def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length],
self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask(
[self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length],
self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size],
self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length],
self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def check_ctc_training(self, config, input_values, *args):
config.ctc_zero_infinity = True
model = Data2VecAudioForCTC(config=config)
model.to(torch_device)
model.train()
# freeze feature encoder
model.freeze_feature_encoder()
input_values = input_values[:3]
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
max_length_labels = model._get_feat_extract_output_lengths(
torch.tensor(input_lengths))
labels = ids_tensor(
(input_values.shape[0], max(max_length_labels) - 2),
model.config.vocab_size)
# pad input
for i in range(len(input_lengths)):
input_values[i, input_lengths[i]:] = 0.0
if max_length_labels[i] < labels.shape[-1]:
# it's important that we make sure that target lenghts are at least
# one shorter than logit lenghts to prevent -inf
labels[i, max_length_labels[i] - 1:] = -100
loss = model(input_values, labels=labels).loss
self.parent.assertFalse(torch.isinf(loss).item())
loss.backward()
def check_seq_classifier_loss(self, config, input_values, *args):
model = Data2VecAudioForSequenceClassification(config=config)
model.to(torch_device)
# make sure that dropout is disabled
model.eval()
input_values = input_values[:3]
attention_mask = torch.ones(input_values.shape,
device=torch_device,
dtype=torch.long)
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))
# pad input
for i in range(len(input_lengths)):
input_values[i, input_lengths[i]:] = 0.0
attention_mask[i, input_lengths[i]:] = 0
masked_loss = model(input_values,
attention_mask=attention_mask,
labels=labels).loss.item()
unmasked_loss = model(input_values, labels=labels).loss.item()
self.parent.assertTrue(isinstance(masked_loss, float))
self.parent.assertTrue(isinstance(unmasked_loss, float))
self.parent.assertTrue(masked_loss != unmasked_loss)
def check_ctc_loss(self, config, input_values, *args):
model = SEWDForCTC(config=config)
model.to(torch_device)
# make sure that dropout is disabled
model.eval()
input_values = input_values[:3]
attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long)
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
labels = ids_tensor((input_values.shape[0], min(max_length_labels) - 1), model.config.vocab_size)
# pad input
for i in range(len(input_lengths)):
input_values[i, input_lengths[i] :] = 0.0
attention_mask[i, input_lengths[i] :] = 0
model.config.ctc_loss_reduction = "sum"
sum_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()
model.config.ctc_loss_reduction = "mean"
mean_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()
self.parent.assertTrue(isinstance(sum_loss, float))
self.parent.assertTrue(isinstance(mean_loss, float))
def check_xvector_training(self, config, *args):
config.ctc_zero_infinity = True
model = UniSpeechSatForXVector(config=config)
model.to(torch_device)
model.train()
# freeze everything but the classification head
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_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
labels = ids_tensor((input_values.shape[0], 1),
len(model.config.id2label))
# pad input
for i in range(len(input_lengths)):
input_values[i, input_lengths[i]:] = 0.0
loss = model(input_values, labels=labels).loss
self.parent.assertFalse(torch.isinf(loss).item())
loss.backward()
def prepare_config_and_inputs_for_decoder(self):
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = self.prepare_config_and_inputs()
config.is_decoder = True
encoder_hidden_states = floats_tensor(
[self.batch_size, self.seq_length, self.hidden_size])
encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length],
vocab_size=2)
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def test_retain_grad_hidden_states_attentions(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = True
config.output_attentions = True
# no need to test all models as different heads yield the same functionality
model_class = self.all_model_classes[0]
model = model_class(config)
model.to(torch_device)
# set layer drop to 0
model.config.layerdrop = 0.0
input_values = inputs_dict["input_values"]
input_lengths = torch.tensor(
