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use scale=1.0 in floats_tensor called in speech model testers (#17007)

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Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
2022-04-29 14:41:33 +02:00
committed by GitHub
parent e6f00a11d7
commit e952e049b4
12 changed files with 17 additions and 17 deletions
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2
tests/data2vec/test_modeling_data2vec_audio.py
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@@ -116,7 +116,7 @@ class Data2VecAudioModelTester:
self.adapter_output_seq_length = (self.output_seq_length - 1) // adapter_stride + 1
def prepare_config_and_inputs(self):
input_values = floats_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_values = floats_tensor([self.batch_size, self.seq_length], scale=1.0)
attention_mask = random_attention_mask([self.batch_size, self.seq_length])
config = self.get_config()

2
tests/hubert/test_modeling_hubert.py
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@@ -106,7 +106,7 @@ class HubertModelTester:
self.encoder_seq_length = self.output_seq_length
def prepare_config_and_inputs(self):
input_values = floats_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_values = floats_tensor([self.batch_size, self.seq_length], scale=1.0)
attention_mask = random_attention_mask([self.batch_size, self.seq_length])
config = self.get_config()

2
tests/perceiver/test_modeling_perceiver.py
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@@ -143,7 +143,7 @@ class PerceiverModelTester:
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
if model_class is None or model_class.__name__ == "PerceiverModel":
inputs = floats_tensor([self.batch_size, self.seq_length, config.d_model], self.vocab_size)
inputs = floats_tensor([self.batch_size, self.seq_length, config.d_model], scale=1.0)
return config, inputs, input_mask, sequence_labels, token_labels
elif model_class.__name__ in ["PerceiverForMaskedLM", "PerceiverForSequenceClassification"]:
inputs = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)

2
tests/sew/test_modeling_sew.py
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@@ -108,7 +108,7 @@ class SEWModelTester:
self.encoder_seq_length = self.output_seq_length // self.squeeze_factor
def prepare_config_and_inputs(self):
input_values = floats_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_values = floats_tensor([self.batch_size, self.seq_length], scale=1.0)
attention_mask = random_attention_mask([self.batch_size, self.seq_length])
config = self.get_config()

2
tests/sew_d/test_modeling_sew_d.py
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@@ -122,7 +122,7 @@ class SEWDModelTester:
self.encoder_seq_length = self.output_seq_length // self.squeeze_factor
def prepare_config_and_inputs(self):
input_values = floats_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_values = floats_tensor([self.batch_size, self.seq_length], scale=1.0)
attention_mask = random_attention_mask([self.batch_size, self.seq_length])
config = self.get_config()

8
tests/speech_encoder_decoder/test_modeling_flax_speech_encoder_decoder.py
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@@ -582,7 +582,7 @@ class FlaxWav2Vec2GPT2ModelTest(FlaxEncoderDecoderMixin, unittest.TestCase):
"facebook/wav2vec2-large-lv60", "gpt2-medium"
)
batch_size = 13
input_values = floats_tensor([batch_size, 512], model.config.encoder.vocab_size)
input_values = floats_tensor([batch_size, 512], scale=1.0)
attention_mask = random_attention_mask([batch_size, 512])
decoder_input_ids = ids_tensor([batch_size, 4], model.config.decoder.vocab_size)
decoder_attention_mask = random_attention_mask([batch_size, 4])
@@ -638,7 +638,7 @@ class FlaxWav2Vec2GPT2ModelTest(FlaxEncoderDecoderMixin, unittest.TestCase):
# prepare inputs
batch_size = 13
input_values = floats_tensor([batch_size, 512], fx_model.config.encoder.vocab_size)
input_values = floats_tensor([batch_size, 512], scale=1.0)
attention_mask = random_attention_mask([batch_size, 512])
decoder_input_ids = ids_tensor([batch_size, 4], fx_model.config.decoder.vocab_size)
decoder_attention_mask = random_attention_mask([batch_size, 4])
@@ -699,7 +699,7 @@ class FlaxWav2Vec2BartModelTest(FlaxEncoderDecoderMixin, unittest.TestCase):
"facebook/wav2vec2-large-lv60", "bart-large"
)
batch_size = 13
input_values = floats_tensor([batch_size, 512], model.config.encoder.vocab_size)
input_values = floats_tensor([batch_size, 512], scale=1.0)
attention_mask = random_attention_mask([batch_size, 512])
decoder_input_ids = ids_tensor([batch_size, 4], model.config.decoder.vocab_size)
decoder_attention_mask = random_attention_mask([batch_size, 4])
@@ -755,7 +755,7 @@ class FlaxWav2Vec2BartModelTest(FlaxEncoderDecoderMixin, unittest.TestCase):
# prepare inputs
batch_size = 13
input_values = floats_tensor([batch_size, 512], fx_model.config.encoder.vocab_size)
input_values = floats_tensor([batch_size, 512], scale=1.0)
attention_mask = random_attention_mask([batch_size, 512])
decoder_input_ids = ids_tensor([batch_size, 4], fx_model.config.decoder.vocab_size)
decoder_attention_mask = random_attention_mask([batch_size, 4])

