Update FAQ and documentation

Author: KazunoriSumiya

FAQ.md

@@ -5,7 +5,7 @@
 1. [Why does the size of the quantized model remain the same as the original model size?](#1-why-does-the-size-of-the-quantized-model-remain-the-same-as-the-original-model-size)
 2. [Why does loading a quantized exported model from a file fail?](#2-why-does-loading-a-quantized-exported-model-from-a-file-fail)
 3. [Why am I getting a torch.fx error?](#3-why-am-i-getting-a-torchfx-error)
-
+4. [Does MCT support both per-tensor and per-channel quantization?](#4-does-mct-support-both-per-tensor-and-per-channel-quantization)
 
 ### 1. Why does the size of the quantized model remain the same as the original model size?
 
@@ -57,23 +57,19 @@ Check the `torch.fx` error, and search for an identical replacement. Some exampl
 
 ### 4. Does MCT support both per-tensor and per-channel quantization?
 
-MCT supports both per-tensor and per-channel quantization, as [defined in TPC](https://sonysemiconductorsolutions.github.io/mct-model-optimization/api/api_docs/modules/target_platform_capabilities.html#ug-target-platform-capabilities)
-    
-#model_compression_toolkit.target_platform_capabilities.schema.mct_current_schema.AttributeQuantizationConfig.weights_per_channel_threshold).
-To change this, please set the following parameters.
+MCT supports both per-tensor and per-channel quantization, as [defined in TPC](https://sonysemiconductorsolutions.github.io/mct-model-optimization/api/api_docs/modules/target_platform_capabilities.html#model_compression_toolkit.target_platform_capabilities.schema.mct_current_schema.AttributeQuantizationConfig.weights_per_channel_threshold). To change this, please set the following parameters.
 
-Solution:
-You can switch between per-tensor quantization and per-channel quantization by switching the parameter (weights_per_channel_threshold) as shown below.
+**Solution**: You can switch between per-tensor quantization and per-channel quantization by switching the parameter (weights_per_channel_threshold) as shown below.
 
-In the object that configures the quantizer below:
-・model_compression_toolkit.target_platform_capabilities.schema.mct_current_schema.AttributeQuantizationConfig()
-Set the following parameters:
-・weights_per_channel_threshold(bool) - Indicates whether to quantize the weights per-channel or per-tensor.
-For more details, please refer to [this](https://sonysemiconductorsolutions.github.io/mct-model-optimization/api/api_docs/modules/target_platform_capabilities.html#model_compression_toolkit.target_platform_capabilities.schema.mct_current_schema.AttributeQuantizationConfig.weights_per_channel_threshold) page.
+In the object that configures the quantizer below:  
+* model_compression_toolkit.target_platform_capabilities.schema.mct_current_schema.AttributeQuantizationConfig()  
+Set the following parameter:  
+* weights_per_channel_threshold(bool) - Indicates whether to quantize the weights per-channel or per-tensor.  
+For more details, please refer to [this page](https://sonysemiconductorsolutions.github.io/mct-model-optimization/api/api_docs/modules/target_platform_capabilities.html#model_compression_toolkit.target_platform_capabilities.schema.mct_current_schema.AttributeQuantizationConfig.weights_per_channel_threshold).
 
 
-In QAT, the following object is used to set up a weight-learnable quantizer:
-・model_compression_toolkit.trainable_infrastructure.TrainableQuantizerWeightsConfig()
-Set the following parameters:
-・weights_per_channel_threshold (bool) – Whether to quantize the weights per-channel or not (per-tensor).
-For more details, please refer to [this](https://sonysemiconductorsolutions.github.io/mct-model-optimization/api/api_docs/modules/trainable_infrastructure.html#trainablequantizerweightsconfig) page.
+In QAT, the following object is used to set up a weight-learnable quantizer:  
+* model_compression_toolkit.trainable_infrastructure.TrainableQuantizerWeightsConfig()  
+Set the following parameter:  
+* weights_per_channel_threshold (bool) – Whether to quantize the weights per-channel or not (per-tensor).  
+For more details, please refer to [this page](https://sonysemiconductorsolutions.github.io/mct-model-optimization/api/api_docs/modules/trainable_infrastructure.html#trainablequantizerweightsconfig).

docs/api/api_docs/classes/QuantizationErrorMethod.html

@@ -44,9 +44,7 @@ <h3>Navigation</h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="model_compression_toolkit.core.QuantizationErrorMethod">
 <em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">model_compression_toolkit.core.</span></span><span class="sig-name descname"><span class="pre">QuantizationErrorMethod</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#model_compression_toolkit.core.QuantizationErrorMethod" title="Link to this definition">¶</a></dt>
-<dd><blockquote>
-<div><p>Method for quantization threshold selection:</p>
-</div></blockquote>
+<dd><p>Method for quantization threshold selection:</p>
 <p>NOCLIPPING - Use min/max values as thresholds. This avoids clipping bias but reduces quantization resolution.</p>
 <p>MSE - <strong>(default)</strong> Use mean square error for minimizing quantization noise.</p>
 <p>MAE - Use mean absolute error for minimizing quantization noise.</p>

model_compression_toolkit/core/common/quantization/quantization_config.py

@@ -39,7 +39,7 @@ class CustomOpsetLayers(NamedTuple):
 
 class QuantizationErrorMethod(Enum):
     """
-     Method for quantization threshold selection:
+    Method for quantization threshold selection:
 
     NOCLIPPING - Use min/max values as thresholds. This avoids clipping bias but reduces quantization resolution.