From 40fc8704c4867dea6ac3328bef27da431cfd80f4 Mon Sep 17 00:00:00 2001 From: Zijie Li Date: Wed, 12 Jun 2024 14:29:50 +0800 Subject: [PATCH] Add GPU example for GLM-4 (#11267) * Add GPU example for GLM-4 * Update streamchat.py * Fix pretrianed arguments Fix pretrained arguments in generate and streamchat.py * Update Readme Update install tiktoken required for GLM-4 * Update comments in generate.py --- .../Model/glm4/README.md | 265 ++++++++++++++++++ .../Model/glm4/generate.py | 78 ++++++ .../Model/glm4/streamchat.py | 69 +++++ .../GPU/PyTorch-Models/Model/glm4/README.md | 147 ++++++++++ .../GPU/PyTorch-Models/Model/glm4/generate.py | 77 +++++ 5 files changed, 636 insertions(+) create mode 100644 python/llm/example/GPU/HF-Transformers-AutoModels/Model/glm4/README.md create mode 100644 python/llm/example/GPU/HF-Transformers-AutoModels/Model/glm4/generate.py create mode 100644 python/llm/example/GPU/HF-Transformers-AutoModels/Model/glm4/streamchat.py create mode 100644 python/llm/example/GPU/PyTorch-Models/Model/glm4/README.md create mode 100644 python/llm/example/GPU/PyTorch-Models/Model/glm4/generate.py diff --git a/python/llm/example/GPU/HF-Transformers-AutoModels/Model/glm4/README.md b/python/llm/example/GPU/HF-Transformers-AutoModels/Model/glm4/README.md new file mode 100644 index 00000000000..3d0c5d584a3 --- /dev/null +++ b/python/llm/example/GPU/HF-Transformers-AutoModels/Model/glm4/README.md @@ -0,0 +1,265 @@ +# GLM-4 +In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on GLM-4 models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [THUDM/glm-4-9b-chat](https://huggingface.co/THUDM/glm-4-9b-chat) as a reference InternLM model. + +## 0. Requirements +To run these examples with IPEX-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to [here](../../../README.md#requirements) for more information. + +## Example 1: Predict Tokens using `generate()` API +In the example [generate.py](./generate.py), we show a basic use case for a GLM-4 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations on Intel GPUs. +### 1. Install +#### 1.1 Installation on Linux +We suggest using conda to manage environment: +```bash +conda create -n llm python=3.11 +conda activate llm +# below command will install intel_extension_for_pytorch==2.1.10+xpu as default +pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ + +# install tiktoken required for GLM-4 +pip install tiktoken +``` + +#### 1.2 Installation on Windows +We suggest using conda to manage environment: +```bash +conda create -n llm python=3.11 libuv +conda activate llm + +# below command will install intel_extension_for_pytorch==2.1.10+xpu as default +pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ + +# install tiktoken required for GLM-4 +pip install tiktoken +``` + +### 2. Configures OneAPI environment variables for Linux + +> [!NOTE] +> Skip this step if you are running on Windows. + +This is a required step on Linux for APT or offline installed oneAPI. Skip this step for PIP-installed oneAPI. + +```bash +source /opt/intel/oneapi/setvars.sh +``` + +### 3. Runtime Configurations +For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device. +#### 3.1 Configurations for Linux +
+ +For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series + +```bash +export USE_XETLA=OFF +export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1 +export SYCL_CACHE_PERSISTENT=1 +``` + +
+ +
+ +For Intel Data Center GPU Max Series + +```bash +export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so +export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1 +export SYCL_CACHE_PERSISTENT=1 +export ENABLE_SDP_FUSION=1 +``` +> Note: Please note that `libtcmalloc.so` can be installed by `conda install -c conda-forge -y gperftools=2.10`. +
+ +
+ +For Intel iGPU + +```bash +export SYCL_CACHE_PERSISTENT=1 +export BIGDL_LLM_XMX_DISABLED=1 +``` + +
+ +#### 3.2 Configurations for Windows +
