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transformers/tests/models/qwen2_audio/__init__.py Normal file
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transformers/tests/models/qwen2_audio/test_modeling_qwen2_audio.py Normal file
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# Copyright 2024 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 Qwen2Audio model."""
import tempfile
import unittest
from io import BytesIO
from urllib.request import urlopen
import librosa
import pytest
from transformers import (
AutoProcessor,
Qwen2AudioConfig,
Qwen2AudioForConditionalGeneration,
is_torch_available,
)
from transformers.testing_utils import (
cleanup,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
if is_torch_available():
import torch
class Qwen2AudioModelTester:
def __init__(
self,
parent,
ignore_index=-100,
audio_token_index=0,
seq_length=25,
feat_seq_length=60,
text_config={
"model_type": "qwen2",
"intermediate_size": 36,
"initializer_range": 0.02,
"hidden_size": 32,
"max_position_embeddings": 52,
"num_hidden_layers": 2,
"num_attention_heads": 4,
"num_key_value_heads": 2,
"use_labels": True,
"use_mrope": False,
"vocab_size": 99,
"pad_token_id": 1, # can't be the same as the audio token id
},
is_training=True,
audio_config={
"model_type": "qwen2_audio_encoder",
"d_model": 16,
"encoder_attention_heads": 4,
"encoder_ffn_dim": 16,
"encoder_layers": 2,
"num_mel_bins": 80,
"max_source_positions": 30,
"initializer_range": 0.02,
},
):
self.parent = parent
self.ignore_index = ignore_index
self.audio_token_index = audio_token_index
self.text_config = text_config
self.audio_config = audio_config
self.seq_length = seq_length
self.feat_seq_length = feat_seq_length
self.num_hidden_layers = text_config["num_hidden_layers"]
self.vocab_size = text_config["vocab_size"]
self.hidden_size = text_config["hidden_size"]
self.num_attention_heads = text_config["num_attention_heads"]
self.is_training = is_training
self.batch_size = 3
self.encoder_seq_length = seq_length
def get_config(self):
return Qwen2AudioConfig(
text_config=self.text_config,
audio_config=self.audio_config,
ignore_index=self.ignore_index,
audio_token_index=self.audio_token_index,
)
def prepare_config_and_inputs(self):
input_features_values = floats_tensor(
[
self.batch_size,
self.audio_config["num_mel_bins"],
self.feat_seq_length,
]
)
config = self.get_config()
feature_attention_mask = torch.ones([self.batch_size, self.feat_seq_length], dtype=torch.long).to(torch_device)
return config, input_features_values, feature_attention_mask
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, input_features_values, feature_attention_mask = config_and_inputs
input_length = (input_features_values.shape[-1] - 1) // 2 + 1
num_audio_tokens = (input_length - 2) // 2 + 1
input_ids = ids_tensor([self.batch_size, self.seq_length], config.text_config.vocab_size - 1) + 1
attention_mask = torch.ones(input_ids.shape, dtype=torch.long).to(torch_device)
attention_mask[:, :1] = 0
# we are giving 3 audios let's make sure we pass in 3 audios tokens
input_ids[:, 1 : 1 + num_audio_tokens] = config.audio_token_index
inputs_dict = {
"input_features": input_features_values,
"feature_attention_mask": feature_attention_mask,
"input_ids": input_ids,
"attention_mask": attention_mask,
}
return config, inputs_dict
@require_torch
class Qwen2AudioForConditionalGenerationModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
"""
Model tester for `Qwen2AudioForConditionalGeneration`.
