Refactoring to remove duplicates in pyserini.encode and pyserini.search.faiss

integrations-optional/dense/test_ance.py

@@ -21,8 +21,8 @@
 import unittest
 
 from integrations.utils import clean_files, run_command, parse_score_qa, parse_score_msmarco
+from pyserini.encode import QueryEncoder
 from pyserini.search import get_topics
-from pyserini.search.faiss._searcher import QueryEncoder
 
 
 class TestAnce(unittest.TestCase):

integrations-optional/dense/test_dpr.py

@@ -22,8 +22,8 @@
 import unittest
 
 from integrations.utils import clean_files, run_command, parse_score_qa
+from pyserini.encode import QueryEncoder
 from pyserini.search import get_topics
-from pyserini.search.faiss._searcher import QueryEncoder
 
 
 class TestDpr(unittest.TestCase):

integrations-optional/dense/test_tct_colbert.py

@@ -21,8 +21,8 @@
 import unittest
 
 from integrations.utils import clean_files, run_command, parse_score
+from pyserini.encode import QueryEncoder
 from pyserini.search import get_topics
-from pyserini.search.faiss._searcher import QueryEncoder
 
 
 class TestTctColBert(unittest.TestCase):

integrations-optional/sparse/test_lucenesearcher_check_ltr_msmarco_document.py

@@ -23,7 +23,7 @@
 
 class TestLtrMsmarcoDocument(unittest.TestCase):
     def test_reranking(self):
-        if(os.path.isdir('ltr_test')):
+        if os.path.isdir('ltr_test'):
             rmtree('ltr_test')
             os.mkdir('ltr_test')
         inp = 'run.msmarco-pass-doc.bm25.txt'

integrations-optional/sparse/test_lucenesearcher_check_ltr_msmarco_passage.py

@@ -20,12 +20,10 @@
 import unittest
 from shutil import rmtree
 
-from pyserini.search.lucene import LuceneSearcher
-
 
 class TestLtrMsmarcoPassage(unittest.TestCase):
     def test_reranking(self):
-        if(os.path.isdir('ltr_test')):
+        if os.path.isdir('ltr_test'):
             rmtree('ltr_test')
             os.mkdir('ltr_test')
         inp = 'run.msmarco-passage.bm25tuned.txt'

integrations/clprf/test_trec_covid_r5.py

@@ -14,13 +14,14 @@
 # limitations under the License.
 #
 
+import gzip
+import json
 import os
 import re
 import shutil
 import unittest
-import json
-import gzip
 from random import randint
+
 from pyserini.util import download_url, download_prebuilt_index
 
 

integrations/lucenesearcher_anserini_checker.py

@@ -18,8 +18,8 @@
 import os
 from typing import List
 
-from pyserini.util import get_cache_home
 from pyserini.prebuilt_index_info import TF_INDEX_INFO
+from pyserini.util import get_cache_home
 
 
 class LuceneSearcherAnseriniMatchChecker:

integrations/sparse/test_lucenesearcher_check_irst.py

@@ -16,8 +16,8 @@
 
 import os
 import unittest
-from shutil import rmtree
 from random import randint
+from shutil import rmtree
 
 from integrations.utils import run_command, parse_score
 

integrations/sparse/test_search_pretokenized.py

@@ -17,8 +17,8 @@
 import os
 import shutil
 import unittest
-
 from random import randint
+
 from integrations.lucenesearcher_score_checker import LuceneSearcherScoreChecker
 
 

integrations/utils.py

@@ -15,8 +15,8 @@
 #
 
 import os
-import subprocess
 import shutil
+import subprocess
 
 
 def clean_files(files):

pyserini/analysis/_base.py

@@ -47,7 +47,6 @@
 
 # Wrappers around Anserini classes
 JAnalyzerUtils = autoclass('io.anserini.analysis.AnalyzerUtils')
-JDefaultEnglishAnalyzer = autoclass('io.anserini.analysis.DefaultEnglishAnalyzer')
 JTweetAnalyzer = autoclass('io.anserini.analysis.TweetAnalyzer')
 JHuggingFaceTokenizerAnalyzer = autoclass('io.anserini.analysis.HuggingFaceTokenizerAnalyzer')
 

pyserini/demo/dpr.py

@@ -18,8 +18,9 @@
 import json
 import random
 
+from pyserini.encode import DprQueryEncoder
 from pyserini.search import get_topics
-from pyserini.search.faiss import FaissSearcher, DprQueryEncoder
+from pyserini.search.faiss import FaissSearcher
 from pyserini.search.hybrid import HybridSearcher
 from pyserini.search.lucene import LuceneSearcher
 

pyserini/demo/msmarco.py

@@ -18,8 +18,9 @@
 import json
 import random
 
+from pyserini.encode import AnceQueryEncoder, TctColBertQueryEncoder
 from pyserini.search import get_topics
-from pyserini.search.faiss import FaissSearcher, TctColBertQueryEncoder, AnceQueryEncoder
+from pyserini.search.faiss import FaissSearcher
 from pyserini.search.hybrid import HybridSearcher
 from pyserini.search.lucene import LuceneSearcher
 

