[Qwen2Audio] handle input ids expansion during processing (#35534)

* add audio_token attribute to proc

* expand input_ids

* and legacy and expanded input_ids

* test update

* split lines

* add possibility not to provide eos and bos audio tokens

* raise errors

* test incorrect number of audio tokens

* add example

* fmt

* typo

docs/source/en/model_doc/qwen2_audio.md

@@ -34,6 +34,37 @@ The abstract from the paper is the following:
 
 `Qwen2-Audio-7B` and `Qwen2-Audio-7B-Instruct` can be found on the [Huggingface Hub](https://huggingface.co/Qwen)
 
+### Inference
+
+```python
+from io import BytesIO
+from urllib.request import urlopen
+import librosa
+from transformers import AutoProcessor, Qwen2AudioForConditionalGeneration
+
+model = Qwen2AudioForConditionalGeneration.from_pretrained("Qwen/Qwen2-Audio-7B", trust_remote_code=True, device_map="auto")
+processor = AutoProcessor.from_pretrained("Qwen/Qwen2-Audio-7B", trust_remote_code=True)
+
+prompt = "<|audio_bos|><|AUDIO|><|audio_eos|>Generate the caption in English:"
+url = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Audio/glass-breaking-151256.mp3"
+audio, sr = librosa.load(BytesIO(urlopen(url).read()), sr=processor.feature_extractor.sampling_rate)
+inputs = processor(text=prompt, audios=audio, return_tensors="pt").to(model.device)
+
+generate_ids = model.generate(**inputs, max_length=256)
+generate_ids = generate_ids[:, inputs.input_ids.size(1):]
+
+response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+
+# We can also omit the audio_bos and audio_eos tokens
+prompt = "<|AUDIO|>Generate the caption in English:"
+inputs = processor(text=prompt, audios=audio, return_tensors="pt").to(model.device)
+
+generate_ids = model.generate(**inputs, max_length=256)
+generate_ids = generate_ids[:, inputs.input_ids.size(1):]
+
+response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+```
+
 In the following, we demonstrate how to use `Qwen2-Audio-7B-Instruct` for the inference, supporting both voice chat and audio analysis modes. Note that we have used the ChatML format for dialog, in this demo we show how to leverage `apply_chat_template` for this purpose.
 
 ### Voice Chat Inference

src/transformers/models/qwen2_audio/modeling_qwen2_audio.py

@@ -1197,9 +1197,34 @@ class Qwen2AudioForConditionalGeneration(Qwen2AudioPreTrainedModel, GenerationMi
                 selected_audio_feature = audio_outputs.last_hidden_state
                 audio_features = self.multi_modal_projector(selected_audio_feature)
 
-                inputs_embeds, attention_mask, labels, position_ids, _ = self._merge_input_ids_with_audio_features(
+                # if we have consecutive audio tokens, then it means we expanded input_ids in processing
-                    audio_features, audio_output_lengths, inputs_embeds, input_ids, attention_mask, labels
+                audio_tokens = input_ids == self.config.audio_token_index
-                )
+                legacy_processing = (audio_tokens[:, :-1] & audio_tokens[:, 1:]).sum() == 0
+
+                if legacy_processing:
+                    logger.warning_once(
+                        "Expanding inputs for audio tokens in Qwen2Audio should be done in processing."
+                    )
+                    inputs_embeds, attention_mask, labels, position_ids, _ = self._merge_input_ids_with_audio_features(
+                        audio_features, audio_output_lengths, inputs_embeds, input_ids, attention_mask, labels
+                    )
+                else:
+                    num_audios, max_audio_tokens, embed_dim = audio_features.shape
+                    audio_features_mask = torch.arange(max_audio_tokens, device=audio_output_lengths.device)[None, :]
+                    audio_features_mask = audio_features_mask < audio_output_lengths[:, None]
+                    audio_features = audio_features[audio_features_mask]
+
+                    n_audio_tokens = (input_ids == self.config.audio_token_index).sum().item()
+                    n_audio_features = audio_features.shape[0]
+
+                    if n_audio_tokens != n_audio_features:
+                        raise ValueError(
+                            f"Audio features and audio tokens do not match: tokens: {n_audio_tokens}, features {n_audio_features}"
+                        )
+                    special_audio_mask = (input_ids == self.config.audio_token_index).to(inputs_embeds.device)
+                    special_audio_mask = special_audio_mask.unsqueeze(-1).expand_as(inputs_embeds)
+                    audio_features = audio_features.to(inputs_embeds.device, inputs_embeds.dtype)
+                    inputs_embeds = inputs_embeds.masked_scatter(special_audio_mask, audio_features)
 