+ +For Intel iGPU + +```cmd +set SYCL_CACHE_PERSISTENT=1 +set BIGDL_LLM_XMX_DISABLED=1 +``` + +
+ +
+ +For Intel Arc™ A-Series Graphics + +```cmd +set SYCL_CACHE_PERSISTENT=1 +``` + +
+ +> [!NOTE] +> For the first time that each model runs on Intel iGPU/Intel Arc™ A300-Series or Pro A60, it may take several minutes to compile. +### 4. Running examples + +``` +python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT +``` + +Arguments info: +- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the GLM-4 model (e.g. `THUDM/glm-4-9b-chat`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'THUDM/glm-4-9b-chat'`. +- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'AI是什么？'`. +- `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`. + +#### Sample Output +##### [THUDM/glm-4-9b-chat](https://huggingface.co/THUDM/glm-4-9b-chat) +```log +Inference time: xxxx s +-------------------- Prompt -------------------- +<|user|> +AI是什么？ +<|assistant|> +-------------------- Output -------------------- + +AI是什么？ + +AI，即人工智能（Artificial Intelligence），是指由人创造出来的，能够模拟、延伸和扩展人的智能的计算机系统或机器。人工智能的目标 +``` + +```log +Inference time: xxxx s +-------------------- Prompt -------------------- +<|user|> +What is AI? +<|assistant|> +-------------------- Output -------------------- + +What is AI? + +Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term "art +``` + +## Example 2: Stream Chat using `stream_chat()` API +In the example [streamchat.py](./streamchat.py), we show a basic use case for a GLM-4 model to stream chat, with IPEX-LLM INT4 optimizations. +### 1. Install +#### 1.1 Installation on Linux +We suggest using conda to manage environment: +```bash +conda create -n llm python=3.11 +conda activate llm +# below command will install intel_extension_for_pytorch==2.1.10+xpu as default +pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ + +# install tiktoken required for GLM-4 +pip install tiktoken +``` + +#### 1.2 Installation on Windows +We suggest using conda to manage environment: +```bash +conda create -n llm python=3.11 libuv +conda activate llm + +# below command will install intel_extension_for_pytorch==2.1.10+xpu as default +pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ + +# install tiktoken required for GLM-4 +pip install tiktoken +``` + +### 2. Configures OneAPI environment variables for Linux + +> [!NOTE] +> Skip this step if you are running on Windows. + +This is a required step on Linux for APT or offline installed oneAPI. Skip this step for PIP-installed oneAPI. + +```bash +source /opt/intel/oneapi/setvars.sh +``` + +### 3. Runtime Configurations +For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device. +#### 3.1 Configurations for Linux +
+ +For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series + +```bash +export USE_XETLA=OFF +export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1 +export SYCL_CACHE_PERSISTENT=1 +``` + +
+ +
+ +For Intel Data Center GPU Max Series + +```bash +export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so +export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1 +export SYCL_CACHE_PERSISTENT=1 +export ENABLE_SDP_FUSION=1 +``` +> Note: Please note that `libtcmalloc.so` can be installed by `conda install -c conda-forge -y gperftools=2.10`. +
+ +
+ +For Intel iGPU + +```bash +export SYCL_CACHE_PERSISTENT=1 +export BIGDL_LLM_XMX_DISABLED=1 +``` + +
+ +#### 3.2 Configurations for Windows +
+ +For Intel iGPU + +```cmd +set SYCL_CACHE_PERSISTENT=1 +set BIGDL_LLM_XMX_DISABLED=1 +``` + +
+ +
+ +For Intel Arc™ A-Series Graphics + +```cmd +set SYCL_CACHE_PERSISTENT=1 +``` + +
