Add cohere example (#10954)
* add link first * add_cpu_example * add GPU example
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Wang, Jian4
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@ -184,6 +184,7 @@ Over 50 models have been optimized/verified on `ipex-llm`, including *LLaMA/LLaM
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| Deepseek | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/deepseek) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/deepseek) |
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| StableLM | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/stablelm) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/stablelm) |
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| CodeGemma | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/codegemma) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/codegemma) |
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| Command-R/cohere | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/cohere) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/cohere) |
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## Get Support
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- Please report a bug or raise a feature request by opening a [Github Issue](https://github.com/intel-analytics/ipex-llm/issues)
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@ -587,6 +587,13 @@ Verified Models
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<td>
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<a href="https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels/Model/codegemma">link</a></td>
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</tr>
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<tr>
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<td>Command-R/cohere</td>
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<td>
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<a href="https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/cohere">link</a></td>
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<td>
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<a href="https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels/Model/cohere">link</a></td>
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</tr>
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</tbody>
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</table>
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@ -0,0 +1,64 @@
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# CoHere/command-r
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In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on cohere models. For illustration purposes, we utilize the [CohereForAI/c4ai-command-r-v01](https://huggingface.co/CohereForAI/c4ai-command-r-v01) as reference model.
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## 0. Requirements
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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.
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## Example: Predict Tokens using `generate()` API
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In the example [generate.py](./generate.py), we show a basic use case for a cohere model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
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### 1. Install
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We suggest using conda to manage environment:
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```bash
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conda create -n llm python=3.11
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conda activate llm
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pip install --pre --upgrade ipex-llm[all] # install ipex-llm with 'all' option
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pip install tansformers==4.40.0
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```
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### 2. Run
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```
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python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT
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```
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Arguments info:
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- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the cohere model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'CohereForAI/c4ai-command-r-v01'`.
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- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'What is AI?'`.
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- `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`.
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> **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.
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>
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> Please select the appropriate size of the cohere model based on the capabilities of your machine.
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#### 2.1 Client
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On client Windows machine, it is recommended to run directly with full utilization of all cores:
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```powershell
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python ./generate.py
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```
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#### 2.2 Server
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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.
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E.g. on Linux,
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```bash
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# set IPEX-LLM env variables
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source ipex-llm-init -t
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# e.g. for a server with 48 cores per socket
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export OMP_NUM_THREADS=48
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numactl -C 0-47 -m 0 python ./generate.py
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```
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#### 2.3 Sample Output
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#### [CohereForAI/c4ai-command-r-v01](https://huggingface.co/CohereForAI/c4ai-command-r-v01)
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```log
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Inference time: xxxxx s
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-------------------- Prompt --------------------
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<BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>What is AI?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
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-------------------- Output --------------------
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<|START_OF_TURN_TOKEN|><|USER_TOKEN|>What is AI?<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
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Artificial Intelligence, or AI, is a fascinating field of study that aims to create intelligent machines that can mimic human cognitive functions and perform complex tasks. AI strives to
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```
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@ -0,0 +1,69 @@
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#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import torch
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import time
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import argparse
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from ipex_llm.transformers import AutoModelForCausalLM
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from transformers import AutoTokenizer
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# you could tune the prompt based on your own model,
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# Refer to https://huggingface.co/CohereForAI/c4ai-command-r-v01
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COHERE_PROMPT_FORMAT = """
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<BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>{prompt}<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
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"""
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for cohere model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="CohereForAI/c4ai-command-r-v01",
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help='The huggingface repo id for the cohere to be downloaded'
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', or the path to the huggingface checkpoint folder')
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parser.add_argument('--prompt', type=str, default="What is AI?",
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help='Prompt to infer')
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parser.add_argument('--n-predict', type=int, default=32,
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help='Max tokens to predict')
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args = parser.parse_args()
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model_path = args.repo_id_or_model_path
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# Load model in 4 bit,
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# which convert the relevant layers in the model into INT4 format
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model = AutoModelForCausalLM.from_pretrained(model_path,
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load_in_4bit=True,
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trust_remote_code=True)
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# Load tokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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# Generate predicted tokens
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with torch.inference_mode():
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prompt = COHERE_PROMPT_FORMAT.format(prompt=args.prompt)
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input_ids = tokenizer.encode(prompt, return_tensors="pt")
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st = time.time()
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# if your selected model is capable of utilizing previous key/value attentions
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# to enhance decoding speed, but has `"use_cache": false` in its model config,
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# it is important to set `use_cache=True` explicitly in the `generate` function
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# to obtain optimal performance with IPEX-LLM INT4 optimizations
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output = model.generate(input_ids,
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max_new_tokens=args.n_predict)
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end = time.time()
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output_str = tokenizer.decode(output[0], skip_special_tokens=True)
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print(f'Inference time: {end-st} s')
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print('-'*20, 'Prompt', '-'*20)
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print(prompt)
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print('-'*20, 'Output', '-'*20)
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print(output_str)
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64
python/llm/example/CPU/PyTorch-Models/Model/cohere/README.md
Normal file
64
python/llm/example/CPU/PyTorch-Models/Model/cohere/README.md
Normal file
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@ -0,0 +1,64 @@
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# CoHere/command-r
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In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on cohere models. For illustration purposes, we utilize the [CohereForAI/c4ai-command-r-v01](https://huggingface.co/CohereForAI/c4ai-command-r-v01) as reference model.
