【Paddle Tensor】Fix bugs related to converting unit tests about collect shape (#71305)

* fix scale

* fix codestyle

* fix argsort

* fix elementwise

* fix reduce

* fix codestyle

* fix collect_shape

* fix codestyle

* add split test

* add time

* fix codestyle

* fix conv2d

* fix codestyle

* delete conv2d

* delete conv2d

* update

* last fix

* fix timeout

* Update CMakeLists.txt

Author: PolaKuma

test/ir/inference/CMakeLists.txt

@@ -175,6 +175,11 @@ if(WITH_GPU AND TENSORRT_FOUND)
                        PROPERTIES TIMEOUT 1000)
   set_tests_properties(test_trt_convert_pool2d PROPERTIES TIMEOUT 600)
   set_tests_properties(test_trt_convert_slice PROPERTIES TIMEOUT 600)
+  set_tests_properties(test_trt_convert_scale PROPERTIES TIMEOUT 600)
+  set_tests_properties(test_trt_convert_elementwise PROPERTIES TIMEOUT 600)
+  set_tests_properties(test_trt_convert_reduce PROPERTIES TIMEOUT 600)
+  set_tests_properties(test_trt_convert_compare_and_logical PROPERTIES TIMEOUT
+                                                                       600)
 
   if(NOT WIN32)
 

test/ir/inference/test_trt_convert_argsort.py

@@ -30,7 +30,7 @@ def is_program_valid(self, program_config: ProgramConfig) -> bool:
     def sample_program_configs(self):
         def generate_input1():
             if self.dims == 4:
-                return np.random.random([1, 3, 3, 4000]).astype(np.float32)
+                return np.random.random([1, 3, 3, 3840]).astype(np.float32)
             elif self.dims == 3:
                 return np.random.random([1, 3, 24]).astype(np.int32)
             elif self.dims == 2:
@@ -69,50 +69,52 @@ def generate_input1():
                     )
                     yield program_config
 
+    def generate_dynamic_shape(self):
+        if self.dims == 4:
+            self.dynamic_shape.min_input_shape = {
+                "input_data": [1, 3, 3, 3840],
+            }
+            self.dynamic_shape.max_input_shape = {
+                "input_data": [9, 3, 3, 3840],
+            }
+            self.dynamic_shape.opt_input_shape = {
+                "input_data": [6, 3, 3, 3840],
+            }
+        elif self.dims == 3:
+            self.dynamic_shape.min_input_shape = {
+                "input_data": [1, 3, 24],
+            }
+            self.dynamic_shape.max_input_shape = {
+                "input_data": [9, 3, 24],
+            }
+            self.dynamic_shape.opt_input_shape = {
+                "input_data": [6, 3, 24],
+            }
+        elif self.dims == 2:
+            self.dynamic_shape.min_input_shape = {
+                "input_data": [1, 24],
+            }
+            self.dynamic_shape.max_input_shape = {
+                "input_data": [9, 24],
+            }
+            self.dynamic_shape.opt_input_shape = {
+                "input_data": [6, 24],
+            }
+        else:
+            self.dynamic_shape.min_input_shape = {
+                "input_data": [24],
+            }
+            self.dynamic_shape.max_input_shape = {
+                "input_data": [24],
+            }
+            self.dynamic_shape.opt_input_shape = {
+                "input_data": [24],
+            }
+        return self.dynamic_shape
+
     def sample_predictor_configs(
-        self, program_config
+        self, program_config, run_pir=False
     ) -> tuple[paddle_infer.Config, list[int], float]:
-        def generate_dynamic_shape(attrs):
-            if self.dims == 4:
-                self.dynamic_shape.min_input_shape = {
-                    "input_data": [1, 3, 3, 4000],
-                }
-                self.dynamic_shape.max_input_shape = {
-                    "input_data": [9, 3, 3, 4000],
-                }
-                self.dynamic_shape.opt_input_shape = {
-                    "input_data": [6, 3, 3, 4000],
-                }
-            elif self.dims == 3:
-                self.dynamic_shape.min_input_shape = {
-                    "input_data": [1, 3, 24],
-                }
-                self.dynamic_shape.max_input_shape = {
-                    "input_data": [9, 3, 24],
-                }
-                self.dynamic_shape.opt_input_shape = {
-                    "input_data": [6, 3, 24],
-                }
-            elif self.dims == 2:
-                self.dynamic_shape.min_input_shape = {
-                    "input_data": [1, 24],
-                }
-                self.dynamic_shape.max_input_shape = {
-                    "input_data": [9, 24],
-                }
-                self.dynamic_shape.opt_input_shape = {
-                    "input_data": [6, 24],
-                }
-            else:
-                self.dynamic_shape.min_input_shape = {
-                    "input_data": [24],
-                }
-                self.dynamic_shape.max_input_shape = {
-                    "input_data": [25],
-                }
-                self.dynamic_shape.opt_input_shape = {
-                    "input_data": [24],
-                }
 
         def clear_dynamic_shape():
             self.dynamic_shape.max_input_shape = {}
@@ -124,14 +126,14 @@ def clear_dynamic_shape():
         ]
         self.trt_param.workspace_size = 1073741824
         # for dynamic_shape
-        generate_dynamic_shape(attrs)
+        self.generate_dynamic_shape()
         self.trt_param.precision = paddle_infer.PrecisionType.Float32
         yield self.create_inference_config(), (1, 3), 1e-5
         self.trt_param.precision = paddle_infer.PrecisionType.Half
         yield self.create_inference_config(), (1, 3), 1e-3
 
     def test(self):
-        self.run_test()
+        self.run_test(run_pir=True)
 
 
 if __name__ == "__main__":