From 66a1df3cd2e2eb516aec079bcebc09fad1f152cf Mon Sep 17 00:00:00 2001
From: Chang Xu <molixu7@gmail.com>
Date: Thu, 3 Nov 2022 10:23:05 +0800
Subject: [PATCH] Avoid Quant Weight Repeatedly (#47587)

---
 .../slim/quantization/quantization_pass.py    | 75 ++++++++++---------
 1 file changed, 41 insertions(+), 34 deletions(-)

diff --git a/python/paddle/fluid/contrib/slim/quantization/quantization_pass.py b/python/paddle/fluid/contrib/slim/quantization/quantization_pass.py
index 7a9b89866eb..020bdcec489 100644
--- a/python/paddle/fluid/contrib/slim/quantization/quantization_pass.py
+++ b/python/paddle/fluid/contrib/slim/quantization/quantization_pass.py
@@ -1119,6 +1119,7 @@ class QuantizationFreezePass(object):
         self._op_input_rename_map = collections.OrderedDict()
         self._op_output_rename_map = collections.OrderedDict()
         self._quant_var_scale_map = collections.OrderedDict()
+        self._quantized_ops = set()
 
     def apply(self, graph):
         """
@@ -1173,24 +1174,27 @@ class QuantizationFreezePass(object):
                             quant_axis = 1
                         else:
                             quant_axis = 0
-                        quantized_param_v = utils.quant_tensor(
-                            param_v.copy(),
-                            scale_v,
-                            quant_axis,
-                            self._weight_bits,
-                        )
-                        quantized_param_v = np.round(quantized_param_v)
-                        # Weight bias correction
-                        if self._bias_correction == True:
-                            quantized_param_v = utils.bias_correction_w(
-                                param_v,
-                                quantized_param_v,
+                        if input_arg_name not in self._quantized_ops:
+                            self._quantized_ops.add(input_arg_name)
+                            quantized_param_v = utils.quant_tensor(
+                                param_v.copy(),
                                 scale_v,
                                 quant_axis,
-                                weight_bits=self._weight_bits,
+                                self._weight_bits,
                             )
                             quantized_param_v = np.round(quantized_param_v)
-                        self._restore_var(input_arg_name, quantized_param_v)
+                            # Weight bias correction
+                            if self._bias_correction == True:
+                                quantized_param_v = utils.bias_correction_w(
+                                    param_v,
+                                    quantized_param_v,
+                                    scale_v,
+                                    quant_axis,
+                                    weight_bits=self._weight_bits,
+                                )
+                                quantized_param_v = np.round(quantized_param_v)
+                            self._restore_var(input_arg_name, quantized_param_v)
+
                     self._remove_fake_quant_and_dequant_op(graph, op_node)
 
         # Remove all fake dequant op
@@ -3029,6 +3033,7 @@ class QuantWeightPass(object):
         self._save_int_weight = save_int_weight
         assert self._scope is not None, "scope must not be None."
         assert self._place is not None, "place must not be None."
+        self._quantized_ops = set()
 
     def apply(self, graph):
         assert isinstance(
@@ -3066,29 +3071,31 @@ class QuantWeightPass(object):
                 param_v = self._load_var(x_node.name())
                 quant_axis = _op.op().attr("quant_axis")
                 bits_length = _op.op().attr("bit_length")
-                quantized_param_v = utils.quant_tensor(
-                    param_v.copy(),
-                    scale_v,
-                    quant_axis,
-                    bits_length,
-                    onnx_format=True,
-                )
-                if self._bias_correction == True:
-                    quantized_param_v = utils.bias_correction_w(
-                        param_v,
-                        quantized_param_v,
+                if x_node.name() not in self._quantized_ops:
+                    self._quantized_ops.add(x_node.name())
+                    quantized_param_v = utils.quant_tensor(
+                        param_v.copy(),
                         scale_v,
                         quant_axis,
-                        weight_bits=bits_length,
+                        bits_length,
+                        onnx_format=True,
                     )
-                if self._save_int_weight:
-                    # cast weight type to int
-                    if self._quant_bits == 8:
-                        save_weight_dtype = np.int8
-                    quantized_param_v = quantized_param_v.astype(
-                        save_weight_dtype
-                    )
-                self._restore_var(x_node.name(), quantized_param_v)
+                    if self._bias_correction == True:
+                        quantized_param_v = utils.bias_correction_w(
+                            param_v,
+                            quantized_param_v,
+                            scale_v,
+                            quant_axis,
+                            weight_bits=bits_length,
+                        )
+                    if self._save_int_weight:
+                        # cast weight type to int
+                        if self._quant_bits == 8:
+                            save_weight_dtype = np.int8
+                        quantized_param_v = quantized_param_v.astype(
+                            save_weight_dtype
+                        )
+                    self._restore_var(x_node.name(), quantized_param_v)
 
                 for next_op_node in out_node.outputs:
                     graph.update_input_link(out_node, x_node, next_op_node)
-- 
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