transformers/tests/models/clvp/test_modeling_clvp.py
cyyever 1e6b546ea6
Use Python 3.9 syntax in tests (#37343)
Signed-off-by: cyy <cyyever@outlook.com>
2025-04-08 14:12:08 +02:00

641 lines
26 KiB
Python

# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Testing suite for the PyTorch Clvp model."""
import tempfile
import unittest
import datasets
import numpy as np
from transformers import ClvpConfig, ClvpDecoderConfig, ClvpEncoderConfig
from transformers.testing_utils import (
cleanup,
require_torch,
slow,
torch_device,
)
from transformers.utils import is_torch_available
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import (
ModelTesterMixin,
_config_zero_init,
ids_tensor,
random_attention_mask,
)
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import ClvpEncoder, ClvpForCausalLM, ClvpModel, ClvpModelForConditionalGeneration
from transformers import ClvpFeatureExtractor, ClvpTokenizer
class ClvpEncoderTester:
def __init__(
self,
parent,
batch_size=2,
seq_length=7,
is_training=False,
use_input_mask=True,
use_labels=True,
vocab_size=50,
hidden_size=128,
projection_dim=16,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=32,
dropout=0.1,
attention_dropout=0.1,
initializer_range=0.02,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.projection_dim = projection_dim
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.dropout = dropout
self.attention_dropout = attention_dropout
self.initializer_range = initializer_range
self.scope = scope
self.bos_token_id = vocab_size - 1
self.eos_token_id = vocab_size - 1
def get_config(self):
encoder_config = ClvpEncoderConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
projection_dim=self.projection_dim,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
dropout=self.dropout,
attention_dropout=self.attention_dropout,
initializer_range=self.initializer_range,
bos_token_id=self.bos_token_id,
eos_token_id=self.eos_token_id,
)
return encoder_config
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])
if input_mask is not None:
batch_size, seq_length = input_mask.shape
rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,))
for batch_idx, start_index in enumerate(rnd_start_indices):
input_mask[batch_idx, :start_index] = 1
input_mask[batch_idx, start_index:] = 0
encoder_config = self.get_config()
return encoder_config, input_ids, input_mask
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
speech_config, input_ids, input_mask = config_and_inputs
inputs_dict = {"input_ids": input_ids.to(torch_device), "attention_mask": input_mask.to(torch_device)}
return speech_config, inputs_dict
def create_and_check_model(self, speech_config, input_ids, input_mask):
text_config = ClvpEncoderConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
projection_dim=self.projection_dim,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
dropout=self.dropout,
attention_dropout=self.attention_dropout,
initializer_range=self.initializer_range,
)
text_encoder_model = ClvpEncoder(config=text_config)
text_encoder_model.to(torch_device)
text_encoder_model.eval()
with torch.no_grad():
result = text_encoder_model(input_ids, attention_mask=input_mask)
result = text_encoder_model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result[0].shape, (self.batch_size, self.projection_dim))
# now check with speech config
speech_encoder_model = ClvpEncoder(config=speech_config)
speech_encoder_model.to(torch_device)
speech_encoder_model.eval()
with torch.no_grad():
result = speech_encoder_model(input_ids, attention_mask=input_mask)
result = speech_encoder_model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result[0].shape, (self.batch_size, self.projection_dim))
@require_torch
class ClvpEncoderTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (ClvpEncoder,) if is_torch_available() else ()
test_pruning = False
test_head_masking = False
test_torchscript = False
def setUp(self):
self.model_tester = ClvpEncoderTester(self)
self.encoder_config_tester = ConfigTester(self, config_class=ClvpEncoderConfig, hidden_size=32)
def tearDown(self):
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
cleanup(torch_device)
def test_config(self):
self.encoder_config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
@unittest.skip(reason="ClvpEncoder does not output loss")
def test_training(self):
pass
@unittest.skip(reason="ClvpEncoder does not output loss")
def test_training_gradient_checkpointing(self):
pass
class ClvpDecoderTester:
def __init__(
self,
parent,
batch_size=2,
seq_length=3,
is_training=False,
vocab_size=300,
max_position_embeddings=256,
max_text_tokens=256,
use_input_mask=True,
hidden_size=128,
num_hidden_layers=2,
num_attention_heads=2,
bos_token_id=97,
eos_token_id=98,
relative_attention_num_buckets=4,
relative_attention_max_distance=16,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.max_text_tokens = max_text_tokens
self.use_input_mask = use_input_mask
self.hidden_size = hidden_size
self.num_attention_heads = num_attention_heads
self.num_hidden_layers = num_hidden_layers
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
