[quant][graphmode][fx] Add additional_object_mapping argument to convert

torch/quantization/fx/quantization_patterns.py

@@ -177,6 +177,9 @@ def convert(self, quantizer, node, load_arg, debug=False, convert_custom_config_
             # note that relu should already be fused into conv module in the fusion step
             assert self.relu_node is None, 'conv module and relu fusion is not executed, ' \
                 'please make sure to run fusion before prepare'
+            if convert_custom_config_dict is None:
+                convert_custom_config_dict = {}
+            additional_static_quant_mapping = convert_custom_config_dict.get("static", {})
             # 1. attach activation post process to module
             if type(self.conv) in [
                     torch.nn.intrinsic.ConvReLU1d,
@@ -187,7 +190,8 @@ def convert(self, quantizer, node, load_arg, debug=False, convert_custom_config_
             else:
                 self.conv.activation_post_process = quantizer.activation_post_process_map[node.name]
             # 2. select quantized class
-            qconv_cls = get_static_quant_module_class(type(self.conv))
+            qconv_cls = get_static_quant_module_class(
+                type(self.conv), additional_static_quant_mapping)
             quantized = qconv_cls.from_float(self.conv)
             parent_name, name = _parent_name(self.conv_node.target)
             setattr(quantizer.modules[parent_name], name, quantized)
@@ -363,6 +367,9 @@ def __init__(self, quantizer, node):
         self.bn = quantizer.modules[self.bn_node.target]
 
     def convert(self, quantizer, node, load_arg, debug=False, convert_custom_config_dict=None):
+        if convert_custom_config_dict is None:
+            convert_custom_config_dict = {}
+        additional_static_quant_mapping = convert_custom_config_dict.get("static", {})
         # 1. attach activation post process to module
         activation_post_process = quantizer.activation_post_process_map[node.name]
         if type(self.bn) in \
@@ -371,7 +378,7 @@ def convert(self, quantizer, node, load_arg, debug=False, convert_custom_config_
             self.bn[1].activation_post_process = activation_post_process
         else:
             self.bn.activation_post_process = activation_post_process
-        qbn_cls = get_static_quant_module_class(type(self.bn))
+        qbn_cls = get_static_quant_module_class(type(self.bn), additional_static_quant_mapping)
         quantized = qbn_cls.from_float(self.bn)
         parent_name, name = _parent_name(self.bn_node.target)
         setattr(quantizer.modules[parent_name], name, quantized)
@@ -405,11 +412,15 @@ def convert(self, quantizer, node, load_arg, debug=False, convert_custom_config_
             return NotImplemented
         assert node.op in ['call_module', 'call_function'], 'Only call_module and ' + \
             'call_function are handled in DefaultNode'
+        if convert_custom_config_dict is None:
+            convert_custom_config_dict = {}
+        additional_static_quant_mapping = convert_custom_config_dict.get("static", {})
         activation_post_process = quantizer.activation_post_process_map[node.name]
         if node.op == 'call_module':
             module = quantizer.modules[node.target]
             module.activation_post_process = activation_post_process
-            quantized_module_cls = get_static_quant_module_class(type(module))
+            quantized_module_cls = get_static_quant_module_class(
+                type(module), additional_static_quant_mapping)
             quantized_module = quantized_module_cls.from_float(module)
             parent_name, name = _parent_name(node.target)
             setattr(quantizer.modules[parent_name], name, quantized_module)

torch/quantization/quantization_mappings.py

@@ -96,14 +96,17 @@ def get_default_static_quant_module_mappings():
     '''
     return DEFAULT_STATIC_QUANT_MODULE_MAPPINGS
 
-def get_static_quant_module_class(float_module_class):
-    ''' Get the statically quantized module class corresponding to
+def get_static_quant_module_class(float_module_class, additional_static_quant_mapping=None):
+    r"""n Get the statically quantized module class corresponding to
     the floating point module class
-    '''
-    static_quant_module_class = DEFAULT_STATIC_QUANT_MODULE_MAPPINGS.get(float_module_class, None)
+    """
+    if additional_static_quant_mapping is None:
+        additional_static_quant_mapping = {}
+    all_mappings = dict(DEFAULT_STATIC_QUANT_MODULE_MAPPINGS, **additional_static_quant_mapping)
+    static_quant_module_class = all_mappings.get(float_module_class, None)
     assert static_quant_module_class is not None, \
-        'Floating point module class {}'.format(float_module_class) + \
-        ' does not have a corresponding quantized module class'
+        "Floating point module class {}".format(float_module_class) + \
+        " does not have a corresponding quantized module class"
     return static_quant_module_class
 
 def get_default_qat_module_mappings():

torch/quantization/quantize_fx.py

@@ -253,6 +253,18 @@ def convert_fx(graph_module, inplace=False, debug=False, convert_custom_config_d
         `debug`: flag for producing a debug friendly model (preserve weight attribute)
         `convert_custom_config_dict`: dictionary for custom configurations for convert function:
         convert_custom_config_dict = {
+          # addtional object (module/operator) mappings that will overwrite the default
+          # module mappingn
+          "additional_object_mapping": {
+             "static": {
+                FloatModule: QuantizedModule,
+                float_op: quantized_op
+             },
+             "dynamic": {
+                FloatModule: DynamicallyQuantizedModule,
+                float_op: dynamically_quantized_op
+             },
+          }
           # user will manually define the corresponding quantized
           # module class which has a from_observed class method that converts
           # observed custom module to quantized custom module