SUMIN/HuggingFace_transformer
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[generate] do_sample default back to False (#3298)

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* change do_samples back

* None better default as boolean

* adapt do_sample to True in test example

* make style
This commit is contained in:
Patrick von Platen
2020-03-17 15:52:37 +01:00
committed by GitHub
parent 2187c49f5c
commit e8f44af5bf
6 changed files with 30 additions and 22 deletions
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7
tests/test_modeling_bart.py
View File

@@ -285,7 +285,12 @@ class BartHeadTests(unittest.TestCase):
max_length = 5
new_input_ids = lm_model.generate(
input_ids.clone(), num_return_sequences=1, num_beams=2, no_repeat_ngram_size=3, max_length=max_length
input_ids.clone(),
do_sample=True,
num_return_sequences=1,
num_beams=2,
no_repeat_ngram_size=3,
max_length=max_length,
)
self.assertEqual(new_input_ids.shape, (input_ids.shape[0], max_length - 1))
# TODO(SS): uneven length batches, empty inputs

16
tests/test_modeling_common.py
View File

@@ -638,16 +638,16 @@ class ModelTesterMixin:
if config.bos_token_id is None:
with self.assertRaises(AssertionError):
model.generate(max_length=5)
model.generate(do_sample=True, max_length=5)
# batch_size = 1
self._check_generated_tokens(model.generate(input_ids))
self._check_generated_tokens(model.generate(input_ids, do_sample=True))
# batch_size = 1, num_beams > 1
self._check_generated_tokens(model.generate(input_ids, num_beams=3))
self._check_generated_tokens(model.generate(input_ids, do_sample=True, num_beams=3))
else:
# batch_size = 1
self._check_generated_tokens(model.generate(max_length=5))
self._check_generated_tokens(model.generate(do_sample=True, max_length=5))
# batch_size = 1, num_beams > 1
self._check_generated_tokens(model.generate(max_length=5, num_beams=3))
self._check_generated_tokens(model.generate(do_sample=True, max_length=5, num_beams=3))
with self.assertRaises(AssertionError):
# generating multiple sequences when greedy no beam generation
@@ -659,12 +659,14 @@ class ModelTesterMixin:
model.generate(input_ids, do_sample=False, num_return_sequences=3, num_beams=2)
# batch_size > 1, sample
self._check_generated_tokens(model.generate(input_ids, num_return_sequences=3))
self._check_generated_tokens(model.generate(input_ids, do_sample=True, num_return_sequences=3))
# batch_size > 1, greedy
self._check_generated_tokens(model.generate(input_ids, do_sample=False))
# batch_size > 1, num_beams > 1, sample
self._check_generated_tokens(model.generate(input_ids, num_beams=3, num_return_sequences=3,))
self._check_generated_tokens(
model.generate(input_ids, do_sample=True, num_beams=3, num_return_sequences=3,)
)
# batch_size > 1, num_beams > 1, greedy
self._check_generated_tokens(
model.generate(input_ids, do_sample=False, num_beams=3, num_return_sequences=3)

16
tests/test_modeling_tf_common.py
View File

@@ -422,16 +422,16 @@ class TFModelTesterMixin:
if config.bos_token_id is None:
with self.assertRaises(AssertionError):
model.generate(max_length=5)
model.generate(do_sample=True, max_length=5)
# batch_size = 1
self._check_generated_tokens(model.generate(input_ids))
self._check_generated_tokens(model.generate(input_ids, do_sample=True))
# batch_size = 1, num_beams > 1
self._check_generated_tokens(model.generate(input_ids, num_beams=3))
self._check_generated_tokens(model.generate(input_ids, do_sample=True, num_beams=3))
else:
# batch_size = 1
self._check_generated_tokens(model.generate(max_length=5))
self._check_generated_tokens(model.generate(do_sample=True, max_length=5))
# batch_size = 1, num_beams > 1
self._check_generated_tokens(model.generate(max_length=5, num_beams=3))
self._check_generated_tokens(model.generate(do_sample=True, max_length=5, num_beams=3))
with self.assertRaises(AssertionError):
# generating multiple sequences when greedy no beam generation
@@ -443,12 +443,14 @@ class TFModelTesterMixin:
model.generate(input_ids, do_sample=False, num_return_sequences=3, num_beams=2)
# batch_size > 1, sample
self._check_generated_tokens(model.generate(input_ids, num_return_sequences=3))
self._check_generated_tokens(model.generate(input_ids, do_sample=True, num_return_sequences=3))
# batch_size > 1, greedy
self._check_generated_tokens(model.generate(input_ids, do_sample=False))
# batch_size > 1, num_beams > 1, sample
self._check_generated_tokens(model.generate(input_ids, num_beams=3, num_return_sequences=3,))
self._check_generated_tokens(
model.generate(input_ids, do_sample=True, num_beams=3, num_return_sequences=3,)
)
# batch_size > 1, num_beams > 1, greedy
self._check_generated_tokens(
model.generate(input_ids, do_sample=False, num_beams=3, num_return_sequences=3)