trt support serialize and deserialize (#35828)

python/paddle/fluid/tests/unittests/ir/inference/trt_layer_auto_scan_test.py

@@ -25,6 +25,7 @@ import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
 import paddle.inference as paddle_infer
+import shutil
 
 from paddle import compat as cpt
 from typing import Optional, List, Callable, Dict, Any, Set
@@ -68,18 +69,21 @@ class TrtLayerAutoScanTest(AutoScanTest):
             max_batch_size=4,
             min_subgraph_size=0,
             precision=paddle_infer.PrecisionType.Float32,
-            use_static=False,
+            use_static=True,
             use_calib_mode=False)
         self.dynamic_shape = self.DynamicShapeParam({}, {}, {}, False)
         self.num_percent_cases = float(
             os.getenv(
                 'TEST_NUM_PERCENT_CASES', default='1.0'))
+        abs_dir = os.path.abspath(os.path.dirname(__file__))
+        cache_dir = str(self.__module__) + '_trt_cache_dir'
+        self.trt_cache_dir = os.path.join(abs_dir, cache_dir)
 
     def create_inference_config(self, use_trt=True) -> paddle_infer.Config:
         config = paddle_infer.Config()
-        config.disable_glog_info()
+        # config.disable_glog_info()
         config.enable_use_gpu(100, 0)
-        config.set_optim_cache_dir('trt_convert_cache_dir')
+        config.set_optim_cache_dir(self.trt_cache_dir)
         if use_trt:
             config.switch_ir_debug()
             config.enable_tensorrt_engine(
@@ -218,6 +222,9 @@ class TrtLayerAutoScanTest(AutoScanTest):
             for pred_config, nodes_num, threshold in self.sample_predictor_configs(
                     prog_config):
 
+                if os.path.exists(self.trt_cache_dir):
+                    shutil.rmtree(self.trt_cache_dir)
+
                 if isinstance(threshold, float):
                     atol = threshold
                     rtol = 1e-8
@@ -261,9 +268,9 @@ class TrtLayerAutoScanTest(AutoScanTest):
                     if not skip_flag:
                         self.assert_op_size(nodes_num[0], nodes_num[1])
                     # deserialize test
-                    #if nodes_num[0] > 0:
-                    #    self.run_test_config(model, params, prog_config,
-                    #                         pred_config_deserialize, feed_data)
+                    if nodes_num[0] > 0:
+                        self.run_test_config(model, params, prog_config,
+                                             pred_config_deserialize, feed_data)
                 except Exception as e:
                     self.fail_log(
                         str(prog_config) + ' vs ' + self.inference_config_str(