_path),
            config=config,
            cache_dir=model_args.cache_dir,
        )
    else:
        logger.info("Training new model from scratch")
        model = AutoModelWithLMHead.from_config(config)

    model.resize_token_embeddings(len(tokenizer))

    # Setup adapters
    if adapter_args.train_adapter:
        language = adapter_args.language
        if not language:
            raise ValueError(
                "--language flag must be set when training an adapter")
        # check if language adapter already exists, otherwise add it
        if language not in model.config.adapters.adapter_list(
                AdapterType.text_lang):
            # resolve the adapter config
            adapter_config = AdapterConfig.load(
                adapter_args.adapter_config,
                non_linearity=adapter_args.adapter_non_linearity,
                reduction_factor=adapter_args.adapter_reduction_factor,
            )
            # load a pre-trained from Hub if specified
            if adapter_args.load_adapter:
                model.load_adapter(
                    adapter_args.load_adapter,
                    AdapterType.text_lang,
                    config=adapter_config,
                    load_as=language,
                )
            # otherwise, add a fresh adapter
            else:
                model.add_adapter(language,
                                  AdapterType.text_lang,
                                  config=adapter_config)
        # Freeze all model weights except of those of this adapter & use this adapter in every forward pass
        model.train_adapter([language])

    if config.model_type in ["bert", "roberta", "distilbert", "camembert"
                             ] and not data_args.mlm:
        raise ValueError(
            "BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the"
            "--mlm flag (masked language modeling).")

    if data_args.block_size <= 0:
        data_args.block_size = tokenizer.max_len
        # Our input block size will be the max possible for the model
    else:
        data_args.block_size = min(data_args.block_size, tokenizer.max_len)

    # Get datasets

    train_dataset = (get_dataset(
        data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir)
                     if training_args.do_train else None)
    eval_dataset = (get_dataset(data_args,
                                tokenizer=tokenizer,
                                evaluate=True,
                                cache_dir=model_args.cache_dir)
                    if training_args.do_eval else None)
    if config.model_type == "xlnet":
        data_collator = DataCollatorForPermutationLanguageModeling(
            tokenizer=tokenizer,
            plm_probability=data_args.plm_probability,
            max_span_length=data_args.max_span_length,
        )
    else:
        data_collator = DataCollatorForLanguageModeling(
            tokenizer=tokenizer,
            mlm=data_args.mlm,
            mlm_probability=data_args.mlm_probability)

    # Initialize our Trainer
    trainer = Trainer(
        model=model,
        args=training_args,
        data_collator=data_collator,
        train_dataset=train_dataset,
        eval_dataset=eval_dataset,
        prediction_loss_only=True,
        do_save_full_model=not adapter_args.train_adapter,
        do_save_adapters=adapter_args.train_adapter,
    )

    # Training
    if training_args.do_train:
        model_path = (model_args.model_name_or_path
                      if model_args.model_name_or_path is not None
                      and os.path.isdir(model_args.model_name_or_path) else
                      None)
        trainer.train(model_path=model_path)
        trainer.save_model()
        # For convenience, we also re-save the tokenizer to the same directory,
        # so that you can share your model easily on huggingface.co/models =)
        if trainer.is_world_master():
            tokenizer.save_pretrained(training_args.output_dir)

    # Evaluation
    results = {}
    if training_args.do_eval:
        logger.info("*** Evaluate ***")

        eval_output = trainer.evaluate()

        perplexity = math.exp(eval_output["eval_loss"])
        result = {"perplexity": perplexity}

        output_eval_file = os.path.join(training_args.output_dir,
                                        "eval_results_lm.txt")
        if trainer.is_world_master():
            with open(output_eval_file, "w") as writer:
                logger.info("***** Eval results *****")
                for key in sorted(result.keys()):
                    logger.info("  %s = %s", key, str(result[key]))
                    writer.write("%s = %s\n" % (key, str(result[key])))

        results.update(result)

    return results
    overwrite_cache=overwrite_tokenizer,
    mode=Split.test,
) if training_args.do_eval else None)

config = BertConfig.from_pretrained(
    model_name_or_path,
    num_labels=num_labels,
    finetuning_task=task_name,
    #cache_dir=cache_dir,
)

model = AutoModelWithHeads.from_pretrained(model_name_or_path, config=config)

#model.add_multiple_choice_head(adapter_config_name_or_path, num_choices=5)
model.add_multiple_choice_head(adapter_config_name_or_path, num_choices=5)
adapter_config = AdapterConfig.load(adapter_config_name_or_path)
#model.load_adapter("comsense/csqa@ukp", "text_task", config=config) # --> original
model.load_adapter(adapter_name_or_path, "text_task",
                   config=adapter_config)  # --> removed
model.add_apdapter("csqa", AdapterType.text_task)
model.train_adapter([adapter_name_or_path])
model.set_active_adapters([[adapter_name_or_path]])
# task adapter - only add if not existing
if task_name not in model.config.adapters.adapter_list(AdapterType.text_task):
    # add a new adapter
    model.add_adapter(task_name,
                      AdapterType.text_task,
                      config=adapter_args.adapter_config)
# enable adapter training
model.train_adapter([task_name])