class DensePassageRetriever(BaseRetriever):
"""
Retriever that uses a bi-encoder (one transformer for query, one transformer for passage).
See the original paper for more details:
Karpukhin, Vladimir, et al. (2020): "Dense Passage Retrieval for Open-Domain Question Answering."
(https://arxiv.org/abs/2004.04906).
"""
def __init__(self,
document_store: BaseDocumentStore,
query_embedding_model: Union[
Path,
str] = "facebook/dpr-question_encoder-single-nq-base",
passage_embedding_model: Union[
Path, str] = "facebook/dpr-ctx_encoder-single-nq-base",
max_seq_len_query: int = 64,
max_seq_len_passage: int = 256,
use_gpu: bool = True,
batch_size: int = 16,
embed_title: bool = True,
use_fast_tokenizers: bool = True,
similarity_function: str = "dot_product"):
"""
Init the Retriever incl. the two encoder models from a local or remote model checkpoint.
The checkpoint format matches huggingface transformers' model format
**Example:**
```python
| # remote model from FAIR
| DensePassageRetriever(document_store=your_doc_store,
| query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
| passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base")
| # or from local path
| DensePassageRetriever(document_store=your_doc_store,
| query_embedding_model="model_directory/question-encoder",
| passage_embedding_model="model_directory/context-encoder")
```
:param document_store: An instance of DocumentStore from which to retrieve documents.
:param query_embedding_model: Local path or remote name of question encoder checkpoint. The format equals the
one used by hugging-face transformers' modelhub models
Currently available remote names: ``"facebook/dpr-question_encoder-single-nq-base"``
:param passage_embedding_model: Local path or remote name of passage encoder checkpoint. The format equals the
one used by hugging-face transformers' modelhub models
Currently available remote names: ``"facebook/dpr-ctx_encoder-single-nq-base"``
:param max_seq_len_query: Longest length of each query sequence. Maximum number of tokens for the query text. Longer ones will be cut down."
:param max_seq_len_passage: Longest length of each passage/context sequence. Maximum number of tokens for the passage text. Longer ones will be cut down."
:param use_gpu: Whether to use gpu or not
:param batch_size: Number of questions or passages to encode at once
:param embed_title: Whether to concatenate title and passage to a text pair that is then used to create the embedding.
This is the approach used in the original paper and is likely to improve performance if your
titles contain meaningful information for retrieval (topic, entities etc.) .
The title is expected to be present in doc.meta["name"] and can be supplied in the documents
before writing them to the DocumentStore like this:
{"text": "my text", "meta": {"name": "my title"}}.
"""
self.document_store = document_store
self.batch_size = batch_size
self.max_seq_len_passage = max_seq_len_passage
self.max_seq_len_query = max_seq_len_query
if use_gpu and torch.cuda.is_available():
self.device = torch.device("cuda")
else:
self.device = torch.device("cpu")
self.embed_title = embed_title
# Init & Load Encoders
self.query_tokenizer = Tokenizer.load(
pretrained_model_name_or_path=query_embedding_model,
do_lower_case=True,
use_fast=use_fast_tokenizers)
self.query_encoder = LanguageModel.load(
pretrained_model_name_or_path=query_embedding_model,
language_model_class="DPRQuestionEncoder")
self.passage_tokenizer = Tokenizer.load(
pretrained_model_name_or_path=passage_embedding_model,
do_lower_case=True,
use_fast=use_fast_tokenizers)
self.passage_encoder = LanguageModel.load(
pretrained_model_name_or_path=passage_embedding_model,
language_model_class="DPRContextEncoder")
self.processor = TextSimilarityProcessor(
tokenizer=self.query_tokenizer,
passage_tokenizer=self.passage_tokenizer,
max_seq_len_passage=self.max_seq_len_passage,
max_seq_len_query=self.max_seq_len_query,
label_list=["hard_negative", "positive"],
metric="text_similarity_metric",
embed_title=self.embed_title,
num_hard_negatives=0,
num_negatives=0)
prediction_head = TextSimilarityHead(
similarity_function=similarity_function)
self.model = BiAdaptiveModel(
language_model1=self.query_encoder,
language_model2=self.passage_encoder,
prediction_heads=[prediction_head],
embeds_dropout_prob=0.1,
lm1_output_types=["per_sequence"],
lm2_output_types=["per_sequence"],
device=self.device,
)
self.model.connect_heads_with_processor(self.processor.tasks,
require_labels=False)
def retrieve(self,
query: str,
filters: dict = None,
top_k: int = 10,
index: str = None) -> List[Document]:
if index is None:
index = self.document_store.index
query_emb = self.embed_queries(texts=[query])
documents = self.document_store.query_by_embedding(
query_emb=query_emb[0], top_k=top_k, filters=filters, index=index)
return documents
def _get_predictions(self, dicts, tokenizer):
"""
Feed a preprocessed dataset to the model and get the actual predictions (forward pass + formatting).