pyserini/dsearch.py

@@ -20,10 +20,8 @@
 import os
 import sys
 
-from pyserini.search.faiss import FaissSearcher
-from pyserini.search.faiss._searcher import TctColBertQueryEncoder, BinaryDenseSearcher
-
-__all__ = ['SimpleDenseSearcher', 'BinaryDenseSearcher', 'TctColBertQueryEncoder']
+from pyserini.encode import TctColBertQueryEncoder
+from pyserini.search.faiss import FaissSearcher, BinaryDenseFaissSearcher
 
 
 class SimpleDenseSearcher(FaissSearcher):
@@ -33,7 +31,7 @@ def __new__(cls, *args, **kwargs):
         return super().__new__(cls)
 
 
-class BinaryDenseSearcher(BinaryDenseSearcher):
+class BinaryDenseSearcher(BinaryDenseFaissSearcher):
     def __new__(cls, *args, **kwargs):
         print('pyserini.dsearch.BinaryDenseSearcher class has been deprecated, '
               'please use BinaryDenseSearcher from pyserini.search.faiss instead')

pyserini/encode/__init__.py

@@ -21,8 +21,10 @@
 from ._aggretriever import AggretrieverDocumentEncoder, AggretrieverQueryEncoder
 from ._ance import AnceEncoder, AnceDocumentEncoder, AnceQueryEncoder
 from ._auto import AutoQueryEncoder, AutoDocumentEncoder
+from ._bpr import BprQueryEncoder
 from ._cached_data import CachedDataQueryEncoder
 from ._cosdpr import CosDprEncoder, CosDprDocumentEncoder, CosDprQueryEncoder
+from ._dkrr import DkrrDprQueryEncoder
 from ._dpr import DprDocumentEncoder, DprQueryEncoder
 from ._openai import OpenAIDocumentEncoder, OpenAIQueryEncoder, OPENAI_API_RETRY_DELAY
 from ._slim import SlimQueryEncoder

pyserini/encode/_aggretriever.py

@@ -27,7 +27,8 @@
 from transformers import AutoModelForMaskedLM, AutoTokenizer, PreTrainedModel
 from pyserini.encode import DocumentEncoder, QueryEncoder
 
-class BERTAggretrieverEncoder(PreTrainedModel):
+
+class BertAggretrieverEncoder(PreTrainedModel):
     config_class = BertConfig
     base_model_prefix = 'encoder'
     load_tf_weights = None
@@ -120,7 +121,7 @@ def forward(
         return torch.cat((semantic_reps, lexical_reps), -1)
 
 
-class DistlBERTAggretrieverEncoder(BERTAggretrieverEncoder):
+class DistlBertAggretrieverEncoder(BertAggretrieverEncoder):
     config_class = DistilBertConfig
     base_model_prefix = 'encoder'
     load_tf_weights = None
@@ -130,9 +131,9 @@ class AggretrieverDocumentEncoder(DocumentEncoder):
     def __init__(self, model_name: str, tokenizer_name=None, device='cuda:0'):
         self.device = device
         if 'distilbert' in model_name.lower():
-            self.model = DistlBERTAggretrieverEncoder.from_pretrained(model_name)
+            self.model = DistlBertAggretrieverEncoder.from_pretrained(model_name)
         else:
-            self.model = BERTAggretrieverEncoder.from_pretrained(model_name)
+            self.model = BertAggretrieverEncoder.from_pretrained(model_name)
         self.model.to(self.device)
         self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name or model_name)
 
@@ -160,30 +161,33 @@ def encode(self, texts, titles=None, fp16=False,  max_length=512, **kwargs):
 
 
 class AggretrieverQueryEncoder(QueryEncoder):
-    def __init__(self, model_name: str, tokenizer_name=None, device='cuda:0'):
-        self.device = device
-        if 'distilbert' in model_name.lower():
-            self.model = DistlBERTAggretrieverEncoder.from_pretrained(model_name)
-        else:
-            self.model = BERTAggretrieverEncoder.from_pretrained(model_name)
-        self.model.to(self.device)
-        self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name or model_name)
-
-    def encode(self, texts, fp16=False,  max_length=32, **kwargs):
-        texts = [text for text in texts]
-        inputs = self.tokenizer(
-            texts,
-            max_length=max_length,
-            padding="longest",
-            truncation=True,
-            add_special_tokens=True,
-            return_tensors='pt'
-        )
-        inputs.to(self.device)
-        if fp16:
-            with autocast():
-                with torch.no_grad():
-                    outputs = self.model(**inputs)
-        else:
+    def __init__(self, encoder_dir: str = None, tokenizer_name: str = None,
+                 encoded_query_dir: str = None, device: str = 'cpu', **kwargs):
+        if encoder_dir:
+            self.device = device
+            if 'distilbert' in encoder_dir.lower():
+                self.model = DistlBertAggretrieverEncoder.from_pretrained(encoder_dir)
+            else:
+                self.model = BertAggretrieverEncoder.from_pretrained(encoder_dir)
+            self.model.to(self.device)
+            self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name or encoder_dir)
+            self.has_model = True
+        if (not self.has_model) and (not self.has_encoded_query):
+            raise Exception('Neither query encoder model nor encoded queries provided. Please provide at least one')
+
+    def encode(self, query: str,  max_length: int=32):
+        if self.has_model:
+            inputs = self.tokenizer(
+                query,
+                max_length=max_length,
+                padding="longest",
+                truncation=True,
+                add_special_tokens=True,
+                return_tensors='pt'
+            )
+            inputs.to(self.device)
             outputs = self.model(**inputs)
-        return outputs.detach().cpu().numpy()
+            embeddings = outputs.detach().cpu().numpy()
+            return embeddings.flatten()
+        else:
+            return super().encode(query)