         outputs = self.language_model(
             attention_mask=attention_mask,

src/transformers/models/qwen2_audio/processing_qwen2_audio.py

@@ -40,15 +40,32 @@ class Qwen2AudioProcessor(ProcessorMixin):
         chat_template (`Optional[str]`, *optional*):
                 The Jinja template to use for formatting the conversation. If not provided, the default chat template
                 is used.
+        audio_token (`str`, *optional*, defaults to `"<|AUDIO|>"`):
+            The token to use for audio tokens.
+        audio_bos_token (`str`, *optional*, defaults to `"<|audio_bos|>"`):
+            The token to use for audio bos tokens.
+        audio_eos_token (`str`, *optional*, defaults to `"<|audio_eos|>"`):
+            The token to use for audio eos tokens.
     """
 
     attributes = ["feature_extractor", "tokenizer"]
     feature_extractor_class = "WhisperFeatureExtractor"
     tokenizer_class = "AutoTokenizer"
 
-    def __init__(self, feature_extractor=None, tokenizer=None, chat_template=None):
+    def __init__(
+        self,
+        feature_extractor=None,
+        tokenizer=None,
+        chat_template=None,
+        audio_token="<|AUDIO|>",
+        audio_bos_token="<|audio_bos|>",
+        audio_eos_token="<|audio_eos|>",
+    ):
         if chat_template is None:
             chat_template = self.default_chat_template
+        self.audio_token = tokenizer.audio_token if hasattr(tokenizer, "audio_token") else audio_token
+        self.audio_bos_token = tokenizer.audio_bos_token if hasattr(tokenizer, "audio_bos_token") else audio_bos_token
+        self.audio_eos_token = tokenizer.audio_eos_token if hasattr(tokenizer, "audio_eos_token") else audio_eos_token
         super().__init__(feature_extractor, tokenizer, chat_template=chat_template)
 
     def __call__(
@@ -88,7 +105,18 @@ class Qwen2AudioProcessor(ProcessorMixin):
 
         if text is None:
             raise ValueError("You need to specify either a `text` input to process.")
-        inputs = self.tokenizer(text, padding=padding, **kwargs)
+        elif isinstance(text, str):
+            text = [text]
+        elif not isinstance(text, list) and not isinstance(text[0], str):
+            raise ValueError("Invalid input text. Please provide a string, or a list of strings")
+
+        # ensure we have as much audios as audio tokens
+        num_audio_tokens = sum(sample.count(self.audio_token) for sample in text)
+        num_audios = 1 if type(audios) == np.ndarray else len(audios)
+        if num_audio_tokens != num_audios:
+            raise ValueError(
+                f"Found {num_audio_tokens} {self.audio_token} token{'s' if num_audio_tokens > 1 else ''} in provided text but received {num_audios} audio{'s' if num_audios > 1 else ''}"
+            )
 
