Add GLM-4 CPU example (#11223)

* Add GLM-4 example

* add tiktoken dependency

* fix

* fix

README.md

@@ -154,6 +154,7 @@ Over 50 models have been optimized/verified on `ipex-llm`, including *LLaMA/LLaM
 | ChatGLM    | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/chatglm)   |    | 
 | ChatGLM2   | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/chatglm2)  | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/chatglm2)   |
 | ChatGLM3   | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/chatglm3)  | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/chatglm3)   |
+| GLM-4      | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/glm4)      | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/glm4)       |
 | Mistral    | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/mistral)   | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/mistral)    |
 | Mixtral    | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/mixtral)   | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/mixtral)    |
 | Falcon     | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/falcon)    | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/falcon)     |

docs/readthedocs/source/index.rst

@@ -257,6 +257,13 @@ Verified Models
          <td>
            <a href="https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels/Model/chatglm3">link</a></td>
        </tr>
+       <tr>
+         <td>GLM-4</td>
+         <td>
+           <a href="https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/glm4">link</a></td>
+         <td>
+           <a href="https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels/Model/glm4">link</a></td>
+       </tr>
        <tr>
          <td>Mistral</td>
          <td>

python/llm/example/CPU/HF-Transformers-AutoModels/Model/glm4/README.md

@@ -0,0 +1,166 @@
+# GLM-4
+
+In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on GLM-4 models. 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, we have some recommended requirements for your machine, please refer to [here](../README.md#recommended-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.
+### 1. Install
+We suggest using conda to manage environment:
+
+On Linux:
+
+```bash
+conda create -n llm python=3.11 # recommend to use Python 3.11
+conda activate llm
+
+# install the latest ipex-llm nightly build with 'all' option
+pip install --pre --upgrade ipex-llm[all] --extra-index-url https://download.pytorch.org/whl/cpu
+
+# install tiktoken required for GLM-4
+pip install tiktoken
+```
+
+On Windows:
+
+```cmd
+conda create -n llm python=3.11
+conda activate llm
+
+pip install --pre --upgrade ipex-llm[all]
+
+pip install tiktoken
+```
+
+### 2. Run
+```
+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 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`.
+
+> **Note**: When loading the model in 4-bit, IPEX-LLM converts linear layers in the model into INT4 format. In theory, a *X*B model saved in 16-bit will requires approximately 2*X* GB of memory for loading, and ~0.5*X* GB memory for further inference.
+>
+> Please select the appropriate size of the GLM-4 model based on the capabilities of your machine.
+
+#### 2.1 Client
+On client Windows machine, it is recommended to run directly with full utilization of all cores:
+```cmd
+python ./generate.py 
+```
+
+#### 2.2 Server
+For optimal performance on server, it is recommended to set several environment variables (refer to [here](../README.md#best-known-configuration-on-linux) for more information), and run the example with all the physical cores of a single socket.
+
+E.g. on Linux,
+```bash
+# set IPEX-LLM env variables
+source ipex-llm-init
+
+# e.g. for a server with 48 cores per socket
+export OMP_NUM_THREADS=48
+numactl -C 0-47 -m 0 python ./generate.py
+```
+
+#### 2.3 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。人工智能技术
+```
+
+```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
+We suggest using conda to manage environment:
+
+On Linux:
+
+```bash
+conda create -n llm python=3.11 # recommend to use Python 3.11
+conda activate llm
+
+# install the latest ipex-llm nightly build with 'all' option
+pip install --pre --upgrade ipex-llm[all] --extra-index-url https://download.pytorch.org/whl/cpu
+
+# install tiktoken required for GLM-4
+pip install tiktoken
+```
+
+On Windows:
+
+```cmd
+conda create -n llm python=3.11
+conda activate llm
+
+pip install --pre --upgrade ipex-llm[all]
+
+pip install tiktoken
+```
+
+### 2. Run
+**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 `"晚上睡不着应该怎么办"`.
+- `--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.
+
+> **Note**: When loading the model in 4-bit, IPEX-LLM converts linear layers in the model into INT4 format. In theory, a *X*B model saved in 16-bit will requires approximately 2*X* GB of memory for loading, and ~0.5*X* GB memory for further inference.
+>
+> Please select the appropriate size of the GLM-4 model based on the capabilities of your machine.
+
+#### 2.1 Client
+On client Windows machine, it is recommended to run directly with full utilization of all cores:
+```cmd
+$env:PYTHONUNBUFFERED=1  # ensure stdout and stderr streams are sent straight to terminal without being first buffered
+python ./streamchat.py
+```
+
+#### 2.2 Server
+For optimal performance on server, it is recommended to set several environment variables (refer to [here](../README.md#best-known-configuration-on-linux) for more information), and run the example with all the physical cores of a single socket.
+
+E.g. on Linux,
+```bash
+# set IPEX-LLM env variables
+source ipex-llm-init
+
+# e.g. for a server with 48 cores per socket
+export OMP_NUM_THREADS=48
+export PYTHONUNBUFFERED=1  # ensure stdout and stderr streams are sent straight to terminal without being first buffered
+numactl -C 0-47 -m 0 python ./streamchat.py
+```