"""
all_model_classes = (Qwen2AudioForConditionalGeneration,) if is_torch_available() else ()
test_pruning = False
test_head_masking = False
_is_composite = True
def setUp(self):
self.model_tester = Qwen2AudioModelTester(self)
self.config_tester = ConfigTester(self, config_class=Qwen2AudioConfig, has_text_modality=False)
@unittest.skip(reason="Compile not yet supported because in Qwen2Audio models")
@pytest.mark.torch_compile_test
def test_sdpa_can_compile_dynamic(self):
pass
@unittest.skip(reason="Compile not yet supported because in Qwen2Audio models")
def test_sdpa_can_dispatch_on_flash(self):
pass
def test_sdpa_can_dispatch_composite_models(self):
# overwrite because Qwen2 is audio+text model (not vision+text)
if not self.has_attentions:
self.skipTest(reason="Model architecture does not support attentions")
if not self._is_composite:
self.skipTest(f"{self.all_model_classes[0].__name__} does not support SDPA")
for model_class in self.all_model_classes:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
model = model_class(config)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model_sdpa = model_class.from_pretrained(tmpdirname)
model_sdpa = model_sdpa.eval().to(torch_device)
text_attn = "sdpa" if model.language_model._supports_sdpa else "eager"
vision_attn = "sdpa" if model.audio_tower._supports_sdpa else "eager"
# `None` as it is the requested one which will be assigned to each sub-config
# Sub-model will dispatch to SDPA if it can (checked below that `SDPA` layers are present)
self.assertTrue(model_sdpa.config._attn_implementation == "sdpa")
self.assertTrue(model.language_model.config._attn_implementation == text_attn)
self.assertTrue(model.audio_tower.config._attn_implementation == vision_attn)
model_eager = model_class.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.language_model.config._attn_implementation == "eager")
self.assertTrue(model_eager.audio_tower.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 Qwen2AudioForConditionalGenerationIntegrationTest(unittest.TestCase):
def setUp(self):
cleanup(torch_device, gc_collect=True)
self.processor = AutoProcessor.from_pretrained("Qwen/Qwen2-Audio-7B-Instruct")
def tearDown(self):
cleanup(torch_device, gc_collect=True)
@slow
def test_small_model_integration_test_single(self):
# Let' s make sure we test the preprocessing to replace what is used
model = Qwen2AudioForConditionalGeneration.from_pretrained(
"Qwen/Qwen2-Audio-7B-Instruct", device_map=torch_device, dtype=torch.float16
)
url = "https://huggingface.co/datasets/raushan-testing-hf/audio-test/resolve/main/glass-breaking-151256.mp3"
messages = [
{
"role": "user",
"content": [
{"type": "audio", "audio_url": url},
{"type": "text", "text": "What's that sound?"},
],
}
]
raw_audio, _ = librosa.load(BytesIO(urlopen(url).read()), sr=self.processor.feature_extractor.sampling_rate)
formatted_prompt = self.processor.apply_chat_template(messages, add_generation_prompt=True)
inputs = self.processor(text=formatted_prompt, audio=[raw_audio], return_tensors="pt", padding=True).to(
torch_device
)
torch.manual_seed(42)
output = model.generate(**inputs, max_new_tokens=32)
# fmt: off
EXPECTED_INPUT_IDS = torch.tensor(
[[151644, 8948, 198, 2610, 525, 264, 10950, 17847, 13, 151645, 198, 151644, 872, 198, 14755, 220, 16, 25, 220, 151647, *[151646] * 101 , 151648, 198, 3838, 594, 429, 5112, 30, 151645, 198, 151644, 77091, 198]],
device=torch_device
)
# fmt: on
torch.testing.assert_close(inputs["input_ids"], EXPECTED_INPUT_IDS)
# fmt: off
EXPECTED_DECODED_TEXT = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\nAudio 1: <|audio_bos|>" + "<|AUDIO|>" * 101 + "<|audio_eos|>\nWhat's that sound?<|im_end|>\n<|im_start|>assistant\nIt is the sound of glass breaking.<|im_end|>"
# fmt: on
self.assertEqual(
self.processor.decode(output[0], skip_special_tokens=False),
EXPECTED_DECODED_TEXT,
)
@slow
