[Bug-fix][1.6] Improve QAT accuracy (#20174)

* Leave fake quantization around mul

* Replace Fake with Real Quantized Mul

* Gather all scales from fake_quantize_ops

* Enable uint8 in conv_relu tensors

* Disable int8 mul and restore fake mul

* Fix buf for running QAT on VGG16 and 19

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

@@ -321,10 +321,11 @@ class FakeQAT2MkldnnINT8PerfPass(object):
 
         graph = self._gather_scales(graph)
         graph = self._remove_fake_ops(graph)
-        graph = self._update_pooling_scales(graph)
         graph = self._dequantize_weights(graph)
         graph = self._optimize_fp32_graph(graph)
         graph = self._compute_weight_scales(graph)
+        graph = self._update_conv_relu_scales(graph)
+        graph = self._update_pooling_scales(graph)
         graph = self._quantize_fp32_graph(graph)
         graph = self._remove_unused_var_nodes(graph)
         return graph
@@ -350,6 +351,8 @@ class FakeQAT2MkldnnINT8PerfPass(object):
                 use_unsigned_int = False
                 self._var_quant_scales[input_name] = (use_unsigned_int,
                                                       lod_tensor)
+                self._var_quant_scales[scale_name.replace(".scale", "")] = (
+                    use_unsigned_int, lod_tensor)
 
             if op.name() in self._fake_dequantize_types:
                 input_name = op.input("X")[0]
@@ -378,12 +381,12 @@ class FakeQAT2MkldnnINT8PerfPass(object):
                 next_op = op_out.outputs[0]
                 if next_op.name() not in self._mul_ops:
                     self._remove_fake_quantize(graph, op)
-                else:
-                    quant_op = self._transform_to_quantize_mkldnn(graph, op)
-                    self._transform_to_mul_mkldnn(graph, next_op, quant_op)
 
         for op in graph.all_op_nodes():
             if op.name() in self._fake_dequantize_types:
+                op_in = graph._find_node_by_name(op.inputs, op.input("X")[0])
+                prev_op = op_in.inputs[0]
+                if prev_op.name() not in self._mul_ops:
                     self._remove_fake_dequantize(graph, op)
         return graph
 
@@ -530,7 +533,7 @@ class FakeQAT2MkldnnINT8PerfPass(object):
             if op.name() in self._pool_ops:
                 if op.op().attr("pooling_type") == "avg":
                     ids.append(op.id())
-        return set(ids)
+        return set(ids) if len(ids) else set([-1])
 
     def _transform_to_quantize_mkldnn(self, graph, op_node):
         """
@@ -557,13 +560,16 @@ class FakeQAT2MkldnnINT8PerfPass(object):
         graph.safe_remove_nodes(op_node)
         return quant_op_node
 
-    def _transform_to_mul_mkldnn(self, graph, op_node, quantize_node):
-        input_name = op_node.input("X")[0]
-        scale_in = quantize_node.op().attr("Scale")
-        op_node.set_attr("scale_y", [1.0])
-        op_node.set_attr("scale_x", scale_in)
-        op_node.set_attr("scale_out", 1.0)
-        op_node.set_attr("force_fp32_output", True)
+    def _update_conv_relu_scales(self, graph):
+        for op in graph.all_op_nodes():
+            if op.name() in self._conv_ops:
+                out_name = op.output("Output")[0]
+                if out_name in self._var_quant_scales and \
+                   op.op().attr("fuse_activation") == 'relu' and \
+                   op.op().attr("fuse_residual_connection") == False:
+                    _, tensor = self._var_quant_scales[out_name]
+                    self._var_quant_scales[out_name] = (True, tensor)
+        return graph
 
     def _quantize_fp32_graph(self, graph):
         ir_pass = self._core.get_pass('cpu_quantize_placement_pass')