Merge branch 'master' into kaihui/core_dump

intel · Jul 29, 2024 · 5226448 · 5226448
2 parents 663921a + 50eb6fb
commit 5226448
Show file tree

Hide file tree

Showing 25 changed files with 2,970 additions and 2 deletions.
diff --git a/.azure-pipelines/scripts/install_nc.sh b/.azure-pipelines/scripts/install_nc.sh
@@ -3,6 +3,10 @@
 echo -e "\n Install Neural Compressor ... "
 cd /neural-compressor
 if [[ $1 = *"3x_pt"* ]]; then
+    if [[ $1 != *"3x_pt_fp8"* ]]; then
+        echo -e "\n Install torch CPU ... "
+        pip install torch==2.3.0 --index-url https://download.pytorch.org/whl/cpu
+    fi
     python -m pip install --no-cache-dir -r requirements_pt.txt
     python setup.py pt bdist_wheel
     pip install --no-deps dist/neural_compressor*.whl --force-reinstall

diff --git a/README.md b/README.md
@@ -31,11 +31,29 @@ support AMD CPU, ARM CPU, and NVidia GPU through ONNX Runtime with limited testi
 * [2024/07] Performance optimizations and usability improvements on [client-side](https://github.com/intel/neural-compressor/blob/master/docs/source/3x/client_quant.md).
 
 ## Installation
+### Install Framework
+#### Install torch for CPU
+```Shell
+pip install torch --index-url https://download.pytorch.org/whl/cpu
+```
+#### Use Docker Image with torch installed for HPU
+https://docs.habana.ai/en/latest/Installation_Guide/Bare_Metal_Fresh_OS.html#bare-metal-fresh-os-single-click 
+
+#### Install torch/intel_extension_for_pytorch for Intel GPU
+https://intel.github.io/intel-extension-for-pytorch/index.html#installation 
+
+#### Install torch for other platform
+https://pytorch.org/get-started/locally
+
+#### Install tensorflow
+```Shell
+pip install tensorflow
+```
 
 ### Install from pypi
 ```Shell
 # Install 2.X API + Framework extension API + PyTorch dependency
-pip install neural-compressor[pt] 
+pip install neural-compressor[pt]
 # Install 2.X API + Framework extension API + TensorFlow dependency
 pip install neural-compressor[tf]
 ```

diff --git a/docs/source/installation_guide.md b/docs/source/installation_guide.md
@@ -27,6 +27,25 @@ The following prerequisites and requirements must be satisfied for a successful
 > Notes:
 > - If you get some build issues, please check [frequently asked questions](faq.md) at first.
 
