skip quant matmul in mha (#1232)

paddleslim/auto_compression/compressor.py

@@ -313,6 +313,17 @@ class AutoCompression:
             model_filename=model_filename, params_filename=params_filename,
             executor=exe))
         _, _, model_type = get_patterns(inference_program)
+        if self.model_filename is None:
+            new_model_filename = '__new_model__'
+        else:
+            new_model_filename = 'new_' + self.model_filename
+        program_bytes = inference_program._remove_training_info(
+            clip_extra=False).desc.serialize_to_string()
+        with open(os.path.join(self.model_dir, new_model_filename), "wb") as f:
+            f.write(program_bytes)
+        shutil.move(
+            os.path.join(self.model_dir, new_model_filename),
+            os.path.join(self.model_dir, self.model_filename))
         _logger.info(f"Detect model type: {model_type}")
         return model_type
 

paddleslim/common/patterns.py

@@ -43,7 +43,7 @@ def find_final_nodes(program):
     return final_nodes
 
 
-def _is_mha(pattern_ops, pattern_ops_type):
+def _is_mha(pattern_ops, pattern_ops_type, skip_quant_tensor_list=[]):
     """ judge whether this pattern is multihead attention """
     if pattern_ops_type.count('softmax') != 1 or pattern_ops_type.count(
             'fetch') > 0:
@@ -53,6 +53,7 @@ def _is_mha(pattern_ops, pattern_ops_type):
     for op in pattern_ops:
         if op.type() in ['matmul', 'matmul_v2']:
             if not is_dynamic_weight_op(op):
+                skip_quant_tensor_list.extend(op._op.input('X'))
                 matmul_num += 1
     if matmul_num == 2:
         return True
@@ -81,6 +82,7 @@ def _is_ffn(pattern_ops, pattern_ops_type):
 def get_patterns(program, only_final_node=True):
     """ distinguish the pattern in the program and get distillation node """
     distill_node = []
+    skip_quant_tensor_list = []
     patterns = {}
     graph = GraphWrapper(program)
     block_num = 0
@@ -110,7 +112,8 @@ def get_patterns(program, only_final_node=True):
                     pattern_name = shortcut_start_op.type() + '$' + str(op.idx(
                     ))
 
-                    if _is_mha(pattern_ops, pattern_ops_type):
+                    if _is_mha(pattern_ops, pattern_ops_type,
+                               skip_quant_tensor_list):
                         model_type = 'transformer'
                         pattern_name = 'MHA$' + str(block_num)
 
@@ -145,4 +148,12 @@ def get_patterns(program, only_final_node=True):
         distill_node.append('teacher_' + out_var.name())
         distill_node.append(out_var.name())
 
+    #### skip quant matmul in attention
+    if model_type == 'transformer':
+        for block_id in range(len(program.blocks)):
+            for op in program.blocks[block_id].ops:
+                for inp_name in op.input_arg_names:
+                    if inp_name in skip_quant_tensor_list:
+                        op._set_attr("op_namescope", "skip_quant")
+
     return patterns, distill_node, model_type