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2025-10-09 16:47:16 +08:00
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transformers/tests/models/speech_encoder_decoder/__init__.py Normal file
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transformers/tests/models/speech_encoder_decoder/test_modeling_speech_encoder_decoder.py Normal file
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# Copyright 2021 HuggingFace Inc. team.
#
# 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.
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import (
require_deterministic_for_xpu,
require_torch,
slow,
torch_device,
)
from ...test_modeling_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_bert import BertModelTester
from ..speech_to_text.test_modeling_speech_to_text import Speech2TextModelTester
from ..wav2vec2.test_modeling_wav2vec2 import Wav2Vec2ModelTester
if is_torch_available():
import numpy as np
import torch
from transformers import (
BertLMHeadModel,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
Wav2Vec2Model,
)
from transformers.modeling_outputs import BaseModelOutput
from transformers.models.speech_to_text.modeling_speech_to_text import Speech2TextEncoder
@require_torch
class EncoderDecoderMixin:
def get_encoder_decoder_model(self, config, decoder_config):
pass
def prepare_config_and_inputs(self):
pass
def get_pretrained_model_and_inputs(self):
pass
def check_encoder_decoder_model_from_pretrained_configs(
self,
config,
attention_mask,
decoder_config,
decoder_input_ids,
decoder_attention_mask,
input_values=None,
input_features=None,
**kwargs,
):
encoder_decoder_config = SpeechEncoderDecoderConfig.from_encoder_decoder_configs(config, decoder_config)
self.assertTrue(encoder_decoder_config.decoder.is_decoder)
enc_dec_model = SpeechEncoderDecoderModel(encoder_decoder_config)
enc_dec_model.to(torch_device)
enc_dec_model.eval()
self.assertTrue(enc_dec_model.config.is_encoder_decoder)
self.assertFalse(enc_dec_model.config.tie_word_embeddings)
outputs_encoder_decoder = enc_dec_model(
input_values=input_values,
input_features=input_features,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
self.assertEqual(
outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
)
def check_encoder_decoder_model(
self,
config,
attention_mask,
decoder_config,
decoder_input_ids,
decoder_attention_mask,
input_values=None,
input_features=None,
**kwargs,
):
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
enc_dec_model = SpeechEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
self.assertTrue(enc_dec_model.config.decoder.is_decoder)
self.assertTrue(enc_dec_model.config.decoder.add_cross_attention)
self.assertTrue(enc_dec_model.config.is_encoder_decoder)
enc_dec_model.to(torch_device)
outputs_encoder_decoder = enc_dec_model(
input_values=input_values,
input_features=input_features,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
output_hidden_states=True,
)
self.assertEqual(
outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
)
encoder_outputs = BaseModelOutput(last_hidden_state=outputs_encoder_decoder.encoder_hidden_states[-1])
outputs_encoder_decoder = enc_dec_model(
encoder_outputs=encoder_outputs,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
self.assertEqual(
outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
)
def check_encoder_decoder_model_with_inputs(
self,
config,
attention_mask,
decoder_config,
decoder_input_ids,
decoder_attention_mask,
input_values=None,
input_features=None,
**kwargs,
):
inputs = input_values if input_features is None else input_features
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
enc_dec_model = SpeechEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
enc_dec_model.to(torch_device)
outputs_encoder_decoder = enc_dec_model(
inputs,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
output_hidden_states=True,
)
self.assertEqual(
outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
)