+ +> [!NOTE] +> For the first time that each model runs on Intel iGPU/Intel Arc™ A300-Series or Pro A60, it may take several minutes to compile. +### 4. Running examples + +**Stream Chat using `stream_chat()` API**: +``` +python ./streamchat.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --question QUESTION +``` + +**Chat using `chat()` API**: +``` +python ./streamchat.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --question QUESTION --disable-stream +``` + +Arguments info: +- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the GLM-4 model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'THUDM/glm-4-9b-chat'`. +- `--question QUESTION`: argument defining the question to ask. It is default to be `"AI是什么？"`. +- `--disable-stream`: argument defining whether to stream chat. If include `--disable-stream` when running the script, the stream chat is disabled and `chat()` API is used. diff --git a/python/llm/example/GPU/HF-Transformers-AutoModels/Model/glm4/generate.py b/python/llm/example/GPU/HF-Transformers-AutoModels/Model/glm4/generate.py new file mode 100644 index 00000000000..ebef3dae4c2 --- /dev/null +++ b/python/llm/example/GPU/HF-Transformers-AutoModels/Model/glm4/generate.py @@ -0,0 +1,78 @@ +# +# 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 ipex_llm.transformers import AutoModel +from transformers import AutoTokenizer + +# you could tune the prompt based on your own model, +# here the prompt tuning refers to https://huggingface.co/THUDM/glm-4-9b-chat/blob/main/tokenization_chatglm.py +GLM4_PROMPT_FORMAT = "<|user|>\n{prompt}\n<|assistant|>" + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for GLM-4 model') + parser.add_argument('--repo-id-or-model-path', type=str, default="THUDM/glm-4-9b-chat", + help='The huggingface repo id for the GLM-4 model to be downloaded' + ', or the path to the huggingface checkpoint folder') + parser.add_argument('--prompt', type=str, default="AI是什么？", + help='Prompt to infer') + parser.add_argument('--n-predict', type=int, default=32, + help='Max tokens to predict') + + args = parser.parse_args() + model_path = args.repo_id_or_model_path + + # Load model in 4 bit, + # which convert the relevant layers in the model into INT4 format + # When running LLMs on Intel iGPUs for Windows users, we recommend setting `cpu_embedding=True` in the from_pretrained function. + # This will allow the memory-intensive embedding layer to utilize the CPU instead of iGPU. + model = AutoModel.from_pretrained(model_path, + load_in_4bit=True, + optimize_model=True, + trust_remote_code=True, + use_cache=True) + model = model.to("xpu") + + # Load tokenizer + tokenizer = AutoTokenizer.from_pretrained(model_path, + trust_remote_code=True) + + # Generate predicted tokens + with torch.inference_mode(): + prompt = GLM4_PROMPT_FORMAT.format(prompt=args.prompt) + input_ids = tokenizer.encode(prompt, return_tensors="pt").to('xpu') + + # ipex_llm model needs a warmup, then inference time can be accurate + output = model.generate(input_ids, + max_new_tokens=args.n_predict) + + st = time.time() + + output = model.generate(input_ids, + max_new_tokens=args.n_predict) + + torch.xpu.synchronize() + end = time.time() + output_str = tokenizer.decode(output[0], skip_special_tokens=True) + print(f'Inference time: {end-st} s') + print('-'*20, 'Prompt', '-'*20) + print(prompt) + print('-'*20, 'Output', '-'*20) + print(output_str) diff --git a/python/llm/example/GPU/HF-Transformers-AutoModels/Model/glm4/streamchat.py b/python/llm/example/GPU/HF-Transformers-AutoModels/Model/glm4/streamchat.py new file mode 100644 index 00000000000..31be35d9639 --- /dev/null +++ b/python/llm/example/GPU/HF-Transformers-AutoModels/Model/glm4/streamchat.py @@ -0,0 +1,69 @@ +# +# 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 ipex_llm.transformers import AutoModel +from transformers import AutoTokenizer + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Stream Chat for GLM-4 model') + parser.add_argument('--repo-id-or-model-path', type=str, default="THUDM/glm-4-9b-chat", + help='The huggingface repo id for the GLM-4 model to be downloaded' + ', or the path to the huggingface checkpoint folder') + parser.add_argument('--question', type=str, default="晚上睡不着应该怎么办", + help='Qustion you want to