add phixtral and optimize phi-moe (#10052)
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@ -177,6 +177,7 @@ Over 20 models have been optimized/verified on `bigdl-llm`, including *LLaMA/LLa
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| Yi | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/yi) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/yi) |
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| Yi | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/yi) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/yi) |
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| BlueLM | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/bluelm) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/bluelm) |
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| BlueLM | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/bluelm) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/bluelm) |
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| SOLAR | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/solar) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/solar) |
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| SOLAR | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/solar) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/solar) |
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| Phixtral | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/phixtral) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/phixtral) |
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| InternLM2 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/internlm2) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/internlm2) |
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| InternLM2 | [link](python/llm/example/CPU/HF-Transformers-AutoModels/Model/internlm2) | [link](python/llm/example/GPU/HF-Transformers-AutoModels/Model/internlm2) |
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***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).***
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***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).***
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@ -75,6 +75,7 @@ Over 20 models have been optimized/verified on `bigdl-llm`, including *LLaMA/LLa
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| Yi | [link](example/CPU/HF-Transformers-AutoModels/Model/yi) | [link](example/GPU/HF-Transformers-AutoModels/Model/yi) |
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| Yi | [link](example/CPU/HF-Transformers-AutoModels/Model/yi) | [link](example/GPU/HF-Transformers-AutoModels/Model/yi) |
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| BlueLM | [link](example/CPU/HF-Transformers-AutoModels/Model/bluelm) | [link](example/GPU/HF-Transformers-AutoModels/Model/bluelm) |
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| BlueLM | [link](example/CPU/HF-Transformers-AutoModels/Model/bluelm) | [link](example/GPU/HF-Transformers-AutoModels/Model/bluelm) |
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| SOLAR | [link](example/CPU/HF-Transformers-AutoModels/Model/solar) | [link](example/GPU/HF-Transformers-AutoModels/Model/solar) |
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| SOLAR | [link](example/CPU/HF-Transformers-AutoModels/Model/solar) | [link](example/GPU/HF-Transformers-AutoModels/Model/solar) |
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| Phixtral | [link](example/CPU/HF-Transformers-AutoModels/Model/phixtral) | [link](example/GPU/HF-Transformers-AutoModels/Model/phixtral) |
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| InternLM2 | [link](example/CPU/HF-Transformers-AutoModels/Model/internlm2) | [link](example/GPU/HF-Transformers-AutoModels/Model/internlm2) |
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| InternLM2 | [link](example/CPU/HF-Transformers-AutoModels/Model/internlm2) | [link](example/GPU/HF-Transformers-AutoModels/Model/internlm2) |
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### Working with `bigdl-llm`
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### Working with `bigdl-llm`
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# Phixtral-4x2_8
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In this directory, you will find examples on how you could apply BigDL-LLM INT4 optimizations on phi models. For illustration purposes, we utilize the [microsoft/phixtral-4x2_8](https://huggingface.co/mlabonne/phixtral-4x2_8) as a reference phixtral model.
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> **Note**: If you want to download the Hugging Face *Transformers* model, please refer to [here](https://huggingface.co/docs/hub/models-downloading#using-git).
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>
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> BigDL-LLM optimizes the *Transformers* model in INT4 precision at runtime, and thus no explicit conversion is needed.
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## Requirements
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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.
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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 phixtral model to predict the next N tokens using `generate()` API, with BigDL-LLM INT4 optimizations.
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### 1. Install
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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#).
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After installing conda, create a Python environment for BigDL-LLM:
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```bash
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conda create -n llm python=3.9 # recommend to use Python 3.9
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conda activate llm
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pip install --pre --upgrade bigdl-llm[all] # install the latest bigdl-llm nightly build with 'all' option
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pip install einops # additional package required for phi to conduct generation
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```
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### 2. Run
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After setting up the Python environment, you could run the example by following steps.
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> **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.
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>
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> Please select the appropriate size of the phixtral model based on the capabilities of your machine.
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#### 2.1 Client
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On client Windows machines, it is recommended to run directly with full utilization of all cores:
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```powershell
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python ./generate.py --prompt 'What is AI?'
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```
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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.
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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 BigDL-LLM env variables
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source bigdl-llm-init
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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 --prompt 'What is AI？'
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```
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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.
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#### 2.3 Arguments Info
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In the example, several arguments can be passed to satisfy your requirements:
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- `--repo-id-or-model-path`: str, argument defining the huggingface repo id for the phixtral model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'mlabonne/phixtral-4x2_8'`.
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- `--prompt`: str, argument defining the prompt to be inferred (with integrated prompt format for chat). It is default to be `What is AI?`.
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- `--n-predict`: int, argument defining the max number of tokens to predict. It is default to be `32`.
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#### 2.4 Sample Output
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#### [mlabonne/phixtral-4x2_8](https://huggingface.co/mlabonne/phixtral-4x2_8)
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```log
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Inference time: xxxx s
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-------------------- Prompt --------------------
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Question:What is AI?
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Answer:
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-------------------- Output --------------------
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Question:What is AI?
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Answer: AI, or artificial intelligence, is the simulation of human intelligence in machines that are programmed to think and learn like humans.
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```
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@ -0,0 +1,72 @@
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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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import numpy as np
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from transformers import AutoTokenizer, GenerationConfig
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from bigdl.llm import optimize_model
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# you could tune the prompt based on your own model,
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# here the prompt tuning refers to # TODO: https://huggingface.co/microsoft/phi-1_5/blob/main/modeling_mixformer_sequential.py
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PHI1_5_PROMPT_FORMAT = " Question:{prompt}\n\n Answer:"
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generation_config = GenerationConfig(use_cache = True)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for phixtral model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="mlabonne/phixtral-4x2_8",
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help='The huggingface repo id for the phi model 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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from bigdl.llm.transformers import AutoModelForCausalLM
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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,
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trust_remote_code=True)
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# Generate predicted tokens
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with torch.inference_mode():
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prompt = PHI1_5_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 BigDL-LLM INT4 optimizations
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# Note that phixtral uses GenerationConfig to enable 'use_cache'
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output = model.generate(input_ids, do_sample=False, max_new_tokens=args.n_predict, generation_config = generation_config)
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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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# Phixtral
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In this directory, you will find examples on how you could use BigDL-LLM `optimize_model` API to accelerate Qwen-VL models. For illustration purposes, we utilize the [mlabonne/phixtral-4x2_8](https://huggingface.co/mlabonne/phixtral-4x2_8) as a reference Phixtral model.
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## Requirements
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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.
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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 phixtral model to predict the next N tokens using `generate()` API, with BigDL-LLM INT4 optimizations.
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### 1. Install
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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#).
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After installing conda, create a Python environment for BigDL-LLM:
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```bash
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conda create -n llm python=3.9 # recommend to use Python 3.9
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conda activate llm
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pip install --pre --upgrade bigdl-llm[all] # install the latest bigdl-llm nightly build with 'all' option
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pip install einops
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```
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### 2. Run
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After setting up the Python environment, you could run the example by following steps.
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#### 2.1 Client
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On client Windows machines, it is recommended to run directly with full utilization of all cores:
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```powershell
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python ./generate.py --prompt 'What is AI?'
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```
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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.
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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 BigDL-LLM env variables
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source bigdl-llm-init
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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 --prompt 'What is AI?'
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```
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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.
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#### 2.3 Arguments Info
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In the example, several arguments can be passed to satisfy your requirements:
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- `--repo-id-or-model-path`: str, argument defining the huggingface repo id for the phixtral model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'mlabonne/phixtral-4x2_8'`.
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- `--prompt`: str, argument defining the prompt to be inferred (with integrated prompt format for chat). It is default to be `'What is AI?'`.
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- `--n-predict`: int, argument defining the max number of tokens to predict. It is default to be `32`.
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#### 2.4 Sample Output
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#### [mlabonne/phixtral-4x2_8](https://huggingface.co/mlabonne/phixtral-4x2_8)
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```log
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Inference time: xxxx s
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-------------------- Prompt --------------------
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Question:What is AI?
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Answer:
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-------------------- Output --------------------
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Question:What is AI?
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Answer: AI, or artificial intelligence, is the simulation of human intelligence in machines that are programmed to think and learn like humans.
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```
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@ -0,0 +1,66 @@
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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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import numpy as np
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from transformers import AutoTokenizer, GenerationConfig
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from bigdl.llm import optimize_model
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# you could tune the prompt based on your own model,
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# here the prompt tuning refers to # TODO: https://huggingface.co/microsoft/phi-1_5/blob/main/modeling_mixformer_sequential.py
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PHI1_5_PROMPT_FORMAT = " Question:{prompt}\n\n Answer:"
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generation_config = GenerationConfig(use_cache = True)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for phixtral model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="mlabonne/phixtral-4x2_8",
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help='The huggingface repo id for the phi model 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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from transformers import AutoModelForCausalLM
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model = AutoModelForCausalLM.from_pretrained(model_path,
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trust_remote_code=True)
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model = optimize_model(model)
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# Load tokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_path,
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trust_remote_code=True)
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# Generate predicted tokens
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with torch.inference_mode():
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prompt = PHI1_5_PROMPT_FORMAT.format(prompt=args.prompt)
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||||||
|
input_ids = tokenizer.encode(prompt, return_tensors="pt")
|
||||||
|
st = time.time()
|
||||||
|
|
||||||
|
# Note that phixtral uses GenerationConfig to enable 'use_cache'
|
||||||
|
output = model.generate(input_ids, do_sample=False, max_new_tokens=args.n_predict, generation_config = generation_config)
|
||||||
|
|
||||||
|
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)
|
||||||
|
|
@ -0,0 +1,119 @@
|
||||||
|
# Phixtral
|
||||||
|
In this directory, you will find examples on how you could apply BigDL-LLM INT4 optimizations on phixtral models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [mlabonne/phixtral-4x2_8](https://huggingface.co/mlabonne/phixtral-4x2_8) as a reference phixtral model.
|
||||||
|
|
||||||
|
## 0. Requirements
|
||||||
|
To run these examples with BigDL-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 InternLM model to predict the next N tokens using `generate()` API, with BigDL-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=3.9
|
||||||
|
conda activate llm
|
||||||
|
# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
|
||||||
|
pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
|
||||||
|
```
|
||||||
|
|
||||||
|
#### 1.2 Installation on Windows
|
||||||
|
We suggest using conda to manage environment:
|
||||||
|
```bash
|
||||||
|
conda create -n llm python=3.9 libuv
|
||||||
|
conda activate llm
|
||||||
|
# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
|
||||||
|
pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
|
||||||
|
```
|
||||||
|
|
||||||
|
### 2. Configures OneAPI environment variables
|
||||||
|
#### 2.1 Configurations for Linux
|
||||||
|
```bash
|
||||||
|
source /opt/intel/oneapi/setvars.sh
|
||||||
|
```
|
||||||
|
|
||||||
|
#### 2.2 Configurations for Windows
|
||||||
|
```cmd
|
||||||
|
call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat"
|
||||||
|
```
|
||||||
|
> Note: Please make sure you are using **CMD** (**Anaconda Prompt** if using conda) to run the command as PowerShell is not supported.
|
||||||
|
### 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
|
||||||
|
```
|
||||||
|
|
||||||
|
</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 ENABLE_SDP_FUSION=1
|
||||||
|
```
|
||||||
|
> Note: Please note that `libtcmalloc.so` can be installed by `conda install -c conda-forge -y gperftools=2.10`.
|
||||||
|
</details>
|
||||||
|
|
||||||
|
#### 3.2 Configurations for Windows
|
||||||
|
<details>
|
||||||
|
|
||||||
|
<summary>For Intel iGPU</summary>
|
||||||
|
|
||||||
|
```cmd
|
||||||
|
set SYCL_CACHE_PERSISTENT=1
|
||||||
|
set BIGDL_LLM_XMX_DISABLED=1
|
||||||
|
```
|
||||||
|
|
||||||
|
</details>
|
||||||
|
|
||||||
|
<details>
|
||||||
|
|
||||||
|
<summary>For Intel Arc™ A300-Series or Pro A60</summary>
|
||||||
|
|
||||||
|
```cmd
|
||||||
|
set SYCL_CACHE_PERSISTENT=1
|
||||||
|
```
|
||||||
|
|
||||||
|
</details>
|
||||||
|
|
||||||
|
<details>
|
||||||
|
|
||||||
|
<summary>For other Intel dGPU Series</summary>
|
||||||
|
|
||||||
|
There is no need to set further environment variables.
|
||||||
|
|
||||||
|
</details>
|
||||||
|
|
||||||
|
> 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 phi model (e.g. `mlabonne/phixtral-4x2_8`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'mlabonne/phixtral-4x2_8'`.
|
||||||
|
- `--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
|
||||||
|
#### [mlabonne/phixtral-4x2_8](https://huggingface.co/mlabonne/phixtral-4x2_8)
|
||||||
|
```log
|
||||||
|
Inference time: xxxx s
|
||||||
|
-------------------- Prompt --------------------
|
||||||
|
Question:What is AI?
