[DOC]: Added INT4 weight compression description (#20812)

* Added INT4 information into weight compression doc

* Added GPTQ info. Fixed comments

* Fixed list

* Fixed issues. Updated Gen.AI doc

* Applied comments

* Added additional infor about GPTQ support

* Fixed typos

* Update docs/articles_en/openvino_workflow/gen_ai.md

Co-authored-by: Nico Galoppo <nico.galoppo@intel.com>

* Update docs/articles_en/openvino_workflow/gen_ai.md

Co-authored-by: Nico Galoppo <nico.galoppo@intel.com>

* Update docs/optimization_guide/nncf/code/weight_compression_openvino.py

Co-authored-by: Nico Galoppo <nico.galoppo@intel.com>

* Applied changes

* Update docs/articles_en/openvino_workflow/gen_ai.md

Co-authored-by: Tatiana Savina <tatiana.savina@intel.com>

* Update docs/articles_en/openvino_workflow/gen_ai.md

Co-authored-by: Tatiana Savina <tatiana.savina@intel.com>

* Update docs/articles_en/openvino_workflow/gen_ai.md

Co-authored-by: Tatiana Savina <tatiana.savina@intel.com>

* Update docs/articles_en/openvino_workflow/model_optimization_guide/weight_compression.md

Co-authored-by: Tatiana Savina <tatiana.savina@intel.com>

* Update docs/articles_en/openvino_workflow/model_optimization_guide/weight_compression.md

Co-authored-by: Tatiana Savina <tatiana.savina@intel.com>

* Update docs/articles_en/openvino_workflow/model_optimization_guide/weight_compression.md

Co-authored-by: Tatiana Savina <tatiana.savina@intel.com>

* Update docs/articles_en/openvino_workflow/model_optimization_guide/weight_compression.md

Co-authored-by: Tatiana Savina <tatiana.savina@intel.com>

* Update docs/articles_en/openvino_workflow/model_optimization_guide/weight_compression.md

Co-authored-by: Tatiana Savina <tatiana.savina@intel.com>

* Added table with results

* One more comment

---------

Co-authored-by: Nico Galoppo <nico.galoppo@intel.com>
Co-authored-by: Tatiana Savina <tatiana.savina@intel.com>

docs/articles_en/openvino_workflow/gen_ai.md

@@ -115,6 +115,28 @@ Optimum-Intel API also provides out-of-the-box model optimization through weight
 
 Weight compression is applied by default to models larger than one billion parameters and is also available for CLI interface as the ``--int8`` option.
 
+.. note::
+
+   8-bit weight compression is enabled by default for models larger than 1 billion parameters.
+
+`NNCF <https://github.com/openvinotoolkit/nncf>`__ also provides 4-bit weight compression, which is supported by OpenVINO. It can be applied to Optimum objects as follows:
+
+.. code-block:: python
+
+    from nncf import compress_weights, CompressWeightsMode
+
+    model = OVModelForCausalLM.from_pretrained(model_id, export=True, load_in_8bit=False)
+    model.model = compress_weights(model.model, mode=CompressWeightsMode.INT4_SYM, group_size=128, ratio=0.8)
+
+
+The optimized model can be saved as usual with a call to ``save_pretrained()``. For more details on compression options, refer to the :doc:`weight compression guide <weight_compression>`.
+
+.. note::
+
+   OpenVINO also supports 4-bit models from Hugging Face `Transformers <https://github.com/huggingface/transformers>`__ library optimized 
+   with `GPTQ <https://github.com/PanQiWei/AutoGPTQ>`__. In this case, there is no need for an additional model optimization step because model conversion will automatically preserve the INT4 optimization results, allowing model inference to benefit from it.
+
+
 Below are some examples of using Optimum-Intel for model conversion and inference:
 
 * `Stable Diffusion v2.1 using Optimum-Intel OpenVINO <https://github.com/openvinotoolkit/openvino_notebooks/blob/main/notebooks/236-stable-diffusion-v2/236-stable-diffusion-v2-optimum-demo.ipynb>`__

docs/articles_en/openvino_workflow/model_optimization_guide/weight_compression.md

@@ -10,12 +10,14 @@ Weight compression aims to reduce the memory footprint of a model. It can also l
 - enabling the inference of exceptionally large models that cannot be accommodated in the memory of the device; 
 - improving the inference performance of the models by reducing the latency of the memory access when computing the operations with weights, for example, Linear layers.
 
-Currently, `Neural Network Compression Framework (NNCF) <https://github.com/openvinotoolkit/nncf>`__ provides 8-bit weight quantization as a compression method primarily designed to optimize LLMs. The main difference between weights compression and full model quantization (post-training quantization) is that activations remain floating-point in the case of weights compression which leads to a better accuracy. Weight compression for LLMs provides a solid inference performance improvement which is on par with the performance of the full model quantization. In addition, weight compression is data-free and does not require a calibration dataset, making it easy to use.
+Currently, `Neural Network Compression Framework (NNCF) <https://github.com/openvinotoolkit/nncf>`__ provides weight quantization to 8 and 4-bit integer data types as a compression method primarily designed to optimize LLMs. The main difference between weights compression and full model quantization is that activations remain floating-point in the case of weight compression, resulting in better accuracy. Weight compression for LLMs provides a solid inference performance improvement which is on par with the performance of the full model quantization. In addition, weight compression is data-free and does not require a calibration dataset, making it easy to use.
 
