Add init example for omni mode

Author: Oscilloscope98

python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-o-2_6/omni.py

@@ -0,0 +1,135 @@
+#
+# Copyright 2016 The BigDL Authors.
+#
+# 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 os
+import time
+import math
+import torch
+import librosa
+import argparse
+import numpy as np
+from PIL import Image
+from moviepy import VideoFileClip
+from transformers import AutoTokenizer
+from ipex_llm.transformers import AutoModel
+
+
+
+# The video chunk function is adpated from https://huggingface.co/openbmb/MiniCPM-o-2_6#chat-inference
+def get_video_chunk_content(video_path, temp_audio_name, flatten=True):
+    video = VideoFileClip(video_path)
+    print('video_duration:', video.duration)
+    
+    with open(temp_audio_name, 'wb') as temp_audio_file:
+        temp_audio_file_path = temp_audio_file.name
+        video.audio.write_audiofile(temp_audio_file_path, codec="pcm_s16le", fps=16000)
+        audio_np, sr = librosa.load(temp_audio_file_path, sr=16000, mono=True)
+    num_units = math.ceil(video.duration)
+    
+    # 1 frame + 1s audio chunk
+    contents= []
+    for i in range(num_units):
+        frame = video.get_frame(i+1)
+        image = Image.fromarray((frame).astype(np.uint8))
+        audio = audio_np[sr*i:sr*(i+1)]
+        if flatten:
+            contents.extend(["<unit>", image, audio])
+        else:
+            contents.append(["<unit>", image, audio])
+    
+    return contents
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Chat with MiniCPM-o-2_6 in Omni mode')
+    parser.add_argument('--repo-id-or-model-path', type=str,
+                        help='The Hugging Face or ModelScope repo id for the MiniCPM-o-2_6 model to be downloaded'
+                             ', or the path to the checkpoint folder')
+    parser.add_argument('--video-path', type=str, required=True,
+                        help='The path to the video, which the model uses to conduct inference '
+                             'based on its images and audio.')
+    parser.add_argument('--n-predict', type=int, default=32,
+                        help='Max tokens to predict')
+    
+    args = parser.parse_args()
+
+    model_path = args.repo_id_or_model_path
+    video_path = args.video_path
+
+    # Load model in 4 bit,
+    # which convert the relevant layers in the model into INT4 format
+    model = AutoModel.from_pretrained(model_path, 
+                                      load_in_low_bit="sym_int4",
+                                      optimize_model=True,
+                                      trust_remote_code=True,
+                                      attn_implementation='sdpa',
+                                      use_cache=True,
+                                      init_vision=True,
+                                      init_audio=True,
+                                      init_tts=False,
+                                      modules_to_not_convert=["vpm", "resampler"])
+    
+    model = model.half().to('xpu')
+
+    tokenizer = AutoTokenizer.from_pretrained(model_path,
+                                              trust_remote_code=True)
+    
+
+    # The following code for generation is adapted from https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct#quickstart
+    temp_audio_name = "temp_audio.wav"
+    contents = get_video_chunk_content(video_path, temp_audio_name)
+    messages = [{"role":"user", "content": contents}]
+
+    import os
+    if os.path.exists(temp_audio_name):
+        os.remove(temp_audio_name)
+
+    with torch.inference_mode():
+        # ipex_llm model needs a warmup, then inference time can be accurate
+        model.chat(
+            msgs=messages,
+            tokenizer=tokenizer,
+            sampling=True,
+            temperature=0.5,
+            max_new_tokens=args.n_predict,
+            omni_input=True, # need to set omni_input=True when omni inference
+            use_tts_template=False,
+            generate_audio=False,
+            max_slice_nums=1,
+            use_image_id=False,
+        )
+
+        st = time.time()
+        response = model.chat(
+            msgs=messages,
+            tokenizer=tokenizer,
+            sampling=True,
+            temperature=0.5,
+            max_new_tokens=args.n_predict,
+            omni_input=True, # need to set omni_input=True when omni inference
+            use_tts_template=False,
+            generate_audio=False,
+            max_slice_nums=1,
+            use_image_id=False,
+        )
+        torch.xpu.synchronize()
+        end = time.time()
+
+    print(f'Inference time: {end-st} s')
+    print('-'*20, 'Input Video Path', '-'*20)
+    print(video_path)
+    print('-'*20, 'Chat Output', '-'*20)
+    print(response)