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## 0. Requirements
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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.
|
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|
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## Example: Predict Tokens using `generate()` API
|
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In the example [generate.py](./generate.py), we show a basic use case for a cohere model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
|
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### 1. Install
|
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We suggest using conda to manage environment:
|
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```bash
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conda create -n llm python=3.11
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conda activate llm
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pip install --pre --upgrade ipex-llm[all] # install ipex-llm with 'all' option
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pip install tansformers==4.40.0
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```
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### 2. Run
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```
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python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT
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```
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Arguments info:
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- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the cohere model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'CohereForAI/c4ai-command-r-v01'`.
|
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- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'What is AI?'`.
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- `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`.
|
||||
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> **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.
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>
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> Please select the appropriate size of the cohere model based on the capabilities of your machine.
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#### 2.1 Client
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On client Windows machine, it is recommended to run directly with full utilization of all cores:
|
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```powershell
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python ./generate.py
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```
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#### 2.2 Server
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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.
|
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E.g. on Linux,
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```bash
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# set IPEX-LLM env variables
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source ipex-llm-init -t
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# e.g. for a server with 48 cores per socket
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export OMP_NUM_THREADS=48
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numactl -C 0-47 -m 0 python ./generate.py
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```
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#### 2.3 Sample Output
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#### [CohereForAI/c4ai-command-r-v01](https://huggingface.co/CohereForAI/c4ai-command-r-v01)
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```log
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Inference time: xxxxx s
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-------------------- Prompt --------------------
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<BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>What is AI?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
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-------------------- Output --------------------
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<|START_OF_TURN_TOKEN|><|USER_TOKEN|>What is AI?<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
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Artificial Intelligence, or AI, is a fascinating field of study that aims to create intelligent machines that can mimic human cognitive functions and perform complex tasks. AI strives to
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```
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@ -0,0 +1,69 @@
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#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import torch
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import time
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import argparse
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from ipex_llm.transformers import AutoModelForCausalLM
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from transformers import AutoTokenizer
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# you could tune the prompt based on your own model,
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# Refer to https://huggingface.co/CohereForAI/c4ai-command-r-v01
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COHERE_PROMPT_FORMAT = """
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<BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>{prompt}<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
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"""
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for cohere model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="CohereForAI/c4ai-command-r-v01",
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help='The huggingface repo id for the cohere to be downloaded'
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', or the path to the huggingface checkpoint folder')
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parser.add_argument('--prompt', type=str, default="What is AI?",
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help='Prompt to infer')
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parser.add_argument('--n-predict', type=int, default=32,
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help='Max tokens to predict')
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args = parser.parse_args()
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model_path = args.repo_id_or_model_path
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# Load model in 4 bit,
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# which convert the relevant layers in the model into INT4 format
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model = AutoModelForCausalLM.from_pretrained(model_path,
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load_in_4bit=True,
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trust_remote_code=True)
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# Load tokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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# Generate predicted tokens
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with torch.inference_mode():
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prompt = COHERE_PROMPT_FORMAT.format(prompt=args.prompt)
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input_ids = tokenizer.encode(prompt, return_tensors="pt")
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st = time.time()
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# if your selected model is capable of utilizing previous key/value attentions
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# to enhance decoding speed, but has `"use_cache": false` in its model config,
|
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# it is important to set `use_cache=True` explicitly in the `generate` function
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# to obtain optimal performance with IPEX-LLM INT4 optimizations
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output = model.generate(input_ids,
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max_new_tokens=args.n_predict)
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end = time.time()
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output_str = tokenizer.decode(output[0], skip_special_tokens=True)
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print(f'Inference time: {end-st} s')
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print('-'*20, 'Prompt', '-'*20)
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print(prompt)
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print('-'*20, 'Output', '-'*20)
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print(output_str)
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@ -0,0 +1,101 @@
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# CoHere/command-r
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In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on cohere models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [CohereForAI/c4ai-command-r-v01](https://huggingface.co/CohereForAI/c4ai-command-r-v01) as a reference model.