:param dicts: list of dictionaries
examples:[{'query': "where is florida?"}, {'query': "who wrote lord of the rings?"}, ...]
[{'passages': [{
"title": 'Big Little Lies (TV series)',
"text": 'series garnered several accolades. It received..',
"label": 'positive',
"external_id": '18768923'},
{"title": 'Framlingham Castle',
"text": 'Castle on the Hill "Castle on the Hill" is a song by English..',
"label": 'positive',
"external_id": '19930582'}, ...]
:return: dictionary of embeddings for "passages" and "query"
"""
dataset, tensor_names, baskets = self.processor.dataset_from_dicts(
dicts, indices=[i for i in range(len(dicts))], return_baskets=True)
data_loader = NamedDataLoader(dataset=dataset,
sampler=SequentialSampler(dataset),
batch_size=self.batch_size,
tensor_names=tensor_names)
all_embeddings = {"query": [], "passages": []}
self.model.eval()
for i, batch in enumerate(
tqdm(data_loader,
desc=f"Inferencing Samples",
unit=" Batches",
disable=False)):
batch = {key: batch[key].to(self.device) for key in batch}
# get logits
with torch.no_grad():
query_embeddings, passage_embeddings = self.model.forward(
**batch)[0]
if query_embeddings is not None:
all_embeddings["query"].append(
query_embeddings.cpu().numpy())
if passage_embeddings is not None:
all_embeddings["passages"].append(
passage_embeddings.cpu().numpy())
if all_embeddings["passages"]:
all_embeddings["passages"] = np.concatenate(
all_embeddings["passages"])
if all_embeddings["query"]:
all_embeddings["query"] = np.concatenate(all_embeddings["query"])
return all_embeddings
def embed_queries(self, texts: List[str]) -> List[np.array]:
"""
Create embeddings for a list of queries using the query encoder
:param texts: Queries to embed
:return: Embeddings, one per input queries
"""
queries = [{'query': q} for q in texts]
result = self._get_predictions(queries, self.query_tokenizer)["query"]
return result
def embed_passages(self, docs: List[Document]) -> List[np.array]:
"""
Create embeddings for a list of passages using the passage encoder
:param docs: List of Document objects used to represent documents / passages in a standardized way within Haystack.
