[Cherry-pick] Fix Paddle-TRT UT fails

test/ir/inference/program_config.py

@@ -275,6 +275,7 @@ def generate_weight():
         self.outputs = outputs
         self.input_type = input_type
         self.no_cast_list = [] if no_cast_list is None else no_cast_list
+        self.supported_cast_type = [np.float32, np.float16]
 
     def __repr__(self):
         log_str = ''
@@ -292,11 +293,9 @@ def __repr__(self):
         return log_str
 
     def set_input_type(self, _type: np.dtype) -> None:
-        assert _type in [
-            np.float32,
-            np.float16,
-            None,
-        ], "PaddleTRT only supports FP32 / FP16 IO"
+        assert (
+            _type in self.supported_cast_type or _type is None
+        ), "PaddleTRT only supports FP32 / FP16 IO"
 
         ver = paddle.inference.get_trt_compile_version()
         trt_version = ver[0] * 1000 + ver[1] * 100 + ver[2] * 10
@@ -309,15 +308,14 @@ def set_input_type(self, _type: np.dtype) -> None:
     def get_feed_data(self) -> Dict[str, Dict[str, Any]]:
         feed_data = {}
         for name, tensor_config in self.inputs.items():
-            do_casting = (
-                self.input_type is not None and name not in self.no_cast_list
-            )
+            data = tensor_config.data
             # Cast to target input_type
-            data = (
-                tensor_config.data.astype(self.input_type)
-                if do_casting
-                else tensor_config.data
-            )
+            if (
+                self.input_type is not None
+                and name not in self.no_cast_list
+                and data.dtype in self.supported_cast_type
+            ):
+                data = data.astype(self.input_type)
             # Truncate FP32 tensors to FP16 precision for FP16 test stability
             if data.dtype == np.float32 and name not in self.no_cast_list:
                 data = data.astype(np.float16).astype(np.float32)
@@ -334,10 +332,14 @@ def _cast(self) -> None:
         for name, inp in self.inputs.items():
             if name in self.no_cast_list:
                 continue
+            if inp.dtype not in self.supported_cast_type:
+                continue
             inp.convert_type_inplace(self.input_type)
         for name, weight in self.weights.items():
             if name in self.no_cast_list:
                 continue
+            if weight.dtype not in self.supported_cast_type:
+                continue
             weight.convert_type_inplace(self.input_type)
         return self
 

test/ir/inference/test_trt_convert_assign.py

@@ -120,9 +120,8 @@ def clear_dynamic_shape():
             self.dynamic_shape.opt_input_shape = {}
 
         def generate_trt_nodes_num(attrs, dynamic_shape):
-            if not dynamic_shape and (
-                self.has_bool_dtype or self.dims == 1 or self.dims == 0
-            ):
+            # Static shape does not support 0 or 1 dim's input
+            if not dynamic_shape and (self.dims == 1 or self.dims == 0):
                 return 0, 4
             return 1, 2
 

test/ir/inference/test_trt_convert_cast.py

@@ -118,6 +118,7 @@ def generate_input(type):
                             )
                         },
                         outputs=["cast_output_data1"],
+                        no_cast_list=["input_data"],
                     )
 
                     yield program_config