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> **Note**: Because the size of this cohere model is 35B, even running low_bit `sym_int4` still requires about 17.5GB. So currently it can only be run on MAX GPU, or run with [Pipeline-Parallel-Inference](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/Pipeline-Parallel-Inference) on multiple Arc GPUs.
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>
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> Please select the appropriate size of the cohere model based on the capabilities of your machine.
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## 0. Requirements
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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.
|
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|
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## Example: Predict Tokens using `generate()` API
|
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In the example [generate.py](./generate.py), we show a basic use case for a cohere model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations on Intel GPUs.
|
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### 1. Install
|
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#### 1.1 Installation on Linux
|
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We suggest using conda to manage environment:
|
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```bash
|
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conda create -n llm python=3.11
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conda activate llm
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# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
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pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
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pip install tansformers==4.40.0
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conda install -c conda-forge -y gperftools=2.10 # to enable tcmalloc
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```
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#### 1.2 Installation on Windows
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We suggest using conda to manage environment:
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```bash
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conda create -n llm python=3.11 libuv
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conda activate llm
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# below command will use pip to install the Intel oneAPI Base Toolkit 2024.0
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pip install dpcpp-cpp-rt==2024.0.2 mkl-dpcpp==2024.0.0 onednn==2024.0.0
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# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
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pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
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pip install tansformers==4.40.0
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```
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### 2. Configures OneAPI environment variables for Linux
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> [!NOTE]
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> Skip this step if you are running on Windows.
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This is a required step on Linux for APT or offline installed oneAPI. Skip this step for PIP-installed oneAPI.
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```bash
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source /opt/intel/oneapi/setvars.sh
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```
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### 3. Runtime Configurations
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For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device.
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#### 3.1 Configurations for Linux
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<details>
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<summary>For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series</summary>
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```bash
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export USE_XETLA=OFF
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export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
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export SYCL_CACHE_PERSISTENT=1
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```
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</details>
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<details>
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<summary>For Intel Data Center GPU Max Series</summary>
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```bash
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export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so
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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`.
|
||||
</details>
|
||||
|
||||
### 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 cohere model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'CohereForAI/c4ai-command-r-v01'`.
|
||||
- `--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
|
||||
#### [CohereForAI/c4ai-command-r-v01](https://huggingface.co/CohereForAI/c4ai-command-r-v01)
|
||||
```log
|
||||
Inference time: xxxxx s
|
||||
-------------------- Prompt --------------------
|
||||
|
||||
<BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>What is AI?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
|
||||
|
||||
-------------------- Output --------------------
|
||||
|
||||
<|START_OF_TURN_TOKEN|><|USER_TOKEN|>What is AI?<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
|
||||
Artificial Intelligence Quora User,
|
||||
|
||||
Artificial Intelligence (AI) is the simulation of human intelligence in machines, typically by machines, that have become a very complex
|
||||
```
|
||||
|
|
@ -0,0 +1,81 @@
|
|||
#
|
||||
# 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 ipex_llm.transformers import AutoModelForCausalLM
|
||||
from transformers import AutoTokenizer
|
||||
|
||||
# you could tune the prompt based on your own model,
|
||||
# Refer to https://huggingface.co/CohereForAI/c4ai-command-r-v01
|
||||
COHERE_PROMPT_FORMAT = """
|
||||
<BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>{prompt}<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
|
||||
"""
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for cohere model')
|
||||
parser.add_argument('--repo-id-or-model-path', type=str, default="CohereForAI/c4ai-command-r-v01",
|
||||
help='The huggingface repo id for the cohere to be downloaded'
|
||||
', or the path to the huggingface checkpoint folder')
|
||||
parser.add_argument('--prompt', type=str, default="What is AI?",
|
||||
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 = AutoModelForCausalLM.from_pretrained(model_path,
|
||||
load_in_4bit=True,
|
||||
optimize_model=True,
|
||||
trust_remote_code=True,
|
||||
use_cache=True)
|
||||
model = model.half().to('xpu')
|
||||
|
||||
# Load tokenizer
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
|
||||
|
||||
# Generate predicted tokens
|
||||
with torch.inference_mode():
|
||||
prompt = COHERE_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)
|
||||
|
||||
# start inference
|
||||
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 IPEX-LLM INT4 optimizations
|
||||
output = model.generate(input_ids,
|
||||
max_new_tokens=args.n_predict)
|
||||
torch.xpu.synchronize()
|
||||
end = time.time()
|
||||
output = output.cpu()
|
||||
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)
|
||||
101
python/llm/example/GPU/PyTorch-Models/Model/cohere/README.md
Normal file
101
python/llm/example/GPU/PyTorch-Models/Model/cohere/README.md
Normal file
|
|
@ -0,0 +1,101 @@
|
|||
# CoHere/command-r
|
||||
In this directory, you will find examples on how you could apply IPEX-LLM INT4 optimizations on cohere models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [CohereForAI/c4ai-command-r-v01](https://huggingface.co/CohereForAI/c4ai-command-r-v01) as a reference model.
