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TensorFlow tests: having from_pt set to True requires torch to be installed. (#10664)

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* TF model exists for Blenderbot 400M

* Marian

* RAG
This commit is contained in:
Lysandre Debut
2021-03-12 06:16:40 -05:00
committed by GitHub
parent 543d0549f8
commit 184ef8ecd0
3 changed files with 7 additions and 11 deletions
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2
tests/test_modeling_tf_blenderbot.py
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@@ -309,7 +309,7 @@ class TFBlenderbot400MIntegrationTests(unittest.TestCase):
@cached_property
def model(self):
model = TFAutoModelForSeq2SeqLM.from_pretrained(self.model_name, from_pt=True)
model = TFAutoModelForSeq2SeqLM.from_pretrained(self.model_name)
return model
@slow

2
tests/test_modeling_tf_marian.py
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@@ -350,7 +350,7 @@ class AbstractMarianIntegrationTest(unittest.TestCase):
@cached_property
def model(self):
warnings.simplefilter("error")
model: TFMarianMTModel = TFAutoModelForSeq2SeqLM.from_pretrained(self.model_name, from_pt=True)
model: TFMarianMTModel = TFAutoModelForSeq2SeqLM.from_pretrained(self.model_name)
assert isinstance(model, TFMarianMTModel)
c = model.config
self.assertListEqual(c.bad_words_ids, [[c.pad_token_id]])

14
tests/test_modeling_tf_rag.py
View File

@@ -562,7 +562,7 @@ class TFRagModelIntegrationTests(unittest.TestCase):
)
def token_model_nq_checkpoint(self, retriever):
return TFRagTokenForGeneration.from_pretrained("facebook/rag-token-nq", from_pt=True, retriever=retriever)
return TFRagTokenForGeneration.from_pretrained("facebook/rag-token-nq", retriever=retriever)
def get_rag_config(self):
question_encoder_config = AutoConfig.from_pretrained("facebook/dpr-question_encoder-single-nq-base")
@@ -799,7 +799,7 @@ class TFRagModelIntegrationTests(unittest.TestCase):
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)
rag_token = TFRagTokenForGeneration.from_pretrained("facebook/rag-token-nq", retriever=retriever, from_pt=True)
rag_token = TFRagTokenForGeneration.from_pretrained("facebook/rag-token-nq", retriever=retriever)
# check first two questions
input_dict = tokenizer(
@@ -833,7 +833,7 @@ class TFRagModelIntegrationTests(unittest.TestCase):
# 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)
rag_token = TFRagTokenForGeneration.from_pretrained("facebook/rag-token-nq", retriever=retriever, from_pt=True)
rag_token = TFRagTokenForGeneration.from_pretrained("facebook/rag-token-nq", retriever=retriever)
input_dict = tokenizer(
self.test_data_questions,
@@ -877,9 +877,7 @@ class TFRagModelIntegrationTests(unittest.TestCase):
retriever = RagRetriever.from_pretrained(
"facebook/rag-sequence-nq", index_name="exact", use_dummy_dataset=True
)
rag_sequence = TFRagSequenceForGeneration.from_pretrained(
"facebook/rag-sequence-nq", retriever=retriever, from_pt=True
)
rag_sequence = TFRagSequenceForGeneration.from_pretrained("facebook/rag-sequence-nq", retriever=retriever)
input_dict = tokenizer(
self.test_data_questions,
@@ -923,9 +921,7 @@ class TFRagModelIntegrationTests(unittest.TestCase):
retriever = RagRetriever.from_pretrained(
"facebook/rag-sequence-nq", index_name="exact", use_dummy_dataset=True
)
rag_sequence = TFRagSequenceForGeneration.from_pretrained(
"facebook/rag-sequence-nq", retriever=retriever, from_pt=True
)
rag_sequence = TFRagSequenceForGeneration.from_pretrained("facebook/rag-sequence-nq", retriever=retriever)
input_dict = tokenizer(
self.test_data_questions,
return_tensors="tf",