+### Install Framework
+#### Install torch for CPU
+```Shell
+pip install torch --index-url https://download.pytorch.org/whl/cpu
+```
+#### Use Docker Image with torch installed for HPU
+https://docs.habana.ai/en/latest/Installation_Guide/Bare_Metal_Fresh_OS.html#bare-metal-fresh-os-single-click 
+
+#### Install torch/intel_extension_for_pytorch for Intel GPU
+https://intel.github.io/intel-extension-for-pytorch/index.html#installation 
+
+#### Install torch for other platform
+https://pytorch.org/get-started/locally
+
+#### Install tensorflow
+```Shell
+pip install tensorflow
+```
+
 ### Install from Binary
 - Install from Pypi
 ```Shell

diff --git a/examples/.config/model_params_tensorflow_3x.json b/examples/.config/model_params_tensorflow_3x.json
@@ -93,6 +93,13 @@
       "main_script": "main.py",
       "batch_size": 1000
     },
+    "yolo_v5": {
+      "model_src_dir": "object_detection/yolo_v5/quantization/ptq",
+      "dataset_location": "/tf_dataset2/datasets/coco_yolov5/coco",
+      "input_model": "/tf_dataset2/models/tensorflow/yolo_v5/yolov5s.pb",
+      "main_script": "main.py", 
+      "batch_size": 1
+    },     
     "faster_rcnn_resnet50": {
       "model_src_dir": "object_detection/faster_rcnn_resnet50/quantization/ptq",
       "dataset_location": "/tf_dataset/tensorflow/coco_val.record",

diff --git a/...h/image_recognition/torchvision_models/quantization/static_quant/ipex/README.md b/...h/image_recognition/torchvision_models/quantization/static_quant/ipex/README.md
@@ -0,0 +1,100 @@
+Step-by-Step
+============
+
+This document describes the step-by-step instructions for reproducing PyTorch tuning results with Intel® Neural Compressor.
+
+# Prerequisite
+
+## 1. Environment
+
+We verified examples with IPEX backend on Python 3.10, recommended.
+
+```shell
+pip install -r requirements.txt
+```
+
+## 2. Install Intel-Pytorch-Extension
+
+Please refer to [intel/intel-extension-for-pytorch(github.com)](https://github.com/intel/intel-extension-for-pytorch).
+
+### Install IPEX CPU
+
+   > Note: GCC9 compiler is recommended
+
+   ```shell
+   python -m pip install intel_extension_for_pytorch -f https://software.intel.com/ipex-whl-stable
+   ```
+
+### Install IPEX XPU
+Please build an IPEX docker container according to the [official guide](https://intel.github.io/intel-extension-for-pytorch/index.html#installation?platform=gpu&version=v2.1.30%2bxpu&os=linux%2fwsl2&package=docker).
+
+You can run a simple sanity test to double confirm if the correct version is installed, and if the software stack can get correct hardware information onboard your system. The command should return PyTorch and IPEX versions installed, as well as GPU card(s) information detected.
+```bash
+source {DPCPPROOT}/env/vars.sh
+source {MKLROOT}/env/vars.sh
+source {CCLROOT}/env/vars.sh
+source {MPIROOT}/env/vars.sh
+python -c "import torch; import intel_extension_for_pytorch as ipex; print(torch.__version__); print(ipex.__version__); [print(f'[{i}]: {torch.xpu.get_device_properties(i)}') for i in range(torch.xpu.device_count())];"
+```
+Please also refer to this [tutorial](https://intel.github.io/intel-extension-for-pytorch/index.html#installation?platform=gpu&version=v2.1.30%2bxpu&os=linux%2fwsl2&package=conda) to check system requirements and install dependencies.
+
+## 3. Prepare Dataset
+
+Download [ImageNet](http://www.image-net.org/) Raw image to dir: /path/to/imagenet. The dir include below folder:
+
+```bash
+ls /path/to/imagenet
+train  val
+```
+
+# Run with CPU
+
+> Note: All torchvision model names can be passed as long as they are included in `torchvision.models`, below are some examples.
+
+### 1. ResNet18 With Intel PyTorch Extension
+
+```shell
+python main.py -t -a resnet18 --ipex --pretrained /path/to/imagenet
+```
+or
+```shell
+bash run_quant.sh --input_model=resnet18 --dataset_location=/path/to/imagenet
+bash run_benchmark.sh --input_model=resnet18 --dataset_location=/path/to/imagenet --mode=performance/accuracy --int8=true/false
+```
+
+### 2. ResNet50 With Intel PyTorch Extension
+
+```shell
+python main.py -t -a resnet50 --ipex --pretrained /path/to/imagenet
+```
+or
+```shell
+bash run_quant.sh --input_model=resnet50 --dataset_location=/path/to/imagenet
+bash run_benchmark.sh --input_model=resnet50 --dataset_location=/path/to/imagenet --mode=performance/accuracy --int8=true/false
+```
+
+### 3. ResNext101_32x16d With Intel PyTorch Extension
+
+```shell
+python main.py -t -a resnext101_32x16d_wsl --hub --ipex --pretrained /path/to/imagenet
+```
+or
+```shell
+bash run_quant.sh --input_model=resnext101_32x16d_wsl --dataset_location=/path/to/imagenet
+bash run_benchmark.sh --input_model=resnext101_32x16d_wsl --dataset_location=/path/to/imagenet --mode=performance/accuracy --int8=true/false
+```
+
+# Run with XPU
+
+> Note: All torchvision model names can be passed as long as they are included in `torchvision.models`, below are some examples.
+
+### 1. ResNet18 With Intel PyTorch Extension
+
+```shell
+python main.py -t -a resnet18 --ipex --pretrained /path/to/imagenet --xpu
+```
+or
+```shell
+bash run_quant.sh --input_model=resnet18 --dataset_location=/path/to/imagenet
+bash run_benchmark.sh --input_model=resnet18 --dataset_location=/path/to/imagenet --mode=performance/accuracy --int8=true/false --xpu=true/false
+```