4
tests/speech_encoder_decoder/test_modeling_speech_encoder_decoder.py
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@@ -425,7 +425,7 @@ class Wav2Vec2BertModelTest(EncoderDecoderMixin, unittest.TestCase):
"facebook/wav2vec2-base-960h", "bert-base-cased"
)
batch_size = 13
input_values = floats_tensor([batch_size, 512], model.encoder.config.vocab_size)
input_values = floats_tensor([batch_size, 512], scale=1.0)
attention_mask = random_attention_mask([batch_size, 512])
decoder_input_ids = ids_tensor([batch_size, 4], model.decoder.config.vocab_size)
decoder_attention_mask = random_attention_mask([batch_size, 4])
@@ -489,7 +489,7 @@ class Speech2TextBertModelTest(EncoderDecoderMixin, unittest.TestCase):
"facebook/s2t-small-librispeech-asr", "bert-base-cased"
)
batch_size = 13
input_features = floats_tensor([batch_size, 7, 80], model.encoder.config.vocab_size)
input_features = floats_tensor([batch_size, 7, 80], scale=1.0)
attention_mask = random_attention_mask([batch_size, 7])
decoder_input_ids = ids_tensor([batch_size, 4], model.decoder.config.vocab_size)
decoder_attention_mask = random_attention_mask([batch_size, 4])

2
tests/unispeech/test_modeling_unispeech.py
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@@ -107,7 +107,7 @@ class UniSpeechModelTester:
self.encoder_seq_length = self.output_seq_length
def prepare_config_and_inputs(self):
input_values = floats_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_values = floats_tensor([self.batch_size, self.seq_length], scale=1.0)
attention_mask = random_attention_mask([self.batch_size, self.seq_length])
config = self.get_config()

4
tests/unispeech_sat/test_modeling_unispeech_sat.py
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@@ -121,7 +121,7 @@ class UniSpeechSatModelTester:
self.encoder_seq_length = self.output_seq_length
def prepare_config_and_inputs(self):
input_values = floats_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_values = floats_tensor([self.batch_size, self.seq_length], scale=1.0)
attention_mask = random_attention_mask([self.batch_size, self.seq_length])
config = self.get_config()
@@ -306,7 +306,7 @@ class UniSpeechSatModelTester:
model.freeze_base_model()
# use a longer sequence length to account for TDNN temporal downsampling
input_values = floats_tensor([self.batch_size, self.seq_length * 2], self.vocab_size)
input_values = floats_tensor([self.batch_size, self.seq_length * 2], scale=1.0)
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label))

2
tests/wav2vec2/test_modeling_flax_wav2vec2.py
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@@ -117,7 +117,7 @@ class FlaxWav2Vec2ModelTester:
self.encoder_seq_length = self.output_seq_length
def prepare_config_and_inputs(self):
input_values = floats_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_values = floats_tensor([self.batch_size, self.seq_length], scale=1.0)
attention_mask = random_attention_mask([self.batch_size, self.seq_length])
config = Wav2Vec2Config(

2
tests/wav2vec2/test_modeling_wav2vec2.py
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@@ -150,7 +150,7 @@ class Wav2Vec2ModelTester:
self.adapter_output_seq_length = (self.output_seq_length - 1) // adapter_stride + 1
def prepare_config_and_inputs(self):
input_values = floats_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_values = floats_tensor([self.batch_size, self.seq_length], scale=1.0)
attention_mask = random_attention_mask([self.batch_size, self.seq_length])
config = self.get_config()

2
tests/wavlm/test_modeling_wavlm.py
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@@ -114,7 +114,7 @@ class WavLMModelTester:
self.encoder_seq_length = self.output_seq_length
def prepare_config_and_inputs(self):
input_values = floats_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_values = floats_tensor([self.batch_size, self.seq_length], scale=1.0)
attention_mask = random_attention_mask([self.batch_size, self.seq_length])
config = self.get_config()