self.relative_attention_num_buckets = relative_attention_num_buckets
self.relative_attention_max_distance = relative_attention_max_distance
def get_config(self):
decoder_config = ClvpDecoderConfig(
vocab_size=self.vocab_size,
max_position_embeddings=self.max_position_embeddings,
max_text_tokens=self.max_text_tokens,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
bos_token_id=self.bos_token_id,
eos_token_id=self.eos_token_id,
relative_attention_num_buckets=self.relative_attention_num_buckets,
relative_attention_max_distance=self.relative_attention_max_distance,
)
return decoder_config
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])
if input_mask is not None:
batch_size, seq_length = input_mask.shape
rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,))
for batch_idx, start_index in enumerate(rnd_start_indices):
input_mask[batch_idx, :start_index] = 1
input_mask[batch_idx, start_index:] = 0
decoder_config = self.get_config()
return decoder_config, input_ids, input_mask
def create_and_check_model(self, config, input_ids, attention_mask):
model = ClvpForCausalLM(config).to(torch_device).eval()
with torch.no_grad():
result = model(input_ids=input_ids, attention_mask=attention_mask)
self.parent.assertEqual(result[0].shape, (self.batch_size, self.seq_length, self.vocab_size))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, input_ids, attention_mask = config_and_inputs
inputs_dict = {
"input_ids": input_ids.to(torch_device),
"attention_mask": attention_mask.to(torch_device),
}
return config, inputs_dict
@require_torch
class ClvpDecoderTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (ClvpModel, ClvpForCausalLM) if is_torch_available() else ()
pipeline_model_mapping = {"feature-extraction": ClvpModelForConditionalGeneration} if is_torch_available() else {}
test_pruning = False
def setUp(self):
self.model_tester = ClvpDecoderTester(self)
self.decoder_config_tester = ConfigTester(self, config_class=ClvpDecoderConfig, hidden_size=32)
def tearDown(self):
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
cleanup(torch_device)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
if return_labels and model_class == ClvpForCausalLM:
inputs_dict["labels"] = torch.zeros(
[self.model_tester.batch_size, self.model_tester.seq_length], device=torch_device
).long()
return inputs_dict
def test_training(self):
# we will only test the ClvpForCausalLM since it outputs loss
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
model = ClvpForCausalLM(config)
model.to(torch_device)
model.train()
inputs = self._prepare_for_class(inputs_dict, ClvpForCausalLM, return_labels=True)
loss = model(**inputs).loss
loss.backward()
def test_training_gradient_checkpointing(self):
# we will only test the ClvpForCausalLM since it outputs loss
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.use_cache = False
config.return_dict = True
model = ClvpForCausalLM(config)
model.to(torch_device)
model.gradient_checkpointing_enable()
model.train()
inputs = self._prepare_for_class(inputs_dict, ClvpForCausalLM, return_labels=True)
loss = model(**inputs).loss
loss.backward()
@unittest.skip(reason="Clvp `prepare_inputs_for_generation` function doesn't have cache position.")
def test_generate_continue_from_inputs_embeds(self):
pass
class ClvpModelForConditionalGenerationTester:
def __init__(self, parent, is_training=False):
self.parent = parent
self.clvp_encoder_tester = ClvpEncoderTester(parent)
self.is_training = is_training
self.batch_size = self.clvp_encoder_tester.batch_size # need bs for batching_equivalence test
def get_config(self):
decoder_config = ClvpDecoderConfig(
vocab_size=50,
max_position_embeddings=30,
max_text_tokens=30,
hidden_size=128,
num_hidden_layers=1,
num_attention_heads=2,
bos_token_id=97,
eos_token_id=98,
relative_attention_num_buckets=4,
relative_attention_max_distance=16,
)
text_config = self.clvp_encoder_tester.get_config()
speech_config = self.clvp_encoder_tester.get_config()
speech_config.vocab_size = 300
return ClvpConfig.from_sub_model_configs(
text_config,
speech_config,
decoder_config,
projection_dim=16,
)
def prepare_config_and_inputs(self):
_, input_ids, attention_mask = self.clvp_encoder_tester.prepare_config_and_inputs()
ds = datasets.load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
ds = ds.cast_column("audio", datasets.Audio(sampling_rate=22050))
_, audio, sr = ds.sort("id").select(range(1))[:1]["audio"][0].values()
feature_extractor = ClvpFeatureExtractor()
input_features = feature_extractor(raw_speech=audio, sampling_rate=sr, return_tensors="pt")[
"input_features"
].to(torch_device)
config = self.get_config()
return config, input_ids, attention_mask, input_features
def create_and_check_model(self, config, input_ids, attention_mask, input_features):
model = ClvpModelForConditionalGeneration(config).to(torch_device).eval()
with torch.no_grad():
result = model(input_ids=input_ids, input_features=input_features, attention_mask=attention_mask)
self.parent.assertEqual(result.logits_per_speech.shape, (2, self.clvp_encoder_tester.batch_size))