outputs_encoder_decoder_kwarg = enc_dec_model(
inputs=inputs,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
output_hidden_states=True,
)
self.assertEqual(
outputs_encoder_decoder_kwarg["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
)
def check_encoder_decoder_model_from_pretrained(
self,
config,
attention_mask,
decoder_config,
decoder_input_ids,
decoder_attention_mask,
return_dict,
input_values=None,
input_features=None,
**kwargs,
):
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
kwargs = {"encoder_model": encoder_model, "decoder_model": decoder_model, "return_dict": return_dict}
enc_dec_model = SpeechEncoderDecoderModel.from_encoder_decoder_pretrained(**kwargs)
enc_dec_model.to(torch_device)
outputs_encoder_decoder = enc_dec_model(
input_values=input_values,
input_features=input_features,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
output_hidden_states=True,
return_dict=True,
)
self.assertEqual(
outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
)
def check_save_and_load(
self,
config,
attention_mask,
decoder_config,
decoder_input_ids,
decoder_attention_mask,
input_values=None,
input_features=None,
**kwargs,
):
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
enc_dec_model = SpeechEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
enc_dec_model.to(torch_device)
enc_dec_model.eval()
with torch.no_grad():
outputs = enc_dec_model(
input_values=input_values,
input_features=input_features,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
out_2 = outputs[0].cpu().numpy()
out_2[np.isnan(out_2)] = 0
with tempfile.TemporaryDirectory() as tmpdirname:
enc_dec_model.save_pretrained(tmpdirname)
enc_dec_model = SpeechEncoderDecoderModel.from_pretrained(tmpdirname)
enc_dec_model.to(torch_device)
after_outputs = enc_dec_model(
input_values=input_values,
input_features=input_features,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
out_1 = after_outputs[0].cpu().numpy()
out_1[np.isnan(out_1)] = 0
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
def check_save_and_load_encoder_decoder_model(
self,
config,
attention_mask,
decoder_config,
decoder_input_ids,
decoder_attention_mask,
input_values=None,
input_features=None,
**kwargs,
):
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
enc_dec_model = SpeechEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
enc_dec_model.to(torch_device)
enc_dec_model.eval()
with torch.no_grad():
outputs = enc_dec_model(
input_values=input_values,
input_features=input_features,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
out_2 = outputs[0].cpu().numpy()
out_2[np.isnan(out_2)] = 0
with (
tempfile.TemporaryDirectory() as encoder_tmp_dirname,
tempfile.TemporaryDirectory() as decoder_tmp_dirname,
):
enc_dec_model.encoder.save_pretrained(encoder_tmp_dirname)
enc_dec_model.decoder.save_pretrained(decoder_tmp_dirname)
SpeechEncoderDecoderModel.from_encoder_decoder_pretrained(
encoder_pretrained_model_name_or_path=encoder_tmp_dirname,
decoder_pretrained_model_name_or_path=decoder_tmp_dirname,
)
after_outputs = enc_dec_model(
input_values=input_values,
input_features=input_features,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
out_1 = after_outputs[0].cpu().numpy()
out_1[np.isnan(out_1)] = 0
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
def check_encoder_decoder_model_output_attentions(
self,
config,
attention_mask,
decoder_config,
decoder_input_ids,
decoder_attention_mask,
labels=None,
input_values=None,
input_features=None,
**kwargs,
):
# force eager attention to support output attentions
config._attn_implementation = "eager"
decoder_config._attn_implementation = "eager"
# make the decoder inputs a different shape from the encoder inputs to harden the test
decoder_input_ids = decoder_input_ids[:, :-1]
decoder_attention_mask = decoder_attention_mask[:, :-1]
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
enc_dec_model = SpeechEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
