3.X API installation update (#1935)

Signed-off-by: chensuyue <suyue.chen@intel.com>

Author: chensuyue

.azure-pipelines/scripts/install_nc.sh

@@ -10,10 +10,6 @@ elif [[ $1 = *"3x_tf"* ]]; then
     python -m pip install --no-cache-dir -r requirements_tf.txt
     python setup.py tf bdist_wheel
     pip install dist/neural_compressor*.whl --force-reinstall
-elif [[ $1 = *"3x_ort" ]]; then
-    python -m pip install --no-cache-dir -r requirements_ort.txt
-    python setup.py ort bdist_wheel
-    pip install dist/neural_compressor*.whl --force-reinstall
 else
     python -m pip install --no-cache-dir -r requirements.txt
     python setup.py bdist_wheel

.azure-pipelines/scripts/ut/3x/coverage.3x_ort

@@ -140,16 +140,3 @@ subprojects:
       - "UT-3x-Torch (Coverage Compare CollectDatafiles)"
       - "UT-3x-Torch (Unit Test 3x Torch Unit Test 3x Torch)"
       - "UT-3x-Torch (Unit Test 3x Torch baseline Unit Test 3x Torch baseline)"
-
-  - id: "Unit Tests 3x-ONNXRT workflow"
-    paths:
-      - "neural_compressor/common/**"
-      - "neural_compressor/onnxrt/**"
-      - "test/3x/onnxrt/**"
-      - "setup.py"
-      - "requirements_ort.txt"
-    checks:
-      - "UT-3x-ONNXRT"
-      - "UT-3x-ONNXRT (Coverage Compare CollectDatafiles)"
-      - "UT-3x-ONNXRT (Unit Test 3x ONNXRT Unit Test 3x ONNXRT)"
-      - "UT-3x-ONNXRT (Unit Test 3x ONNXRT baseline Unit Test 3x ONNXRT baseline)"

.azure-pipelines/scripts/ut/3x/run_3x_ort.sh

@@ -19,21 +19,25 @@ Intel® Neural Compressor aims to provide popular model compression techniques s
 as well as Intel extensions such as [Intel Extension for TensorFlow](https://github.com/intel/intel-extension-for-tensorflow) and [Intel Extension for PyTorch](https://github.com/intel/intel-extension-for-pytorch).
 In particular, the tool provides the key features, typical examples, and open collaborations as below:
 
-* Support a wide range of Intel hardware such as [Intel Xeon Scalable Processors](https://www.intel.com/content/www/us/en/products/details/processors/xeon/scalable.html), [Intel Xeon CPU Max Series](https://www.intel.com/content/www/us/en/products/details/processors/xeon/max-series.html), [Intel Data Center GPU Flex Series](https://www.intel.com/content/www/us/en/products/details/discrete-gpus/data-center-gpu/flex-series.html), and [Intel Data Center GPU Max Series](https://www.intel.com/content/www/us/en/products/details/discrete-gpus/data-center-gpu/max-series.html) with extensive testing; support AMD CPU, ARM CPU, and NVidia GPU through ONNX Runtime with limited testing
+* Support a wide range of Intel hardware such as [Intel Gaudi Al Accelerators](https://www.intel.com/content/www/us/en/products/details/processors/ai-accelerators/gaudi-overview.html), [Intel Core Ultra Processors](https://www.intel.com/content/www/us/en/products/details/processors/core-ultra.html), [Intel Xeon Scalable Processors](https://www.intel.com/content/www/us/en/products/details/processors/xeon/scalable.html), [Intel Xeon CPU Max Series](https://www.intel.com/content/www/us/en/products/details/processors/xeon/max-series.html), [Intel Data Center GPU Flex Series](https://www.intel.com/content/www/us/en/products/details/discrete-gpus/data-center-gpu/flex-series.html), and [Intel Data Center GPU Max Series](https://www.intel.com/content/www/us/en/products/details/discrete-gpus/data-center-gpu/max-series.html) with extensive testing; 
+support AMD CPU, ARM CPU, and NVidia GPU through ONNX Runtime with limited testing; support NVidia GPU for some WOQ algorithms like AutoRound and HQQ. 
 
 * Validate popular LLMs such as [LLama2](/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/llm), [Falcon](/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/llm), [GPT-J](/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/llm), [Bloom](/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/llm), [OPT](/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/llm), and more than 10,000 broad models such as [Stable Diffusion](/examples/pytorch/nlp/huggingface_models/text-to-image/quantization), [BERT-Large](/examples/pytorch/nlp/huggingface_models/text-classification/quantization/ptq_static/fx), and [ResNet50](/examples/pytorch/image_recognition/torchvision_models/quantization/ptq/cpu/fx) from popular model hubs such as [Hugging Face](https://huggingface.co/), [Torch Vision](https://pytorch.org/vision/stable/index.html), and [ONNX Model Zoo](https://github.com/onnx/models#models), with automatic [accuracy-driven](/docs/source/design.md#workflow) quantization strategies
 
