[SW-189684] Add description to functions in HQT

Change-Id: Id5822a21abd1f60f28999574c2ca0e89acc70bf6

neural_compressor/torch/algorithms/fp8_quant/_core/measure.py

@@ -28,6 +28,16 @@
 
 
 def patch_module_measure(mod, mconfig, mod_dict):
+    """Replaces the module with patched module according to mconfig.
+
+    Args:
+        mod (nn.module): The module that will be replaced with patched module that measures the inputs.
+        mconfig (e.g. MaxAbsObserver/MaxAbsPerChannelObserver): The observer object that will measure the parameters.
+        mod_dict (dict): dictionary from module name to its patched module.
+
+    Returns:
+        nn.module: The new module after patching.
+    """
     parent = parent_child_mod_dict[mod].parent
     name = parent_child_mod_dict[mod].name
     patched_mod = mod_dict[mod.__class__.__name__].patched_module(mod, mconfig, name)
@@ -72,6 +82,12 @@ def init_measure_object(mod, name, observer_class, mod_type, skip_measure_output
 
 
 def prepare_model(model, mod_list=None):
+    """Defines the observer class and modules for measurement as preparation.
+
+    Args:
+        model (nn.module): The model that will be measured.
+        mod_list (list, optional): The specific submodules that will be measured in the model. Defaults to None.
+    """
     config = get_hqt_config(model).cfg
     observer_class = observer_types[config["observer"]]
     if (config["shape_file"] is not None) and (observer_class != ShapeObserver):
@@ -85,6 +101,16 @@ def prepare_model(model, mod_list=None):
 
 
 def register_patched_measure_modules(model, mod_list, observer_class, d_shapes=None):
+    """Replace the submodules of the model that appear in mod_list with a patched submodule that uses the given observer_class
+    so the submodule will preform measurement on inputs/outputs in forward stage.
+    Weights measurement is done during model preparation as they are static.
+
+    Args:
+        model (nn.module): The model that will be measured.
+        mod_list (list): The specific submodules that will be measured in the model.
+        observer_class (e.g. MaxAbsObserver/MaxAbsPerChannelObserver): The observer type that will measure the weights.
+        d_shapes (dict, optional): Defaults to None.
+    """
     top_level_config = get_hqt_config(model)
     config = top_level_config.cfg
     skip_outputs_measurements = config["measure_exclude"] & (MeasureExclude.OUTPUT | MeasureExclude.ALL)

neural_compressor/torch/algorithms/fp8_quant/_core/quantize.py

@@ -25,6 +25,16 @@
 
 
 def patch_module(mod, qconfig, mod_dict, patched_mod=None):
+    """Replaces the module with patched module according to mod_dict.
+
+    Args:
+        mod (nn.module): The module that will be replaced with a patched module that quantize the inputs/outputs.
+        qconfig (ModuleExtraConfig): The quantization config object with the information how to quantize the inputs/outputs.
+        mod_dict (dict): dictionary from module name to its patched module.
+
+    Returns:
+        nn.module: The new patched module after patching.
+    """
     parent = parent_child_mod_dict[mod].parent
     name = parent_child_mod_dict[mod].name
     if patched_mod is None:
@@ -33,6 +43,8 @@ def patch_module(mod, qconfig, mod_dict, patched_mod=None):
 
 
 def apply_hf_hook(module):
+    """Applies hf_hook on a given module so its weights will be loaded from disk to cpu and then we can quantize it.
+    """
     if hasattr(module, "_hf_hook"):
         module._hf_hook.pre_forward(module)
         module._hf_hook.detach_hook(module)
@@ -43,6 +55,12 @@ def apply_hf_hook(module):
 
 
 def quantize_params(mod, mod_extra_config):
+    """Quantizes the weights of the given module according to the quantization info from mod_extra_config.
+
+    Args:
+        mod (nn.module): The module that its weights will be quantized.
+        mod_extra_config (ModuleExtraConfig): The quantization config object with the information how to quantize the inputs/outputs.
+    """
     for param_name in mod_extra_config.params:
         quantizer = mod_extra_config.params[param_name]
         param = getattr(mod, param_name)
@@ -55,6 +73,15 @@ def quantize_params(mod, mod_extra_config):
 
 
 def prepare_model(model, qconfig, mod_list, hp_dtype=torch.float):
+    """Replaces the model submodules according to the mod_list with patched quantization modules.
+    Configures patched modules with the quantization/dequantization methods to apply on their input and output tensors.
+    Quantizes the model parameters as they are static.
+
+    Args:
+        model (nn.module): The model to quantize.
+        qconfig (dict): Dict that maps between patched module and its quantization info.
+        mod_list (list): The specific submodules that will be quantized in the model.
+    """
     config = get_hqt_config(model)
     patched_modules = []
     patched_module_types = set()
@@ -82,6 +109,12 @@ def prepare_model(model, qconfig, mod_list, hp_dtype=torch.float):
 
 
 def quantize(model, mod_list):
+    """Builds quantization config object that contains for each submodule its quantization functions as preparation for quantization.
+
+    Args:
+        model (nn.module): The model that will be quantized.
+        mod_list (list, optional): The specific modules that will be quantized in the model.
+    """
     config = get_hqt_config(model)
     generate_model_info(model)
     hp_dtype = config.cfg["hp_dtype"]

neural_compressor/torch/algorithms/fp8_quant/_core/utils.py

@@ -42,6 +42,13 @@ def is_substr(substr_list, target):
 
 
 def prepare_model(model):
+    """Receives the parent module to quantize.
+    Replaces its submodules with patched submodules that perform calibration and quantization.
+    Returns the patched parent module that can perform calibration or quantization according to the configuration.
+
+    Args:
+        model (nn.module): The model that will be measured/quantized.
+    """
     config = get_hqt_config(model)
     update_mod_dict(config)
     allowlist = set(config.cfg["mod_dict"].keys())