fix the bug of quanting matmul (#52833)

python/paddle/static/quantization/post_training_quantization.py

@@ -701,6 +701,10 @@ class PostTrainingQuantization:
                     in self.quant_config.activation_quant_operation_types
                     or is_conv1d_quant
                 ):
+                    trans_y = (op_type == 'matmul_v2') and op.op().attr(
+                        'trans_y'
+                    )
+                    op_type = op_type + '_trans_y' if trans_y else op_type
                     collect_var_name(
                         utils._get_op_input_var_names(op),
                         persistable_var_names,

python/paddle/static/quantization/quantization_pass.py

@@ -86,20 +86,6 @@ def _is_input_all_not_persistable(graph, op_node):
     return is_input_all_not_persistable
 
 
-def _check_grandchild_op_node(op_node, grandchild_op_name):
-    '''
-    Check whether the fake_quant node has a grandchild op node named
-    grandchild_op_name.
-    '''
-    for out1_var_node in op_node.outputs:
-        for out1_op_node in out1_var_node.outputs:
-            for out2_var_node in out1_op_node.outputs:
-                for out2_op_node in out2_var_node.outputs:
-                    if out2_op_node.name() == grandchild_op_name:
-                        return True
-    return False
-
-
 class QuantizationTransformPass:
     """
     Quantize the ops that have weights. Add quant and dequant ops for
@@ -360,9 +346,14 @@ class QuantizationTransformPass:
                     if (
                         quant_type == 'channel_wise_abs_max'
                     ):  # Weight quantization
+                        op_type = op.name()
+                        trans_y = (op_type == 'matmul_v2') and op.op().attr(
+                            'trans_y'
+                        )
+                        op_type = op_type + '_trans_y' if trans_y else op_type
                         quant_axis = (
                             1
-                            if op.name() in utils._channelwise_quant_axis1_ops
+                            if op_type in utils._channelwise_quant_axis1_ops
                             else 0
                         )
                         (
@@ -1184,13 +1175,9 @@ class QuantizationFreezePass:
                     # Quantize weight and restore
                     if self._round_type == 'round':
                         param_v = self._load_var(input_arg_name)
-                        if any(
-                            _check_grandchild_op_node(op_node, op)
-                            for op in utils._channelwise_quant_axis1_ops
-                        ):
-                            quant_axis = 1
-                        else:
-                            quant_axis = 0
+                        quant_axis = 0
+                        if op_node.op().has_attr('quant_axis'):
+                            quant_axis = op_node.op().attr('quant_axis')
                         if input_arg_name not in self._quantized_ops:
                             self._quantized_ops.add(input_arg_name)
                             quantized_param_v = utils.quant_tensor(
@@ -2605,9 +2592,14 @@ class QuantizationTransformPassV2(QuantizationTransformPass):
                 channel_wise = False
                 if quant_type == 'channel_wise_abs_max':  # Weight quantization
                     channel_wise = True
+                    op_type = op.name()
+                    trans_y = (op_type == 'matmul_v2') and op.op().attr(
+                        'trans_y'
+                    )
+                    op_type = op_type + '_trans_y' if trans_y else op_type
                     quant_axis = (
                         1
-                        if op.name() in utils._channelwise_quant_axis1_ops
+                        if op_type in utils._channelwise_quant_axis1_ops
                         else 0
                     )
                 insert_quant_pass = InsertQuantizeLinear(