Add dataset_revision argument to RagConfig (#29610)

* add arg

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

src/transformers/models/rag/configuration_rag.py

@@ -111,6 +111,7 @@ class RagConfig(PretrainedConfig):
         output_retrieved=False,
         use_cache=True,
         forced_eos_token_id=None,
+        dataset_revision=None,
         **kwargs,
     ):
         super().__init__(
@@ -156,6 +157,7 @@ class RagConfig(PretrainedConfig):
         self.passages_path = passages_path
         self.index_path = index_path
         self.use_dummy_dataset = use_dummy_dataset
+        self.dataset_revision = dataset_revision
 
         self.output_retrieved = output_retrieved
 

src/transformers/models/rag/retrieval_rag.py

@@ -266,6 +266,7 @@ class CanonicalHFIndex(HFIndexBase):
         index_name: Optional[str] = None,
         index_path: Optional[str] = None,
         use_dummy_dataset=False,
+        dataset_revision=None,
     ):
         if int(index_path is None) + int(index_name is None) != 1:
             raise ValueError("Please provide `index_name` or `index_path`.")
@@ -274,9 +275,14 @@ class CanonicalHFIndex(HFIndexBase):
         self.index_name = index_name
         self.index_path = index_path
         self.use_dummy_dataset = use_dummy_dataset
+        self.dataset_revision = dataset_revision
         logger.info(f"Loading passages from {self.dataset_name}")
         dataset = load_dataset(
-            self.dataset_name, with_index=False, split=self.dataset_split, dummy=self.use_dummy_dataset
+            self.dataset_name,
+            with_index=False,
+            split=self.dataset_split,
+            dummy=self.use_dummy_dataset,
+            revision=dataset_revision,
         )
         super().__init__(vector_size, dataset, index_initialized=False)
 
@@ -293,6 +299,7 @@ class CanonicalHFIndex(HFIndexBase):
                 split=self.dataset_split,
                 index_name=self.index_name,
                 dummy=self.use_dummy_dataset,
+                revision=self.dataset_revision,
             )
             self.dataset.set_format("numpy", columns=["embeddings"], output_all_columns=True)
         self._index_initialized = True
@@ -427,6 +434,7 @@ class RagRetriever:
                 index_name=config.index_name,
                 index_path=config.index_path,
                 use_dummy_dataset=config.use_dummy_dataset,
+                dataset_revision=config.dataset_revision,
             )
 
     @classmethod

tests/models/rag/test_modeling_rag.py

@@ -730,6 +730,7 @@ class RagModelIntegrationTests(unittest.TestCase):
             use_dummy_dataset=True,
             retrieval_vector_size=768,
             retrieval_batch_size=8,
+            dataset_revision="b24a417",
         )
 
     @slow
@@ -905,7 +906,7 @@ class RagModelIntegrationTests(unittest.TestCase):
     def test_rag_sequence_generate_batch(self):
         tokenizer = RagTokenizer.from_pretrained("facebook/rag-sequence-nq")
         retriever = RagRetriever.from_pretrained(
-            "facebook/rag-sequence-nq", index_name="exact", use_dummy_dataset=True
+            "facebook/rag-sequence-nq", index_name="exact", use_dummy_dataset=True, dataset_revision="b24a417"
         )
         rag_sequence = RagSequenceForGeneration.from_pretrained("facebook/rag-sequence-nq", retriever=retriever).to(
             torch_device
@@ -944,7 +945,10 @@ class RagModelIntegrationTests(unittest.TestCase):
     def test_rag_sequence_generate_batch_from_context_input_ids(self):
         tokenizer = RagTokenizer.from_pretrained("facebook/rag-sequence-nq")
         retriever = RagRetriever.from_pretrained(
-            "facebook/rag-sequence-nq", index_name="exact", use_dummy_dataset=True
+            "facebook/rag-sequence-nq",
+            index_name="exact",
+            use_dummy_dataset=True,
+            dataset_revision="b24a417",
         )
         rag_sequence = RagSequenceForGeneration.from_pretrained("facebook/rag-sequence-nq", retriever=retriever).to(
             torch_device
@@ -993,7 +997,9 @@ class RagModelIntegrationTests(unittest.TestCase):
     @slow
     def test_rag_token_generate_batch(self):
         tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-nq")
-        retriever = RagRetriever.from_pretrained("facebook/rag-token-nq", index_name="exact", use_dummy_dataset=True)
+        retriever = RagRetriever.from_pretrained(
+            "facebook/rag-token-nq", index_name="exact", use_dummy_dataset=True, dataset_revision="b24a417"
+        )
         rag_token = RagTokenForGeneration.from_pretrained("facebook/rag-token-nq", retriever=retriever).to(
             torch_device
         )
@@ -1063,6 +1069,7 @@ class RagModelSaveLoadTests(unittest.TestCase):
             use_dummy_dataset=True,
             retrieval_vector_size=768,
             retrieval_batch_size=8,
+            dataset_revision="b24a417",
         )
 
