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Add `dataset_revision` argument to `RagConfig` (#29610) 

* add arg

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar 2024-03-14 16:48:11 +01:00 committed by GitHub
parent 956f44f11a
commit 2cc3cc835f
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GPG Key ID: B5690EEEBB952194
4 changed files with 34 additions and 8 deletions
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2
src/transformers/models/rag/configuration_rag.py
View File

@ -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

10
src/transformers/models/rag/retrieval_rag.py
View File

@ -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

13
tests/models/rag/test_modeling_rag.py
View File

@ -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

17
tests/models/rag/test_modeling_tf_rag.py
View File

@ -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