Allow user to set `per_channel` and `reduce_range` quantization params (#156) (#157)

* Allow user to set `per_channel` and `reduce_range` quantization parameters (#156)

Also save quantization options

* Get operators of graph and subgraphs

scripts/convert.py

@@ -1,4 +1,5 @@
 
+import json
 import os
 import shutil
 from dataclasses import dataclass, field
@@ -77,8 +78,36 @@ class ConversionArguments:
         }
     )
 
+    per_channel: bool = field(
+        default=True,
+        metadata={
+            "help": "Whether to quantize weights per channel"
+        }
+    )
+    reduce_range: bool = field(
+        default=True,
+        metadata={
+            "help": "Whether to quantize weights with 7-bits. It may improve the accuracy for some models running on non-VNNI machine, especially for per-channel mode"
+        }
+    )
 
-def quantize(model_names_or_paths):
+
+def get_operators(model):
+    operators = set()
+
+    def traverse_graph(graph):
+        for node in graph.node:
+            operators.add(node.op_type)
+            for attr in node.attribute:
+                if attr.type == onnx.AttributeProto.GRAPH:
+                    subgraph = attr.g
+                    traverse_graph(subgraph)
+
+    traverse_graph(model.graph)
+    return operators
+
+
+def quantize(model_names_or_paths, conv_args: ConversionArguments):
     """
     Quantize the weights of the model from float32 to int8 to allow very efficient inference on modern CPU
 
@@ -90,6 +119,12 @@ def quantize(model_names_or_paths):
     Returns: The Path generated for the quantized
     """
 
+    quant_config = dict(
+        per_channel=conv_args.per_channel,
+        reduce_range=conv_args.reduce_range,
+        per_model_config={}
+    )
+
     for model in tqdm(model_names_or_paths, desc='Quantizing'):
         directory_path = os.path.dirname(model)
         file_name_without_extension = os.path.splitext(
@@ -104,8 +139,8 @@ def quantize(model_names_or_paths):
         #  - https://github.com/microsoft/onnxruntime/issues/3130#issuecomment-1105200621
         #  - https://github.com/microsoft/onnxruntime/issues/2339
 
-        model_nodes = onnx.load_model(model).graph.node
-        op_types = set([node.op_type for node in model_nodes])
+        loaded_model = onnx.load_model(model)
+        op_types = get_operators(loaded_model)
         weight_type = QuantType.QUInt8 if 'Conv' in op_types else QuantType.QInt8
 
         quantize_dynamic(
@@ -113,9 +148,8 @@ def quantize(model_names_or_paths):
             model_output=os.path.join(
                 directory_path, f'{file_name_without_extension}_quantized.onnx'),
 
-            # TODO allow user to specify these or choose based on hardware
-            per_channel=True,
-            reduce_range=True,
+            per_channel=conv_args.per_channel,
+            reduce_range=conv_args.reduce_range,
 
             weight_type=weight_type,
             optimize_model=False,
@@ -127,6 +161,15 @@ def quantize(model_names_or_paths):
             )
         )
 
+        quant_config['per_model_config'][file_name_without_extension] = dict(
+            op_types=list(op_types),
+            weight_type=str(weight_type),
+        )
+
+    # Save quantization config
+    with open(os.path.join(directory_path, 'quant_config.json'), 'w') as fp:
+        json.dump(quant_config, fp, indent=4)
+
 
 def copy_if_exists(model_path, file_name, destination):
     file = cached_file(model_path, file_name,
@@ -192,7 +235,7 @@ def main():
             os.path.join(output_model_folder, x)
             for x in os.listdir(output_model_folder)
             if x.endswith('.onnx') and not x.endswith('_quantized.onnx')
-        ])
+        ], conv_args)
 
     # Step 3. Move .onnx files to the 'onnx' subfolder
     os.makedirs(os.path.join(output_model_folder, 'onnx'), exist_ok=True)