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

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merged 1 commit into from
Apr 29, 2022
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use scale=1.0 in floats_tensor called in speech model testers #17007

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2 changes: 1 addition & 1 deletion tests/data2vec/test_modeling_data2vec_audio.py
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Original file line number Diff line number Diff line change
Expand Up @@ -116,7 +116,7 @@ def __init__(
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)
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@patrickvonplaten patrickvonplaten Apr 29, 2022 •
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wow good catch!

attention_mask = random_attention_mask([self.batch_size, self.seq_length])

config = self.get_config()
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2 changes: 1 addition & 1 deletion tests/hubert/test_modeling_hubert.py
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Expand Up @@ -106,7 +106,7 @@ def __init__(
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()
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2 changes: 1 addition & 1 deletion tests/perceiver/test_modeling_perceiver.py
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Expand Up @@ -143,7 +143,7 @@ def prepare_config_and_inputs(self, model_class=None):
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)
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2 changes: 1 addition & 1 deletion tests/sew/test_modeling_sew.py
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Expand Up @@ -108,7 +108,7 @@ def __init__(
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()
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2 changes: 1 addition & 1 deletion tests/sew_d/test_modeling_sew_d.py
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Expand Up @@ -122,7 +122,7 @@ def __init__(
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()
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8 changes: 4 additions & 4 deletions tests/speech_encoder_decoder/test_modeling_flax_speech_encoder_decoder.py
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Expand Up @@ -582,7 +582,7 @@ def get_pretrained_model_and_inputs(self):
"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])
Expand Down Expand Up @@ -638,7 +638,7 @@ def test_flaxwav2vec2gpt2_pt_flax_equivalence(self):

# 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])
Expand Down Expand Up @@ -699,7 +699,7 @@ def get_pretrained_model_and_inputs(self):
"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])
Expand Down Expand Up @@ -755,7 +755,7 @@ def test_flaxwav2vec2bart_pt_flax_equivalence(self):

# 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])
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4 changes: 2 additions & 2 deletions tests/speech_encoder_decoder/test_modeling_speech_encoder_decoder.py
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Expand Up @@ -425,7 +425,7 @@ def get_pretrained_model_and_inputs(self):
"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])
Expand Down Expand Up @@ -489,7 +489,7 @@ def get_pretrained_model_and_inputs(self):
"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])
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2 changes: 1 addition & 1 deletion tests/unispeech/test_modeling_unispeech.py
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Original file line number Diff line number Diff line change
Expand Up @@ -107,7 +107,7 @@ def __init__(
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()
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4 changes: 2 additions & 2 deletions tests/unispeech_sat/test_modeling_unispeech_sat.py
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Expand Up @@ -121,7 +121,7 @@ def __init__(
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()
Expand Down Expand Up @@ -306,7 +306,7 @@ def check_xvector_training(self, config, *args):
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))
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2 changes: 1 addition & 1 deletion tests/wav2vec2/test_modeling_flax_wav2vec2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -117,7 +117,7 @@ def __init__(
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(
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2 changes: 1 addition & 1 deletion tests/wav2vec2/test_modeling_wav2vec2.py
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Expand Up @@ -150,7 +150,7 @@ def __init__(
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()
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2 changes: 1 addition & 1 deletion tests/wavlm/test_modeling_wavlm.py
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Expand Up @@ -114,7 +114,7 @@ def __init__(
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()
Expand Down