 * Collaborate with cloud marketplaces such as [Google Cloud Platform](https://console.cloud.google.com/marketplace/product/bitnami-launchpad/inc-tensorflow-intel?project=verdant-sensor-286207), [Amazon Web Services](https://aws.amazon.com/marketplace/pp/prodview-yjyh2xmggbmga#pdp-support), and [Azure](https://azuremarketplace.microsoft.com/en-us/marketplace/apps/bitnami.inc-tensorflow-intel), software platforms such as [Alibaba Cloud](https://www.intel.com/content/www/us/en/developer/articles/technical/quantize-ai-by-oneapi-analytics-on-alibaba-cloud.html), [Tencent TACO](https://new.qq.com/rain/a/20221202A00B9S00) and [Microsoft Olive](https://github.com/microsoft/Olive), and open AI ecosystem such as [Hugging Face](https://huggingface.co/blog/intel), [PyTorch](https://pytorch.org/tutorials/recipes/intel_neural_compressor_for_pytorch.html), [ONNX](https://github.com/onnx/models#models), [ONNX Runtime](https://github.com/microsoft/onnxruntime), and [Lightning AI](https://github.com/Lightning-AI/lightning/blob/master/docs/source-pytorch/advanced/post_training_quantization.rst)
 
 ## What's New
+* [2024/07] From 3.0 release, framework extension API is recommended to be used for quantization.
 * [2024/07] Performance optimizations and usability improvements on [client-side](https://github.com/intel/neural-compressor/blob/master/docs/3x/client_quant.md).
-* [2024/03] A new SOTA approach [AutoRound](https://github.com/intel/auto-round) Weight-Only Quantization on [Intel Gaudi2 AI accelerator](https://habana.ai/products/gaudi2/) is available for LLMs.
 
 ## Installation
 
 ### Install from pypi
 ```Shell
-pip install neural-compressor
+# Install 2.X API + Framework extension API + PyTorch dependency
+pip install neural-compressor[pt] 
+# Install 2.X API + Framework extension API + TensorFlow dependency
+pip install neural-compressor[tf]
 ```
 > **Note**: 
 > Further installation methods can be found under [Installation Guide](https://github.com/intel/neural-compressor/blob/master/docs/source/installation_guide.md). check out our [FAQ](https://github.com/intel/neural-compressor/blob/master/docs/source/faq.md) for more details.

.azure-pipelines/ut-3x-ort.yml

@@ -29,28 +29,28 @@ The following prerequisites and requirements must be satisfied for a successful
 
 ### Install from Binary
 - Install from Pypi
-  ```Shell
-  # install stable basic version from pypi
-  pip install neural-compressor
-  ```
-  ```Shell
-  # [Experimental] install stable basic + PyTorch framework extension API from pypi 
-  pip install neural-compressor[pt]
-  ```
-  ```Shell
-  # [Experimental] install stable basic + TensorFlow framework extension API from pypi 
-  pip install neural-compressor[tf]
-  ```
-
-- Install from test Pypi
-  ```Shell
-  # install nightly version
-  git clone https://github.com/intel/neural-compressor.git
-  cd neural-compressor
-  pip install -r requirements.txt
-  # install nightly basic version from pypi
-  pip install -i https://test.pypi.org/simple/ neural-compressor
-  ```
+```Shell
+# Install 2.X API + Framework extension API + PyTorch dependency
+pip install neural-compressor[pt]
+```
+```Shell
+# Install 2.X API + Framework extension API + TensorFlow dependency
+pip install neural-compressor[tf]
+```
+```Shell
+# Install 2.X API + Framework extension API
+# With this install CMD, some dependencies for framework extension API not installed, 
+# you can install them separately by `pip install -r requirements_pt.txt` or `pip install -r requirements_tf.txt`.
+pip install neural-compressor
+```
+```Shell
+# Framework extension API + TensorFlow dependency
+pip install neural-compressor-pt
+```
+```Shell
+# Framework extension API + TensorFlow dependency
+pip install neural-compressor-tf
+```
 
 ### Install from Source
 
@@ -76,15 +76,20 @@ The AI Kit is distributed through many common channels, including from Intel's w
 ## System Requirements
 
 ### Validated Hardware Environment
+
+#### Intel® Neural Compressor supports HPUs based on heterogeneous architecture with two compute engines (MME and TPC): 
+* Intel Gaudi Al Accelerators (Gaudi2)
+
 #### Intel® Neural Compressor supports CPUs based on [Intel 64 architecture or compatible processors](https://en.wikipedia.org/wiki/X86-64):
 
-* Intel Xeon Scalable processor (formerly Skylake, Cascade Lake, Cooper Lake, Ice Lake, and Sapphire Rapids)
-* Intel Xeon CPU Max Series (formerly Sapphire Rapids HBM)
+* Intel Xeon Scalable processor (Skylake, Cascade Lake, Cooper Lake, Ice Lake, and Sapphire Rapids)
+* Intel Xeon CPU Max Series (Sapphire Rapids HBM)
+* Intel Core Ultra Processors (Meteor Lake)
 
 #### Intel® Neural Compressor supports GPUs built on Intel's Xe architecture:
 
-* Intel Data Center GPU Flex Series (formerly Arctic Sound-M)
-* Intel Data Center GPU Max Series (formerly Ponte Vecchio)
+* Intel Data Center GPU Flex Series (Arctic Sound-M)
+* Intel Data Center GPU Max Series (Ponte Vecchio)
 
 #### Intel® Neural Compressor quantized ONNX models support multiple hardware vendors through ONNX Runtime: