Reduce squeeze2_matmul_fuse_pass, flattent tests time (#47098)

* Add missing fp32 config and reduce the testing combination

* Reduce trt matmul pass test max examples

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

@@ -31,7 +31,7 @@ def sample_program_configs(self):
         def generate_input(batch):
             return np.random.random([batch, 32]).astype(np.float32)
 
-        for batch in [1, 2, 4]:
+        for batch in [1, 4]:
             for axis in [0, 1]:
                 for type in ["flatten", "flatten2"]:
                     if type == "flatten":
@@ -128,7 +128,7 @@ def sample_program_configs(self):
         def generate_input(batch):
             return np.random.random([batch, 32, 64]).astype(np.float32)
 
-        for batch in [1, 2, 4]:
+        for batch in [1, 4]:
             for axis in [0, 1, 2]:
                 for type in ["flatten", "flatten2"]:
                     if type == "flatten":
@@ -166,8 +166,8 @@ def sample_predictor_configs(
 
         def generate_dynamic_shape(attrs):
             self.dynamic_shape.min_input_shape = {"input_data": [1, 8, 8]}
-            self.dynamic_shape.max_input_shape = {"input_data": [4, 64, 768]}
-            self.dynamic_shape.opt_input_shape = {"input_data": [2, 32, 256]}
+            self.dynamic_shape.max_input_shape = {"input_data": [4, 32, 64]}
+            self.dynamic_shape.opt_input_shape = {"input_data": [2, 32, 64]}
 
         def clear_dynamic_shape():
             self.dynamic_shape.max_input_shape = {}
@@ -226,7 +226,7 @@ def sample_program_configs(self):
         def generate_input(batch):
             return np.random.random([batch, 8, 8, 8]).astype(np.float32)
 
-        for batch in [1, 2, 4]:
+        for batch in [1, 4]:
             for axis in [0, 1, 2, 3]:
                 for type in ["flatten", "flatten2"]:
                     if type == "flatten":
@@ -264,7 +264,7 @@ def sample_predictor_configs(
 
         def generate_dynamic_shape(attrs):
             self.dynamic_shape.min_input_shape = {"input_data": [1, 4, 4, 4]}
-            self.dynamic_shape.max_input_shape = {"input_data": [4, 32, 64, 64]}
+            self.dynamic_shape.max_input_shape = {"input_data": [4, 32, 32, 32]}
             self.dynamic_shape.opt_input_shape = {"input_data": [2, 16, 16, 8]}
 
         def clear_dynamic_shape():
@@ -294,6 +294,7 @@ def generate_trt_nodes_num(attrs, dynamic_shape):
 
         # for static_shape
         clear_dynamic_shape()
+        self.trt_param.precision = paddle_infer.PrecisionType.Float32
         yield self.create_inference_config(), generate_trt_nodes_num(
             attrs, False), 1e-5
         self.trt_param.precision = paddle_infer.PrecisionType.Half
@@ -323,7 +324,7 @@ def sample_program_configs(self):
         def generate_input(batch):
             return np.random.random([batch, 8, 8, 8]).astype(np.float32)
 
-        for batch in [1, 2, 4]:
+        for batch in [1, 4]:
             for axis in [0, 1, 2, 3, 4]:
                 for type in ["flatten", "flatten2"]:
                     if type == "flatten":
@@ -361,7 +362,7 @@ def sample_predictor_configs(
 
         def generate_dynamic_shape(attrs):
             self.dynamic_shape.min_input_shape = {"input_data": [1, 4, 4, 4]}
-            self.dynamic_shape.max_input_shape = {"input_data": [4, 32, 64, 64]}
+            self.dynamic_shape.max_input_shape = {"input_data": [4, 16, 16, 8]}
             self.dynamic_shape.opt_input_shape = {"input_data": [2, 16, 16, 8]}
 
         def clear_dynamic_shape():
@@ -391,6 +392,7 @@ def generate_trt_nodes_num(attrs, dynamic_shape):
 
         # for static_shape
         clear_dynamic_shape()
+        self.trt_param.precision = paddle_infer.PrecisionType.Float32
         yield self.create_inference_config(), generate_trt_nodes_num(
             attrs, False), 1e-5
         self.trt_param.precision = paddle_infer.PrecisionType.Half

python/paddle/fluid/tests/unittests/ir/inference/test_trt_squeeze2_matmul_fuse_pass.py

@@ -153,7 +153,7 @@ def sample_program_config(self, draw):
 
     def test(self):
         self.run_and_statis(quant=False,
-                            max_examples=50,
+                            max_examples=25,
                             passes=["trt_squeeze2_matmul_fuse_pass"])