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[Tests] Cast Hubert test models to fp16 (#13755)
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@anton-l
anton-l authored Sep 26, 2021
1 parent 400c5a1 commit e0d31a8
Showing 1 changed file with 35 additions and 17 deletions.
52 changes: 35 additions & 17 deletions tests/test_modeling_hubert.py
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Original file line number Diff line number Diff line change
Expand Up @@ -635,14 +635,16 @@ def _load_superb(self, task, num_samples):
return ds[:num_samples]

def test_inference_ctc_batched(self):
model = HubertForCTC.from_pretrained("facebook/hubert-large-ls960-ft").to(torch_device)
model = HubertForCTC.from_pretrained("facebook/hubert-large-ls960-ft", torch_dtype=torch.float16).to(
torch_device
)
processor = Wav2Vec2Processor.from_pretrained("facebook/hubert-large-ls960-ft", do_lower_case=True)

input_speech = self._load_datasamples(2)

inputs = processor(input_speech, return_tensors="pt", padding=True)

input_values = inputs.input_values.to(torch_device)
input_values = inputs.input_values.half().to(torch_device)
attention_mask = inputs.attention_mask.to(torch_device)

with torch.no_grad():
Expand All @@ -658,31 +660,35 @@ def test_inference_ctc_batched(self):
self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)

def test_inference_keyword_spotting(self):
model = HubertForSequenceClassification.from_pretrained("superb/hubert-base-superb-ks").to(torch_device)
model = HubertForSequenceClassification.from_pretrained(
"superb/hubert-base-superb-ks", torch_dtype=torch.float16
).to(torch_device)
processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-base-superb-ks")
input_data = self._load_superb("ks", 4)
inputs = processor(input_data["speech"], return_tensors="pt", padding=True)

input_values = inputs.input_values.to(torch_device)
input_values = inputs.input_values.half().to(torch_device)
attention_mask = inputs.attention_mask.to(torch_device)
with torch.no_grad():
outputs = model(input_values, attention_mask=attention_mask)
predicted_logits, predicted_ids = torch.max(outputs.logits, dim=-1)

expected_labels = [2, 6, 10, 9]
# s3prl logits for the same batch
expected_logits = torch.tensor([7.6692, 17.7795, 11.1562, 11.8232], device=torch_device)
expected_logits = torch.tensor([7.6692, 17.7795, 11.1562, 11.8232], dtype=torch.float16, device=torch_device)

self.assertListEqual(predicted_ids.tolist(), expected_labels)
self.assertTrue(torch.allclose(predicted_logits, expected_logits, atol=1e-2))
self.assertTrue(torch.allclose(predicted_logits, expected_logits, atol=2e-2))

def test_inference_intent_classification(self):
model = HubertForSequenceClassification.from_pretrained("superb/hubert-base-superb-ic").to(torch_device)
model = HubertForSequenceClassification.from_pretrained(
"superb/hubert-base-superb-ic", torch_dtype=torch.float16
).to(torch_device)
processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-base-superb-ic")
input_data = self._load_superb("ic", 4)
inputs = processor(input_data["speech"], return_tensors="pt", padding=True)

input_values = inputs.input_values.to(torch_device)
input_values = inputs.input_values.half().to(torch_device)
attention_mask = inputs.attention_mask.to(torch_device)
with torch.no_grad():
outputs = model(input_values, attention_mask=attention_mask)
Expand All @@ -692,11 +698,17 @@ def test_inference_intent_classification(self):
predicted_logits_location, predicted_ids_location = torch.max(outputs.logits[:, 20:24], dim=-1)

expected_labels_action = [1, 0, 4, 3]
expected_logits_action = torch.tensor([5.9052, 12.5865, 4.4840, 10.0240], device=torch_device)
expected_logits_action = torch.tensor(
[5.9052, 12.5865, 4.4840, 10.0240], dtype=torch.float16, device=torch_device
)
expected_labels_object = [1, 10, 3, 4]
expected_logits_object = torch.tensor([5.5316, 11.7946, 8.1672, 23.2415], device=torch_device)
expected_logits_object = torch.tensor(
[5.5316, 11.7946, 8.1672, 23.2415], dtype=torch.float16, device=torch_device
)
expected_labels_location = [0, 0, 0, 1]
expected_logits_location = torch.tensor([5.2053, 8.9577, 10.0447, 8.1481], device=torch_device)
expected_logits_location = torch.tensor(
[5.2053, 8.9577, 10.0447, 8.1481], dtype=torch.float16, device=torch_device
)

self.assertListEqual(predicted_ids_action.tolist(), expected_labels_action)
self.assertListEqual(predicted_ids_object.tolist(), expected_labels_object)
Expand All @@ -708,42 +720,48 @@ def test_inference_intent_classification(self):
self.assertTrue(torch.allclose(predicted_logits_location, expected_logits_location, atol=3e-1))

def test_inference_speaker_identification(self):
model = HubertForSequenceClassification.from_pretrained("superb/hubert-base-superb-sid").to(torch_device)
model = HubertForSequenceClassification.from_pretrained(
"superb/hubert-base-superb-sid", torch_dtype=torch.float16
).to(torch_device)
processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-base-superb-sid")
input_data = self._load_superb("si", 4)

output_logits = []
with torch.no_grad():
for example in input_data["speech"]:
input = processor(example, return_tensors="pt", padding=True)
output = model(input.input_values.to(torch_device), attention_mask=None)
output = model(input.input_values.half().to(torch_device), attention_mask=None)
output_logits.append(output.logits[0])
output_logits = torch.stack(output_logits)
predicted_logits, predicted_ids = torch.max(output_logits, dim=-1)

expected_labels = [5, 1, 1, 3]
# s3prl logits for the same batch
expected_logits = torch.tensor([78231.5547, 123166.6094, 122785.4141, 84851.2969], device=torch_device)
expected_logits = torch.tensor(
[78231.5547, 123166.6094, 122785.4141, 84851.2969], dtype=torch.float16, device=torch_device
)

self.assertListEqual(predicted_ids.tolist(), expected_labels)
# TODO: lower the tolerance after merging the padding fix https://github.com/pytorch/fairseq/pull/3572
self.assertTrue(torch.allclose(predicted_logits, expected_logits, atol=10))

def test_inference_emotion_recognition(self):
model = HubertForSequenceClassification.from_pretrained("superb/hubert-base-superb-er").to(torch_device)
model = HubertForSequenceClassification.from_pretrained(
"superb/hubert-base-superb-er", torch_dtype=torch.float16
).to(torch_device)
processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/hubert-base-superb-er")
input_data = self._load_superb("er", 4)
inputs = processor(input_data["speech"], return_tensors="pt", padding=True)

input_values = inputs.input_values.to(torch_device)
input_values = inputs.input_values.half().to(torch_device)
attention_mask = inputs.attention_mask.to(torch_device)
with torch.no_grad():
outputs = model(input_values, attention_mask=attention_mask)
predicted_logits, predicted_ids = torch.max(outputs.logits, dim=-1)

expected_labels = [1, 1, 2, 2]
# s3prl logits for the same batch
expected_logits = torch.tensor([2.8384, 2.3389, 3.8564, 4.5558], device=torch_device)
expected_logits = torch.tensor([2.8384, 2.3389, 3.8564, 4.5558], dtype=torch.float16, device=torch_device)

self.assertListEqual(predicted_ids.tolist(), expected_labels)
# TODO: lower the tolerance after merging the padding fix https://github.com/pytorch/fairseq/pull/3572
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