[inference]add reduce converter test (#35145)

* add test

* add test

* add test

paddle/fluid/inference/tensorrt/op_teller.cc

@@ -1079,6 +1079,16 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
         for (auto x : dim) {
           if (!x) return false;
         }
+      } else {
+        if (BOOST_GET_CONST(bool, desc.GetAttr("reduce_all")) &&
+            !BOOST_GET_CONST(bool, desc.GetAttr("keep_dim")))
+          return false;
+      }
+      if (desc.HasAttr("reduce_all")) {
+        int out_dtype = BOOST_GET_CONST(int32_t, desc.GetAttr("out_dtype"));
+        if (out_dtype != -1) {
+          return false;
+        }
       }
     }
 #if IS_TRT_VERSION_GE(7000)

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

+# 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
+import unittest
+
+
+class TrtConvertReduceMeanTest(TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        inputs = program_config.inputs
+        attrs = [
+            program_config.ops[i].attrs
+            for i in range(len(program_config.ops))
+        ]
+
+        ## dim should be in (-rank, rank), and not NONE
+        rank = len(inputs['input_data'].shape)
+        for x in attrs[0]["dim"]:
+            if x >= rank or x <= -rank:
+                return False
+        if len(attrs[0]["dim"]) == 0:
+            return False
+        ## skip not use 
+        if attrs[0]["out_dtype"] != -1:
+            return False
+
+        return True
+
+    def sample_program_configs(self):
+        def generate_input1(attrs: List[Dict[str, Any]]):
+            return np.random.random([1, 3, 64, 64]).astype(np.float32)
+
+        for keep_dim in [False, True]:
+            for dim in [[], [1], [0], [0, 1], [1, 2, 3], [-2, 0, 3], [-3],
+                        [-4, 1], [3, 4, 5]]:
+                for reduce_all in [False, True]:
+                    for out_dtype in [-1, 0, 1]:
+                        dics = [{
+                            "keep_dim": keep_dim,
+                            "dim": dim,
+                            "reduce_all": reduce_all,
+                            "out_dtype": out_dtype
+                        }, {}]
+
+                        ops_config = [{
+                            "op_type": "reduce_mean",
+                            "op_inputs": {
+                                "X": ["input_data"]
+                            },
+                            "op_outputs": {
+                                "Out": ["reduce_output_data"]
+                            },
+                            "op_attrs": dics[0]
+                        }]
+                        ops = self.generate_op_config(ops_config)
+
+                        program_config = ProgramConfig(
+                            ops=ops,
+                            weights={},
+                            inputs={
+                                "input_data": TensorConfig(data_gen=partial(
+                                    generate_input1, dics))
+                            },
+                            outputs=["reduce_output_data"])
+
+                        if not self.is_program_valid(program_config):
+                            continue
+
+                        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, 3, 32, 32]}
+            self.dynamic_shape.max_input_shape = {"input_data": [4, 3, 64, 64]}
+            self.dynamic_shape.opt_input_shape = {"input_data": [1, 3, 64, 64]}
+
+        def clear_dynamic_shape():
+            self.dynamic_shape.min_input_shape = {}
+            self.dynamic_shape.max_input_shape = {}
+            self.dynamic_shape.opt_input_shape = {}
+
+        def generate_trt_nodes_num(attrs, dynamic_shape):
+            if dynamic_shape:
+                if (not attrs[0]['keep_dim']) and attrs[0]['reduce_all']:
+                    return 0, 3
+                else:
+                    return 1, 2
+            else:
+                if 0 in attrs[0]['dim'] or attrs[0]['reduce_all']:
+                    return 0, 3
+                else:
+                    return 1, 2
+
+        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, 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, 1e-5)
+
+        pass
+
+    def add_skip_trt_case(self):
+        def teller1(program_config, predictor_config):
+            if program_config.ops[0].attrs['out_dtype'] != -1:
+                return True
+            return False
+
+        self.add_skip_case(
+            teller1, SkipReasons.TRT_NOT_IMPLEMENTED,
+            "NOT Implemented: we will add out_dtype not equal to  -1 in the future"
+        )
+
+        pass
+
+    def test(self):
+        self.add_skip_trt_case()
+        self.run_test()
+
+
+if __name__ == "__main__":
+    unittest.main()