def test_small_model_integration_test_batch(self):
# Let' s make sure we test the preprocessing to replace what is used
model = Qwen2AudioForConditionalGeneration.from_pretrained(
"Qwen/Qwen2-Audio-7B-Instruct", device_map=torch_device, dtype=torch.float16
)
conversation1 = [
{
"role": "user",
"content": [
{
"type": "audio",
"audio_url": "https://huggingface.co/datasets/raushan-testing-hf/audio-test/resolve/main/glass-breaking-151256.mp3",
},
{"type": "text", "text": "What's that sound?"},
],
},
{"role": "assistant", "content": "It is the sound of glass shattering."},
{
"role": "user",
"content": [
{
"type": "audio",
"audio_url": "https://huggingface.co/datasets/raushan-testing-hf/audio-test/resolve/main/f2641_0_throatclearing.wav",
},
{"type": "text", "text": "What can you hear?"},
],
},
]
conversation2 = [
{
"role": "user",
"content": [
{
"type": "audio",
"audio_url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-Audio/audio/1272-128104-0000.flac",
},
{"type": "text", "text": "What does the person say?"},
],
},
]
conversations = [conversation1, conversation2]
text = [
self.processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False)
for conversation in conversations
]
audios = []
for conversation in conversations:
for message in conversation:
if isinstance(message["content"], list):
for ele in message["content"]:
if ele["type"] == "audio":
audios.append(
librosa.load(
BytesIO(urlopen(ele["audio_url"]).read()),
sr=self.processor.feature_extractor.sampling_rate,
)[0]
)
inputs = self.processor(text=text, audio=audios, return_tensors="pt", padding=True).to(torch_device)
torch.manual_seed(42)
output = model.generate(**inputs, max_new_tokens=32)
EXPECTED_DECODED_TEXT = [
"system\nYou are a helpful assistant.\nuser\nAudio 1: \nWhat's that sound?\nassistant\nIt is the sound of glass shattering.\nuser\nAudio 2: \nWhat can you hear?\nassistant\ncough and throat clearing.",
"system\nYou are a helpful assistant.\nuser\nAudio 1: \nWhat does the person say?\nassistant\nThe original content of this audio is: 'Mister Quiller is the apostle of the middle classes and we are glad to welcome his gospel.'",
]
self.assertEqual(
self.processor.batch_decode(output, skip_special_tokens=True),
EXPECTED_DECODED_TEXT,
)
@slow
def test_small_model_integration_test_multiurn(self):
# Let' s make sure we test the preprocessing to replace what is used
model = Qwen2AudioForConditionalGeneration.from_pretrained(
"Qwen/Qwen2-Audio-7B-Instruct", device_map=torch_device, dtype=torch.float16
)
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{
"role": "user",
"content": [
{
"type": "audio",
"audio_url": "https://huggingface.co/datasets/raushan-testing-hf/audio-test/resolve/main/glass-breaking-151256.mp3",
},
{"type": "text", "text": "What's that sound?"},
],
},
{"role": "assistant", "content": "It is the sound of glass shattering."},
{
"role": "user",
"content": [
{
"type": "audio",
"audio_url": "https://huggingface.co/datasets/raushan-testing-hf/audio-test/resolve/main/f2641_0_throatclearing.wav",
},
{"type": "text", "text": "How about this one?"},
],
},
]
formatted_prompt = self.processor.apply_chat_template(messages, add_generation_prompt=True)
audios = []
for message in messages:
if isinstance(message["content"], list):
for ele in message["content"]:
if ele["type"] == "audio":
audios.append(
librosa.load(
BytesIO(urlopen(ele["audio_url"]).read()),
sr=self.processor.feature_extractor.sampling_rate,
)[0]
)
inputs = self.processor(text=formatted_prompt, audio=audios, return_tensors="pt", padding=True).to(
torch_device
)
torch.manual_seed(42)
output = model.generate(**inputs, max_new_tokens=32, top_k=1)
EXPECTED_DECODED_TEXT = [
"system\nYou are a helpful assistant.\nuser\nAudio 1: \nWhat's that sound?\nassistant\nIt is the sound of glass shattering.\nuser\nAudio 2: \nHow about this one?\nassistant\nThroat clearing."