         if audios is not None:
             audio_inputs = self.feature_extractor(
@@ -97,6 +125,46 @@ class Qwen2AudioProcessor(ProcessorMixin):
             audio_inputs["feature_attention_mask"] = audio_inputs.pop(
                 "attention_mask"
             )  # rename attention_mask to prevent conflicts later on
+
+            expanded_text = []
+            audio_lengths = audio_inputs["feature_attention_mask"].sum(-1).tolist()
+
+            for sample in text:
+                replace_str = []
+                while self.audio_token in sample:
+                    audio_length = audio_lengths.pop(0)
+                    input_length = (audio_length - 1) // 2 + 1
+                    num_audio_tokens = (input_length - 2) // 2 + 1
+
+                    expanded_audio_token = self.audio_token * num_audio_tokens
+
+                    audio_token_start_idx = sample.find(self.audio_token)
+                    audio_token_end_idx = audio_token_start_idx + len(self.audio_token)
+
+                    has_bos = (
+                        sample[audio_token_start_idx - len(self.audio_bos_token) : audio_token_start_idx]
+                        == self.audio_bos_token
+                    )
+                    has_eos = (
+                        sample[audio_token_end_idx : audio_token_end_idx + len(self.audio_eos_token)]
+                        == self.audio_eos_token
+                    )
+
+                    # Check if this audio token is surrounded by bos/eos tokens
+                    if not has_bos and not has_eos:
+                        expanded_audio_token = self.audio_bos_token + expanded_audio_token + self.audio_eos_token
+
+                    replace_str.append(expanded_audio_token)
+                    sample = sample.replace(self.audio_token, "<placeholder>", 1)
+
+                while "<placeholder>" in sample:
+                    sample = sample.replace("<placeholder>", replace_str.pop(0), 1)
+                expanded_text.append(sample)
+            text = expanded_text
+
+        inputs = self.tokenizer(text, padding=padding, **kwargs)
+
+        if audios is not None:
             inputs.update(audio_inputs)
 
         return BatchFeature(data={**inputs})

tests/models/qwen2_audio/test_modeling_qwen2_audio.py

@@ -49,7 +49,7 @@ class Qwen2AudioModelTester:
         parent,
         ignore_index=-100,
         audio_token_index=0,
-        seq_length=7,
+        seq_length=25,
         feat_seq_length=60,
         text_config={
             "model_type": "qwen2",
@@ -91,7 +91,7 @@ class Qwen2AudioModelTester:
         self.is_training = is_training
 
         self.batch_size = 3
-        self.encoder_seq_length = audio_config["max_source_positions"] // 2 + seq_length - 1
+        self.encoder_seq_length = seq_length
 
     def get_config(self):
         return Qwen2AudioConfig(
@@ -116,11 +116,13 @@ class Qwen2AudioModelTester:
     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] = config.audio_token_index
+        input_ids[:, 1 : 1 + num_audio_tokens] = config.audio_token_index
         inputs_dict = {
             "input_features": input_features_values,
             "feature_attention_mask": feature_attention_mask,
@@ -237,54 +239,39 @@ class Qwen2AudioForConditionalGenerationIntegrationTest(unittest.TestCase):
 
         output = model.generate(**inputs, max_new_tokens=32)
 
-        EXPECTED_INPUT_IDS = torch.tensor(
+        # 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,
-                    151644,
+            *[151646] * 101,
-                    8948,
+            151648, 198, 3838, 594, 429, 5112, 30, 151645, 198, 151644, 77091, 198,
-                    198,
+        ]])
-                    2610,
+        # fmt: on
-                    525,
-                    264,
-                    10950,
-                    17847,
-                    13,
-                    151645,
-                    198,
-                    151644,
-                    872,
-                    198,
-                    14755,
-                    220,
-                    16,
-                    25,
-                    220,
-                    151647,
-                    151646,
-                    151648,
-                    198,
-                    3838,
-                    594,
-                    429,
-                    5112,
-                    30,
-                    151645,
-                    198,
-                    151644,
-                    77091,
-                    198,
-                ]
-            ]
-        )
         self.assertTrue(torch.equal(inputs["input_ids"], EXPECTED_INPUT_IDS))
 
-        EXPECTED_DECODED_TEXT = "<|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 breaking.<|im_end|>"
+        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|>"
+        )
 
         self.assertEqual(
             self.processor.decode(output[0], skip_special_tokens=False),
             EXPECTED_DECODED_TEXT,
         )
 
+        # test the error when incorrect number of audio tokens
+        # fmt: off
+        inputs["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] * 200,
+            151648, 198, 3838, 594, 429, 5112, 30, 151645, 198, 151644, 77091, 198,
+        ]])
+        # fmt: on
+        with self.assertRaisesRegex(
+            ValueError, "Audio features and audio tokens do not match: tokens: 200, features 101"
+        ):
+            model.generate(**inputs, max_new_tokens=32)
+
     @slow
     def test_small_model_integration_test_batch(self):
         # Let' s make sure we test the preprocessing to replace what is used