:return: Embeddings of documents / passages shape (batch_size, embedding_dim)
"""
passages = [{
'passages': [{
"title":
d.meta["name"] if d.meta and "name" in d.meta else "",
"text":
d.text,
"label":
d.meta["label"]
if d.meta and "label" in d.meta else "positive",
"external_id":
d.id
}]
} for d in docs]
embeddings = self._get_predictions(passages,
self.passage_tokenizer)["passages"]
return embeddings
def train(self,
data_dir: str,
train_filename: str,
dev_filename: str = None,
test_filename: str = None,
batch_size: int = 2,
embed_title: bool = True,
num_hard_negatives: int = 1,
num_negatives: int = 0,
n_epochs: int = 3,
evaluate_every: int = 1000,
n_gpu: int = 1,
learning_rate: float = 1e-5,
epsilon: float = 1e-08,
weight_decay: float = 0.0,
num_warmup_steps: int = 100,
grad_acc_steps: int = 1,
optimizer_name: str = "TransformersAdamW",
optimizer_correct_bias: bool = True,
save_dir: str = "../saved_models/dpr-tutorial",
query_encoder_save_dir: str = "lm1",
passage_encoder_save_dir: str = "lm2"):
"""
train a DensePassageRetrieval model
:param data_dir: Directory where training file, dev file and test file are present
:param train_filename: training filename
:param dev_filename: development set filename, file to be used by model in eval step of training
:param test_filename: test set filename, file to be used by model in test step after training
:param batch_size: total number of samples in 1 batch of data
:param embed_title: whether to concatenate passage title with each passage. The default setting in official DPR embeds passage title with the corresponding passage
:param num_hard_negatives: number of hard negative passages(passages which are very similar(high score by BM25) to query but do not contain the answer
:param num_negatives: number of negative passages(any random passage from dataset which do not contain answer to query)
:param n_epochs: number of epochs to train the model on
:param evaluate_every: number of training steps after evaluation is run
:param n_gpu: number of gpus to train on
:param learning_rate: learning rate of optimizer
:param epsilon: epsilon parameter of optimizer
:param weight_decay: weight decay parameter of optimizer
:param grad_acc_steps: number of steps to accumulate gradient over before back-propagation is done
:param optimizer_name: what optimizer to use (default: TransformersAdamW)
:param num_warmup_steps: number of warmup steps
:param optimizer_correct_bias: Whether to correct bias in optimizer
:param save_dir: directory where models are saved
:param query_encoder_save_dir: directory inside save_dir where query_encoder model files are saved
:param passage_encoder_save_dir: directory inside save_dir where passage_encoder model files are saved
"""
self.embed_title = embed_title
self.processor = TextSimilarityProcessor(
tokenizer=self.query_tokenizer,
passage_tokenizer=self.passage_tokenizer,
max_seq_len_passage=self.max_seq_len_passage,
max_seq_len_query=self.max_seq_len_query,
label_list=["hard_negative", "positive"],
metric="text_similarity_metric",
data_dir=data_dir,
train_filename=train_filename,
dev_filename=dev_filename,
test_filename=test_filename,
embed_title=self.embed_title,
num_hard_negatives=num_hard_negatives,
num_negatives=num_negatives)
self.model.connect_heads_with_processor(self.processor.tasks,
require_labels=True)
data_silo = DataSilo(processor=self.processor,
batch_size=batch_size,
distributed=False)
# 5. Create an optimizer
self.model, optimizer, lr_schedule = initialize_optimizer(
model=self.model,
learning_rate=learning_rate,
optimizer_opts={
"name": optimizer_name,
"correct_bias": optimizer_correct_bias,
"weight_decay": weight_decay,
"eps": epsilon
},
schedule_opts={
"name": "LinearWarmup",
"num_warmup_steps": num_warmup_steps
},
n_batches=len(data_silo.loaders["train"]),
n_epochs=n_epochs,
grad_acc_steps=grad_acc_steps,
device=self.device)
# 6. Feed everything to the Trainer, which keeps care of growing our model and evaluates it from time to time
trainer = Trainer(
model=self.model,
optimizer=optimizer,
data_silo=data_silo,
epochs=n_epochs,
n_gpu=n_gpu,
lr_schedule=lr_schedule,
evaluate_every=evaluate_every,
device=self.device,
)
# 7. Let it grow! Watch the tracked metrics live on the public mlflow server: https://public-mlflow.deepset.ai
trainer.train()
self.model.save(Path(save_dir),
lm1_name=query_encoder_save_dir,
lm2_name=passage_encoder_save_dir)
self.processor.save(Path(save_dir))
def save(self, save_dir: Union[Path, str]):
save_dir = Path(save_dir)
self.model.save(save_dir,
lm1_name="query_encoder",
lm2_name="passage_encoder")
save_dir = str(save_dir)
self.query_tokenizer.save_pretrained(save_dir + "/query_encoder")
self.passage_tokenizer.save_pretrained(save_dir + "/passage_encoder")
@classmethod
def load(
cls,
load_dir: Union[Path, str],
document_store: BaseDocumentStore,
max_seq_len_query: int = 64,
max_seq_len_passage: int = 256,
use_gpu: bool = True,
batch_size: int = 16,
embed_title: bool = True,
use_fast_tokenizers: bool = True,
similarity_function: str = "dot_product",
):
load_dir = Path(load_dir)
dpr = cls(document_store=document_store,
query_embedding_model=Path(load_dir) / "query_encoder",
passage_embedding_model=Path(load_dir) / "passage_encoder",
max_seq_len_query=max_seq_len_query,
max_seq_len_passage=max_seq_len_passage,
use_gpu=use_gpu,
batch_size=batch_size,
embed_title=embed_title,
use_fast_tokenizers=use_fast_tokenizers,
similarity_function=similarity_function)
return dpr
class DensePassageRetriever(BaseRetriever):
"""
Retriever that uses a bi-encoder (one transformer for query, one transformer for passage).
