fix act recover params and pattern recognition (#1695)

* fix

* update

paddleslim/common/recover_program.py

@@ -71,6 +71,8 @@ def _recover_param_attr(program, startup_program):
         if param.persistable is True and param.name != 'feed' and param.name != 'fetch']
     with paddle.static.program_guard(program, startup_program):
         for w in all_weights:
+            if w.dtype not in [paddle.float32]:
+                continue
             new_w = paddle.create_parameter(
                 shape=w.shape, dtype=w.dtype, name=w.name)
             new_w.set_value(w.get_value())

paddleslim/common/transformer_pattern.py

@@ -25,13 +25,15 @@ def _find_gemm_op(op, graph):
     return op
 
 
-def _append_transformer_prune_params(op, graph, block_num, params_dict):
-    for next_op in graph.next_ops(op):
+def _append_transformer_prune_params(op_lists, graph, block_num, params_dict):
+    first_op = op_lists[0]
+    for next_op in graph.next_ops(first_op):
         if next_op.type() == 'elementwise_add':
             continue
         next_op = _find_gemm_op(next_op, graph)
-        if next_op.type() in ['mul', 'matmul', 'matmul_v2'
-                              ] and has_trainable_var(next_op):
+        if next_op.type() in [
+                'mul', 'matmul', 'matmul_v2'
+        ] and has_trainable_var(next_op) and next_op in op_lists:
             if block_num not in params_dict:
                 params_dict[block_num] = {}
                 params_dict[block_num]['P1'] = [get_weight(next_op)]
@@ -41,7 +43,7 @@ def _append_transformer_prune_params(op, graph, block_num, params_dict):
                 get_weight(has_bias(next_op, graph)))
             op = next_op
     next_op = _find_gemm_op(find_weight_op(op, graph), graph)
-    if next_op:
+    if next_op and next_op in op_lists:
         params_dict[block_num]['P2'] = [get_weight(next_op)]
         params_dict[block_num]['P2'].append(
             get_weight(has_bias(next_op, graph)))
@@ -57,14 +59,14 @@ def preprocess_transformer_patterns(patterns, graph):
             continue
         block_num = int(pattern_name.split('$')[-1])
         if 'MHA' in pattern_name:
-            mha_weight = _append_transformer_prune_params(pattern_ops[0], graph,
-                                                          block_num, mha_weight)
+            mha_weight = _append_transformer_prune_params(
+                pattern_ops, graph, block_num, mha_weight)
             mha_weight[block_num]['reshape_op'] = []
             for op in pattern_ops:
                 if op.type() in ['reshape', 'reshape2']:
                     mha_weight[block_num]['reshape_op'].append(op)
         elif 'FFN' in pattern_name:
-            ffn_weight = _append_transformer_prune_params(pattern_ops[0], graph,
-                                                          block_num, ffn_weight)
+            ffn_weight = _append_transformer_prune_params(
+                pattern_ops, graph, block_num, ffn_weight)
 
     return mha_weight, ffn_weight