Update optimizer descriptions in quantization_facade.py for Keras and PyTorch

Author: KazunoriSumiya

docs/api/api_docs/methods/get_keras_gptq_config.html

@@ -51,8 +51,8 @@ <h3>Navigation</h3>
 <dt class="field-even">Parameters<span class="colon">:</span></dt>
 <dd class="field-even"><ul class="simple">
 <li><p><strong>n_epochs</strong> (<em>int</em>) – Number of epochs for running the representative dataset for fine-tuning.</p></li>
-<li><p><strong>optimizer</strong> (<em>OptimizerV2</em>) – Keras optimizer to use for fine-tuning for auxiliry variable with a default learning rate set to 0.2.</p></li>
-<li><p><strong>optimizer_rest</strong> (<em>OptimizerV2</em>) – Keras optimizer to use for fine-tuning of the bias variable.</p></li>
+<li><p><strong>optimizer</strong> (<em>OptimizerV2</em>) – Keras optimizer to use for fine-tuning for auxiliary variable. Default: Adam(learning rate set to 3e-2).</p></li>
+<li><p><strong>optimizer_rest</strong> (<em>OptimizerV2</em>) – Keras optimizer to use for fine-tuning of the bias variable. Default: Adam(learning rate set to 1e-4).</p></li>
 <li><p><strong>loss</strong> (<em>Callable</em>) – loss to use during fine-tuning. should accept 4 lists of tensors. 1st list of quantized tensors, the 2nd list is the float tensors, the 3rd is a list of quantized weights and the 4th is a list of float weights.</p></li>
 <li><p><strong>log_function</strong> (<em>Callable</em>) – Function to log information about the gptq process.</p></li>
 <li><p><strong>use_hessian_based_weights</strong> (<em>bool</em>) – Whether to use Hessian-based weights for weighted average loss.</p></li>

docs/api/api_docs/methods/get_pytroch_gptq_config.html

@@ -51,8 +51,8 @@ <h3>Navigation</h3>
 <dt class="field-even">Parameters<span class="colon">:</span></dt>
 <dd class="field-even"><ul class="simple">
 <li><p><strong>n_epochs</strong> (<em>int</em>) – Number of epochs for running the representative dataset for fine-tuning.</p></li>
-<li><p><strong>optimizer</strong> (<em>Optimizer</em>) – Pytorch optimizer to use for fine-tuning for auxiliry variable.</p></li>
-<li><p><strong>optimizer_rest</strong> (<em>Optimizer</em>) – Pytorch optimizer to use for fine-tuning of the bias variable.</p></li>
+<li><p><strong>optimizer</strong> (<em>Optimizer</em>) – Pytorch optimizer to use for fine-tuning for auxiliary variable. Default: Adam(learning rate set to 3e-2).</p></li>
+<li><p><strong>optimizer_rest</strong> (<em>Optimizer</em>) – Pytorch optimizer to use for fine-tuning of the bias variable. Default: Adam(learning rate set to 1e-4).</p></li>
 <li><p><strong>loss</strong> (<em>Callable</em>) – loss to use during fine-tuning. See the default loss function for the exact interface.</p></li>
 <li><p><strong>log_function</strong> (<em>Callable</em>) – Function to log information about the gptq process.</p></li>
 <li><p><strong>use_hessian_based_weights</strong> (<em>bool</em>) – Whether to use Hessian-based weights for weighted average loss.</p></li>

docs/searchindex.js

@@ -75,8 +75,8 @@ def get_keras_gptq_config(n_epochs: int,
 
         args:
             n_epochs (int): Number of epochs for running the representative dataset for fine-tuning.
-            optimizer (OptimizerV2): Keras optimizer to use for fine-tuning for auxiliry variable with a default learning rate set to 0.2.
-            optimizer_rest (OptimizerV2): Keras optimizer to use for fine-tuning of the bias variable.
+            optimizer (OptimizerV2): Keras optimizer to use for fine-tuning for auxiliary variable. Default: Adam(learning rate set to 3e-2).
+            optimizer_rest (OptimizerV2): Keras optimizer to use for fine-tuning of the bias variable. Default: Adam(learning rate set to 1e-4).
             loss (Callable): loss to use during fine-tuning. should accept 4 lists of tensors. 1st list of quantized tensors, the 2nd list is the float tensors, the 3rd is a list of quantized weights and the 4th is a list of float weights.
             log_function (Callable): Function to log information about the gptq process.
             use_hessian_based_weights (bool): Whether to use Hessian-based weights for weighted average loss.

model_compression_toolkit/gptq/keras/quantization_facade.py

@@ -69,8 +69,8 @@ def get_pytorch_gptq_config(n_epochs: int,
 
         args:
             n_epochs (int): Number of epochs for running the representative dataset for fine-tuning.
-            optimizer (Optimizer): Pytorch optimizer to use for fine-tuning for auxiliry variable.
-            optimizer_rest (Optimizer): Pytorch optimizer to use for fine-tuning of the bias variable.
+            optimizer (Optimizer): Pytorch optimizer to use for fine-tuning for auxiliary variable. Default: Adam(learning rate set to 3e-2).
+            optimizer_rest (Optimizer): Pytorch optimizer to use for fine-tuning of the bias variable. Default: Adam(learning rate set to 1e-4).
             loss (Callable): loss to use during fine-tuning. See the default loss function for the exact interface.
             log_function (Callable): Function to log information about the gptq process.
             use_hessian_based_weights (bool): Whether to use Hessian-based weights for weighted average loss.