add CodeShell CPU example (#9345)

* add CodeShell CPU example

* fix some problems

README.md

@@ -161,6 +161,7 @@ Over 20 models have been optimized/verified on `bigdl-llm`, including *LLaMA/LLa
 | Skywork      | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/skywork)                 |    |
 | InternLM-XComposer  | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/internlm-xcomposer)   |    |
 | WizardCoder-Python | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/wizardcoder-python) | |
+| CodeShell | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/CodeShell) | |
 
 
 ***For more details, please refer to the `bigdl-llm` [Document](https://test-bigdl-llm.readthedocs.io/en/main/doc/LLM/index.html), [Readme](python/llm), [Tutorial](https://github.com/intel-analytics/bigdl-llm-tutorial) and [API Doc](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/LLM/index.html).***

python/llm/README.md

@@ -68,6 +68,7 @@ Over 20 models have been optimized/verified on `bigdl-llm`, including *LLaMA/LLa
 | Skywork    | [link](example/CPU/HF-Transformers-AutoModels/Model/skywork)                 |    |
 | InternLM-XComposer    | [link](example/CPU/HF-Transformers-AutoModels/Model/internlm-xcomposer)   |   |
 | WizardCoder-Python | [link](example/CPU/HF-Transformers-AutoModels/Model/wizardcoder-python) | |
+| CodeShell | [link](example/CPU/HF-Transformers-AutoModels/Model/CodeShell) | |
 
 ### Working with `bigdl-llm`
 

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

@@ -0,0 +1,79 @@
+# CodeShell-7B
+
+In this directory, you will find examples on how you could apply BigDL-LLM INT4 optimizations on CodeShell models. For illustration purposes, we utilize the [WisdomShell/CodeShell-7B](https://huggingface.co/WisdomShell/CodeShell-7B) as a reference CodeShell model.
+
+> **Note**: If you want to download the Hugging Face *Transformers* model, please refer to [here](https://huggingface.co/docs/hub/models-downloading#using-git).
+>
+> BigDL-LLM optimizes the *Transformers* model in INT4 precision at runtime, and thus no explicit conversion is needed.
+
+## Requirements
+To run these examples with BigDL-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 CodeShell model to predict the next N tokens using `generate()` API, with BigDL-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://docs.conda.io/en/latest/miniconda.html#).
+
+After installing conda, create a Python environment for BigDL-LLM:
+```bash
+conda create -n llm python=3.9 # recommend to use Python 3.9
+conda activate llm
+
+pip install --pre --upgrade bigdl-llm[all] # install the latest bigdl-llm nightly build with 'all' option
+```
+
+### 2. Run
+After setting up the Python environment, you could run the example by following steps.
+
+> **Note**: When loading the model in 4-bit, BigDL-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 CodeShell model based on the capabilities of your machine.
+
+#### 2.1 Client
+On client Windows machines, it is recommended to run directly with full utilization of all cores:
+```powershell
+python ./generate.py --prompt 'def print_hello_world():'
+```
+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 BigDL-Nano env variables
+source bigdl-nano-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 'def print_hello_world():'
+```
+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 CodeShell model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'WisdomShell/CodeShell-7B'`.
+- `--prompt`: str, argument defining the prompt to be inferred (with integrated prompt format for code). It is default to be `def print_hello_world():`.
+- `--n-predict`: int, argument defining the max number of tokens to predict. It is default to be `50`.
+
+#### 2.4 Sample Output
+#### [WisdomShell/CodeShell-7B ](https://huggingface.co/WisdomShell/CodeShell-7B )
+```log
+Inference time: xxxx s
+-------------------- Prompt --------------------
+def print_hello_world():
+-------------------- Output --------------------
+def print_hello_world():
+    print("Hello World")
+
+print_hello_world()
+
+# Function with parameters
+def print_hello_name(name):
+    print("Hello " + name)
+
+print_hello_name("John")
+print
+
+```

