Initial NPU C++ Example (#12417)

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* meet review, update

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* meet review, add license

* typo

python/llm/example/NPU/HF-Transformers-AutoModels/LLM/CPP_Examples/CMakeLists.txt

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+cmake_minimum_required(VERSION 3.10)
+
+project(LLM_NPU_EXAMPLE VERSION 1.0.0 LANGUAGES CXX)
+
+set (CMAKE_CXX_STANDARD 17)
+SET (CMAKE_CXX_STANDARD_REQUIRED True)
+
+if(DEFINED ENV{CONDA_ENV_DIR})
+    set(ENV_DIR $ENV{CONDA_ENV_DIR})
+    set(LIBRARY_DIR ${ENV_DIR}/bigdl-core-npu)
+    include_directories(${LIBRARY_DIR}/include)
+    set(DLL_DIR ${ENV_DIR}/intel_npu_acceleration_library/lib/Release)
+else()
+    set(LIBRARY_DIR ${CMAKE_CURRENT_SOURCE_DIR})
+    include_directories(${CMAKE_CURRENT_SOURCE_DIR}/include)
+endif()
+
+add_library(npu_llm STATIC IMPORTED)
+set_target_properties(npu_llm PROPERTIES IMPORTED_LOCATION ${LIBRARY_DIR}/npu_llm.lib)
+
+set(TARGET llm-npu-cli)
+add_executable(${TARGET} llm-npu-cli.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE npu_llm)
+target_compile_features(${TARGET} PRIVATE cxx_std_17)
+
+add_custom_command(TARGET llm-npu-cli POST_BUILD
+    COMMAND ${CMAKE_COMMAND} -E copy_if_different
+        ${LIBRARY_DIR}/npu_llm.dll
+        ${CMAKE_BINARY_DIR}/Release/
+    COMMENT "Copying npu_llm.dll to build/Release\n"
+)
+
+add_custom_command(TARGET llm-npu-cli POST_BUILD
+    COMMAND ${CMAKE_COMMAND} -E copy_directory
+        ${DLL_DIR}/
+        ${CMAKE_BINARY_DIR}/Release/
+    COMMENT "Copying dependency to build/Release\n"
+)