 Compress Model Weights
 ######################
 
-The code snippet below shows how to compress the weights of the model represented in OpenVINO IR using NNCF:
+- **8-bit weight quantization** - this method is aimed at accurate optimization of the model, which usually leads to significant performance improvements for Transformer-based models. Models with 8-bit compressed weights are performant on the vast majority of supported CPU and GPU platforms.
+
+The code snippet below shows how to do 8-bit quantization of the model weights represented in OpenVINO IR using NNCF:
 
 .. tab-set::
 
@@ -28,6 +30,103 @@ The code snippet below shows how to compress the weights of the model represente
 
 Now, the model is ready for compilation and inference. It can be also saved into a compressed format, resulting in a smaller binary file.
 
+- **4-bit weight quantization** - this method stands for an INT4-INT8 mixed-precision weight quantization, where INT4 is considered as the primary precision and INT8 is the backup one. It usually results in a smaller model size and lower inference latency, although the accuracy degradation could be higher, depending on the model. The method has several parameters that can provide different performance-accuracy trade-offs after optimization:
+
+  * ``mode`` - there are two modes to choose from: ``INT4_SYM`` - stands for INT4 symmetric weight quantization and results in faster inference and smaller model size, and ``INT4_ASYM`` - INT4 asymmetric weight quantization with variable zero-point for more accurate results.
+
+  * ``group_size`` - controls the size of the group of weights that share the same quantization parameters. Smaller model size results in a more accurate optimized model but with a larger footprint and slower inference. The following group sizes are recommended: ``128``, ``64``, ``32`` (``128`` is default value)
+
+  * ``ratio`` - controls the ratio between INT4 and INT8 compressed layers in the model. For example, 0.8 means that 80% of layers will be compressed to INT4, while the rest will be compressed to INT8 precision.
+
+The example below shows 4-bit weight quantization applied on top of OpenVINO IR:
+
+.. tab-set::
+
+   .. tab-item:: OpenVINO
+      :sync: openvino
+      
+      .. doxygensnippet:: docs/optimization_guide/nncf/code/weight_compression_openvino.py
+         :language: python
+         :fragment: [compression_4bit]
+
+.. note::
+
+   OpenVINO also supports 4-bit models from Hugging Face `Transformers <https://github.com/huggingface/transformers>`__ library optimized 
+   with `GPTQ <https://github.com/PanQiWei/AutoGPTQ>`__. In this case, there is no need for an additional model optimization step because model conversion will automatically preserve the INT4 optimization results, allowing model inference to benefit from it.
+
+
+The table below shows examples of Text Generation models with different optimization settings:
+
+.. list-table::
+   :widths: 40 55 25 25
+   :header-rows: 1
+
+   * - Model
+     - Optimization
+     - Perplexity
+     - Model Size (Gb)
+   * - databricks/dolly-v2-3b
+     - FP32
+     - 5.01
+     - 10.3
+   * - databricks/dolly-v2-3b
+     - INT8
+     - 5.07
+     - 2.6
+   * - databricks/dolly-v2-3b
+     - INT4_ASYM,group_size=32,ratio=0.5
+     - 5.28
+     - 2.2
+   * - facebook/opt-6.7b
+     - FP32
+     - 4.25
+     - 24.8
+   * - facebook/opt-6.7b
+     - INT8
+     - 4.27
+     - 6.2
+   * - facebook/opt-6.7b
+     - INT4_ASYM,group_size=64,ratio=0.8
+     - 4.32
+     - 4.1
+   * - meta-llama/Llama-2-7b-chat-hf
+     - FP32
+     - 3.28
+     - 25.1
+   * - meta-llama/Llama-2-7b-chat-hf
+     - INT8
+     - 3.29
+     - 6.3
+   * - meta-llama/Llama-2-7b-chat-hf
+     - INT4_ASYM,group_size=128,ratio=0.8
+     - 3.41
+     - 4.0
+   * - togethercomputer/RedPajama-INCITE-7B-Instruct
+     - FP32
+     - 4.15
+     - 25.6
+   * - togethercomputer/RedPajama-INCITE-7B-Instruct
+     - INT8
+     - 4.17
+     - 6.4
+   * - togethercomputer/RedPajama-INCITE-7B-Instruct
+     - INT4_ASYM,group_size=128,ratio=1.0
+     - 4.17
+     - 3.6
+   * - meta-llama/Llama-2-13b-chat-hf
+     - FP32
+     - 2.92
+     - 48.5
+   * - meta-llama/Llama-2-13b-chat-hf
+     - INT8
+     - 2.91
+     - 12.1
+   * - meta-llama/Llama-2-13b-chat-hf
+     - INT4_SYM,group_size=64,ratio=0.8
+     - 2.98
+     - 8.0
+   
+
 Additional Resources
 ####################
 

docs/optimization_guide/nncf/code/weight_compression_openvino.py

@@ -4,3 +4,10 @@ from nncf import compress_weights
 ...
 model = compress_weights(model) # model is openvino.Model object
 #! [compression_8bit]
+
+#! [compression_4bit]
+from nncf import compress_weights, CompressWeightsMode
+
+...
+model = compress_weights(model, mode=CompressWeightsMode.INT4_SYM, group_size=128, ratio=0.8) # model is openvino.Model object
+#! [compression_4bit]