Improve stablity of Paddle-TensorRT FP16 UT GitHub (1) (#51554)

* Improve Readability and Overall Clarity of Logging

* Adds the set_input_type API for specifying input data types

* Specifying input data types

test/ir/inference/program_config.py

@@ -54,8 +54,8 @@ class TensorConfig:
         if data_gen is not None:
             self.data_gen = data_gen
             self.data = data_gen()
-            self.dtype = data_gen().dtype
-            self.shape = data_gen().shape
+            self.dtype = self.data.dtype
+            self.shape = self.data.shape
         else:
             assert (
                 shape is not None
@@ -67,6 +67,11 @@ class TensorConfig:
     def __repr__(self):
         return str({'shape': self.shape, 'lod': self.lod, 'dtype': self.dtype})
 
+    def astype(self, type: np.dtype):
+        self.data = self.data.astype(type)
+        self.dtype = self.data.dtype
+        return self
+
 
 class VarType(enum.Enum):
     LOD_TENSOR = 1
@@ -270,6 +275,16 @@ class ProgramConfig:
 
         return log_str
 
+    def set_input_type(self, type: np.dtype):
+        for inp in self.inputs.values():
+            inp.astype(type)
+        for weight in self.weights.values():
+            weight.astype(type)
+        return self
+
+    def get_input_type(self) -> np.dtype:
+        return next(iter(self.inputs.values())).dtype
+
 
 def create_fake_model(program_config):
     '''Create a Paddle model(in memory) according to the given config.'''

test/ir/inference/test_multihead_matmul_fuse_pass_v3.py

@@ -33,6 +33,9 @@ class TestMultiheadMatmulFusePass(PassAutoScanTest):
         def generate_elewise_input():
             return np.random.random([1, 12, 128, 128]).astype(np.float32)
 
+        def generate_weight(shape):
+            return np.random.random(shape).astype(np.float32)
+
         mul_0 = OpConfig(
             "mul",
             inputs={"X": ["mul_x"], "Y": ["mul_0_w"]},
@@ -195,13 +198,27 @@ class TestMultiheadMatmulFusePass(PassAutoScanTest):
                 ),
             },
             weights={
-                "mul_0_w": TensorConfig(shape=[768, 768]),
-                "mul_1_w": TensorConfig(shape=[768, 768]),
-                "mul_2_w": TensorConfig(shape=[768, 768]),
-                "mul_3_w": TensorConfig(shape=[768, 768]),
-                "ele_0_w": TensorConfig(shape=[768]),
-                "ele_1_w": TensorConfig(shape=[768]),
-                "ele_2_w": TensorConfig(shape=[768]),
+                "mul_0_w": TensorConfig(
+                    data_gen=partial(generate_weight, [768, 768])
+                ),
+                "mul_1_w": TensorConfig(
+                    data_gen=partial(generate_weight, [768, 768])
+                ),
+                "mul_2_w": TensorConfig(
+                    data_gen=partial(generate_weight, [768, 768])
+                ),
+                "mul_3_w": TensorConfig(
+                    data_gen=partial(generate_weight, [768, 768])
+                ),
+                "ele_0_w": TensorConfig(
+                    data_gen=partial(generate_weight, [768])
+                ),
+                "ele_1_w": TensorConfig(
+                    data_gen=partial(generate_weight, [768])
+                ),
+                "ele_2_w": TensorConfig(
+                    data_gen=partial(generate_weight, [768])
+                ),
             },
             outputs=[ops[-1].outputs["Out"][0]],
         )

test/ir/inference/test_trt_convert_bitwise_not.py

@@ -103,11 +103,11 @@ class TrtConvertActivationTest(TrtLayerAutoScanTest):
             ver = paddle_infer.get_trt_compile_version()
             trt_version = ver[0] * 1000 + ver[1] * 100 + ver[2] * 10
             if trt_version >= 8400:
-                if self.dims == 1 and not dynamic_shape:
+                if self.dims == 1:
                     return 0, 3
                 return 1, 2
             else:
-                if (self.dims == 1 and not dynamic_shape) or (
+                if self.dims <= 2 or (
                     program_config.inputs['input_data'].dtype
                     in ['bool', 'int8', 'uint8']
                 ):