add group_norm trt converter test case (#35524)

* add group_norm trt converter test case

* update group_norm trt converter test case

Author: baoachun

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

@@ -0,0 +1,134 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+# 
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# 
+#     http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from trt_layer_auto_scan_test import TrtLayerAutoScanTest, SkipReasons
+from program_config import TensorConfig, ProgramConfig
+import numpy as np
+import paddle.inference as paddle_infer
+from functools import partial
+from typing import Optional, List, Callable, Dict, Any, Set
+
+
+class TrtConvertGroupNormTest(TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        return True
+
+    def sample_program_configs(self):
+        def generate_input(attrs: List[Dict[str, Any]], batch):
+            if attrs[0]['data_layout'] == 'NCHW':
+                return np.random.random([batch, 32, 64, 64]).astype(np.float32)
+            else:
+                return np.random.random([batch, 64, 64, 32]).astype(np.float32)
+
+        def generate_scale():
+            return np.random.randn(32).astype(np.float32)
+
+        def generate_bias():
+            return np.random.randn(32).astype(np.float32)
+
+        for batch in [1, 2, 4]:
+            for group in [1, 4, 32]:
+                for epsilon in [0.1, 0.7]:
+                    for data_layout in ['NCHW', 'NHWC']:
+                        for i in [0, 1]:
+                            dics = [{
+                                "epsilon": epsilon,
+                                "groups": group,
+                                "data_layout": data_layout
+                            }, {
+                                "groups": group,
+                                "data_layout": data_layout
+                            }]
+                            ops_config = [{
+                                "op_type": "group_norm",
+                                "op_inputs": {
+                                    "X": ["input_data"],
+                                    "Scale": ["scale_weight"],
+                                    "Bias": ["bias_weight"]
+                                },
+                                "op_outputs": {
+                                    "Y": ["y_output"],
+                                    "Mean": ["mean_output"],
+                                    "Variance": ["variance_output"]
+                                },
+                                "op_attrs": dics[i]
+                            }]
+                            ops = self.generate_op_config(ops_config)
+
+                            program_config = ProgramConfig(
+                                ops=ops,
+                                weights={
+                                    "scale_weight": TensorConfig(
+                                        data_gen=partial(generate_scale)),
+                                    "bias_weight": TensorConfig(
+                                        data_gen=partial(generate_bias))
+                                },
+                                inputs={
+                                    "input_data": TensorConfig(data_gen=partial(
+                                        generate_input, dics, batch))
+                                },
+                                outputs=["y_output"])
+
+                            yield program_config
+
+    def sample_predictor_configs(
+            self, program_config) -> (paddle_infer.Config, List[int], float):
+        def generate_dynamic_shape(attrs):
+            self.dynamic_shape.min_input_shape = {"input_data": [1, 16, 32, 32]}
+            self.dynamic_shape.max_input_shape = {
+                "input_data": [4, 64, 128, 64]
+            }
+            self.dynamic_shape.opt_input_shape = {"input_data": [2, 32, 64, 64]}
+
+        def clear_dynamic_shape():
+            self.dynamic_shape.max_input_shape = {}
+            self.dynamic_shape.min_input_shape = {}
+            self.dynamic_shape.opt_input_shape = {}
+
+        def generate_trt_nodes_num(attrs, dynamic_shape):
+            if len(attrs[0]) == 3:
+                if dynamic_shape:
+                    return 1, 2
+                else:
+                    return 0, 3
+            else:
+                return 0, 3
+
+        attrs = [
+            program_config.ops[i].attrs
+            for i in range(len(program_config.ops))
+        ]
+
+        # 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
+        yield self.create_inference_config(), generate_trt_nodes_num(
+            attrs, False), 1e-5
+
+        # for dynamic_shape
+        generate_dynamic_shape(attrs)
+        # self.trt_param.precision = paddle_infer.PrecisionType.Float32
+        # yield self.create_inference_config(), generate_trt_nodes_num(attrs, True), 1e-5
+        # self.trt_param.precision = paddle_infer.PrecisionType.Half
+        # yield self.create_inference_config(), generate_trt_nodes_num(attrs, True), 1e-5
+
+    def test(self):
+        self.run_test()
+
+
+if __name__ == "__main__":
+    unittest.main()