pyserini/encode/_ance.py

@@ -14,10 +14,10 @@
 # limitations under the License.
 #
 
-from typing import Optional
+from typing import Optional, List
 
 import torch
-from transformers import PreTrainedModel, RobertaConfig, RobertaModel, RobertaTokenizer
+from transformers import PreTrainedModel, RobertaConfig, RobertaModel, RobertaTokenizer, requires_backends
 
 from pyserini.encode import DocumentEncoder, QueryEncoder
 
@@ -30,6 +30,7 @@ class AnceEncoder(PreTrainedModel):
     _keys_to_ignore_on_load_unexpected = [r'pooler', r'classifier']
 
     def __init__(self, config: RobertaConfig):
+        requires_backends(self, 'torch')
         super().__init__(config)
         self.config = config
         self.roberta = RobertaModel(config)
@@ -55,11 +56,7 @@ def init_weights(self):
         self.embeddingHead.apply(self._init_weights)
         self.norm.apply(self._init_weights)
 
-    def forward(
-            self,
-            input_ids: torch.Tensor,
-            attention_mask: Optional[torch.Tensor] = None,
-    ):
+    def forward(self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None):
         input_shape = input_ids.size()
         device = input_ids.device
         if attention_mask is None:
@@ -98,22 +95,60 @@ def encode(self, texts, titles=None,  max_length=256, **kwargs):
 
 
 class AnceQueryEncoder(QueryEncoder):
+    def __init__(self, encoder_dir: str = None, tokenizer_name: str = None,
+                 encoded_query_dir: str = None, device: str = 'cpu', **kwargs):
+        super().__init__(encoded_query_dir)
+        if encoder_dir:
+            self.device = device
+            self.model = AnceEncoder.from_pretrained(encoder_dir)
+            self.model.to(self.device)
+            self.tokenizer = RobertaTokenizer.from_pretrained(tokenizer_name or encoder_dir)
+            self.has_model = True
+            self.tokenizer.do_lower_case = True
+        if (not self.has_model) and (not self.has_encoded_query):
+            raise Exception('Neither query encoder model nor encoded queries provided. Please provide at least one')
+
+    def encode(self, query: str):
+        if self.has_model:
+            inputs = self.tokenizer(
+                [query],
+                max_length=64,
+                padding='longest',
+                truncation=True,
+                add_special_tokens=True,
+                return_tensors='pt'
+            )
+            inputs.to(self.device)
+            embeddings = self.model(inputs["input_ids"]).detach().cpu().numpy()
+            return embeddings.flatten()
+        else:
+            return super().encode(query)
+
+    def prf_encode(self, query: str):
+        if self.has_model:
+            inputs = self.tokenizer(
+                [query],
+                max_length=512,
+                padding='longest',
+                truncation=True,
+                add_special_tokens=False,
+                return_tensors='pt'
+            )
+            inputs.to(self.device)
+            embeddings = self.model(inputs["input_ids"]).detach().cpu().numpy()
+            return embeddings.flatten()
+        else:
+            return super().encode(query)
 
-    def __init__(self, model_name: str, tokenizer_name: str = None, device: str = 'cpu'):
-        self.device = device
-        self.model = AnceEncoder.from_pretrained(model_name)
-        self.model.to(self.device)
-        self.tokenizer = RobertaTokenizer.from_pretrained(tokenizer_name or model_name)
-
-    def encode(self, query: str, **kwargs):
+    def prf_batch_encode(self, query: List[str]):
         inputs = self.tokenizer(
-            [query],
-            max_length=64,
+            query,
+            max_length=512,
             padding='longest',
             truncation=True,
-            add_special_tokens=True,
+            add_special_tokens=False,
             return_tensors='pt'
         )
         inputs.to(self.device)
         embeddings = self.model(inputs["input_ids"]).detach().cpu().numpy()
-        return embeddings.flatten()
+        return embeddings