enc_dec_model.to(torch_device)
outputs_encoder_decoder = enc_dec_model(
input_values=input_values,
input_features=input_features,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
output_attentions=True,
)
inputs = input_values if input_features is None else input_features
encoder_attentions = outputs_encoder_decoder["encoder_attentions"]
self.assertEqual(len(encoder_attentions), config.num_hidden_layers)
seq_len = enc_dec_model.encoder._get_feat_extract_output_lengths(inputs.shape[1])
self.assertEqual(encoder_attentions[0].shape[-3:], (config.num_attention_heads, seq_len, seq_len))
decoder_attentions = outputs_encoder_decoder["decoder_attentions"]
num_decoder_layers = (
decoder_config.num_decoder_layers
if hasattr(decoder_config, "num_decoder_layers")
else decoder_config.num_hidden_layers
)
self.assertEqual(len(decoder_attentions), num_decoder_layers)
self.assertEqual(
decoder_attentions[0].shape[-3:],
(decoder_config.num_attention_heads, decoder_input_ids.shape[-1], decoder_input_ids.shape[-1]),
)
cross_attentions = outputs_encoder_decoder["cross_attentions"]
self.assertEqual(len(cross_attentions), num_decoder_layers)
cross_attention_input_seq_len = decoder_input_ids.shape[-1]
self.assertEqual(
cross_attentions[0].shape[-3:],
(decoder_config.num_attention_heads, cross_attention_input_seq_len, seq_len),
)
def check_encoder_decoder_model_generate(
self, config, decoder_config, input_values=None, input_features=None, **kwargs
):
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
enc_dec_model = SpeechEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
enc_dec_model.to(torch_device)
# make sure EOS token is set to None to prevent early stopping of generation
if hasattr(enc_dec_model.config, "eos_token_id"):
enc_dec_model.config.eos_token_id = None
if hasattr(enc_dec_model.config, "decoder") and hasattr(enc_dec_model.config.decoder, "eos_token_id"):
enc_dec_model.config.decoder.eos_token_id = None
if hasattr(enc_dec_model.generation_config, "eos_token_id"):
enc_dec_model.generation_config.eos_token_id = None
inputs = input_values if input_features is None else input_features
# Bert does not have a bos token id, so use pad_token_id instead
generated_output = enc_dec_model.generate(
inputs,
decoder_start_token_id=enc_dec_model.config.decoder.pad_token_id,
max_length=decoder_config.max_length,
)
self.assertEqual(generated_output.shape, (inputs.shape[0],) + (decoder_config.max_length,))
def test_encoder_decoder_model(self):
input_ids_dict = self.prepare_config_and_inputs()
self.check_encoder_decoder_model(**input_ids_dict)
def test_encoder_decoder_model_with_inputs(self):
input_ids_dict = self.prepare_config_and_inputs()
self.check_encoder_decoder_model_with_inputs(**input_ids_dict)
def test_encoder_decoder_model_from_pretrained_configs(self):
input_ids_dict = self.prepare_config_and_inputs()
self.check_encoder_decoder_model_from_pretrained_configs(**input_ids_dict)
def test_encoder_decoder_model_from_pretrained(self):
input_ids_dict = self.prepare_config_and_inputs()
self.check_encoder_decoder_model_from_pretrained(**input_ids_dict, return_dict=False)
def test_encoder_decoder_model_from_pretrained_return_dict(self):
input_ids_dict = self.prepare_config_and_inputs()
self.check_encoder_decoder_model_from_pretrained(**input_ids_dict, return_dict=True)
def test_save_and_load_from_pretrained(self):
input_ids_dict = self.prepare_config_and_inputs()
self.check_save_and_load(**input_ids_dict)
def test_save_and_load_from_encoder_decoder_pretrained(self):
input_ids_dict = self.prepare_config_and_inputs()
self.check_save_and_load_encoder_decoder_model(**input_ids_dict)
def test_encoder_decoder_model_output_attentions(self):
input_ids_dict = self.prepare_config_and_inputs()
self.check_encoder_decoder_model_output_attentions(**input_ids_dict)
def test_encoder_decoder_model_generate(self):
input_ids_dict = self.prepare_config_and_inputs()
self.check_encoder_decoder_model_generate(**input_ids_dict)