self.parent.assertEqual(result.logits_per_text.shape, (self.clvp_encoder_tester.batch_size, 2))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, input_ids, attention_mask, input_features = config_and_inputs
inputs_dict = {
"input_ids": input_ids.to(torch_device),
"attention_mask": attention_mask.to(torch_device),
"input_features": input_features.to(torch_device),
"return_loss": False,
}
return config, inputs_dict
@require_torch
class ClvpModelForConditionalGenerationTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (ClvpModelForConditionalGeneration,) if is_torch_available() else ()
# Doesn't run generation tests. There are interface mismatches when using `generate` -- TODO @gante
all_generative_model_classes = ()
test_head_masking = False
test_pruning = False
test_resize_embeddings = False
test_attention_outputs = False
test_torchscript = False
def setUp(self):
self.model_tester = ClvpModelForConditionalGenerationTester(self)
common_properties = ["projection_dim", "logit_scale_init_value"]
self.clvp_config_tester = ConfigTester(
self, config_class=ClvpConfig, has_text_modality=False, common_properties=common_properties, hidden_size=32
)
def test_config(self):
self.clvp_config_tester.run_common_tests()
def tearDown(self):
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
cleanup(torch_device)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
# check for decoder model, text encoder model and speech encoder model hidden states
decoder_hidden_states = outputs.decoder_hidden_states
text_encoder_hidden_states = outputs.text_encoder_hidden_states
speech_encoder_hidden_states = outputs.speech_encoder_hidden_states
# check length of the hidden states
expected_decoder_num_layers = config.decoder_config.num_hidden_layers + 1
self.assertEqual(len(decoder_hidden_states), expected_decoder_num_layers)
expected_speech_encoder_num_layers = config.text_config.num_hidden_layers + 1
self.assertEqual(len(text_encoder_hidden_states), expected_speech_encoder_num_layers)
expected_text_encoder_num_layers = config.speech_config.num_hidden_layers + 1
self.assertEqual(len(speech_encoder_hidden_states), expected_text_encoder_num_layers)
# check shapes of each hidden state
# for the decoder model we will only test the dimension because the ClvpConditioningEncoder could increase
# the sequence lengths.
self.assertEqual(decoder_hidden_states[0].shape[-1], config.decoder_config.hidden_size)
# the testing for text encoder stays standard because we just pass the text tokens here.
self.assertListEqual(
list(text_encoder_hidden_states[0].shape[-2:]),
[self.model_tester.clvp_encoder_tester.seq_length, config.text_config.hidden_size],
)
# for the decoder model we will only test the dimension because the fix_decoder_outputs method could increase
# the sequence lengths by adding `decoder_fixing_codes` tokens at the end.
self.assertEqual(speech_encoder_hidden_states[0].shape[-1], config.speech_config.hidden_size)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
@unittest.skip(reason="Retain_grad is tested in individual model tests")
def test_retain_grad_hidden_states_attentions(self):
pass
@unittest.skip(reason="ClvpModelForConditionalGeneration does not have get_input_embeddings")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="ClvpModelForConditionalGeneration does not have get_input_embeddings")
def test_model_get_set_embeddings(self):
pass
# override as the `logit_scale` parameter initialization is different for Clvp
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
if param.requires_grad:
# check if `logit_scale` is initialized as per the original implementation
if name == "logit_scale":
expected_value = np.log(1 / 0.07)
returned_value = param.data.item()
self.assertAlmostEqual(
returned_value,
expected_value,
delta=1e-3,
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
else:
expected_range = [0.0, 1.0]
returned_range = ((param.data.mean() * 1e9).round() / 1e9).item()
self.assertIn(
returned_range,
expected_range,
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
def test_load_speech_text_decoder_config(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
# Save ClvpConfig and check if we can load ClvpEncoderConfig from it
with tempfile.TemporaryDirectory() as tmp_dir_name:
config.save_pretrained(tmp_dir_name)
encoder_config = ClvpEncoderConfig.from_pretrained(tmp_dir_name)
self.assertDictEqual(config.text_config.to_dict(), encoder_config.to_dict())
# Save ClvpConfig and check if we can load ClvpDecoderConfig from it
with tempfile.TemporaryDirectory() as tmp_dir_name:
config.save_pretrained(tmp_dir_name)
decoder_config = ClvpDecoderConfig.from_pretrained(tmp_dir_name)
self.assertDictEqual(config.decoder_config.to_dict(), decoder_config.to_dict())
@slow
def test_model_from_pretrained(self):
model_name = "susnato/clvp_dev"
model = ClvpModelForConditionalGeneration.from_pretrained(model_name)
self.assertIsNotNone(model)
# Since Clvp has a lot of different models connected with each other it's better to test each of them individually along
# with a test_full_model_integration. If the model breaks in future, it could be of a great help to identify the broken part.