See the original paper for more details:
Karpukhin, Vladimir, et al. (2020): "Dense Passage Retrieval for Open-Domain Question Answering."
(https://arxiv.org/abs/2004.04906).
"""
def __init__(self,
document_store: BaseDocumentStore,
query_embedding_model: Union[Path, str] = "facebook/dpr-question_encoder-single-nq-base",
passage_embedding_model: Union[Path, str] = "facebook/dpr-ctx_encoder-single-nq-base",
single_model_path: Optional[Union[Path, str]] = None,
model_version: Optional[str] = None,
max_seq_len_query: int = 64,
max_seq_len_passage: int = 256,
top_k: int = 10,
use_gpu: bool = True,
batch_size: int = 16,
embed_title: bool = True,
use_fast_tokenizers: bool = True,
infer_tokenizer_classes: bool = False,
similarity_function: str = "dot_product",
progress_bar: bool = True
):
"""
Init the Retriever incl. the two encoder models from a local or remote model checkpoint.
The checkpoint format matches huggingface transformers' model format
**Example:**
```python
| # remote model from FAIR
| DensePassageRetriever(document_store=your_doc_store,
| query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
| passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base")
| # or from local path
| DensePassageRetriever(document_store=your_doc_store,
| query_embedding_model="model_directory/question-encoder",
| passage_embedding_model="model_directory/context-encoder")
```
:param document_store: An instance of DocumentStore from which to retrieve documents.
:param query_embedding_model: Local path or remote name of question encoder checkpoint. The format equals the
one used by hugging-face transformers' modelhub models
Currently available remote names: ``"facebook/dpr-question_encoder-single-nq-base"``
:param passage_embedding_model: Local path or remote name of passage encoder checkpoint. The format equals the
one used by hugging-face transformers' modelhub models
Currently available remote names: ``"facebook/dpr-ctx_encoder-single-nq-base"``
:param single_model_path: Local path or remote name of a query and passage embedder in one single model. Those
models are typically trained within FARM.
Currently available remote names: TODO add FARM DPR model to HF modelhub
:param model_version: The version of model to use from the HuggingFace model hub. Can be tag name, branch name, or commit hash.
:param max_seq_len_query: Longest length of each query sequence. Maximum number of tokens for the query text. Longer ones will be cut down."
:param max_seq_len_passage: Longest length of each passage/context sequence. Maximum number of tokens for the passage text. Longer ones will be cut down."
:param top_k: How many documents to return per query.
:param use_gpu: Whether to use gpu or not
:param batch_size: Number of questions or passages to encode at once
:param embed_title: Whether to concatenate title and passage to a text pair that is then used to create the embedding.
This is the approach used in the original paper and is likely to improve performance if your
titles contain meaningful information for retrieval (topic, entities etc.) .
The title is expected to be present in doc.meta["name"] and can be supplied in the documents
before writing them to the DocumentStore like this:
{"text": "my text", "meta": {"name": "my title"}}.
:param use_fast_tokenizers: Whether to use fast Rust tokenizers
:param infer_tokenizer_classes: Whether to infer tokenizer class from the model config / name.
If `False`, the class always loads `DPRQuestionEncoderTokenizer` and `DPRContextEncoderTokenizer`.
:param similarity_function: Which function to apply for calculating the similarity of query and passage embeddings during training.
Options: `dot_product` (Default) or `cosine`
:param progress_bar: Whether to show a tqdm progress bar or not.
Can be helpful to disable in production deployments to keep the logs clean.