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

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+# C++ Example of running LLM on Intel NPU using IPEX-LLM (Experimental)
+In this directory, you will find a C++ example on how to run LLM models on Intel NPUs using IPEX-LLM (leveraging *Intel NPU Acceleration Library*). See the table blow for verified models.
+
+## Verified Models
+
+| Model      | Model Link                                                    |
+|------------|----------------------------------------------------------------|
+| Qwen2 | [Qwen/Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct), [Qwen/Qwen2-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2-1.5B-Instruct) |
+| Qwen2.5 | [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) |
+
+
+## 0. Requirements
+To run this C++ example with IPEX-LLM on Intel NPUs, make sure to install the newest driver version of Intel NPU.
+Go to https://www.intel.com/content/www/us/en/download/794734/intel-npu-driver-windows.html to download and unzip the driver.
+Then go to **Device Manager**, find **Neural Processors** -> **Intel(R) AI Boost**.
+Right click and select **Update Driver** -> **Browse my computer for drivers**. And then manually select the unzipped driver folder to install.
+
+## 1. Install
+### 1.1 Installation on Windows
+We suggest using conda to manage environment:
+```cmd
+conda create -n llm python=3.10
+conda activate llm
+
+:: install ipex-llm with 'npu' option
+pip install --pre --upgrade ipex-llm[npu]
+
+:: [optional] for Llama-3.2-1B-Instruct & Llama-3.2-3B-Instruct
+pip install transformers==4.45.0 accelerate==0.33.0
+```
+
+## 2. Convert Model
+We provide a [convert script](convert_model.py) under current directory, by running it, you can obtain the whole weights and configuration files which are required to run C++ example.
+
+```cmd
+:: to convert Qwen2.5-7b-Instruct
+python convert_model.py --repo-id-or-model-path Qwen/Qwen2.5-7B-Instruct --save-directory <converted_model_path>
+
+```
+
+Arguments info:
+- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the model (e.g. `Qwen/Qwen2.5-7B-Instruct`) to be downloaded, or the path to the huggingface checkpoint folder.
+- `--save-directory SAVE_DIRECTORY`: argument defining the path to save converted model. If it is a non-existing path, the original pretrained model specified by `REPO_ID_OR_MODEL_PATH` will be loaded, and the converted model will be saved into `SAVE_DIRECTORY`.
+- `--max-context-len MAX_CONTEXT_LEN`: Defines the maximum sequence length for both input and output tokens. It is default to be `1024`.
+- `--max-prompt-len MAX_PROMPT_LEN`: Defines the maximum number of tokens that the input prompt can contain. It is default to be `960`.
+- `--disable-transpose-value-cache`: Disable the optimization of transposing value cache.
+
+## 3. Build C++ Example `llm-npu-cli`
+
+You can run below cmake script in cmd to build `llm-npu-cli`, don't forget to replace below conda env dir with your own path.
+
+```cmd
+:: under current directory
+:: please replace below conda env dir with your own path
+set CONDA_ENV_DIR=C:\Users\arda\miniforge3\envs\llm\Lib\site-packages
+mkdir build
+cd build
+cmake ..
+cmake --build . --config Release -j
+cd Release
+```
+
+## 4. Run `llm-npu-cli`
+
+With built `llm-npu-cli`, you can run the example with specified paramaters. For example,
+
+```cmd
+llm-npu-cli.exe -m <converted_model_path> -n 64 "AI是什么?"
+```
+
+Arguments info:
+- `-m` : argument defining the path of saved converted model.
+- `-n` : argument defining how many tokens will be generated.
+- Last argument is your input prompt.
+
+### 5. Sample Output
+#### [`Qwen/Qwen2.5-7B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)
+```cmd
+Input:
+<|im_start|>system
+You are a helpful assistant.<|im_end|>
+<|im_start|>user
+AI是什么?<|im_end|>
+<|im_start|>assistant
+
+
+Prefill 22 tokens cost xxxx ms.
+Output:
+AI是"人工智能"的缩写，是英文"Artificial Intelligence"的翻译。它是研究如何使计算机也具有智能的一种技术和理论。简而言之，人工智能就是让计算机能够模仿人智能行为的一项技术。
+
+Decode 46 tokens cost xxxx ms (avg xx.xx ms each token).
+```

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

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+#
+# 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 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="Convert LLM for C++ NPU inference and save"
+    )
+    parser.add_argument(
+        "--repo-id-or-model-path",
+        type=str,
+        default="Qwen/Qwen2.5-7B-Instruct",  # Or Qwen2-7B-Instruct, Qwen2-1.5B-Instruct
+        help="The huggingface repo id for the Qwen model to be downloaded"
+        ", or the path to the huggingface checkpoint folder",
+    )
+    parser.add_argument("--save-directory", type=str,
+        required=True,
+        help="The path of folder to save converted model, "
+            "If path not exists, lowbit model will be saved there. "
+            "Else, program will raise error.",
+    )
+    parser.add_argument("--max-context-len", type=int, default=1024)
+    parser.add_argument("--max-prompt-len", type=int, default=960)
+    parser.add_argument("--quantization_group_size", type=int, default=0)
+    parser.add_argument('--load_in_low_bit', type=str, default="sym_int4",
+                        help='Load in low bit to use')
+    parser.add_argument("--disable-transpose-value-cache", action="store_true", default=False)
+
+    args = parser.parse_args()
+    model_path = args.repo_id_or_model_path
+    save_dir = args.save_directory
+
+    model = AutoModelForCausalLM.from_pretrained(model_path,
+                                                 optimize_model=True,
+                                                 pipeline=True,
+                                                 load_in_low_bit=args.load_in_low_bit,
+                                                 max_context_len=args.max_context_len,
+                                                 max_prompt_len=args.max_prompt_len,
+                                                 quantization_group_size=args.quantization_group_size,
+                                                 torch_dtype=torch.float16,
+                                                 attn_implementation="eager",
+                                                 transpose_value_cache=not args.disable_transpose_value_cache,
+                                                 mixed_precision=True,
+                                                 trust_remote_code=True,
+                                                 compile_full_model=True,
+                                                 save_directory=save_dir)
+
+    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+    tokenizer.save_pretrained(save_dir)
+
+    print("-" * 80)
+    print(f"finish save model to {save_dir}")
+    print("success shut down")

python/llm/example/NPU/HF-Transformers-AutoModels/LLM/CPP_Examples/llm-npu-cli.cpp