|
||||
> **Note**: Because the size of this cohere model is 35B, even running low_bit `sym_int4` still requires about 17.5GB. So currently it can only be run on MAX GPU, or run with [Pipeline-Parallel-Inference](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/Pipeline-Parallel-Inference) on multiple Arc GPUs.
|
||||
>
|
||||
> Please select the appropriate size of the cohere model based on the capabilities of your machine.
|
||||
|
||||
## 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 cohere 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/
|
||||
pip install tansformers==4.40.0
|
||||
conda install -c conda-forge -y gperftools=2.10 # to enable tcmalloc
|
||||
```
|
||||
|
||||
#### 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 use pip to install the Intel oneAPI Base Toolkit 2024.0
|
||||
pip install dpcpp-cpp-rt==2024.0.2 mkl-dpcpp==2024.0.0 onednn==2024.0.0
|
||||
|
||||
# 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/
|
||||
pip install tansformers==4.40.0
|
||||
```
|
||||
|
||||
### 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
|
||||
<details>
|
||||
|
||||
<summary>For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series</summary>
|
||||
|
||||
```bash
|
||||
export USE_XETLA=OFF
|
||||
export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
|
||||
export SYCL_CACHE_PERSISTENT=1
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
|
||||
<summary>For Intel Data Center GPU Max Series</summary>
|
||||
|
||||
```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`.
|
||||
</details>
|
||||
|
||||
### 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 cohere model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'CohereForAI/c4ai-command-r-v01'`.
|
||||
- `--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
|
||||
#### [CohereForAI/c4ai-command-r-v01](https://huggingface.co/CohereForAI/c4ai-command-r-v01)
|
||||
```log
|
||||
Inference time: xxxxx s
|
||||
-------------------- Prompt --------------------
|
||||
|
||||
<BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>What is AI?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
|
||||
|
||||
-------------------- Output --------------------
|
||||
|
||||
<|START_OF_TURN_TOKEN|><|USER_TOKEN|>What is AI?<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
|
||||
Artificial Intelligence Quora User,
|
||||
|
||||
Artificial Intelligence (AI) is the simulation of human intelligence in machines, typically by machines, that have become a very complex
|
||||
```
|
||||
|
|
@ -0,0 +1,81 @@
|
|||
#
|
||||
# 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 ipex_llm.transformers import AutoModelForCausalLM
|
||||
from transformers import AutoTokenizer
|
||||
|
||||
# you could tune the prompt based on your own model,
|
||||
# Refer to https://huggingface.co/CohereForAI/c4ai-command-r-v01
|
||||
COHERE_PROMPT_FORMAT = """
|
||||
<BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>{prompt}<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
|
||||
"""
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for cohere model')
|
||||
parser.add_argument('--repo-id-or-model-path', type=str, default="CohereForAI/c4ai-command-r-v01",
|
||||
help='The huggingface repo id for the cohere to be downloaded'
|
||||
', or the path to the huggingface checkpoint folder')
|
||||
parser.add_argument('--prompt', type=str, default="What is AI?",
|
||||
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 = AutoModelForCausalLM.from_pretrained(model_path,
|
||||
load_in_4bit=True,
|
||||
optimize_model=True,
|
||||
trust_remote_code=True,
|
||||
use_cache=True)
|
||||
model = model.half().to('xpu')
|
||||
|
||||
# Load tokenizer
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
|
||||
|
||||
# Generate predicted tokens
|
||||
with torch.inference_mode():
|
||||
prompt = COHERE_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)
|
||||
|
||||
# start inference
|
||||
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 IPEX-LLM INT4 optimizations
|
||||
output = model.generate(input_ids,
|
||||
max_new_tokens=args.n_predict)
|
||||
torch.xpu.synchronize()
|
||||
end = time.time()
|
||||
output = output.cpu()
|
||||
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)
|
||||
Loading…
Reference in a new issue