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tests/models/qwen2_audio/test_modeling_qwen2_audio.py

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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 unittest
+from io import BytesIO
+from urllib.request import urlopen
+
+import librosa
+
+from transformers import (
+    AutoProcessor,
+    Qwen2AudioConfig,
+    Qwen2AudioEncoderConfig,
+    Qwen2AudioForConditionalGeneration,
+    Qwen2AudioModel,
+    Qwen2Config,
+    is_torch_available,
+)
+from transformers.testing_utils import (
+    cleanup,
+    require_torch,
+    slow,
+    torch_device,
+)
+
+from ...alm_tester import ALMModelTest, ALMModelTester
+
+
+if is_torch_available():
+    import torch
+
+
+class Qwen2AudioModelTester(ALMModelTester):
+    config_class = Qwen2AudioConfig
+    base_model_class = Qwen2AudioModel
+    conditional_generation_class = Qwen2AudioForConditionalGeneration
+    text_config_class = Qwen2Config
+    audio_config_class = Qwen2AudioEncoderConfig
+    audio_mask_key = "feature_attention_mask"
+
+    def __init__(self, parent, **kwargs):
+        # feat_seq_length=60 → after conv2 s=2: 30 → after avg_pool s=2: 15 audio embed tokens.
+        kwargs.setdefault("feat_seq_length", 60)
+        # Encoder asserts input_features.shape[-1] == max_source_positions * conv1.stride * conv2.stride == 2 * max_source_positions.
+        kwargs.setdefault("max_source_positions", kwargs["feat_seq_length"] // 2)
+        super().__init__(parent, **kwargs)
+
+    def get_audio_embeds_mask(self, audio_mask):
+        # Mirrors Qwen2AudioEncoder._get_feat_extract_output_lengths: conv2 (k=3,s=2,p=1) then avg_pool (k=2,s=2).
+        input_lengths = audio_mask.sum(-1)
+        input_lengths = (input_lengths - 1) // 2 + 1
+        output_lengths = (input_lengths - 2) // 2 + 1
+        max_len = int(output_lengths.max().item())
+        positions = torch.arange(max_len, device=audio_mask.device)[None, :]
+        return (positions < output_lengths[:, None]).long()
+
+
+@require_torch
+class Qwen2AudioForConditionalGenerationModelTest(ALMModelTest, unittest.TestCase):
+    """
+    Model tester for `Qwen2AudioForConditionalGeneration`.
+    """
+
+    model_tester_class = Qwen2AudioModelTester
+    pipeline_model_mapping = {"any-to-any": Qwen2AudioForConditionalGeneration} if is_torch_available() else {}
+
+    @unittest.skip(reason="inputs_embeds is the audio-fused path; can't match raw token-only embeddings.")
+    def test_inputs_embeds_matches_input_ids(self):
+        pass
+
+
+@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,
+        )

tests/models/qwen2_audio/test_processing_qwen2_audio.py

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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 unittest
+
+from transformers import AutoProcessor, AutoTokenizer, Qwen2AudioProcessor
+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
+    model_id = "Qwen/Qwen2-Audio-7B-Instruct"
+
+    @classmethod
+    def _setup_test_attributes(cls, processor):
+        cls.audio_token = processor.audio_token
+
+    def test_can_load_various_tokenizers(self):
+        processor = Qwen2AudioProcessor.from_pretrained(self.model_id)
+        tokenizer = AutoTokenizer.from_pretrained(self.model_id)
+        self.assertEqual(processor.tokenizer.__class__, tokenizer.__class__)
+
+    def test_tokenizer_integration(self):
+        slow_tokenizer = AutoTokenizer.from_pretrained(self.model_id, use_fast=False)
+        fast_tokenizer = AutoTokenizer.from_pretrained(self.model_id, 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.model_id)
+        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)