"""
self.document_store = document_store
self.batch_size = batch_size
self.progress_bar = progress_bar
self.top_k = top_k
if document_store is None:
logger.warning("DensePassageRetriever initialized without a document store. "
"This is fine if you are performing DPR training. "
"Otherwise, please provide a document store in the constructor.")
elif document_store.similarity != "dot_product":
logger.warning(f"You are using a Dense Passage Retriever model with the {document_store.similarity} function. "
"We recommend you use dot_product instead. "
"This can be set when initializing the DocumentStore")
if use_gpu and torch.cuda.is_available():
self.device = torch.device("cuda")
else:
self.device = torch.device("cpu")
self.infer_tokenizer_classes = infer_tokenizer_classes
tokenizers_default_classes = {
"query": "DPRQuestionEncoderTokenizer",
"passage": "DPRContextEncoderTokenizer"
}
if self.infer_tokenizer_classes:
tokenizers_default_classes["query"] = None # type: ignore
tokenizers_default_classes["passage"] = None # type: ignore
# Init & Load Encoders
if single_model_path is None:
self.query_tokenizer = Tokenizer.load(pretrained_model_name_or_path=query_embedding_model,
revision=model_version,
do_lower_case=True,
use_fast=use_fast_tokenizers,
tokenizer_class=tokenizers_default_classes["query"])
self.query_encoder = LanguageModel.load(pretrained_model_name_or_path=query_embedding_model,
revision=model_version,
language_model_class="DPRQuestionEncoder")
self.passage_tokenizer = Tokenizer.load(pretrained_model_name_or_path=passage_embedding_model,
revision=model_version,
do_lower_case=True,
use_fast=use_fast_tokenizers,
tokenizer_class=tokenizers_default_classes["passage"])
self.passage_encoder = LanguageModel.load(pretrained_model_name_or_path=passage_embedding_model,
revision=model_version,
language_model_class="DPRContextEncoder")
self.processor = TextSimilarityProcessor(query_tokenizer=self.query_tokenizer,
passage_tokenizer=self.passage_tokenizer,
max_seq_len_passage=max_seq_len_passage,
max_seq_len_query=max_seq_len_query,
label_list=["hard_negative", "positive"],
metric="text_similarity_metric",
embed_title=embed_title,
num_hard_negatives=0,
num_positives=1)
prediction_head = TextSimilarityHead(similarity_function=similarity_function)
self.model = BiAdaptiveModel(
language_model1=self.query_encoder,
language_model2=self.passage_encoder,
prediction_heads=[prediction_head],
embeds_dropout_prob=0.1,
lm1_output_types=["per_sequence"],
lm2_output_types=["per_sequence"],
device=self.device,
)
else:
self.processor = TextSimilarityProcessor.load_from_dir(single_model_path)
self.processor.max_seq_len_passage = max_seq_len_passage
self.processor.max_seq_len_query = max_seq_len_query
self.processor.embed_title = embed_title
self.processor.num_hard_negatives = 0
self.processor.num_positives = 1 # during indexing of documents only one embedding is created
self.model = BiAdaptiveModel.load(single_model_path, device=self.device)
self.model.connect_heads_with_processor(self.processor.tasks, require_labels=False)
def retrieve(self, query: str, filters: dict = None, top_k: Optional[int] = None, index: str = None) -> List[Document]:
"""
Scan through documents in DocumentStore and return a small number documents
that are most relevant to the query.
:param query: The query
:param filters: A dictionary where the keys specify a metadata field and the value is a list of accepted values for that field
:param top_k: How many documents to return per query.
:param index: The name of the index in the DocumentStore from which to retrieve documents
"""
if top_k is None:
top_k = self.top_k
if not self.document_store:
logger.error("Cannot perform retrieve() since DensePassageRetriever initialized with document_store=None")
return []
if index is None:
index = self.document_store.index
query_emb = self.embed_queries(texts=[query])
documents = self.document_store.query_by_embedding(query_emb=query_emb[0], top_k=top_k, filters=filters, index=index)
return documents
def _get_predictions(self, dicts):
"""
Feed a preprocessed dataset to the model and get the actual predictions (forward pass + formatting).
:param dicts: list of dictionaries
examples:[{'query': "where is florida?"}, {'query': "who wrote lord of the rings?"}, ...]
