fix ut and simplify config

Signed-off-by: Mengni Wang <[email protected]>

onnx_neural_compressor/algorithms/utility.py

@@ -222,14 +222,10 @@ def calculate_scale_zp(rmin, rmax, qType, sym, reduce_range=False):
     dtype = _qType_to_np_type(qType)
     if isinstance(rmax, np.ndarray):
         if sym:
-            mask = abs(rmin) > abs(rmax)
-            scale = np.ones(rmin.shape).astype(rmin.dtype)
-            scale[mask] = rmin[mask]
-            scale[~mask] = rmax[~mask]
-            abs_max = round((qmax - qmin) / 2)
-            scale /= abs_max
-        else:
-            scale = (rmax - rmin) / (qmax - qmin)
+            max_range = np.maximum(abs(rmin), abs(rmax))
+            rmin = -max_range
+            rmax = max_range
+        scale = (rmax - rmin) / (qmax - qmin)
         scale[abs(scale) < np.finfo(rmax.dtype).tiny] = 1
         zero_point = (
             np.multiply(np.ones(rmax.shape), np.round((qmax + qmin) / 2.0)).astype(dtype)

onnx_neural_compressor/constants.py

@@ -20,8 +20,6 @@
 # constants for configs
 GLOBAL = "global"
 LOCAL = "local"
-DEFAULT_WHITE_LIST = "*"
-EMPTY_WHITE_LIST = None
 
 # config name
 BASE_CONFIG = "base_config"

onnx_neural_compressor/quantization/algorithm_entry.py

@@ -192,7 +192,7 @@ def smooth_quant_entry(
         calibration_data_reader,
         execution_provider=getattr(quant_config, "execution_provider", "CPUExecutionProvider"),
     )
-    smoothed_model = smoother.transform(**quant_config.to_dict())
+    smoothed_model = smoother.transform(**quant_config.get_model_params_dict())
     with tempfile.TemporaryDirectory(prefix="ort.quant.") as tmp_dir:
         # ORT quant API requires str input
         onnx.save_model(

test/quantization/post_training_quant/test_operators.py

@@ -78,6 +78,9 @@ def setUpClass(cls):
     @classmethod
     def tearDownClass(cls):
         shutil.rmtree("./onnxrt_test", ignore_errors=True)
+        os.remove("int8.onnx")
+        os.remove("qdq.onnx")
+        os.remove("test.onnx")
 
     def qlinear_test(self, model, q_config, quantize_params, quantizable_op_types, **kwargs):
         quant = quantizer.StaticQuantizer(

test/quantization/test_algorithm_utility.py

@@ -40,3 +40,6 @@ def test_is_B_transposed(self):
             beta=0.35,
         )
         self.assertFalse(quant_utils.is_B_transposed(node))
+
+if __name__ == "__main__":
+    unittest.main()

test/quantization/test_config.py

@@ -329,28 +329,6 @@ def test_static_custom_quant_config(self):
                 self.assertLess(idx, 2)
 
     def test_config_white_lst(self):
-        global_config = config.RTNConfig(weight_bits=4)
-        # set operator instance
-        fc_out_config = config.RTNConfig(weight_dtype="fp32", white_list=["/h.4/mlp/fc_out/MatMul"])
-        # get model and quantize
-        fp32_model = self.gptj
-        qmodel = algos.rtn_quantize_entry(fp32_model, quant_config=global_config + fc_out_config)
-        self.assertIsNotNone(qmodel)
-        self.assertEqual(self._count_woq_matmul(qmodel), 29)
-        self.assertFalse(self._check_node_is_quantized(qmodel, "/h.4/mlp/fc_out/MatMul"))
-
-    def test_config_white_lst2(self):
-        global_config = config.RTNConfig(weight_dtype="fp32")
-        # set operator instance
-        fc_out_config = config.RTNConfig(weight_bits=4, white_list=["/h.4/mlp/fc_out/MatMul"])
-        # get model and quantize
-        fp32_model = self.gptj
-        qmodel = algos.rtn_quantize_entry(fp32_model, quant_config=global_config + fc_out_config)
-        self.assertIsNotNone(qmodel)
-        self.assertEqual(self._count_woq_matmul(qmodel), 1)
-        self.assertTrue(self._check_node_is_quantized(qmodel, "/h.4/mlp/fc_out/MatMul"))
-
-    def test_config_white_lst3(self):
 
         global_config = config.RTNConfig(weight_bits=4)
         # set operator instance

test/utils/test_onnx_model.py

@@ -88,6 +88,7 @@ def tearDownClass(self):
         shutil.rmtree("./gptj", ignore_errors=True)
         shutil.rmtree("./large_model", ignore_errors=True)
         os.remove("matmul_add.onnx")
+        os.remove("model1.onnx")
 
     def setUp(self):
         # print the test name
@@ -102,7 +103,7 @@ def test_model_atrribute(self):
         # model_path
         self.assertEqual(model.model_path, self.matmul_add_model)
         # framework
-        self.assertEqual(model.framework(), "onnxruntime")
+        self.assertEqual(model.framework, "onnxruntime")
         # q_config
         quant_config = config.RTNConfig()
         model.q_config = quant_config