[NPU] Add initial support for minicpm-llama-v2.5 (intel-analytics#11962)

* add initial support for minicpm-llama-v2.5

* update impl

* add minicpm-llama3-v2.5 example

python/llm/example/NPU/HF-Transformers-AutoModels/Multimodal/minicpm-llama3-v2.5.py

@@ -0,0 +1,103 @@
+#
+# 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 torch
+import time
+import argparse
+
+from ipex_llm.transformers.npu_model import AutoModel, AutoModelForCausalLM
+from transformers import AutoTokenizer
+from transformers.utils import logging
+
+import requests
+from PIL import Image
+
+logger = logging.get_logger(__name__)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Predict Tokens using `chat()` API for npu model"
+    )
+    parser.add_argument(
+        "--repo-id-or-model-path",
+        type=str,
+        default="openbmb/MiniCPM-Llama3-V-2_5",
+        help="The huggingface repo id for the MiniCPM-Llama3-V-2_5 model to be downloaded"
+        ", or the path to the huggingface checkpoint folder",
+    )
+    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('--prompt', type=str, default="What is in the image?",
+                        help='Prompt to infer')
+    parser.add_argument("--n-predict", type=int, default=32, help="Max tokens to predict")
+    parser.add_argument("--max-output-len", type=int, default=1024)
+    parser.add_argument("--max-prompt-len", type=int, default=512)
+    parser.add_argument("--disable-transpose-value-cache", action="store_true", default=False)
+    parser.add_argument("--intra-pp", type=int, default=2)
+    parser.add_argument("--inter-pp", type=int, default=2)
+
+    args = parser.parse_args()
+    model_path = args.repo_id_or_model_path
+
+    model = AutoModelForCausalLM.from_pretrained(
+        model_path,
+        torch_dtype=torch.float32,
+        trust_remote_code=True,
+        attn_implementation="eager",
+        load_in_low_bit="sym_int4",
+        optimize_model=True,
+        max_output_len=args.max_output_len,
+        max_prompt_len=args.max_prompt_len,
+        intra_pp=args.intra_pp,
+        inter_pp=args.inter_pp,
+        transpose_value_cache=not args.disable_transpose_value_cache,
+    )
+    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+
+    print("-" * 80)
+    print("done")
+
+    msgs = [{'role': 'user', 'content': args.prompt}]
+    image_path = args.image_url_or_path
+    if os.path.exists(image_path):
+       image = Image.open(image_path).convert('RGB')
+    else:
+       image = Image.open(requests.get(image_path, stream=True).raw).convert('RGB')
+
+    st = time.time()
+    res = model.chat(
+        image=image,
+        msgs=msgs,
+        tokenizer=tokenizer,
+        sampling=True,
+        temperature=0.7,
+        # system_prompt='' # pass system_prompt if needed
+        )
+    end = time.time()
+
+    print(f'Inference time: {end-st} s')
+    print('-'*20, 'Input', '-'*20)
+    print(image_path)
+    print('-'*20, 'Prompt', '-'*20)
+    print(args.prompt)
+    output_str = res
+    print('-'*20, 'Output', '-'*20)
+    print(output_str)
+
+    print("done")
+    print("success shut down")

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

@@ -152,17 +152,25 @@ def from_pretrained(cls, *args, **kwargs):
             )
             from ipex_llm.transformers.npu_models.convert_mp import optimize_llm, optimize_llm_pre
 
+            if model.config.model_type == "minicpmv":
+                llm = model.llm
+                if llm.config.hidden_size == 4096 and llm.config.vocab_size == 128256:
+                    # MiniCPM-llama3-V2.5
+                    llm.config.model_type = "llama"
+            else:
+                llm = model
+
             with torch.no_grad():
-                optimize_llm_pre(model, qtype)
-                cls.load_convert(qtype, model, "cpu", *args, **kwargs)
-                create_npu_kernels(model)
+                optimize_llm_pre(llm, qtype)
+                cls.load_convert(qtype, llm, "cpu", *args, **kwargs)
+                create_npu_kernels(llm)
             model = model.eval()
             logger.info(f"Finish to convert model")
             model.config.update({"bigdl_transformers_low_bit": qtype})
             model.share_memory()
 
             optimize_llm(
-                model,
+                llm,
                 max_output_len=max_output_len,
                 max_prompt_len=max_prompt_len,
                 inter_pp=inter_pp,