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

+# 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
+import unittest
+
+
+class TrtConvertReduceSumTest(TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        inputs = program_config.inputs
+        attrs = [
+            program_config.ops[i].attrs
+            for i in range(len(program_config.ops))
+        ]
+
+        ## dim should be in (-rank, rank), and not NONE
+        rank = len(inputs['input_data'].shape)
+        for x in attrs[0]["dim"]:
+            if x >= rank or x <= -rank:
+                return False
+        if len(attrs[0]["dim"]) == 0:
+            return False
+        ## skip not use 
+        if attrs[0]["out_dtype"] != -1:
+            return False
+
+        return True
+
+    def sample_program_configs(self):
+        def generate_input1(attrs: List[Dict[str, Any]]):
+            return np.random.random([1, 3, 64, 64]).astype(np.float32)
+
+        for keep_dim in [False, True]:
+            for dim in [[], [1], [0], [0, 1], [1, 2, 3], [-2, 0, 3], [-3],
+                        [-4, 1], [3, 4, 5]]:
+                for reduce_all in [False, True]:
+                    for out_dtype in [-1, 0, 1]:
+                        dics = [{
+                            "keep_dim": keep_dim,
+                            "dim": dim,
+                            "reduce_all": reduce_all,
+                            "out_dtype": out_dtype
+                        }, {}]
+
+                        ops_config = [{
+                            "op_type": "reduce_sum",
+                            "op_inputs": {
+                                "X": ["input_data"]
+                            },
+                            "op_outputs": {
+                                "Out": ["reduce_output_data"]
+                            },
+                            "op_attrs": dics[0]
+                        }]
+                        ops = self.generate_op_config(ops_config)
+
+                        program_config = ProgramConfig(
+                            ops=ops,
+                            weights={},
+                            inputs={
+                                "input_data": TensorConfig(data_gen=partial(
+                                    generate_input1, dics))
+                            },
+                            outputs=["reduce_output_data"])
+
+                        if not self.is_program_valid(program_config):
+                            continue
+
+                        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, 3, 32, 32]}
+            self.dynamic_shape.max_input_shape = {"input_data": [4, 3, 64, 64]}
+            self.dynamic_shape.opt_input_shape = {"input_data": [1, 3, 64, 64]}
+
+        def clear_dynamic_shape():
+            self.dynamic_shape.min_input_shape = {}
+            self.dynamic_shape.max_input_shape = {}
+            self.dynamic_shape.opt_input_shape = {}
+
+        def generate_trt_nodes_num(attrs, dynamic_shape):
+            if dynamic_shape:
+                if (not attrs[0]['keep_dim']) and attrs[0]['reduce_all']:
+                    return 0, 3
+                else:
+                    return 1, 2
+            else:
+                if 0 in attrs[0]['dim'] or attrs[0]['reduce_all']:
+                    return 0, 3
+                else:
+                    return 1, 2
+
+        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, 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, 1e-5)
+
+        pass
+
+    def add_skip_trt_case(self):
+        def teller1(program_config, predictor_config):
+            if program_config.ops[0].attrs['out_dtype'] != -1:
+                return True
+            return False
+
+        self.add_skip_case(
+            teller1, SkipReasons.TRT_NOT_IMPLEMENTED,
+            "NOT Implemented: we will add out_dtype not equal to  -1 in the future"
+        )
+
+        pass
+
+    def test(self):
+        self.add_skip_trt_case()
+        self.run_test()
+
+
+if __name__ == "__main__":
+    unittest.main()