|
||||||
|
|
||||||
|
Answer:
|
||||||
|
-------------------- Output --------------------
|
||||||
|
Question:What is AI?
|
||||||
|
|
||||||
|
Answer: AI, or artificial intelligence, is the simulation of human intelligence in machines that are programmed to think and learn like humans. It involves the development of computer systems that
|
||||||
|
```
|
||||||
|
|
@ -0,0 +1,80 @@
|
||||||
|
#
|
||||||
|
# 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 transformers import AutoTokenizer, GenerationConfig
|
||||||
|
import intel_extension_for_pytorch as ipex
|
||||||
|
|
||||||
|
|
||||||
|
# you could tune the prompt based on your own model,
|
||||||
|
# here the prompt tuning refers to # TODO: https://huggingface.co/microsoft/phi-1_5/blob/main/modeling_mixformer_sequential.py
|
||||||
|
PHI1_5_PROMPT_FORMAT = " Question:{prompt}\n\n Answer:"
|
||||||
|
generation_config = GenerationConfig(use_cache = True)
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for phi model')
|
||||||
|
parser.add_argument('--repo-id-or-model-path', type=str, default="mlabonne/phixtral-4x2_8",
|
||||||
|
help='The huggingface repo id for the phixtral model 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.
|
||||||
|
from bigdl.llm.transformers import AutoModel,AutoModelForCausalLM
|
||||||
|
model = AutoModelForCausalLM.from_pretrained(model_path,
|
||||||
|
load_in_4bit=True,
|
||||||
|
trust_remote_code=True)
|
||||||
|
model = model.to('xpu')
|
||||||
|
|
||||||
|
# Load tokenizer
|
||||||
|
tokenizer = AutoTokenizer.from_pretrained(model_path,
|
||||||
|
trust_remote_code=True)
|
||||||
|
|
||||||
|
# Generate predicted tokens
|
||||||
|
# for phi-moe
|
||||||
|
with torch.inference_mode():
|
||||||
|
prompt = PHI1_5_PROMPT_FORMAT.format(prompt=args.prompt)
|
||||||
|
input_ids = tokenizer.encode(prompt, return_tensors="pt").to('xpu')
|
||||||
|
|
||||||
|
# ipex model needs a warmup, then inference time can be accurate
|
||||||
|
output = model.generate(input_ids,
|
||||||
|
max_new_tokens=args.n_predict,
|
||||||
|
generation_config = generation_config)
|
||||||
|
|
||||||
|
# start inference without profiling
|
||||||
|
st = time.time()
|
||||||
|
output = model.generate(input_ids, do_sample=False, max_new_tokens=args.n_predict, generation_config = generation_config)
|
||||||
|
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)
|
||||||
123
python/llm/example/GPU/PyTorch-Models/Model/phixtral/README.md
Normal file
123
python/llm/example/GPU/PyTorch-Models/Model/phixtral/README.md
Normal file
|
|
@ -0,0 +1,123 @@
|
||||||
|
# phixtral
|
||||||
|
In this directory, you will find examples on how you could use BigDL-LLM `optimize_model` API to accelerate phi-1_5 models on [Intel GPUs](../../../README.md). For illustration purposes, we utilize the [mlabonne/phixtral-4x2_8](https://huggingface.co/mlabonne/phixtral-4x2_8) as a reference phixtral model.
|
||||||
|
|
||||||
|
## Requirements
|
||||||
|
To run these examples with BigDL-LLM, 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 phixtral model to predict the next N tokens using `generate()` API, with BigDL-LLM INT4 optimizations on Intel GPUs.
|
||||||
|
### 1. Install
|
||||||
|
#### 1.1 Installation on Linux
|
||||||
|
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[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
|
||||||
|
pip install einops # additional package required for phixtral to conduct generation
|
||||||
|
```
|
||||||
|
|
||||||
|
#### 1.2 Installation on Windows
|
||||||
|
We suggest using conda to manage environment:
|
||||||
|
```bash
|
||||||
|
conda create -n llm python=3.9 libuv
|
||||||
|
conda activate llm
|
||||||
|
# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
|
||||||
|
pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
|
||||||
|
pip install einops # additional package required for phixtral to conduct generation
|
||||||
|
```
|
||||||
|
|
||||||
|
### 2. Configures OneAPI environment variables
|
||||||
|
#### 2.1 Configurations for Linux
|
||||||
|
```bash
|
||||||
|
source /opt/intel/oneapi/setvars.sh
|
||||||
|
```
|
||||||
|
|
||||||
|
#### 2.2 Configurations for Windows
|
||||||
|
```cmd
|
||||||
|
call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat"
|
||||||
|
```
|
||||||
|
> Note: Please make sure you are using **CMD** (**Anaconda Prompt** if using conda) to run the command as PowerShell is not supported.
