skip quantizing ops in cpu inference (#30342)

* skip quantizing ops in cpu inference, test=develop

paddle/fluid/framework/ir/mkldnn/cpu_quantize_placement_pass.cc

@@ -42,6 +42,10 @@ void CPUQuantizePlacementPass::ApplyImpl(ir::Graph* graph) const {
       return;
     }
 
+    if (op->Op()->GetAttrIfExists<int>("skip_quant") == 1) {
+      return;
+    }
+
     if (op->Op()->HasAttr("mkldnn_data_type") ||
         op->Op()->HasProtoAttr("mkldnn_data_type")) {
       // use_quantizer is no longer used

python/paddle/fluid/contrib/slim/quantization/quant2_int8_mkldnn_pass.py

@@ -56,7 +56,8 @@ class Quant2Int8MkldnnPass(object):
         ]
         self._fake_quantize_dequantize_types = [
             'fake_quantize_dequantize_abs_max',
-            'fake_quantize_dequantize_moving_average_abs_max'
+            'fake_quantize_dequantize_moving_average_abs_max',
+            'fake_channel_wise_quantize_dequantize_abs_max'
         ]
         self._ops_to_quantize = _ops_to_quantize
         self._op_ids_to_skip = _op_ids_to_skip if _op_ids_to_skip is not None else set(
@@ -71,7 +72,7 @@ class Quant2Int8MkldnnPass(object):
         self._relu_ops = ['relu', 'relu6']
         self._matmul_ops = ['matmul']
         self._gru_ops = ['fusion_gru', 'multi_gru']
-        self._weight_scales = {}
+        self._weight_thresholds = {}
         # Collect the Input and Output sclaes from Fake quant models
         self._var_quant_scales = {}
         self._max_range = {}
@@ -84,7 +85,8 @@ class Quant2Int8MkldnnPass(object):
                           IrGraph), 'graph must be the instance of IrGraph.'
 
         self._reset_pass_idx_and_group('int8')
-        graph = self._gather_weight_scales_from_fake(graph)
+        graph = self._label_skip_quantized_op(graph)
+        graph = self._gather_weight_thresholds_from_fake(graph)
         graph = self._gather_output_scales_from_attr(graph)
         graph = self._gather_input_scales_from_fake(graph)
         graph = self._remove_fake_ops(graph)
@@ -135,6 +137,30 @@ class Quant2Int8MkldnnPass(object):
     def _is_fc_quantized(self, graph):
         return self._is_any_of_op_types_quantized(self._fc_ops, graph)
 
+    def _label_skip_quantized_op(self, graph):
+        """
+        For some ops(conv2d, depthwise_conv2d, mul, matml), find and label
+        the skip quantized ops. cpu_quantize_placement_pass will use the
+        label to identify it.
+        For static models, the skip quantized ops have `skip_quant` attr.
+        Therefore, it only needs to find and label the skip quantized ops for
+        dygraph models, in which the quantized ops don't have `quantization_type`
+        attr.
+        """
+        target_ops = self._conv_ops + self._mul_ops + self._matmul_ops
+        for op_node in graph.all_op_nodes():
+            if op_node.name() in target_ops and \
+               not op_node.op().has_attr("quantization_type"):
+                is_quantized_op = True
+                for var_node in op_node.inputs:
+                    for front_op_node in var_node.inputs:
+                        if "fake_quantize_dequantize_" not in front_op_node.name(
+                        ):
+                            is_quantized_op = False
+                if not is_quantized_op:
+                    op_node.op()._set_attr("skip_quant", True)
+        return graph
+
     def _gather_input_scales_from_fake(self, graph):
         def _add_scale_for_vars(var_names, use_unsigned_int, lod_tensor):
             scales = self._var_quant_scales
@@ -165,19 +191,19 @@ class Quant2Int8MkldnnPass(object):
 
         return graph
 
-    def _gather_weight_scales_from_fake(self, graph):
+    def _gather_weight_thresholds_from_fake(self, graph):
         for op in graph.all_op_nodes():
             if op.name() in self._fake_dequantize_types:
                 input_name = op.input("X")[0]
                 if op.op().has_attr("max_range"):
                     _max_range = np.array(op.op().attr("max_range")).astype(
                         np.float64)
-                    self._weight_scales[input_name] = np.array(
+                    self._weight_thresholds[input_name] = np.array(
                         self._s8_max * self._s8_max /
                         _max_range).astype(np.float64)
                 else:
                     scale_name = op.input("Scales")[0]
-                    self._weight_scales[input_name] = np.array(
+                    self._weight_thresholds[input_name] = np.array(
                         self._load_param(self._scope, scale_name)).astype(
                             np.float64)
 
@@ -314,7 +340,7 @@ class Quant2Int8MkldnnPass(object):
         weight_var_name = op_node.input(weight_name)[0]
         output_var_name = op_node.output(output_name)[0]
         # Convert int8 range weights to fp32 range weights
-        scales = self._weight_scales[output_var_name]
+        scales = self._weight_thresholds[output_var_name]
         weight = self._load_param(self._scope, weight_var_name)
         if scales.size == 1 or scales.size == weight.shape[0]:
             w_fp32 = np.multiply(np.divide(weight, self._s8_max).T, scales.T).T

python/paddle/fluid/contrib/slim/tests/test_quant2_int8_mkldnn_pass.py

@@ -180,7 +180,7 @@ class TestQuant2Int8MkldnnPass(unittest.TestCase):
                 _place=self.place,
                 _core=core,
                 _debug=False)
-            qpass._weight_scales["mul_output"] = self.mul_output_scale
+            qpass._weight_thresholds["mul_output"] = self.mul_output_scale
             param = self.scope.var("mul_weights").get_tensor()
             param.set(self.variables_mul["mul_weights"], self.place)
             qpass._dequantize_op_weights(graph, op_node, "Y", "Out")