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* Add type annotations in QuestionAnsweringHead * Fix test by increasing max_seq_len * Add SampleBasket type annotation * Remove prediction head param from adaptive model init * Add type ignore for AdaptiveModel init * Fix and rename tests * Adjust folder structure Co-authored-by: Julian Risch <julian.risch@deepset.ai>
82 lines
2.5 KiB
Python
82 lines
2.5 KiB
Python
import logging
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from pathlib import Path
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from haystack.modeling.data_handler.data_silo import DataSilo
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from haystack.modeling.data_handler.processor import SquadProcessor
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from haystack.modeling.model.adaptive_model import AdaptiveModel
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from haystack.modeling.model.language_model import LanguageModel
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from haystack.modeling.model.optimization import initialize_optimizer
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from haystack.modeling.model.prediction_head import QuestionAnsweringHead
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from haystack.modeling.model.tokenization import Tokenizer
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from haystack.modeling.training.base import Trainer
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from haystack.modeling.utils import set_all_seeds, initialize_device_settings
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def test_training(caplog=None):
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if caplog:
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caplog.set_level(logging.CRITICAL)
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set_all_seeds(seed=42)
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device, n_gpu = initialize_device_settings(use_cuda=False)
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batch_size = 2
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n_epochs = 1
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evaluate_every = 4
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base_LM_model = "distilbert-base-uncased"
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tokenizer = Tokenizer.load(
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pretrained_model_name_or_path=base_LM_model,
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do_lower_case=True,
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use_fast=True # TODO parametrize this to test slow as well
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)
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label_list = ["start_token", "end_token"]
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processor = SquadProcessor(
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tokenizer=tokenizer,
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max_seq_len=256,
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doc_stride=10,
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max_query_length=6,
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train_filename="train-sample.json",
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dev_filename="dev-sample.json",
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test_filename=None,
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data_dir=Path("samples/qa"),
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label_list=label_list,
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metric="squad"
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)
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data_silo = DataSilo(processor=processor, batch_size=batch_size, max_processes=1)
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language_model = LanguageModel.load(base_LM_model)
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prediction_head = QuestionAnsweringHead()
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model = AdaptiveModel(
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language_model=language_model,
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prediction_heads=[prediction_head],
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embeds_dropout_prob=0.1,
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lm_output_types=["per_token"],
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device=device,
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)
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model, optimizer, lr_schedule = initialize_optimizer(
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model=model,
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learning_rate=2e-5,
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# optimizer_opts={'name': 'AdamW', 'lr': 2E-05},
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n_batches=len(data_silo.loaders["train"]),
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n_epochs=n_epochs,
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device=device
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)
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trainer = Trainer(
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model=model,
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optimizer=optimizer,
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data_silo=data_silo,
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epochs=n_epochs,
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n_gpu=n_gpu,
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lr_schedule=lr_schedule,
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evaluate_every=evaluate_every,
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device=device
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)
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trainer.train()
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assert type(model) == AdaptiveModel
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assert type(processor) == SquadProcessor
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if __name__ == "__main__":
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test_training()
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