def test_training_gradient_checkpointing(self):
inputs_dict = self.prepare_config_and_inputs()
encoder_model, decoder_model = self.get_encoder_decoder_model(
inputs_dict["config"], inputs_dict["decoder_config"]
)
model = SpeechEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
model.to(torch_device)
model.train()
model.gradient_checkpointing_enable()
model.config.decoder_start_token_id = 0
model.config.pad_token_id = 0
model_inputs = {
"attention_mask": inputs_dict["attention_mask"],
"labels": inputs_dict["labels"],
"decoder_input_ids": inputs_dict["decoder_input_ids"],
}
inputs = inputs_dict["input_features"] if "input_features" in inputs_dict else inputs_dict["input_values"]
loss = model(inputs, **model_inputs).loss
loss.backward()
@slow
@require_deterministic_for_xpu
def test_real_model_save_load_from_pretrained(self):
model_2, inputs = self.get_pretrained_model_and_inputs()
model_2.to(torch_device)
with torch.no_grad():
outputs = model_2(**inputs)
out_2 = outputs[0].cpu().numpy()
out_2[np.isnan(out_2)] = 0
with tempfile.TemporaryDirectory() as tmp_dirname:
model_2.save_pretrained(tmp_dirname)
model_1 = SpeechEncoderDecoderModel.from_pretrained(tmp_dirname)
model_1.to(torch_device)
after_outputs = model_1(**inputs)
out_1 = after_outputs[0].cpu().numpy()
out_1[np.isnan(out_1)] = 0
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
@unittest.skip("TODO Arthur I have to skip for now because I don't understand it")
def test_sdpa_can_dispatch_composite_models(self):
inputs_dict = self.prepare_config_and_inputs()
encoder_config, decoder_config = inputs_dict["config"], inputs_dict["decoder_config"]
config = SpeechEncoderDecoderConfig.from_encoder_decoder_configs(
encoder_config=encoder_config, decoder_config=decoder_config
)
model = SpeechEncoderDecoderModel(config=config)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model_sdpa = SpeechEncoderDecoderModel.from_pretrained(tmpdirname)
model_sdpa = model_sdpa.eval().to(torch_device)
# see https://github.com/huggingface/transformers/pull/32238
# Sub-model will dispatch to SDPA if it can (checked below that `SDPA` layers are present)
encoder_attn = "sdpa" if model.encoder._supports_sdpa else "eager"
decoder_attn = "sdpa" if model.decoder._supports_sdpa else "eager"
self.assertTrue(model_sdpa.config._attn_implementation == "sdpa")
self.assertTrue(model_sdpa.encoder.config._attn_implementation == encoder_attn)
self.assertTrue(model_sdpa.decoder.config._attn_implementation == decoder_attn)
# Also test that nothing break if we request SDPA explicitly, when both sub-parts support it.
# If the model supports sdpa (i.e. all of sub-models supports it) we'll dispatch safely
# Otherwise we should raise error that SDPA is not supported, as some of the sub-models doesn't support
if encoder_attn == "sdpa" and decoder_attn == "sdpa":
model_sdpa_explicit = SpeechEncoderDecoderModel.from_pretrained(tmpdirname, attn_implementation="sdpa")
model_sdpa_explicit = model_sdpa_explicit.eval().to(torch_device)
self.assertTrue(model_sdpa_explicit.config._attn_implementation == "sdpa")
else:
with self.assertRaises(ValueError):
model_sdpa_explicit = SpeechEncoderDecoderModel.from_pretrained(
tmpdirname, attn_implementation="sdpa"
)
model_eager = SpeechEncoderDecoderModel.from_pretrained(
tmpdirname,
attn_implementation="eager",
)
model_eager = model_eager.eval().to(torch_device)
self.assertTrue(model_eager.config._attn_implementation == "eager")
self.assertTrue(model_eager.encoder.config._attn_implementation == "eager")
self.assertTrue(model_eager.decoder.config._attn_implementation == "eager")
for name, submodule in model_eager.named_modules():
class_name = submodule.__class__.__name__
if "SdpaAttention" in class_name or "SdpaSelfAttention" in class_name:
raise ValueError("The eager model should not have SDPA attention layers")
@require_torch
class Wav2Vec2BertModelTest(EncoderDecoderMixin, unittest.TestCase):
def get_pretrained_model_and_inputs(self):
