Fix qwen2.5-omni use_audio_in_video

requirements/framework.txt

@@ -28,7 +28,7 @@ sentencepiece
 tensorboard
 tiktoken
 tqdm
-transformers>=4.33,<4.52
+transformers>=4.33,<4.53
 transformers_stream_generator
 trl>=0.13,<0.17
 uvicorn

swift/llm/model/utils.py

@@ -39,6 +39,7 @@ def update_attn_impl(config: PretrainedConfig,
                          attn_impl_keys: Optional[List[str]] = None) -> None:
         if attn_impl is None:
             return
+        logger.info(f'attn_impl: {attn_impl}')
         use_flash_attn = AttnImpl.to_use_flash_attn(attn_impl)
         if use_flash_attn:
             attn_impl = 'flash_attention_2'

swift/llm/template/base.py

@@ -739,7 +739,7 @@ def _pre_tokenize_images(self, context_list: List[Context], loss_scale_list: Lis
             if context == '<image>' and inputs.is_multimodal and inputs.image_idx < len(inputs.images):
                 c_list = self.replace_tag('image', inputs.image_idx, inputs)
                 inputs.image_idx += 1
-                loss_scale = 0.
+                loss_scale = 0. if self.template_backend == 'swift' else 1.
             else:
                 c_list = [context]
             res += c_list

swift/llm/template/template/gemma.py

@@ -109,7 +109,7 @@ def _encode(self, inputs: StdTemplateInputs) -> Dict[str, Any]:
             input_ids = encoded['input_ids']
             labels = encoded['labels']
             idx_list = findall(input_ids, self.boi_token_id)
-            img_tokens = self.tokenizer.encode(self.processor.full_image_sequence)
+            img_tokens = self._tokenize(self.processor.full_image_sequence)
             input_ids, labels = self._extend_tokens(input_ids, labels, idx_list, lambda _: img_tokens)
 
             # TODO: customize

swift/llm/template/template/qwen.py

@@ -1,5 +1,4 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
-from contextlib import contextmanager
 from dataclasses import dataclass, field
 from functools import partial
 from typing import Any, Dict, List, Literal, Optional, Tuple
@@ -384,32 +383,50 @@ class Qwen2_5OmniTemplate(Qwen2_5VLTemplate):
     version = 'omni'
     placeholder_tokens = ['<|IMAGE|>', '<|AUDIO|>', '<|VIDEO|>']
 
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        from transformers.models.qwen2_5_omni.processing_qwen2_5_omni import Qwen2_5OmniProcessorKwargs
+        default = Qwen2_5OmniProcessorKwargs._defaults
+        self.seconds_per_chunk = default['videos_kwargs']['seconds_per_chunk']
+        self.position_id_per_seconds = default['videos_kwargs']['position_id_per_seconds']
+        self.use_audio_in_video = get_env_args('use_audio_in_video', bool, False)
+        self.sampling_rate = get_env_args('sampling_rate', int, self.processor.feature_extractor.sampling_rate)
+
     def replace_tag(self, media_type: Literal['image', 'video', 'audio'], index: int,
                     inputs: StdTemplateInputs) -> List[Context]:
         from qwen_omni_utils import fetch_image, fetch_video
-        sampling_rate = self.processor.feature_extractor.sampling_rate
         if media_type == 'image':
             inputs.images[index] = fetch_image({'image': inputs.images[index]})
             return ['<|vision_bos|><|IMAGE|><|vision_eos|>']
         elif media_type == 'audio':
-            sampling_rate = get_env_args('sampling_rate', int, sampling_rate)
-            inputs.audios[index] = load_audio(inputs.audios[index], sampling_rate)
+            inputs.audios[index] = load_audio(inputs.audios[index], self.sampling_rate)
             return ['<|audio_bos|><|AUDIO|><|audio_eos|>']
         elif media_type == 'video':
             video = inputs.videos[index]
             inputs.videos[index] = fetch_video({'video': video}).to(torch.uint8)
-            use_audio_in_video = get_env_args('use_audio_in_video', bool, False)
-            if use_audio_in_video:
+            if self.use_audio_in_video:
                 import librosa
-                sampling_rate = get_env_args('sampling_rate', int, sampling_rate)
-                video = librosa.load(video, sr=sampling_rate)[0]
-                inputs.audios.insert(inputs.audio_idx, video)
+                if video.startswith('http://') or video.startswith('https://'):
+                    import audioread
+                    video = audioread.ffdec.FFmpegAudioFile(video)
+                video = librosa.load(video, sr=self.sampling_rate)[0]
+                inputs.audios.insert(inputs.audio_idx, (video, 'video'))
                 inputs.audio_idx += 1
+                return ['<|vision_bos|><|audio_bos|><|VIDEO|><|audio_eos|><|vision_eos|>']
             return ['<|vision_bos|><|VIDEO|><|vision_eos|>']
 
