Add qwen2-vl example

python/llm/example/GPU/HuggingFace/Multimodal/qwen2-vl/generate.py

@@ -0,0 +1,120 @@
+#
+# 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 torch
+import time
+import argparse
+import numpy as np
+
+from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
+from ipex_llm.transformers import Qwen2VLForConditionalGeneration
+from qwen_vl_utils import process_vision_info
+from ipex_llm import optimize_model
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Predict Tokens using generate() API for Qwen2-VL-7B-Instruct model')
+    parser.add_argument('--repo-id-or-model-path', type=str, default="Qwen/Qwen2-VL-7B-Instruct",
+                        help='The huggingface repo id for the Qwen2-VL model to be downloaded'
+                             ', or the path to the huggingface checkpoint folder')
+    parser.add_argument('--prompt', type=str, default="Describe this image.",
+                        help='Prompt to infer') 
+    parser.add_argument('--image-url-or-path', type=str,
+                        default='http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg' ,
+                        help='The URL or path to the image to infer')
+
+    parser.add_argument('--n-predict', type=int, default=32,
+                        help='Max tokens to predict')
+    parser.add_argument('--modelscope', action="store_true", default=False, 
+                        help="Use models from modelscope")
+
+    args = parser.parse_args()
+    model_path = args.repo_id_or_model_path
+
+    model = Qwen2VLForConditionalGeneration.from_pretrained(model_path,
+                                                 load_in_4bit=True,
+                                                 trust_remote_code=True,
+                                                 torch_dtype='auto',
+                                                 low_cpu_mem_usage=True,
+                                                 use_cache=True,)
+
+    model = optimize_model(model, low_bit='sym_int4', modules_to_not_convert=["visual"])
+
+    # Use .float() for better output, and use .half() for better speed
+    model = model.half().to("xpu")
+
+    # The following code for generation is adapted from https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct#quickstart
+
+    # The default range for the number of visual tokens per image in the model is 4-16384. You can set min_pixels and max_pixels according to your needs, such as a token count range of 256-1280, to balance speed and memory usage.
+    min_pixels = 256*28*28
+    max_pixels = 1280*28*28
+    processor = AutoProcessor.from_pretrained(model_path, min_pixels=min_pixels, max_pixels=max_pixels)
+
+    prompt = args.prompt
+    image_path = args.image_url_or_path
+
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "image",
+                    "image": image_path,
+                },
+                {"type": "text", "text": prompt},
+            ],
+        }
+    ]
+    text = processor.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True
+    )
+    image_inputs, video_inputs = process_vision_info(messages)
+    inputs = processor(
+        text=[text],
+        images=image_inputs,
+        videos=video_inputs,
+        padding=True,
+        return_tensors="pt",
+    )
+    inputs = inputs.to('xpu')
+
+    with torch.inference_mode():
+        # warmup
+        generated_ids = model.generate(
+            **inputs,
+            max_new_tokens=args.n_predict
+            )
+
+        st = time.time()
+        generated_ids = model.generate(
+            **inputs,
+            max_new_tokens=args.n_predict
+            )
+        torch.xpu.synchronize()
+        end = time.time()
+        generated_ids = generated_ids.cpu()
+        generated_ids = [
+            output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, generated_ids)
+            ]
+
+        response = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+        print(f'Inference time: {end-st} s')
+        print('-'*20, 'Input Image', '-'*20)
+        print(image_path)
+        print('-'*20, 'Prompt', '-'*20)
+        print(prompt)
+        print('-'*20, 'Output', '-'*20)
+        print(response)

python/llm/src/ipex_llm/transformers/__init__.py

@@ -21,5 +21,10 @@
         AutoModelForSequenceClassification, AutoModelForMaskedLM, \
         AutoModelForNextSentencePrediction, AutoModelForMultipleChoice, \
         AutoModelForTokenClassification
+
+import transformers
+if transformers.__version__ >= '4.45.0':
+    from .model import Qwen2VLForConditionalGeneration
+
 from .modelling_bigdl import *
 from .pipeline_parallel import init_pipeline_parallel, PPModelWorker

python/llm/src/ipex_llm/transformers/model.py

@@ -839,3 +839,7 @@ class AutoModelForMultipleChoice(_BaseAutoModelClass):
 
 class AutoModelForTokenClassification(_BaseAutoModelClass):
     HF_Model = transformers.AutoModelForTokenClassification
+
+if transformers.__version__ >= '4.45.0':
+    class Qwen2VLForConditionalGeneration(_BaseAutoModelClass):
+        HF_MODEL = transformers.Qwen2VLForConditionalGeneration