[NPU] Add example for NPU multi-processing minicpm-1b model (#11935)

* add minicpm example

python/llm/example/NPU/HF-Transformers-AutoModels/LLM/README.md

@@ -81,6 +81,7 @@ The example below shows how to run the **_optimized model implementations_** on
 - [Llama2-7B](./llama.py)
 - [Llama3-8B](./llama.py)
 - [Qwen2-1.5B](./qwen2.py)
+- [MiniCPM-1B](./minicpm.py)
 
 ```bash
 # to run Llama-2-7b-chat-hf
@@ -91,6 +92,9 @@ python llama.py --repo-id-or-model-path meta-llama/Meta-Llama-3-8B-Instruct
 
 # to run Qwen2-1.5B-Instruct
 python qwen2.py
+
+# to run MiniCPM-1B-sft-bf16
+python minicpm.py
 ```
 
 Arguments info:
@@ -113,6 +117,9 @@ python llama.py --repo-id-or-model-path meta-llama/Meta-Llama-3-8B-Instruct --d
 
 # to run Qwen2-1.5B-Instruct
 python qwen2.py --disable-transpose-value-cache
+
+# to run MiniCPM-1B-sft-bf16
+python minicpm.py --disable-transpose-value-cache
 ```
 
 

python/llm/example/NPU/HF-Transformers-AutoModels/LLM/minicpm.py

@@ -0,0 +1,91 @@
+#
+# 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 AutoModelForCausalLM
+from transformers import AutoTokenizer
+
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Predict Tokens using `generate()` API for npu model"
+    )
+    parser.add_argument(
+        "--repo-id-or-model-path",
+        type=str,
+        default="openbmb/MiniCPM-1B-sft-bf16",
+        help="The huggingface repo id for the Llama2 model to be downloaded"
+        ", or the path to the huggingface checkpoint folder",
+    )
+    parser.add_argument('--prompt', type=str, default="What is AI?",
+                        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.float16,
+        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")
+    with torch.inference_mode():
+
+        print("finish to load")
+        for i in range(5):
+            _input_ids = tokenizer.encode("<用户>{}".format(args.prompt), return_tensors="pt")
+            print("input length:", len(_input_ids[0]))
+            st = time.time()
+            output = model.generate(
+                _input_ids, num_beams=1, do_sample=False, max_new_tokens=args.n_predict
+            )
+            end = time.time()
+            print(f"Inference time: {end-st} s")
+            input_str = tokenizer.decode(_input_ids[0], skip_special_tokens=False)
+            print("-" * 20, "Input", "-" * 20)
+            print(input_str)
+            output_str = tokenizer.decode(output[0], skip_special_tokens=False)
+            print("-" * 20, "Output", "-" * 20)
+            print(output_str)
+
+    print("-" * 80)
+    print("done")
+    print("success shut down")