[DOCS] Limitation of BF16 inference precision for LLMs (openvinotoolk…

…it#24501)

Creating `Limitation of the bf16 inference precision` section to
describe an issue of higher accuracy loss than the pre-determined
threshold of 0.5% and present possible solutions.
This PR addresses JIRA ticket: 138832.

docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes/cpu-device.rst

@@ -101,6 +101,11 @@ On platforms that natively support half-precision calculations (``bfloat16`` or
 of ``f32`` to achieve better performance (see the `Execution Mode Hint <#execution-mode-hint>`__).
 Thus, no special steps are required to run a model with ``bf16`` or ``f16`` inference precision.
 
+.. important::
+
+   The ``bf16`` floating-point precision appears to have some limitations that impact the
+   inference accuracy in LLM models. For more details, refer to this :ref:`article <limited_inference_precision>`.
+
 Using the half-precision provides the following performance benefits:
 
 - ``bfloat16`` and ``float16`` data types enable Intel® Advanced Matrix Extension (AMX) on 4+ generation Intel® Xeon® Scalable Processors, resulting in significantly faster computations on the corresponding hardware compared to AVX512 or AVX2 instructions in many deep learning operation implementations.

docs/articles_en/openvino-workflow/running-inference/optimize-inference/precision-control.rst

@@ -18,18 +18,40 @@ of the weights, and it does not affect how the devices execute the model. This c
 a lot of confusion where, for example, you couldn't execute a high-performance model on the GPU
 by default, and the behavior between devices was different.
 
-This guide will focus on how to control inference precision. And using lower precision is important for performance because compute bandwidth tends to be higher for smaller data types, and hardware often has special blocks for efficient multiply-accumulate operations with smaller data types only (e.g. Intel Xᵉ Matrix Extensions (XMX) on GPU and Intel Advanced Matrix Extensions (AMX) on CPU do not support ``f32``). Also, I/O operations requires less memory due to the smaller tensor byte size. This guide will focus on how to control inference precision.
+This guide will focus on how to control inference precision. And using lower precision is
+important for performance because compute bandwidth tends to be higher for smaller data
+types, and hardware often has special blocks for efficient multiply-accumulate operations
+with smaller data types only (e.g. Intel Xᵉ Matrix Extensions (XMX) on GPU and Intel
+Advanced Matrix Extensions (AMX) on CPU do not support ``f32``). Also, I/O operations
+requires less memory due to the smaller tensor byte size. This guide will focus on how
+to control inference precision.
 
 
 Execution Mode
 ##############
 
-``ov::hint::execution_mode`` is a high-level hint to control whether the user wants to keep the best accuracy (**ACCURACY mode**) or if the device can do some optimizations that may lower the accuracy for performance reasons (**PERFORMANCE mode**)
-
-* In **ACCURACY mode**, the device cannot convert floating point tensors to a smaller floating point type, so devices try to keep the accuracy metrics as close as possible to the original values ​​obtained after training relative to the device's real capabilities. This means that most devices will infer with ``f32`` precision if your device supports it.
-* In **PERFORMANCE mode**, the device can convert to smaller data types and apply other optimizations that may have some impact on accuracy rates, although we still try to minimize accuracy loss and may use mixed precision execution in some cases.
-
-If the model has been quantized using :doc:`OpenVINO optimization tools <../../model-optimization-guide/quantizing-models-post-training>` or any other method, the quantized operators will be executed with the target integer precision if the device has hardware acceleration for that type. For example, quantized ``int8`` primitives are executed with ``int8`` precision for both **ACCURACY** and **PERFORMANCE modes** if the device provides higher compute bandwidth for 8-bit data types compared to any available floating-point type. On the other hand, devices without hardware acceleration for the ``int8`` data type can keep such operators in floating point precision, and the exact floating point type will be affected by ``execution_mode`` and ``inference_precision`` properties.
+``ov::hint::execution_mode`` is a high-level hint to control whether the user wants to keep
+the best accuracy (**ACCURACY mode**) or if the device can do some optimizations that
+may lower the accuracy for performance reasons (**PERFORMANCE mode**)
+
+* In **ACCURACY mode**, the device cannot convert floating point tensors to a smaller
+  floating point type, so devices try to keep the accuracy metrics as close as possible to
+  the original values ​​obtained after training relative to the device's real capabilities.
+  This means that most devices will infer with ``f32`` precision if your device supports it.
+* In **PERFORMANCE mode**, the device can convert to smaller data types and apply other
+  optimizations that may have some impact on accuracy rates, although we still try to
+  minimize accuracy loss and may use mixed precision execution in some cases.
+
+If the model has been quantized using
+:doc:`OpenVINO optimization tools <../../model-optimization-guide/quantizing-models-post-training>`
+or any other method, the quantized operators will be executed with the target integer
+precision if the device has hardware acceleration for that type. For example, quantized
+``int8`` primitives are executed with ``int8`` precision for both **ACCURACY** and
+**PERFORMANCE modes** if the device provides higher compute bandwidth for 8-bit data types
+compared to any available floating-point type. On the other hand, devices without hardware
+acceleration for the ``int8`` data type can keep such operators in floating point precision,
+and the exact floating point type will be affected by ``execution_mode`` and
+``inference_precision`` properties.
 