     def _encode(self, inputs: StdTemplateInputs) -> Dict[str, Any]:
         encoded = Template._encode(self, inputs)
-        media_inputs = self.processor(
+        processor = self.processor
+        video_audios_mask = []
+        for i, audio in enumerate(inputs.audios):
+            if isinstance(audio, tuple) and audio[1] == 'video':
+                inputs.audios[i] = audio[0]
+                video_audios_mask.append(True)
+            else:
+                video_audios_mask.append(False)
+        video_audios_mask = torch.tensor(video_audios_mask)
+        media_inputs = processor(
             text='',
             audio=inputs.audios or None,
             images=inputs.images or None,
@@ -420,31 +437,70 @@ def _encode(self, inputs: StdTemplateInputs) -> Dict[str, Any]:
         media_inputs = to_float_dtype(media_inputs, self.model_info.torch_dtype)
         input_ids = encoded['input_ids']
         labels = encoded['labels']
+        # audio
+        audio_token_id = self._tokenize('<|AUDIO|>')
+        idx_list = findall(input_ids, audio_token_id)
+        feature_attention_mask = media_inputs.get('feature_attention_mask')
+        if feature_attention_mask is not None:
+            audio_feature_lengths = torch.sum(feature_attention_mask, dim=1)
+            audio_lengths = (((audio_feature_lengths - 1) // 2 + 1 - 2) // 2 + 1)
+        else:
+            audio_lengths = None
+        audio_lengths_origin = audio_lengths
+        if idx_list:
+            if self.use_audio_in_video:
+                audio_lengths = audio_lengths[~video_audios_mask]
+
+            def _get_new_audio_tokens(i):
+                return audio_token_id * audio_lengths[i]
+
+            input_ids, labels = self._extend_tokens(input_ids, labels, idx_list, _get_new_audio_tokens)
+
         for media_type in ['image', 'video']:
             token = f'<|{media_type.upper()}|>'
             token_id = self._tokenize(token)
             idx_list = findall(input_ids, token_id)
             if idx_list:
-                merge_length = self.processor.image_processor.merge_size**2
+                merge_size = processor.image_processor.merge_size
                 media_grid_thw = media_inputs.get(f'{media_type}_grid_thw')
+                if media_type == 'video' and self.use_audio_in_video:
+                    audio_lengths = audio_lengths_origin[video_audios_mask]
+                    video_second_per_grid = media_inputs['video_second_per_grid']
+
+                    def _get_new_tokens_use_audio_in_video(i):
+                        audio_token_indices = torch.arange(audio_lengths[i])
+                        grid_thw = media_grid_thw[i]
+                        height = grid_thw[1] // merge_size
+                        width = grid_thw[2] // merge_size
+                        video_token_indices = torch.arange(grid_thw[0]).reshape(-1, 1, 1)
+                        video_token_indices = torch.broadcast_to(
+                            video_token_indices, (video_token_indices.shape[0], height, width)).reshape(-1)
+                        video_token_indices = (
+                            video_token_indices * video_second_per_grid[i] * self.position_id_per_seconds)
+                        tokens_per_chunk = int(self.position_id_per_seconds * self.seconds_per_chunk)
+                        video_chunk_indexes = processor.get_chunked_index(video_token_indices, tokens_per_chunk)
+                        audio_chunk_indexes = processor.get_chunked_index(audio_token_indices, tokens_per_chunk)
+
+                        res = []
+                        for j in range(max(len(video_chunk_indexes), len(audio_chunk_indexes))):
+                            if j < len(video_chunk_indexes):
+                                video_seq_length = video_chunk_indexes[j][1] - video_chunk_indexes[j][0]
+                                res += token_id * video_seq_length
+                            if j < len(audio_chunk_indexes):
+                                audio_seq_length = audio_chunk_indexes[j][1] - audio_chunk_indexes[j][0]
+                                res += audio_token_id * audio_seq_length
+                        return res
+
+                    input_ids, labels = self._extend_tokens(input_ids, labels, idx_list,
+                                                            _get_new_tokens_use_audio_in_video)
 
-                def _get_new_tokens(i):
-                    token_len = (media_grid_thw[i].prod() // merge_length)
-                    return token_id * token_len
-
-                input_ids, labels = self._extend_tokens(input_ids, labels, idx_list, _get_new_tokens)
-        # audio
-        feature_attention_mask = media_inputs.get('feature_attention_mask')
-        if feature_attention_mask is not None:
-            audio_feature_lengths = torch.sum(feature_attention_mask, dim=1).tolist()
-            token_id = self._tokenize('<|AUDIO|>')
-            idx_list = findall(input_ids, token_id)
+                else:
 
-            def _get_new_tokens(i):
-                place_num = ((audio_feature_lengths[i] - 1) // 2 + 1 - 2) // 2 + 1
-                return token_id * place_num
+                    def _get_new_tokens(i):
+                        token_len = (media_grid_thw[i].prod() // (merge_size**2))
+                        return token_id * token_len
 
-            input_ids, labels = self._extend_tokens(input_ids, labels, idx_list, _get_new_tokens)
+                    input_ids, labels = self._extend_tokens(input_ids, labels, idx_list, _get_new_tokens)
 
         encoded['input_ids'] = input_ids
         encoded['labels'] = labels
@@ -460,7 +516,6 @@ def _get_position_ids(self, inputs: Dict[str, Any]):
             audio_feature_lengths = torch.sum(feature_attention_mask, dim=1)
         else:
             audio_feature_lengths = None
-        use_audio_in_video = get_env_args('use_audio_in_video', bool, False)
         video_second_per_grid = inputs.pop('video_second_per_grid', None)
         input_ids = inputs['input_ids']
         attention_mask = inputs.get('attention_mask')
@@ -471,7 +526,7 @@ def _get_position_ids(self, inputs: Dict[str, Any]):
             inputs.get('image_grid_thw'),
             inputs.get('video_grid_thw'),
             attention_mask,
-            use_audio_in_video,
+            self.use_audio_in_video,
             audio_feature_lengths,
             video_second_per_grid,
         )
@@ -493,7 +548,7 @@ def _data_collator_mm_data(self, batch: List[Dict[str, Any]]) -> Dict[str, Any]:
 
     def generate(self, model, *args, **kwargs):
         if kwargs.get('video_grid_thw') is not None:
-            kwargs['use_audio_in_video'] = get_env_args('use_audio_in_video', bool, False)
+            kwargs['use_audio_in_video'] = self.use_audio_in_video
         return super().generate(model, *args, **kwargs)
 