ask') + parser.add_argument('--disable-stream', action="store_true", + help='Disable stream chat') + + args = parser.parse_args() + model_path = args.repo_id_or_model_path + disable_stream = args.disable_stream + + # Load model in 4 bit, + # which convert the relevant layers in the model into INT4 format + # When running LLMs on Intel iGPUs for Windows users, we recommend setting `cpu_embedding=True` in the from_pretrained function. + # This will allow the memory-intensive embedding layer to utilize the CPU instead of iGPU. + model = AutoModel.from_pretrained(model_path, + trust_remote_code=True, + load_in_4bit=True, + optimize_model=True, + use_cache=True, + cpu_embedding=True) + + model = model.to('xpu') + + # Load tokenizer + tokenizer = AutoTokenizer.from_pretrained(model_path, + trust_remote_code=True) + + with torch.inference_mode(): + if disable_stream: + # Chat + response, history = model.chat(tokenizer, args.question, history=[]) + print('-'*20, 'Chat Output', '-'*20) + print(response) + else: + # Stream chat + response_ = "" + print('-'*20, 'Stream Chat Output', '-'*20) + for response, history in model.stream_chat(tokenizer, args.question, history=[]): + print(response.replace(response_, ""), end="") + response_ = response diff --git a/python/llm/example/GPU/PyTorch-Models/Model/glm4/README.md b/python/llm/example/GPU/PyTorch-Models/Model/glm4/README.md new file mode 100644 index 00000000000..6993df68438 --- /dev/null +++ b/python/llm/example/GPU/PyTorch-Models/Model/glm4/README.md @@ -0,0 +1,147 @@ +# GLM-4 +In this directory, you will find examples on how you could use IPEX-LLM `optimize_model` API to accelerate GLM-4 models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [THUDM/glm-4-9b-chat](https://huggingface.co/THUDM/glm-4-9b-chat) as a reference GLM-4 model. + +## 0. Requirements +To run these examples with IPEX-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to [here](../../../README.md#requirements) for more information. + +## Example: Predict Tokens using `generate()` API +In the example [generate.py](./generate.py), we show a basic use case for a GLM-4 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations on Intel GPUs. +### 1. Install +#### 1.1 Installation on Linux +We suggest using conda to manage environment: +```bash +conda create -n llm python=3.11 +conda activate llm +# below command will install intel_extension_for_pytorch==2.1.10+xpu as default +pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ + +# install tiktoken required for GLM-4 +pip install tiktoken +``` + +#### 1.2 Installation on Windows +We suggest using conda to manage environment: +```bash +conda create -n llm python=3.11 libuv +conda activate llm + +# below command will install intel_extension_for_pytorch==2.1.10+xpu as default +pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ + +# install tiktoken required for GLM-4 +pip install tiktoken +``` + +### 2. Configures OneAPI environment variables for Linux + +> [!NOTE] +> Skip this step if you are running on Windows. + +This is a required step on Linux for APT or offline installed oneAPI. Skip this step for PIP-installed oneAPI. + +```bash +source /opt/intel/oneapi/setvars.sh +``` + +### 3. Runtime Configurations +For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device. +#### 3.1 Configurations for Linux +
+ +For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series + +```bash +export USE_XETLA=OFF +export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1 +export SYCL_CACHE_PERSISTENT=1 +``` + +
+ +
+ +For Intel Data Center GPU Max Series + +```bash +export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so +export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1 +export SYCL_CACHE_PERSISTENT=1 +export ENABLE_SDP_FUSION=1 +``` +> Note: Please note that `libtcmalloc.so` can be installed by `conda install -c conda-forge -y gperftools=2.10`. +
+ +
+ +For Intel iGPU + +```bash +export SYCL_CACHE_PERSISTENT=1 +export BIGDL_LLM_XMX_DISABLED=1 +``` + +