model = SpeechEncoderDecoderModel.from_encoder_decoder_pretrained(
"facebook/wav2vec2-base-960h", "google-bert/bert-base-cased"
)
batch_size = 13
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])
inputs = {
"input_values": input_values,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
}
return model, inputs
def get_encoder_decoder_model(self, config, decoder_config):
encoder_model = Wav2Vec2Model(config).eval()
decoder_model = BertLMHeadModel(decoder_config).eval()
return encoder_model, decoder_model
def prepare_config_and_inputs(self):
bert_model_tester = BertModelTester(self)
wav2vec2_model_tester = Wav2Vec2ModelTester(self)
encoder_config_and_inputs = wav2vec2_model_tester.prepare_config_and_inputs()
decoder_config_and_inputs = bert_model_tester.prepare_config_and_inputs_for_decoder()
(
config,
input_values,
input_mask,
) = encoder_config_and_inputs
(
decoder_config,
decoder_input_ids,
decoder_token_type_ids,
decoder_input_mask,
decoder_sequence_labels,
decoder_token_labels,
decoder_choice_labels,
encoder_attention_mask,
_,
) = decoder_config_and_inputs
# make sure that cross attention layers are added
decoder_config.add_cross_attention = True
return {
"config": config,
"input_values": input_values,
"attention_mask": input_mask,
"decoder_config": decoder_config,
"decoder_input_ids": decoder_input_ids,
"decoder_token_type_ids": decoder_token_type_ids,
"decoder_attention_mask": decoder_input_mask,
"decoder_sequence_labels": decoder_sequence_labels,
"decoder_token_labels": decoder_token_labels,
"decoder_choice_labels": decoder_choice_labels,
"labels": decoder_token_labels,
}
@require_torch
class Speech2TextBertModelTest(EncoderDecoderMixin, unittest.TestCase):
def get_pretrained_model_and_inputs(self):
model = SpeechEncoderDecoderModel.from_encoder_decoder_pretrained(
"facebook/s2t-small-librispeech-asr", "google-bert/bert-base-cased"
)
batch_size = 13
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])
inputs = {
"input_features": input_features,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
}
return model, inputs
def get_encoder_decoder_model(self, config, decoder_config):
encoder_model = Speech2TextEncoder(config).eval()
decoder_model = BertLMHeadModel(decoder_config).eval()
return encoder_model, decoder_model
def prepare_config_and_inputs(self):
bert_model_tester = BertModelTester(self)
speech2text_model_tester = Speech2TextModelTester(self)
encoder_config_and_inputs = speech2text_model_tester.prepare_config_and_inputs()
decoder_config_and_inputs = bert_model_tester.prepare_config_and_inputs_for_decoder()
config, inputs = encoder_config_and_inputs
input_features = inputs["input_features"]
input_mask = inputs["attention_mask"]
(
decoder_config,
decoder_input_ids,
decoder_token_type_ids,
decoder_input_mask,
decoder_sequence_labels,
decoder_token_labels,
decoder_choice_labels,
encoder_attention_mask,
_,
) = decoder_config_and_inputs
# make sure that cross attention layers are added
decoder_config.add_cross_attention = True
return {
"config": config,
"input_features": input_features,
"attention_mask": input_mask,
"decoder_config": decoder_config,
"decoder_input_ids": decoder_input_ids,
"decoder_token_type_ids": decoder_token_type_ids,
"decoder_attention_mask": decoder_input_mask,
"decoder_sequence_labels": decoder_sequence_labels,
"decoder_token_labels": decoder_token_labels,
"decoder_choice_labels": decoder_choice_labels,
"labels": decoder_token_labels,
}
@unittest.skip(reason="Cannot save full model as Speech2TextModel != Speech2TextEncoder")
def test_encoder_decoder_model_from_pretrained_configs(self):
pass
@require_deterministic_for_xpu
@unittest.skip(reason="Cannot save full model as Speech2TextModel != Speech2TextEncoder")
def test_save_and_load_from_pretrained(self):
pass
@require_deterministic_for_xpu
@unittest.skip(reason="Cannot save full model as Speech2TextModel != Speech2TextEncoder")
def test_real_model_save_load_from_pretrained(self):
pass