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Fix saving tokenizers in DPR training + unify save and load dirs (#682)

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bogdankostic 2020-12-16 17:09:47 +01:00 committed by GitHub
parent 4c2804e38e
commit a9bcabc42d
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1 changed files with 16 additions and 10 deletions
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haystack/retriever/dense.py
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@ -240,9 +240,9 @@ class DensePassageRetriever(BaseRetriever):
grad_acc_steps: int = 1, grad_acc_steps: int = 1,
optimizer_name: str = "TransformersAdamW", optimizer_name: str = "TransformersAdamW",
optimizer_correct_bias: bool = True, optimizer_correct_bias: bool = True,
save_dir: str = "../saved_models/dpr-tutorial", save_dir: str = "../saved_models/dpr",
query_encoder_save_dir: str = "lm1", query_encoder_save_dir: str = "query_encoder",
passage_encoder_save_dir: str = "lm2" passage_encoder_save_dir: str = "passage_encoder"
): ):
""" """
train a DensePassageRetrieval model train a DensePassageRetrieval model
@ -317,20 +317,24 @@ class DensePassageRetriever(BaseRetriever):
trainer.train() trainer.train()
self.model.save(Path(save_dir), lm1_name=query_encoder_save_dir, lm2_name=passage_encoder_save_dir) self.model.save(Path(save_dir), lm1_name=query_encoder_save_dir, lm2_name=passage_encoder_save_dir)
self.processor.save(Path(save_dir)) self.query_tokenizer.save_pretrained(f"{save_dir}/{query_encoder_save_dir}")
self.passage_tokenizer.save_pretrained(f"{save_dir}/{passage_encoder_save_dir}")
def save(self, save_dir: Union[Path, str]): def save(self, save_dir: Union[Path, str], query_encoder_dir: str = "query_encoder",
passage_encoder_dir: str = "passage_encoder"):
""" """
Save DensePassageRetriever to the specified directory. Save DensePassageRetriever to the specified directory.
:param save_dir: Directory to save to. :param save_dir: Directory to save to.
:param query_encoder_dir: Directory in save_dir that contains query encoder model.
:param passage_encoder_dir: Directory in save_dir that contains passage encoder model.
:return: None :return: None
""" """
save_dir = Path(save_dir) save_dir = Path(save_dir)
self.model.save(save_dir, lm1_name="query_encoder", lm2_name="passage_encoder") self.model.save(save_dir, lm1_name=query_encoder_dir, lm2_name=passage_encoder_dir)
save_dir = str(save_dir) save_dir = str(save_dir)
self.query_tokenizer.save_pretrained(save_dir + "/query_encoder") self.query_tokenizer.save_pretrained(save_dir + f"/{query_encoder_dir}")
self.passage_tokenizer.save_pretrained(save_dir + "/passage_encoder") self.passage_tokenizer.save_pretrained(save_dir + f"/{passage_encoder_dir}")
@classmethod @classmethod
def load(cls, def load(cls,
@ -343,6 +347,8 @@ class DensePassageRetriever(BaseRetriever):
embed_title: bool = True, embed_title: bool = True,
use_fast_tokenizers: bool = True, use_fast_tokenizers: bool = True,
similarity_function: str = "dot_product", similarity_function: str = "dot_product",
query_encoder_dir: str = "query_encoder",
passage_encoder_dir: str = "passage_encoder"
): ):
""" """
Load DensePassageRetriever from the specified directory. Load DensePassageRetriever from the specified directory.
@ -351,8 +357,8 @@ class DensePassageRetriever(BaseRetriever):
load_dir = Path(load_dir) load_dir = Path(load_dir)
dpr = cls( dpr = cls(
document_store=document_store, document_store=document_store,
query_embedding_model=Path(load_dir) / "query_encoder", query_embedding_model=Path(load_dir) / query_encoder_dir,
passage_embedding_model=Path(load_dir) / "passage_encoder", passage_embedding_model=Path(load_dir) / passage_encoder_dir,
max_seq_len_query=max_seq_len_query, max_seq_len_query=max_seq_len_query,
max_seq_len_passage=max_seq_len_passage, max_seq_len_passage=max_seq_len_passage,
use_gpu=use_gpu, use_gpu=use_gpu,