     @slow

tests/models/rag/test_modeling_tf_rag.py

@@ -590,6 +590,7 @@ class TFRagModelIntegrationTests(unittest.TestCase):
             use_dummy_dataset=True,
             retrieval_vector_size=768,
             retrieval_batch_size=8,
+            dataset_revision="b24a417",
         )
 
     @slow
@@ -794,7 +795,9 @@ class TFRagModelIntegrationTests(unittest.TestCase):
     @slow
     def test_rag_token_greedy_search(self):
         tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-nq")
-        retriever = RagRetriever.from_pretrained("facebook/rag-token-nq", index_name="exact", use_dummy_dataset=True)
+        retriever = RagRetriever.from_pretrained(
+            "facebook/rag-token-nq", index_name="exact", use_dummy_dataset=True, dataset_revision="b24a417"
+        )
         rag_token = TFRagTokenForGeneration.from_pretrained("facebook/rag-token-nq", retriever=retriever)
 
         # check first two questions
@@ -828,7 +831,9 @@ class TFRagModelIntegrationTests(unittest.TestCase):
     def test_rag_token_generate_batch(self):
         # NOTE: gold labels comes from num_beam=4, so this is effectively beam-search test
         tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-nq")
-        retriever = RagRetriever.from_pretrained("facebook/rag-token-nq", index_name="exact", use_dummy_dataset=True)
+        retriever = RagRetriever.from_pretrained(
+            "facebook/rag-token-nq", index_name="exact", use_dummy_dataset=True, dataset_revision="b24a417"
+        )
         rag_token = TFRagTokenForGeneration.from_pretrained("facebook/rag-token-nq", retriever=retriever)
 
         input_dict = tokenizer(
@@ -871,7 +876,10 @@ class TFRagModelIntegrationTests(unittest.TestCase):
     def test_rag_sequence_generate_batch(self):
         tokenizer = RagTokenizer.from_pretrained("facebook/rag-sequence-nq")
         retriever = RagRetriever.from_pretrained(
-            "facebook/rag-sequence-nq", index_name="exact", use_dummy_dataset=True
+            "facebook/rag-sequence-nq",
+            index_name="exact",
+            use_dummy_dataset=True,
+            dataset_revision="b24a417",
         )
         rag_sequence = TFRagSequenceForGeneration.from_pretrained("facebook/rag-sequence-nq", retriever=retriever)
 
@@ -908,7 +916,7 @@ class TFRagModelIntegrationTests(unittest.TestCase):
     def test_rag_sequence_generate_batch_from_context_input_ids(self):
         tokenizer = RagTokenizer.from_pretrained("facebook/rag-sequence-nq")
         retriever = RagRetriever.from_pretrained(
-            "facebook/rag-sequence-nq", index_name="exact", use_dummy_dataset=True
+            "facebook/rag-sequence-nq", index_name="exact", use_dummy_dataset=True, dataset_revision="b24a417"
         )
         rag_sequence = TFRagSequenceForGeneration.from_pretrained("facebook/rag-sequence-nq", retriever=retriever)
         input_dict = tokenizer(
@@ -976,6 +984,7 @@ class TFRagModelSaveLoadTests(unittest.TestCase):
             use_dummy_dataset=True,
             retrieval_vector_size=768,
             retrieval_batch_size=8,
+            dataset_revision="b24a417",
         )
 
     @slow