 

tests/test_align/test_template/test_audio.py

@@ -56,6 +56,8 @@ def test_step_audio_chat():
 
 
 def test_qwen2_5_omni():
+    USE_AUDIO_IN_VIDEO = True
+    os.environ['USE_AUDIO_IN_VIDEO'] = str(USE_AUDIO_IN_VIDEO)
     pt_engine = PtEngine('Qwen/Qwen2.5-Omni-7B')
     response = _infer_model(pt_engine)
     pt_engine.default_template.template_backend = 'jinja'

tests/test_align/test_template/test_video.py

@@ -131,10 +131,9 @@ def test_qwen2_5_vl():
 
 
 def test_qwen2_5_omni():
-    os.environ['VIDEO_MAX_PIXELS'] = str(28 * 28 * 64)
-    USE_AUDIO_IN_VIDEO = False
+    USE_AUDIO_IN_VIDEO = True
     os.environ['USE_AUDIO_IN_VIDEO'] = str(USE_AUDIO_IN_VIDEO)
-    pt_engine = PtEngine('Qwen/Qwen2.5-Omni-7B')
+    pt_engine = PtEngine('Qwen/Qwen2.5-Omni-7B', attn_impl='flash_attn')
     system = ('You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, '
               'capable of perceiving auditory and visual inputs, as well as generating text and speech.')
     messages = [{'role': 'system', 'content': system}, {'role': 'user', 'content': '<video>'}]
@@ -143,15 +142,11 @@ def test_qwen2_5_omni():
     pt_engine.default_template.template_backend = 'jinja'
     response2 = _infer_model(pt_engine, messages=messages, videos=videos)
     if USE_AUDIO_IN_VIDEO:
-
-        ground_truth = ('Oh, that sounds like a really cool project! Are you using a specific app on the tablet for '
-                        "drawing? And what kind of details are you adding to the guitar? It'd be interesting to hear "
-                        'more about your creative process.')
+        ground_truth = ("Oh, that's a really cool drawing! It looks like a guitar. You've got the body "
+                        'and the neck drawn in a simple yet effective way. The lines are clean and the '
+                        'shape is well-defined. What made you choose to draw a guitar?')
     else:
-        ground_truth = (
-            "Oh, that sounds like a really cool project! So, you're using a tablet to draw a guitar and a key? "
-            "That's a creative way to combine two different things. Have you thought about what you'll do "
-            'with the final drawing? Maybe could use it for a poster or something? Let me know how it turns out!')
+        ground_truth = ('嗯，你是在用平板画画呢。你画的这把吉他，看起来很简洁明了。你用的笔触也很流畅，线条很清晰。你对颜色的运用也很不错，整体看起来很协调。你要是还有啥想法或者问题，随时跟我说哈。')
     assert response == response2 == ground_truth
 
 

tests/test_align/test_template/test_vision.py

@@ -509,10 +509,11 @@ def test_phi4_vision():
 
 def test_gemma3_vision():
     pt_engine = PtEngine('LLM-Research/gemma-3-4b-it')
-    response = _infer_model(pt_engine)
+    response = _infer_model(pt_engine, messages=[{'role': 'user', 'content': '<image>Describe this image in detail.'}])
     pt_engine.default_template.template_backend = 'jinja'
-    response2 = _infer_model(pt_engine)
-    assert response == response2
+    response2 = _infer_model(pt_engine, messages=[{'role': 'user', 'content': '<image>Describe this image in detail.'}])
+    assert response[:80] == response2[:80] == (
+        "Here's a detailed description of the image:\n\n**Overall Impression:**\n\nThe image ")
 
 
 def test_mistral_2503():
@@ -596,9 +597,9 @@ def test_kimi_vl():
     # test_minicpmo()
     # test_valley()
     # test_ui_tars()
-    # test_gemma3_vision()
+    test_gemma3_vision()
     # test_mistral_2503()
     # test_llama4()
     # test_internvl3_8b()
     # test_internvl3_9b()
-    test_kimi_vl()
+    # test_kimi_vl()