|
||||||
|
### 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
|
||||||
|
```
|
||||||
|
|
||||||
|
</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 ENABLE_SDP_FUSION=1
|
||||||
|
```
|
||||||
|
> Note: Please note that `libtcmalloc.so` can be installed by `conda install -c conda-forge -y gperftools=2.10`.
|
||||||
|
</details>
|
||||||
|
#### 3.2 Configurations for Windows
|
||||||
|
<details>
|
||||||
|
|
||||||
|
<summary>For Intel iGPU</summary>
|
||||||
|
|
||||||
|
```cmd
|
||||||
|
set SYCL_CACHE_PERSISTENT=1
|
||||||
|
set BIGDL_LLM_XMX_DISABLED=1
|
||||||
|
```
|
||||||
|
|
||||||
|
</details>
|
||||||
|
|
||||||
|
<details>
|
||||||
|
|
||||||
|
<summary>For Intel Arc™ A300-Series or Pro A60</summary>
|
||||||
|
|
||||||
|
```cmd
|
||||||
|
set SYCL_CACHE_PERSISTENT=1
|
||||||
|
```
|
||||||
|
|
||||||
|
</details>
|
||||||
|
|
||||||
|
<details>
|
||||||
|
|
||||||
|
<summary>For other Intel dGPU Series</summary>
|
||||||
|
|
||||||
|
There is no need to set further environment variables.
|
||||||
|
|
||||||
|
</details>
|
||||||
|
|
||||||
|
> 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 phixtral model (e.g. `mlabonne/phixtral-4x2_8`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'mlabonne/phixtral-4x2_8'`.
|
||||||
|
- `--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
|
||||||
|
#### [mlabonne/phixtral-4x2_8](https://huggingface.co/mlabonne/phixtral-4x2_8)
|
||||||
|
|
||||||
|
```log
|
||||||
|
Inference time: xxxx s
|
||||||
|
-------------------- Prompt --------------------
|
||||||
|
Question:What is AI?
|
||||||
|
|
||||||
|
Answer:
|
||||||
|
-------------------- Output --------------------
|
||||||
|
Question:What is AI?
|
||||||
|
|
||||||
|
Answer: AI, or artificial intelligence, is the simulation of human intelligence in machines that are programmed to think and learn like humans. It involves the development of computer systems that
|
||||||
|
```
|
||||||
|
|
@ -0,0 +1,80 @@
|
||||||
|
#
|
||||||
|
# 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 transformers import AutoTokenizer, GenerationConfig
|
||||||
|
import intel_extension_for_pytorch as ipex
|
||||||
|
from bigdl.llm import optimize_model
|
||||||
|
|
||||||
|
|
||||||
|
# you could tune the prompt based on your own model,
|
||||||
|
# here the prompt tuning refers to # TODO: https://huggingface.co/microsoft/phi-1_5/blob/main/modeling_mixformer_sequential.py
|
||||||
|
PHI1_5_PROMPT_FORMAT = " Question:{prompt}\n\n Answer:"
|
||||||
|
generation_config = GenerationConfig(use_cache = True)
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for phi model')
|
||||||
|
parser.add_argument('--repo-id-or-model-path', type=str, default="mlabonne/phixtral-4x2_8",
|
||||||
|
help='The huggingface repo id for the phixtral model 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 huggingface model with optimize_model in BigDL
|
||||||
|
from transformers import AutoModelForCausalLM
|
||||||
|
model = AutoModelForCausalLM.from_pretrained(model_path,
|
||||||
|
trust_remote_code=True)
|
||||||
|
model = optimize_model(model)
|
||||||
|
|
||||||
|
model = model.to('xpu')
|
||||||
|
|
||||||
|
# Load tokenizer
|
||||||
|
tokenizer = AutoTokenizer.from_pretrained(model_path,
|
||||||
|
trust_remote_code=True)
|
||||||
|
|
||||||
|
# Generate predicted tokens
|
||||||
|
# for phi-moe
|
||||||
|
with torch.inference_mode():
|
||||||
|
prompt = PHI1_5_PROMPT_FORMAT.format(prompt=args.prompt)
|
||||||
|
input_ids = tokenizer.encode(prompt, return_tensors="pt").to('xpu')
|
||||||
|
|
||||||
|
# ipex model needs a warmup, then inference time can be accurate
|