]
self.assertEqual(
self.processor.batch_decode(output, skip_special_tokens=True),
EXPECTED_DECODED_TEXT,
)

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transformers/tests/models/qwen2_audio/test_processing_qwen2_audio.py Normal file
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# Copyright 2024 The HuggingFace 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.
import shutil
import tempfile
import unittest
from transformers import AutoProcessor, AutoTokenizer, Qwen2AudioProcessor, WhisperFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_processing_common import ProcessorTesterMixin, url_to_local_path
@require_torch
@require_torchaudio
class Qwen2AudioProcessorTest(ProcessorTesterMixin, unittest.TestCase):
processor_class = Qwen2AudioProcessor
@classmethod
def setUpClass(cls):
cls.checkpoint = "Qwen/Qwen2-Audio-7B-Instruct"
cls.tmpdirname = tempfile.mkdtemp()
processor = Qwen2AudioProcessor.from_pretrained(cls.checkpoint)
processor.save_pretrained(cls.tmpdirname)
cls.audio_token = processor.audio_token
def get_tokenizer(self, **kwargs):
return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).tokenizer
def get_audio_processor(self, **kwargs):
return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).audio_processor
def get_processor(self, **kwargs):
return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs)
@classmethod
def tearDownClass(cls):
shutil.rmtree(cls.tmpdirname, ignore_errors=True)
def test_can_load_various_tokenizers(self):
processor = Qwen2AudioProcessor.from_pretrained(self.checkpoint)
tokenizer = AutoTokenizer.from_pretrained(self.checkpoint)
self.assertEqual(processor.tokenizer.__class__, tokenizer.__class__)
def test_save_load_pretrained_default(self):
tokenizer = AutoTokenizer.from_pretrained(self.checkpoint)
processor = Qwen2AudioProcessor.from_pretrained(self.checkpoint)
feature_extractor = processor.feature_extractor
processor = Qwen2AudioProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
with tempfile.TemporaryDirectory() as tmpdir:
processor.save_pretrained(tmpdir)
processor = Qwen2AudioProcessor.from_pretrained(tmpdir)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string())
self.assertIsInstance(processor.feature_extractor, WhisperFeatureExtractor)
def test_tokenizer_integration(self):
slow_tokenizer = AutoTokenizer.from_pretrained(self.checkpoint, use_fast=False)
fast_tokenizer = AutoTokenizer.from_pretrained(self.checkpoint, from_slow=True, legacy=False)
prompt = "<|im_start|>system\nAnswer the questions.<|im_end|><|im_start|>user\n<|audio_bos|><|AUDIO|><|audio_eos|>\nWhat is it in this audio?<|im_end|><|im_start|>assistant\n"
EXPECTED_OUTPUT = [
"<|im_start|>",
"system",
"Ċ",
"Answer",
"Ġthe",
"Ġquestions",
".",
"<|im_end|>",
"<|im_start|>",
"user",
"Ċ",
"<|audio_bos|>",
"<|AUDIO|>",
"<|audio_eos|>",
"Ċ",
"What",
"Ġis",
"Ġit",
"Ġin",
"Ġthis",
"Ġaudio",
"?",
"<|im_end|>",
"<|im_start|>",
"assistant",
"Ċ",
]
self.assertEqual(slow_tokenizer.tokenize(prompt), EXPECTED_OUTPUT)
self.assertEqual(fast_tokenizer.tokenize(prompt), EXPECTED_OUTPUT)
def test_chat_template(self):
processor = AutoProcessor.from_pretrained(self.checkpoint)
expected_prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\nAudio 1: <|audio_bos|><|AUDIO|><|audio_eos|>\nWhat's that sound?<|im_end|>\n<|im_start|>assistant\nIt is the sound of glass shattering.<|im_end|>\n<|im_start|>user\nAudio 2: <|audio_bos|><|AUDIO|><|audio_eos|>\nHow about this one?<|im_end|>\n<|im_start|>assistant\n"
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{
"role": "user",
"content": [
{
"type": "audio",
"audio_url": url_to_local_path(
"https://huggingface.co/datasets/raushan-testing-hf/audio-test/resolve/main/glass-breaking-151256.mp3"
),
},
{"type": "text", "text": "What's that sound?"},
],
},
{"role": "assistant", "content": "It is the sound of glass shattering."},
{
"role": "user",
"content": [
{
"type": "audio",
"audio_url": url_to_local_path(
"https://huggingface.co/datasets/raushan-testing-hf/audio-test/resolve/main/f2641_0_throatclearing.wav"
),
},
{"type": "text", "text": "How about this one?"},
],
},
]
formatted_prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
self.assertEqual(expected_prompt, formatted_prompt)