[input_values.shape[1] for _ in range(input_values.shape[0])], dtype=torch.long, device=torch_device
)
output_lengths = model._get_feat_extract_output_lengths(input_lengths)
labels = ids_tensor((input_values.shape[0], output_lengths[0] - 2), self.model_tester.vocab_size)
inputs_dict["attention_mask"] = torch.ones_like(inputs_dict["attention_mask"])
inputs_dict["labels"] = labels
outputs = model(**inputs_dict)
output = outputs[0]
# Encoder-/Decoder-only models
hidden_states = outputs.hidden_states[0]
hidden_states.retain_grad()
output.flatten()[0].backward(retain_graph=True)
self.assertIsNotNone(hidden_states.grad)
def test_save_load_custom_head(self):
model_name = "bert-base-uncased"
model_config = AutoConfig.from_pretrained(
model_name, custom_heads={"tag": CustomHead})
model1 = AutoModelWithHeads.from_pretrained(model_name,
config=model_config)
model2 = AutoModelWithHeads.from_pretrained(model_name,
config=model_config)
config = {
"head_type": "tag",
"num_labels": 3,
"layers": 2,
"activation_function": "tanh"
}
model1.add_custom_head("custom_head", config)
with tempfile.TemporaryDirectory() as temp_dir:
model1.save_head(temp_dir, "custom_head")
model2.load_head(temp_dir)
model1.eval()
model2.eval()
in_data = ids_tensor((1, 128), 1000)
output1 = model1(in_data)
output2 = model2(in_data)
self.assertEqual(output1[0].size(), output2[0].size())
state1 = model1.config.prediction_heads["custom_head"].state_dict()
state2 = model2.config.prediction_heads["custom_head"].state_dict()
for ((k1, v1), (k2, v2)) in zip(state1.items(), state2.items()):
self.assertTrue(torch.equal(v1, v2))
def test_custom_head_from_model_config(self):
model_name = "bert-base-uncased"
model_config = AutoConfig.from_pretrained(model_name, custom_heads={"tag": CustomHead})
model = AutoModelWithHeads.from_pretrained(model_name, config=model_config)
config = {"head_type": "tag", "num_labels": 3, "layers": 2, "activation_function": "tanh"}
model.add_custom_head("custom_head", config)
model.eval()
in_data = ids_tensor((1, 128), 1000)
output1 = model(in_data)
model.add_tagging_head("tagging_head", num_labels=3, layers=2)
output2 = model(in_data)
self.assertEqual(output1[0].size(), output2[0].size())
def check_labels_out_of_vocab(self, config, input_values, *args):
model = SEWDForCTC(config)
model.to(torch_device)
model.train()
input_values = input_values[:3]
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size + 100)
with pytest.raises(ValueError):
model(input_values, labels=labels)
def check_seq_classifier_training(self, config, input_values, *args):
config.ctc_zero_infinity = True
model = SEWDForSequenceClassification(config=config)
model.to(torch_device)
model.train()
# freeze everything but the classification head
model.freeze_base_model()
input_values = input_values[:3]
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))
# pad input
for i in range(len(input_lengths)):
input_values[i, input_lengths[i] :] = 0.0
loss = model(input_values, labels=labels).loss
self.parent.assertFalse(torch.isinf(loss).item())
loss.backward()
def create_and_check_decoder_model_past_large_inputs(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.is_decoder = True
config.add_cross_attention = True
model = Data2VecTextForCausalLM(config=config).to(torch_device).eval()
# make sure that ids don't start with pad token
mask = input_ids.ne(config.pad_token_id).long()
input_ids = input_ids * mask
# first forward pass
outputs = model(
input_ids,
attention_mask=input_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=True,
)
past_key_values = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
# make sure that ids don't start with pad token
mask = next_tokens.ne(config.pad_token_id).long()
next_tokens = next_tokens * mask
next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
output_from_no_past = model(
next_input_ids,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_hidden_states=True,
)["hidden_states"][0]
output_from_past = model(
next_tokens,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
output_hidden_states=True,
)["hidden_states"][0]
# select random slice
random_slice_idx = ids_tensor((1, ), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:,
random_slice_idx].detach(
)
output_from_past_slice = output_from_past[:, :,
random_slice_idx].detach()
self.parent.assertTrue(
output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(
torch.allclose(output_from_past_slice,
output_from_no_past_slice,
atol=1e-3))
