support skip_op_list in PostTrainingQuantization (#42378)

python/paddle/fluid/contrib/slim/quantization/post_training_quantization.py

@@ -126,6 +126,7 @@ class PostTrainingQuantization(object):
                  onnx_format=False,
                  optimize_model=False,
                  is_use_cache_file=False,
+                 skip_tensor_list=None,
                  cache_dir=None):
         '''
         Constructor.
@@ -198,6 +199,7 @@ class PostTrainingQuantization(object):
                 the model accuracy is usually higher when it is 'channel_wise_abs_max'.
             onnx_format(bool): Whether to export the quantized model with format of ONNX.
                 Default is False.
+            skip_tensor_list(list): List of skip quant tensor name.
             optimize_model(bool, optional): If set optimize_model as True, it applies
                 some passes to the model before quantization, and it supports
                 `conv2d/depthwise_conv2d + bn` pass so far. Some targets require the
@@ -301,6 +303,7 @@ class PostTrainingQuantization(object):
         self._activation_quantize_type = activation_quantize_type
         self._weight_quantize_type = weight_quantize_type
         self._onnx_format = onnx_format
+        self._skip_tensor_list = skip_tensor_list
         self._is_full_quantize = is_full_quantize
         if is_full_quantize:
             self._quantizable_op_type = self._support_quantize_op_type
@@ -547,6 +550,12 @@ class PostTrainingQuantization(object):
         persistable_var_names = _all_persistable_var_names(self._program)
         for block_id in range(len(self._program.blocks)):
             for op in self._program.blocks[block_id].ops:
+                # skip quant form self._skip_tensor_list
+                if self._skip_tensor_list is not None:
+                    for inp_name in utils._get_op_input_var_names(op):
+                        if inp_name in self._skip_tensor_list:
+                            op._set_attr("op_namescope", "skip_quant")
+
                 op_type = op.type
                 if self._is_full_quantize and \
                     op_type not in self._quantizable_op_type:

python/paddle/fluid/contrib/slim/tests/test_post_training_quantization_mnist.py

@@ -117,7 +117,8 @@ class TestPostTrainingQuantization(unittest.TestCase):
                                  is_optimize_model=False,
                                  batch_size=10,
                                  batch_nums=10,
-                                 onnx_format=False):
+                                 onnx_format=False,
+                                 skip_tensor_list=None):
 
         place = fluid.CPUPlace()
         exe = fluid.Executor(place)
@@ -136,6 +137,7 @@ class TestPostTrainingQuantization(unittest.TestCase):
             is_full_quantize=is_full_quantize,
             optimize_model=is_optimize_model,
             onnx_format=onnx_format,
+            skip_tensor_list=skip_tensor_list,
             is_use_cache_file=is_use_cache_file)
         ptq.quantize()
         ptq.save_quantized_model(self.int8_model_path)
@@ -154,7 +156,8 @@ class TestPostTrainingQuantization(unittest.TestCase):
                  batch_size=10,
                  infer_iterations=10,
                  quant_iterations=5,
-                 onnx_format=False):
+                 onnx_format=False,
+                 skip_tensor_list=None):
 
         origin_model_path = self.download_model(data_url, data_md5, model_name)
         origin_model_path = os.path.join(origin_model_path, model_name)
@@ -166,10 +169,10 @@ class TestPostTrainingQuantization(unittest.TestCase):
 
         print("Start INT8 post training quantization for {0} on {1} images ...".
               format(model_name, quant_iterations * batch_size))
-        self.generate_quantized_model(origin_model_path, algo, round_type,
-                                      quantizable_op_type, is_full_quantize,
-                                      is_use_cache_file, is_optimize_model,
-                                      batch_size, quant_iterations, onnx_format)
+        self.generate_quantized_model(
+            origin_model_path, algo, round_type, quantizable_op_type,
+            is_full_quantize, is_use_cache_file, is_optimize_model, batch_size,
+            quant_iterations, onnx_format, skip_tensor_list)
 
         print("Start INT8 inference for {0} on {1} images ...".format(
             model_name, infer_iterations * batch_size))
@@ -426,5 +429,38 @@ class TestPostTrainingmseForMnistONNXFormatFullQuant(
             onnx_format=onnx_format)
 
 
+class TestPostTrainingavgForMnistSkipOP(TestPostTrainingQuantization):
+    def test_post_training_avg_skip_op(self):
+        model_name = "mnist_model"
+        data_url = "http://paddle-inference-dist.bj.bcebos.com/int8/mnist_model.tar.gz"
+        data_md5 = "be71d3997ec35ac2a65ae8a145e2887c"
+        algo = "avg"
+        round_type = "round"
+        quantizable_op_type = ["conv2d", "depthwise_conv2d", "mul"]
+        is_full_quantize = False
+        is_use_cache_file = False
+        is_optimize_model = True
+        diff_threshold = 0.01
+        batch_size = 10
+        infer_iterations = 50
+        quant_iterations = 5
+        skip_tensor_list = ["fc_0.w_0"]
+        self.run_test(
+            model_name,
+            data_url,
+            data_md5,
+            algo,
+            round_type,
+            quantizable_op_type,
+            is_full_quantize,
+            is_use_cache_file,
+            is_optimize_model,
+            diff_threshold,
+            batch_size,
+            infer_iterations,
+            quant_iterations,
+            skip_tensor_list=skip_tensor_list)
+
+
 if __name__ == '__main__':
     unittest.main()