[Example]Llama merge

examples/BuddyF16Llama/.gitignore

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+# model params file
+*.data
+# model mlir files
+*.mlir
+*.log
+core

examples/BuddyF16Llama/README.md

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+# Buddy Compiler LLaMA FP16/BF16 Example
+
+1. Download LLaMA2 model
+
+You should download llama model. You can get model from [meta ai](https://ai.meta.com/llama/).
+
+2. Enter Python virtual environment
+
+We recommend you to use anaconda3 to create python virtual environment. You should install python packages as buddy-mlir/requirements.
+
+```
+$ conda activate <your virtual environment name>
+$ cd buddy-mlir
+$ pip install -r requirements.txt
+```
+
+3. LLaMA2 model convert to HuggingFace format
+
+You should convert LLaMA2 model which download from meta ai to HuggingFace format. Because we use HuggingFace api to get LLaMA2 model.
+
+```
+$ cd examples/BuddyF16Llama
+$ python llama2-to-hf.py --input_dir path-to-llama2-model --model_size 7B --output_dir path-to-save-llama-hf-model
+```
+
+Such as you have a 7B LLaMA2 model, in your input_dir path-to-llama-model, you should have a tokenizer.model and a directory named "7B". You should put your 7B LLaMA2 model inside the "7B" directory.
+
+In addition, set an environment variable for the generated LLaMA model.
+```
+$ export LLAMA_MODEL_PATH=/path-to-save-llama-hf-model/
+```
+
+4. Build and check LLVM/MLIR
+
+```
+$ cd buddy-mlir
+$ mkdir llvm/build
+$ cd llvm/build
+$ cmake -G Ninja ../llvm \
+    -DLLVM_ENABLE_PROJECTS="mlir;clang;openmp" \
+    -DLLVM_TARGETS_TO_BUILD="host;RISCV" \
+    -DLLVM_ENABLE_ASSERTIONS=ON \
+    -DOPENMP_ENABLE_LIBOMPTARGET=OFF \
+    -DCMAKE_BUILD_TYPE=RELEASE \
+    -DMLIR_ENABLE_BINDINGS_PYTHON=ON \
+    -DPython3_EXECUTABLE=$(which python3)
+$ ninja check-clang check-mlir omp
+```
+
+5. Build and check buddy-mlir
+
+```
+$ cd buddy-mlir
+$ mkdir build
+$ cd build
+$ cmake -G Ninja .. \
+    -DMLIR_DIR=$PWD/../llvm/build/lib/cmake/mlir \
+    -DLLVM_DIR=$PWD/../llvm/build/lib/cmake/llvm \
+    -DLLVM_ENABLE_ASSERTIONS=ON \
+    -DCMAKE_BUILD_TYPE=RELEASE \
+    -DBUDDY_MLIR_ENABLE_PYTHON_PACKAGES=ON \
+    -DPython3_EXECUTABLE=$(which python3)
+$ ninja
+$ ninja check-buddy
+```
+
+Set the `PYTHONPATH` environment variable. Make sure that the `PYTHONPATH` variable includes the directory of LLVM/MLIR python bindings and the directory of Buddy MLIR python packages.
+
+```
+$ export PYTHONPATH=/path-to-buddy-mlir/llvm/build/tools/mlir/python_packages/mlir_core:/path-to-buddy-mlir/build/python_packages:${PYTHONPATH}
+
+// For example:
+// Navigate to your buddy-mlir/build directory
+$ cd buddy-mlir/build
+$ export BUDDY_MLIR_BUILD_DIR=$PWD
+$ export LLVM_MLIR_BUILD_DIR=$PWD/../llvm/build
+$ export PYTHONPATH=${LLVM_MLIR_BUILD_DIR}/tools/mlir/python_packages/mlir_core:${BUDDY_MLIR_BUILD_DIR}/python_packages:${PYTHONPATH}
+```
+
+6. Build and run LLaMA example
+
+```
+$ cmake -G Ninja .. -DBUDDY_LLAMA_EXAMPLES=ON
+$ ninja buddy-fp16-llama-run
+$ cd bin
+$ ./buddy-fp16-llama-run
+```
+This build will spend a few minutes. We recommend you to use better cpu such as server-level cpu to run buddy-fp16-llama-run.
+
+If you wish to utilize `mimalloc` as a memory allocator, you need to set `BUDDY_MLIR_USE_MIMALLOC` and `MIMALLOC_BUILD_DIR`.
+For more details, please see [here](../../thirdparty/README.md#the-mimalloc-allocator).