@slow
@require_torch
class ClvpIntegrationTest(unittest.TestCase):
def setUp(self):
self.text = "This is an example text."
ds = datasets.load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
ds = ds.cast_column("audio", datasets.Audio(sampling_rate=22050))
_, self.speech_samples, self.sr = ds.sort("id").select(range(1))[:1]["audio"][0].values()
self.model = ClvpModelForConditionalGeneration.from_pretrained("susnato/clvp_dev").to(torch_device)
self.model.eval()
tokenizer = ClvpTokenizer.from_pretrained("susnato/clvp_dev")
feature_extractor = ClvpFeatureExtractor.from_pretrained("susnato/clvp_dev")
tokenizer_output = tokenizer(self.text, return_tensors="pt")
self.text_tokens = tokenizer_output["input_ids"].to(torch_device)
self.input_features = feature_extractor(
raw_speech=self.speech_samples, sampling_rate=self.sr, return_tensors="pt"
)["input_features"].to(torch_device)
def tearDown(self):
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
cleanup(torch_device, gc_collect=True)
def test_conditional_encoder(self):
with torch.no_grad():
conditioning_encoder_outputs = self.model.conditioning_encoder(
input_features=self.input_features, input_ids=self.text_tokens
).to("cpu")
self.assertEqual(
conditioning_encoder_outputs.shape,
torch.Size((self.input_features.shape[0], 18, self.model.config.decoder_config.hidden_size)),
)
EXPECTED_OUTPUTS = torch.tensor(
[[-0.8582, 0.5228, 1.9944], [-0.0465, -1.1017, -0.0093], [-0.0466, -0.6030, -0.1280]]
)
torch.testing.assert_close(conditioning_encoder_outputs[0, :3, :3], EXPECTED_OUTPUTS, rtol=1e-4, atol=1e-4)
def test_decoder_model_generate(self):
autoregressive_model_output = self.model.speech_decoder_model.generate(input_ids=self.text_tokens).cpu()
EXPECTED_OUTPUTS = torch.tensor([[147, 2, 54, 2, 43, 2, 169, 122, 29, 64, 2, 136, 37, 33, 9, 8193]])
torch.testing.assert_close(autoregressive_model_output, EXPECTED_OUTPUTS)
def test_text_and_speech_encoder_models(self):
# check for text embeds
text_embeds = self.model.text_encoder_model(input_ids=self.text_tokens, return_dict=True)[0].cpu()
# fmt: off
EXPECTED_TEXT_EMBEDS = torch.tensor([1.4798, -2.0005, 2.3902, -0.5042, 1.6401, -2.4135, -1.4800, 3.0118, -2.4422, 1.3266, 2.2339, 1.4761, -4.8983, -1.3592, 6.0251, 6.7364, 2.2576, 3.7229, -10.0436, 4.6676])
# fmt: on
torch.testing.assert_close(text_embeds[0, :20], EXPECTED_TEXT_EMBEDS, rtol=1e-4, atol=1e-4)
# check for speech embeds
speech_embeds = self.model.speech_encoder_model(input_ids=self.text_tokens, return_dict=True)[0].cpu()
# fmt: off
EXPECTED_SPEECH_EMBEDS = torch.tensor([3.1202, -3.1183, -1.4264, -6.1339, 1.8885, -0.1983, 0.9461, -1.7414, 0.3320, -3.8400, -1.5715, 1.5096, -1.7576, 0.2387, 4.9758, 5.8450, -6.2534, 2.8587, -5.5816, 4.7821])
# fmt: on
torch.testing.assert_close(speech_embeds[0, :20], EXPECTED_SPEECH_EMBEDS, rtol=1e-4, atol=1e-4)
def test_full_model_integration(self):
full_model_output = self.model.generate(
input_ids=self.text_tokens,
input_features=self.input_features,
do_sample=False,
num_beams=4,
num_return_sequences=4,
max_new_tokens=10,
)
EXPECTED_SPEECH_IDS = torch.tensor([[1953, 1080, 612], [1953, 612, 493], [1953, 612, 716]])
EXPECTED_SIMILARITY_SCORES = torch.tensor([[14.7660, 14.4569, 13.6472, 13.5683]])
torch.testing.assert_close(full_model_output.speech_ids.cpu()[-3:, -3:], EXPECTED_SPEECH_IDS)
torch.testing.assert_close(full_model_output.logits_per_text.cpu(), EXPECTED_SIMILARITY_SCORES)