python/llm/example/CPU/HF-Transformers-AutoModels/Model/glm4/generate.py

@@ -0,0 +1,67 @@
+#
+# 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
+    model = AutoModel.from_pretrained(model_path,
+                                      load_in_4bit=True,
+                                      optimize_model=True,
+                                      trust_remote_code=True,
+                                      use_cache=True)
+
+    # 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")
+        st = time.time()
+        output = model.generate(input_ids,
+                                max_new_tokens=args.n_predict)
+        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)

python/llm/example/CPU/HF-Transformers-AutoModels/Model/glm4/streamchat.py

@@ -0,0 +1,62 @@
+#
+# 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
+    model = AutoModel.from_pretrained(model_path,
+                                      load_in_4bit=True,
+                                      trust_remote_code=True)
+
+    # 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

python/llm/example/CPU/PyTorch-Models/Model/glm4/README.md

@@ -0,0 +1,87 @@
+# GLM-4
+In this directory, you will find examples on how you could use IPEX-LLM `optimize_model` API to accelerate GLM-4 models. 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.
+
+## Requirements
+To run these examples with IPEX-LLM, we have some recommended requirements for your machine, please refer to [here](../README.md#recommended-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.
+### 1. Install
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
+
+After installing conda, create a Python environment for IPEX-LLM:
+
+On Linux:
+
+```bash
+conda create -n llm python=3.11 # recommend to use Python 3.11
+conda activate llm
+
+# install the latest ipex-llm nightly build with 'all' option
+pip install --pre --upgrade ipex-llm[all] --extra-index-url https://download.pytorch.org/whl/cpu
+
+# install tiktoken required for GLM-4
+pip install tiktoken
+```
+
+On Windows:
+
+```cmd
+conda create -n llm python=3.11
+conda activate llm
+
+pip install --pre --upgrade ipex-llm[all]
+
+pip install tiktoken
+```
+
+### 2. Run
+After setting up the Python environment, you could run the example by following steps.
+
+#### 2.1 Client
+On client Windows machines, it is recommended to run directly with full utilization of all cores:
+```cmd
+python ./generate.py --prompt 'AI是什么？'
+```
+More information about arguments can be found in [Arguments Info](#23-arguments-info) section. The expected output can be found in [Sample Output](#24-sample-output) section.
+
+#### 2.2 Server
+For optimal performance on server, it is recommended to set several environment variables (refer to [here](../README.md#best-known-configuration-on-linux) for more information), and run the example with all the physical cores of a single socket.
+
+E.g. on Linux,
+```bash
+# set IPEX-LLM env variables
+source ipex-llm-init
+
+# e.g. for a server with 48 cores per socket
+export OMP_NUM_THREADS=48
+numactl -C 0-47 -m 0 python ./generate.py --prompt 'AI是什么？'
+```
+More information about arguments can be found in [Arguments Info](#23-arguments-info) section. The expected output can be found in [Sample Output](#24-sample-output) section.
+
+#### 2.3 Arguments Info
+In the example, several arguments can be passed to satisfy your requirements:
+
+- `--repo-id-or-model-path`: str, 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'`.
+- `--prompt`: str, argument defining the prompt to be inferred (with integrated prompt format for chat). It is default to be `'AI是什么？'`.
+- `--n-predict`: int, argument defining the max number of tokens to predict. It is default to be `32`.
+
+#### 2.4 Sample Output
+##### [THUDM/glm-4-9b-chat](https://huggingface.co/THUDM/glm-4-9b-chat)
+```log
+Inference time: xxxx s
+-------------------- 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。人工智能技术
+```
+
+```
+Inference time: xxxx s
+-------------------- 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
+```

python/llm/example/CPU/PyTorch-Models/Model/glm4/generate.py

@@ -0,0 +1,65 @@
+#
+# 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
+    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)
+    
+    # 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")
+        st = time.time()
+        output = model.generate(input_ids,
+                                max_new_tokens=args.n_predict)
+        end = time.time()
+        output_str = tokenizer.decode(output[0], skip_special_tokens=True)
+        print(f'Inference time: {end-st} s')
+        print('-'*20, 'Output', '-'*20)
+        print(output_str)