python/llm/example/CPU/HF-Transformers-AutoModels/Model/codeshell/generate.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 bigdl.llm.transformers import AutoModelForCausalLM
+from transformers import AutoTokenizer
+
+# you could tune the prompt based on your own model,
+# here the prompt tuning refers to: https://huggingface.co/WisdomShell/CodeShell-7B
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for CodeShell model')
+    parser.add_argument('--repo-id-or-model-path', type=str, default="WisdomShell/CodeShell-7B",
+                        help='The huggingface repo id for the CodeShell model to be downloaded'
+                             ', or the path to the huggingface checkpoint folder')
+    parser.add_argument('--prompt', type=str, default="def print_hello_world():",
+                        help='Prompt to infer')
+    parser.add_argument('--n-predict', type=int, default=50,
+                        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 = AutoModelForCausalLM.from_pretrained(model_path,
+                                                 load_in_4bit=True,
+                                                 trust_remote_code=True)
+
+    # Load tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(model_path,
+                                              trust_remote_code=True)
+    
+    # Generate predicted tokens
+    with torch.inference_mode():
+        prompt = args.prompt
+        input_ids = tokenizer.encode(prompt, return_tensors="pt")
+        st = time.time()
+        # if your selected model is capable of utilizing previous key/value attentions
+        # to enhance decoding speed, but has `"use_cache": false` in its model config,
+        # it is important to set `use_cache=True` explicitly in the `generate` function
+        # to obtain optimal performance with BigDL-LLM INT4 optimizations
+
+        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/PyTorch-Models/Model/codeshell/README.md

@@ -0,0 +1,70 @@
+# CodeShell
+In this directory, you will find examples on how you could use BigDL-LLM `optimize_model` API to accelerate CodeShell models. For illustration purposes, we utilize the [WisdomShell/CodeShell-7B](https://huggingface.co/WisdomShell/CodeShell-7B ) as a reference CodeShell model.
+
+## Requirements
+To run these examples with BigDL-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 CodeShell model to predict the next N tokens using `generate()` API, with BigDL-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://docs.conda.io/en/latest/miniconda.html#).
+
+After installing conda, create a Python environment for BigDL-LLM:
+```bash
+conda create -n llm python=3.9 # recommend to use Python 3.9
+conda activate llm
+
+pip install --pre --upgrade bigdl-llm[all] # install the latest bigdl-llm nightly build with 'all' option
+```
+
+### 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:
+```powershell
+python ./generate.py --prompt 'def print_hello_world():'
+```
+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 BigDL-Nano env variables
+source bigdl-nano-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 'def print_hello_world():'
+```
+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 CodeShell model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'WisdomShell/CodeShell-7B'`.
+- `--prompt`: str, argument defining the prompt to be inferred (with integrated prompt format for chat). It is default to be `def print_hello_world():`.
+- `--n-predict`: int, argument defining the max number of tokens to predict. It is default to be `50`.
+
+#### 2.4 Sample Output
+#### [WisdomShell/CodeShell-7B](https://huggingface.co/WisdomShell/CodeShell-7B)
+```log
+Inference time: xxxx s
+-------------------- Prompt --------------------
+def print_hello_world():
+-------------------- Output --------------------
+def print_hello_world():
+    print("Hello World")
+
+print_hello_world()
+
+# 2.
+
+def print_hello_world_times(n):
+    for i in range(n):
+        print("Hello World")
+
+print
+```

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

@@ -0,0 +1,61 @@
+#
+# 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  AutoTokenizer, AutoModelForCausalLM
+from bigdl.llm import optimize_model
+
+# you could tune the prompt based on your own model,
+# here the prompt tuning refers to https://huggingface.co/WisdomShell/CodeShell-7B
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for CodeShell model')
+    parser.add_argument('--repo-id-or-model-path', type=str, default="WisdomShell/CodeShell-7B",
+                        help='The huggingface repo id for the CodeShell model to be downloaded'
+                             ', or the path to the huggingface checkpoint folder')
+    parser.add_argument('--prompt', type=str, default="def print_hello_world():",
+                        help='Prompt to infer')
+    parser.add_argument('--n-predict', type=int, default=50,
+                        help='Max tokens to predict')
+
+    args = parser.parse_args()
+    model_path = args.repo_id_or_model_path
+
+    # Load model
+    model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True)
+
+    # With only one line to enable BigDL-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 = 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)