 Code examples:
 
@@ -53,11 +75,43 @@ Code examples:
 Inference Precision
 ###################
 
-``ov::hint::inference_precision`` precision is a lower-level property that allows you to specify the exact precision the user wants, but is less portable. For example, CPU supports ``f32`` inference precision and ``bf16`` on some platforms, GPU supports ``f32`` and ``f16``, so if a user wants to an application that uses multiple devices, they have to handle all these combinations manually or let OV do it automatically by using higher level ``execution_mode`` property. Another thing is that ``inference_precision`` is also a hint, so the value provided is not guaranteed to be used by Runtime (mainly in cases where the current device does not have the required hardware capabilities).
+``ov::hint::inference_precision`` precision is a lower-level property that allows you
+to specify the exact precision the user wants, but is less portable. For example, CPU
+supports ``f32`` inference precision and ``bf16`` on some platforms, GPU supports ``f32``
+and ``f16``, so if a user wants to an application that uses multiple devices, they have
+to handle all these combinations manually or let OV do it automatically by using higher
+level ``execution_mode`` property. Another thing is that ``inference_precision`` is also
+a hint, so the value provided is not guaranteed to be used by Runtime (mainly in cases
+where the current device does not have the required hardware capabilities).
 
 .. note::
 
-   All devices only support floating-point data types (``f32``, ``f16``, ``bf16``) as a value for ``inference_precision`` attribute, because quantization cannot be done in Runtime.
+   All devices only support floating-point data types (``f32``, ``f16``, ``bf16``) as a value
+   for ``inference_precision`` attribute, because quantization cannot be done in Runtime.
+
+
+.. _limited_inference_precision:
+
+Limitation of the ``bf16`` inference precision
+++++++++++++++++++++++++++++++++++++++++++++++
+
+It is important to mention that inferring FP16 and FP32 LLM models with the ``bf16`` runtime
+precision may result in higher accuracy loss than the pre-determined threshold of 0.5%.
+Higher accuracy drop may occur when inferring **dolly-v2-12b**, **dolly-v2-3b**, and
+**gpt-neox-20b** original Pytorch models with ``bf16``, and is caused by a limited
+precision representation.
+
+To solve the issue, you might use an INT8 model and force the FP32 inference precision.
+The accuracy of an INT8 model with FP32 is nearly the same as of an FP16 model with ``f32``.
+Additionally, selective FP32 execution of ops on CPU plugin together with the NNCF ``bf16``
+calibration could potentially mitigate the accuracy loss.
+
+However, the solutions mentioned above would, unfortunately, also result in significant
+performance drop during a large batch size inference task on machines with Intel AMX-BF16 SPR.
+In such cases, the fused multiply-add operation (FMA) is used instead of AMX. Also,
+in a compute-bound case, such as the LLM batch inference/serving, these workarounds
+would drastically reduce the throughput by more than 60%.
+
 
 
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