||||||
|
output = model.generate(input_ids,
|
||||||
|
max_new_tokens=args.n_predict,
|
||||||
|
generation_config = generation_config)
|
||||||
|
|
||||||
|
# start inference without profiling
|
||||||
|
st = time.time()
|
||||||
|
output = model.generate(input_ids, do_sample=False, max_new_tokens=args.n_predict, generation_config = generation_config)
|
||||||
|
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)
|
||||||
|
|
@ -912,6 +912,17 @@ def _optimize_post(model, lightweight_bmm=False):
|
||||||
convert_forward(model,
|
convert_forward(model,
|
||||||
module.MixtralBLockSparseTop2MLP,
|
module.MixtralBLockSparseTop2MLP,
|
||||||
mixtral_mlp_forward)
|
mixtral_mlp_forward)
|
||||||
|
elif model.config.model_type == "phi-msft":
|
||||||
|
modeling_module_name = model.__class__.__module__
|
||||||
|
module = importlib.import_module(modeling_module_name)
|
||||||
|
from bigdl.llm.transformers.models.phixtral import phixtral_moeblock_forward, \
|
||||||
|
phixtral_mlp_forward
|
||||||
|
convert_forward(model,
|
||||||
|
module.MoE,
|
||||||
|
phixtral_moeblock_forward)
|
||||||
|
convert_forward(model,
|
||||||
|
module.MLP,
|
||||||
|
phixtral_mlp_forward)
|
||||||
elif model.config.model_type == "mistral":
|
elif model.config.model_type == "mistral":
|
||||||
if model.config.architectures is not None and \
|
if model.config.architectures is not None and \
|
||||||
model.config.architectures[0] == "MixtralForCausalLM":
|
model.config.architectures[0] == "MixtralForCausalLM":
|
||||||
|
|
|
||||||
144
python/llm/src/bigdl/llm/transformers/models/phixtral.py
Normal file
144
python/llm/src/bigdl/llm/transformers/models/phixtral.py
Normal file
|
|
@ -0,0 +1,144 @@
|
||||||
|
#
|
||||||
|
# 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.
|
||||||
|
#
|
||||||
|
# Some parts of this file is adapted from
|
||||||
|
# https://github.com/huggingface/transformers/blob/main/src/transformers/models/mixtral/modeling_mixtral.py
|
||||||
|
|
||||||
|
# coding=utf-8
|
||||||
|
# Copyright 2023 Mistral AI and the HuggingFace Inc. team. All rights reserved.
|
||||||
|
#
|
||||||
|
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
||||||
|
# and OPT implementations in this library. It has been modified from its
|
||||||
|
# original forms to accommodate minor architectural differences compared
|
||||||
|
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
||||||
|
#
|
||||||
|
# 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.
|
||||||
|
|
||||||
|
""" PyTorch Phixtral model."""
|
||||||
|
import math
|
||||||
|
from typing import Optional, Tuple
|
||||||
|
|
||||||
|
import torch
|
||||||
|
from torch import nn
|
||||||
|
import torch.nn.functional as F
|
||||||
|
from bigdl.llm.ggml.quantize import ggml_tensor_qtype
|
||||||
|
from bigdl.llm.utils.common import invalidInputError
|
||||||
|
from bigdl.llm.transformers.models.utils import init_kv_cache, extend_kv_cache, append_kv_cache
|
||||||
|
from bigdl.llm.transformers.models.utils import apply_rotary_pos_emb,\
|
||||||
|
apply_rotary_pos_emb_no_cache_xpu, is_enough_kv_cache_room_4_36
|
||||||
|
from bigdl.llm.transformers.models.mistral import should_use_fuse_rope, use_decoding_fast_path
|
||||||
|
from bigdl.llm.transformers.models.utils import use_flash_attention
|
||||||
|
from bigdl.llm.transformers.models.utils import mlp_fusion_check
|
||||||
|
|
||||||
|
|
||||||
|
KV_CACHE_ALLOC_BLOCK_LENGTH = 256
|
||||||
|
|
||||||
|
|
||||||
|
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
||||||
|
"""
|
||||||
|
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep).
|
||||||
|
The hidden states go from (batch, num_key_value_heads, seqlen, head_dim)
|
||||||
|
to (batch, num_attention_heads, seqlen, head_dim)
|
||||||
|
"""
|
||||||
|
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
|
||||||
|
if n_rep == 1:
|
||||||
|
return hidden_states
|
||||||
|
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads,
|
||||||
|
n_rep, slen, head_dim)
|
||||||
|
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
|
||||||
|
|
||||||
|
|
||||||
|
def phixtral_moeblock_forward(self, hidden_states: torch.Tensor):
|
||||||
|
batch_size, sequence_length, hidden_dim = hidden_states.shape
|
||||||
|
hidden_states = hidden_states.view(-1, hidden_dim)
|
||||||
|
bs = hidden_states.shape[0]
|
||||||
|
# router_logits: (batch * sequence_length, n_experts)
|
||||||
|
router_logits = self.gate(hidden_states)
|
||||||
|
|
||||||
|
num_local_experts = len(self.mlp)
|
||||||
|
|
||||||
|
routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
|
||||||
|
top_k = self.num_experts_per_tok
|
||||||
|
routing_weights, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
|
||||||
|
routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
|
||||||
|
# we cast back to the input dtype
|
||||||
|
routing_weights = routing_weights.to(hidden_states.dtype)
|
||||||
|
|
||||||
|
if bs > 1:
|
||||||
|
final_hidden_states = torch.zeros(
|
||||||
|
(batch_size * sequence_length, hidden_dim),
|
||||||
|
dtype=hidden_states.dtype,
|
||||||
|
device=hidden_states.device
|
||||||
|
)
|
||||||
|
# One hot encode the selected experts to create an expert mask
|
||||||
|
# this will be used to easily index which expert is going to be sollicitated
|
||||||
|
expert_mask = torch.nn.functional.one_hot(selected_experts,
|
||||||
|
num_classes=num_local_experts).permute(2, 1, 0)
|
||||||
|
|
||||||
|
# Loop over all available experts in the model and perform the computation on each expert
|
||||||
|
for expert_idx in range(num_local_experts):
|
||||||
|
expert_layer = self.mlp[expert_idx]
|
||||||
|
idx, top_x = torch.where(expert_mask[expert_idx])
|
||||||
|
|
||||||
|
if top_x.shape[0] == 0:
|
||||||
|
continue
|
||||||
|
|
||||||
|
# in torch it is faster to index using lists than torch tensors
|
||||||
|
top_x_list = top_x.tolist()
|
||||||
|
idx_list = idx.tolist()
|
||||||
|
|
||||||
|
# Index the correct hidden states and compute the expert hidden state for
|
||||||
|
# the current expert. We need to make sure to multiply the output hidden
|
||||||
|
# states by `routing_weights` on the corresponding tokens (top-1 and top-2)
|
||||||
|
current_state = hidden_states[None, top_x_list].reshape(-1, hidden_dim)
|
||||||
|
current_hidden_states = expert_layer(current_state)
|
||||||
|
|
||||||
|
# However `index_add_` only support torch tensors for indexing so we'll use
|
||||||
|
# the `top_x` tensor here.
|
||||||
|
final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
|
||||||
|
else:
|
||||||
|
selected_experts = selected_experts[0].cpu().tolist()
|
||||||
|
for idx in range(top_k):
|
||||||
|
exp_id = selected_experts[idx]
|
||||||
|
expert_layer = self.mlp[exp_id]
|
||||||
|
weight = routing_weights[:, idx]
|
||||||
|
if idx == 0:
|
||||||
|
final_hidden_states = expert_layer(hidden_states)
|
||||||
|
else:
|
||||||
|
final_hidden_states = final_hidden_states + expert_layer(hidden_states)
|
||||||
|
|
||||||
|
final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
|
||||||
|
return final_hidden_states
|
||||||
|
|
||||||
|
|
||||||
|
def phixtral_mlp_forward(
|
||||||
|
self,
|
||||||
|
x: torch.Tensor,
|
||||||
|
) -> torch.Tensor:
|
||||||
|
hidden_states = self.fc1(x)
|
||||||
|
hidden_states = self.act(hidden_states)
|
||||||
|
hidden_states = self.fc2(hidden_states)
|
||||||
|
|
||||||
|
return hidden_states
|
||||||
Loading…
Reference in a new issue