[{'passages': [{
"title": 'Big Little Lies (TV series)',
"text": 'series garnered several accolades. It received..',
"label": 'positive',
"external_id": '18768923'},
{"title": 'Framlingham Castle',
"text": 'Castle on the Hill "Castle on the Hill" is a song by English..',
"label": 'positive',
"external_id": '19930582'}, ...]
:return: dictionary of embeddings for "passages" and "query"
"""
dataset, tensor_names, _, baskets = self.processor.dataset_from_dicts(
dicts, indices=[i for i in range(len(dicts))], return_baskets=True
)
data_loader = NamedDataLoader(
dataset=dataset, sampler=SequentialSampler(dataset), batch_size=self.batch_size, tensor_names=tensor_names
)
all_embeddings = {"query": [], "passages": []}
self.model.eval()
# When running evaluations etc., we don't want a progress bar for every single query
if len(dataset) == 1:
disable_tqdm=True
else:
disable_tqdm = not self.progress_bar
for i, batch in enumerate(tqdm(data_loader, desc=f"Creating Embeddings", unit=" Batches", disable=disable_tqdm)):
batch = {key: batch[key].to(self.device) for key in batch}
# get logits
with torch.no_grad():
query_embeddings, passage_embeddings = self.model.forward(**batch)[0]
if query_embeddings is not None:
all_embeddings["query"].append(query_embeddings.cpu().numpy())
if passage_embeddings is not None:
all_embeddings["passages"].append(passage_embeddings.cpu().numpy())
if all_embeddings["passages"]:
all_embeddings["passages"] = np.concatenate(all_embeddings["passages"])
if all_embeddings["query"]:
all_embeddings["query"] = np.concatenate(all_embeddings["query"])
return all_embeddings
def embed_queries(self, texts: List[str]) -> List[np.ndarray]:
"""
Create embeddings for a list of queries using the query encoder
:param texts: Queries to embed
:return: Embeddings, one per input queries
"""
queries = [{'query': q} for q in texts]
result = self._get_predictions(queries)["query"]
return result
def embed_passages(self, docs: List[Document]) -> List[np.ndarray]:
"""
Create embeddings for a list of passages using the passage encoder
:param docs: List of Document objects used to represent documents / passages in a standardized way within Haystack.
:return: Embeddings of documents / passages shape (batch_size, embedding_dim)
"""
passages = [{'passages': [{
"title": d.meta["name"] if d.meta and "name" in d.meta else "",
"text": d.text,
"label": d.meta["label"] if d.meta and "label" in d.meta else "positive",
"external_id": d.id}]
} for d in docs]
embeddings = self._get_predictions(passages)["passages"]
return embeddings
def train(self,
data_dir: str,
train_filename: str,
dev_filename: str = None,
test_filename: str = None,
max_processes: int = 128,
dev_split: float = 0,
batch_size: int = 2,
embed_title: bool = True,
num_hard_negatives: int = 1,
num_positives: int = 1,
n_epochs: int = 3,
evaluate_every: int = 1000,
n_gpu: int = 1,
learning_rate: float = 1e-5,
epsilon: float = 1e-08,
weight_decay: float = 0.0,
num_warmup_steps: int = 100,
grad_acc_steps: int = 1,
optimizer_name: str = "TransformersAdamW",
optimizer_correct_bias: bool = True,
save_dir: str = "../saved_models/dpr",
query_encoder_save_dir: str = "query_encoder",
passage_encoder_save_dir: str = "passage_encoder"
):
"""
train a DensePassageRetrieval model
:param data_dir: Directory where training file, dev file and test file are present
:param train_filename: training filename
:param dev_filename: development set filename, file to be used by model in eval step of training
:param test_filename: test set filename, file to be used by model in test step after training
:param max_processes: the maximum number of processes to spawn in the multiprocessing.Pool used in DataSilo.
It can be set to 1 to disable the use of multiprocessing or make debugging easier.