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+//
+// 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.
+//
+
+#include <iostream>
+#include <fstream>
+#include <string>
+#include <chrono>
+
+#include "common.h"
+#include "npu_llm.h"
+
+
+static void print_usage(int, char ** argv) {
+    printf("\nexample usage:\n");
+    printf("\n    %s -m npu_model_dir [-n n_predict] [prompt]\n", argv[0]);
+    printf("\n");
+}
+
+
+int main(int argc, char ** argv) {
+    common_params params;
+
+    // path to the npu model directory
+    std::string model_dir;
+    // prompt to generate text from
+    std::string prompt = "AI是什么?";
+    // number of tokens to predict
+    int n_predict = 32;
+
+    // parse command line arguments
+
+    {
+        int i = 1;
+        for (; i < argc; i++) {
+            if (strcmp(argv[i], "-m") == 0) {
+                if (i + 1 < argc) {
+                    model_dir = argv[++i];
+                } else {
+                    print_usage(argc, argv);
+                    return 1;
+                }
+            } else if (strcmp(argv[i], "-n") == 0) {
+                if (i + 1 < argc) {
+                    try {
+                        n_predict = std::stoi(argv[++i]);
+                    } catch (...) {
+                        print_usage(argc, argv);
+                        return 1;
+                    }
+                } else {
+                    print_usage(argc, argv);
+                    return 1;
+                }
+            } else {
+                // prompt starts here
+                break;
+            }
+        }
+        if (model_dir.empty()) {
+            print_usage(argc, argv);
+            return 1;
+        }
+        if (i < argc) {
+            prompt = argv[i++];
+            for (; i < argc; i++) {
+                prompt += " ";
+                prompt += argv[i];
+            }
+        }
+    }
+
+    params.n_predict = n_predict;
+    params.model = model_dir;
+    params.prompt = prompt;
+
+    npu_model_params model_params;
+    NPUModel* model = load_model_from_file(model_params, params.model);
+
+    tokenizer_params tok_params;
+    load_tokenizer(tok_params, params.model);
+
+    std::string full_prompt = add_chat_template(model_params, params.prompt);
+    std::cout << "Input: " << std::endl;
+    std::cout << full_prompt << std::endl;
+
+    // tokenize input
+    std::vector<int32_t> embd_inp = llm_tokenize(full_prompt, false);
+
+    std::vector<int32_t> embd;  // output ids
+    auto start = std::chrono::high_resolution_clock::now();
+    float* logits = run_prefill(model, embd_inp);
+    int32_t token = llm_sample_token(logits, true, model_params);
+    auto end = std::chrono::high_resolution_clock::now();
+    auto duration = std::chrono::duration_cast<std::chrono::milliseconds>(end - start);
+    printf("\nPrefill %d tokens cost %d ms.\n", embd_inp.size(), duration.count());
+    embd.push_back(token);
+
+    int token_nums = 0;
+    start = std::chrono::high_resolution_clock::now();
+    for (int i = 1; i < params.n_predict; i++){
+        auto logits = run_decode(model, embd[i-1]);
+        int32_t token = llm_sample_token(logits, true, model_params);
+        if (token != tok_params.eos_token_id) {
+            embd.push_back(token);
+            token_nums ++;
+        } else {
+            break;
+        }
+    }
+    end = std::chrono::high_resolution_clock::now();
+    duration = std::chrono::duration_cast<std::chrono::milliseconds>(end - start);
+
+    std::string output = llm_decode(embd);
+
+    std::cout << "Output: " << std::endl;
+    std::cout << output << std::endl;
+
+    printf("\nDecode %d tokens cost %d ms (avg %f ms each token).\n", token_nums, duration.count(), (float)duration.count() / token_nums);
+
+    return 0;
+}