+ +#### 3.2 Configurations for Windows +
+ +For Intel iGPU + +```cmd +set SYCL_CACHE_PERSISTENT=1 +set BIGDL_LLM_XMX_DISABLED=1 +``` + +
+ +
+ +For Intel Arc™ A-Series Graphics + +```cmd +set SYCL_CACHE_PERSISTENT=1 +``` + +
+ +> [!NOTE] +> For the first time that each model runs on Intel iGPU/Intel Arc™ A300-Series or Pro A60, it may take several minutes to compile. +### 4. Running examples + +``` +python ./generate.py --prompt 'What is AI?' +``` + +Arguments info: +- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the GLM-4 model (e.g. `THUDM/glm-4-9b-chat`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'THUDM/glm-4-9b-chat'`. +- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'What is AI?'`. +- `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`. + +#### Sample Output +#### [THUDM/glm-4-9b-chat](https://huggingface.co/THUDM/glm-4-9b-chat) + +```log +Inference time: xxxx s +-------------------- Prompt -------------------- +<|user|> +AI是什么？ +<|assistant|> +-------------------- Output -------------------- + +AI是什么？ + +AI，即人工智能（Artificial Intelligence），是指由人创造出来的，能够模拟、延伸和扩展人的智能的计算机系统或机器。人工智能的目标 +``` + +```log +Inference time: xxxx s +-------------------- Prompt -------------------- +<|user|> +What is AI? +<|assistant|> +-------------------- Output -------------------- + +What is AI? + +Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term "art +``` diff --git a/python/llm/example/GPU/PyTorch-Models/Model/glm4/generate.py b/python/llm/example/GPU/PyTorch-Models/Model/glm4/generate.py new file mode 100644 index 00000000000..3433ba29167 --- /dev/null +++ b/python/llm/example/GPU/PyTorch-Models/Model/glm4/generate.py @@ -0,0 +1,77 @@ +# +# 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 + +from transformers import AutoModel, AutoTokenizer +from ipex_llm import optimize_model + +# you could tune the prompt based on your own model, +# here the prompt tuning refers to https://huggingface.co/THUDM/glm-4-9b-chat/blob/main/tokenization_chatglm.py +GLM4_PROMPT_FORMAT = "<|user|>\n{prompt}\n<|assistant|>" + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for GLM-4 model') + parser.add_argument('--repo-id-or-model-path', type=str, default="THUDM/glm-4-9b-chat", + help='The huggingface repo id for the GLM-4 model to be downloaded' + ', or the path to the huggingface checkpoint folder') + parser.add_argument('--prompt', type=str, default="AI是什么？", + help='Prompt to infer') + parser.add_argument('--n-predict', type=int, default=32, + help='Max tokens to predict') + + args = parser.parse_args() + model_path = args.repo_id_or_model_path + + # Load model + # When running LLMs on Intel iGPUs for Windows users, we recommend setting `cpu_embedding=True` in the from_pretrained function. + # This will allow the memory-intensive embedding layer to utilize the CPU instead of iGPU. + model = AutoModel.from_pretrained(model_path, + trust_remote_code=True, + torch_dtype = 'auto', + low_cpu_mem_usage=True, + use_cache=True) + + # With only one line to enable IPEX-LLM optimization on model + model = optimize_model(model) + model = model.to('xpu') + + # Load tokenizer + tokenizer = AutoTokenizer.from_pretrained(model_path, + trust_remote_code=True) + + # Generate predicted tokens + with torch.inference_mode(): + prompt = GLM4_PROMPT_FORMAT.format(prompt=args.prompt) + input_ids = tokenizer.encode(prompt, return_tensors="pt").to('xpu') + + # ipex_llm model needs a warmup, then inference time can be accurate + output = model.generate(input_ids, + max_new_tokens=args.n_predict) + + st = time.time() + output = model.generate(input_ids, + max_new_tokens=args.n_predict) + torch.xpu.synchronize() + end = time.time() + output_str = tokenizer.decode(output[0], skip_special_tokens=True) + print(f'Inference time: {end-st} s') + print('-'*20, 'Prompt', '-'*20) + print(prompt) + print('-'*20, 'Output', '-'*20) + print(output_str)