:param dev_split: The proportion of the train set that will sliced. Only works if dev_filename is set to None
:param batch_size: total number of samples in 1 batch of data
:param embed_title: whether to concatenate passage title with each passage. The default setting in official DPR embeds passage title with the corresponding passage
:param num_hard_negatives: number of hard negative passages(passages which are very similar(high score by BM25) to query but do not contain the answer
:param num_positives: number of positive passages
:param n_epochs: number of epochs to train the model on
:param evaluate_every: number of training steps after evaluation is run
:param n_gpu: number of gpus to train on
:param learning_rate: learning rate of optimizer
:param epsilon: epsilon parameter of optimizer
:param weight_decay: weight decay parameter of optimizer
:param grad_acc_steps: number of steps to accumulate gradient over before back-propagation is done
:param optimizer_name: what optimizer to use (default: TransformersAdamW)
:param num_warmup_steps: number of warmup steps
:param optimizer_correct_bias: Whether to correct bias in optimizer
:param save_dir: directory where models are saved
:param query_encoder_save_dir: directory inside save_dir where query_encoder model files are saved
:param passage_encoder_save_dir: directory inside save_dir where passage_encoder model files are saved
"""
self.processor.embed_title = embed_title
self.processor.data_dir = data_dir
self.processor.train_filename = train_filename
self.processor.dev_filename = dev_filename
self.processor.test_filename = test_filename
self.processor.dev_split = dev_split
self.processor.num_hard_negatives = num_hard_negatives
self.processor.num_positives = num_positives
self.model.connect_heads_with_processor(self.processor.tasks, require_labels=True)
data_silo = DataSilo(processor=self.processor, batch_size=batch_size, distributed=False, max_processes=max_processes)
# 5. Create an optimizer
self.model, optimizer, lr_schedule = initialize_optimizer(
model=self.model,
learning_rate=learning_rate,
optimizer_opts={"name": optimizer_name, "correct_bias": optimizer_correct_bias,
"weight_decay": weight_decay, "eps": epsilon},
schedule_opts={"name": "LinearWarmup", "num_warmup_steps": num_warmup_steps},
n_batches=len(data_silo.loaders["train"]),
n_epochs=n_epochs,
grad_acc_steps=grad_acc_steps,
device=self.device
)
# 6. Feed everything to the Trainer, which keeps care of growing our model and evaluates it from time to time
trainer = Trainer(
model=self.model,
optimizer=optimizer,
data_silo=data_silo,
epochs=n_epochs,
n_gpu=n_gpu,
lr_schedule=lr_schedule,
evaluate_every=evaluate_every,
device=self.device,
)
# 7. Let it grow! Watch the tracked metrics live on the public mlflow server: https://public-mlflow.deepset.ai
trainer.train()
self.model.save(Path(save_dir), lm1_name=query_encoder_save_dir, lm2_name=passage_encoder_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], query_encoder_dir: str = "query_encoder",
passage_encoder_dir: str = "passage_encoder"):
"""
Save DensePassageRetriever to the specified directory.
: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
"""
save_dir = Path(save_dir)
self.model.save(save_dir, lm1_name=query_encoder_dir, lm2_name=passage_encoder_dir)
save_dir = str(save_dir)
self.query_tokenizer.save_pretrained(save_dir + f"/{query_encoder_dir}")
self.passage_tokenizer.save_pretrained(save_dir + f"/{passage_encoder_dir}")
@classmethod
def load(cls,
load_dir: Union[Path, str],
document_store: BaseDocumentStore,
max_seq_len_query: int = 64,
max_seq_len_passage: int = 256,
use_gpu: bool = True,
batch_size: int = 16,
embed_title: bool = True,
use_fast_tokenizers: bool = True,
similarity_function: str = "dot_product",
query_encoder_dir: str = "query_encoder",
passage_encoder_dir: str = "passage_encoder"
):
"""
Load DensePassageRetriever from the specified directory.
"""
load_dir = Path(load_dir)
dpr = cls(
document_store=document_store,
query_embedding_model=Path(load_dir) / query_encoder_dir,
passage_embedding_model=Path(load_dir) / passage_encoder_dir,
max_seq_len_query=max_seq_len_query,
max_seq_len_passage=max_seq_len_passage,
use_gpu=use_gpu,
batch_size=batch_size,
embed_title=embed_title,
use_fast_tokenizers=use_fast_tokenizers,
similarity_function=similarity_function
)
logger.info(f"DPR model loaded from {load_dir}")
return dpr