[Paddle Inference]Add Concat op TRT converter unittest (#35545)

* add_concat_teller

* add_concat_teller

* add_concat_teller

* add_concat_teller

* add_concat_teller

paddle/fluid/inference/tensorrt/op_teller.cc

@@ -305,6 +305,12 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
         } else {
           if (axis <= 0) return false;
         }
+        auto concat_inputs = desc.Inputs();
+        if (concat_inputs.find("AxisTensor") != concat_inputs.end()) {
+          if (desc.Input("AxisTensor").size() >= 1) {
+            return false;
+          }
+        }
       }
     }
     if (op_type == "transpose2" || op_type == "transpose") {

python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_concat.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
+
+
+class TrtConvertConcatTest(TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        inputs = program_config.inputs
+        weights = program_config.weights
+        outputs = program_config.outputs
+
+        attrs = [
+            program_config.ops[i].attrs
+            for i in range(len(program_config.ops))
+        ]
+        #The input dimension should be less than or equal to the set axis.
+        if len(inputs['concat_input1'].shape) <= attrs[0]['axis']:
+            return False
+
+        return True
+
+    def sample_program_configs(self):
+        def generate_input1(attrs: List[Dict[str, Any]], batch):
+            if self.dims == 4:
+                return np.ones([batch, 3, 24, 24]).astype(np.float32)
+            elif self.dims == 3:
+                return np.ones([batch, 3, 24]).astype(np.float32)
+            elif self.dims == 2:
+                return np.ones([batch, 24]).astype(np.float32)
+            elif self.dims == 1:
+                return np.ones([24]).astype(np.float32)
+
+        def generate_input2(attrs: List[Dict[str, Any]], batch):
+            if self.dims == 4:
+                return np.ones([batch, 3, 24, 24]).astype(np.float32)
+            elif self.dims == 3:
+                return np.ones([batch, 3, 24]).astype(np.float32)
+            elif self.dims == 2:
+                return np.ones([batch, 24]).astype(np.float32)
+            elif self.dims == 1:
+                return np.ones([24]).astype(np.float32)
+
+        def generate_input3(attrs: List[Dict[str, Any]], batch):
+            if self.dims == 4:
+                return np.ones([batch, 3, 24, 24]).astype(np.float32)
+            elif self.dims == 3:
+                return np.ones([batch, 3, 24]).astype(np.float32)
+            elif self.dims == 2:
+                return np.ones([batch, 24]).astype(np.float32)
+            elif self.dims == 1:
+                return np.ones([24]).astype(np.float32)
+
+        def generate_weight1(attrs: List[Dict[str, Any]]):
+            return np.zeros([1]).astype(np.int32)
+
+        for dims in [1, 2, 3, 4]:
+            for num_input in [0, 1]:
+                for batch in [1, 2, 4]:
+                    for axis in [-1, 0, 1, 2, 3]:
+                        self.num_input = num_input
+                        self.dims = dims
+                        dics = [{"axis": axis}, {}]
+                        dics_intput = [{
+                            "X": [
+                                "concat_input1", "concat_input2",
+                                "concat_input3"
+                            ],
+                            "AxisTensor": ["AxisTensor"],
+                        }, {
+                            "X": [
+                                "concat_input1", "concat_input2",
+                                "concat_input3"
+                            ]
+                        }]
+                        dics_inputs = [{
+                            "concat_input1": TensorConfig(data_gen=partial(
+                                generate_input1, dics, batch)),
+                            "concat_input2": TensorConfig(data_gen=partial(
+                                generate_input2, dics, batch)),
+                            "concat_input3": TensorConfig(data_gen=partial(
+                                generate_input3, dics, batch)),
+                            "AxisTensor": TensorConfig(data_gen=partial(
+                                generate_weight1, dics))
+                        }, {
+                            "concat_input1": TensorConfig(data_gen=partial(
+                                generate_input1, dics, batch)),
+                            "concat_input2": TensorConfig(data_gen=partial(
+                                generate_input2, dics, batch)),
+                            "concat_input3": TensorConfig(data_gen=partial(
+                                generate_input3, dics, batch))
+                        }]
+                        ops_config = [{
+                            "op_type": "concat",
+                            "op_inputs": dics_intput[num_input],
+                            "op_outputs": {
+                                "Out": ["concat_output"]
+                            },
+                            "op_attrs": dics[0]
+                        }]
+                        ops = self.generate_op_config(ops_config)
+                        program_config = ProgramConfig(
+                            ops=ops,
+                            weights={},
+                            inputs=dics_inputs[num_input],
+                            outputs=["concat_output"])
+
+                        yield program_config
+
+    def sample_predictor_configs(
+            self, program_config) -> (paddle_infer.Config, List[int], float):
+        def generate_dynamic_shape(attrs):
+            if self.num_input == 0:
+                if self.dims == 4:
+                    self.dynamic_shape.min_input_shape = {
+                        "concat_input1": [1, 3, 24, 24],
+                        "concat_input2": [1, 3, 24, 24],
+                        "concat_input3": [1, 3, 24, 24],
+                        "AxisTensor": [1]
+                    }
+                    self.dynamic_shape.max_input_shape = {
+                        "concat_input1": [4, 3, 48, 48],
+                        "concat_input2": [4, 3, 48, 48],
+                        "concat_input3": [4, 3, 48, 48],
+                        "AxisTensor": [1]
+                    }
+                    self.dynamic_shape.opt_input_shape = {
+                        "concat_input1": [1, 3, 24, 24],
+                        "concat_input2": [1, 3, 24, 24],
+                        "concat_input3": [1, 3, 24, 24],
+                        "AxisTensor": [1]
+                    }
+                elif self.dims == 3:
+                    self.dynamic_shape.min_input_shape = {
+                        "concat_input1": [1, 3, 24],
+                        "concat_input2": [1, 3, 24],
+                        "concat_input3": [1, 3, 24],
+                        "AxisTensor": [1]
+                    }
+                    self.dynamic_shape.max_input_shape = {
+                        "concat_input1": [4, 12, 48],
+                        "concat_input2": [4, 12, 48],
+                        "concat_input3": [4, 12, 48],
+                        "AxisTensor": [1]
+                    }
+                    self.dynamic_shape.opt_input_shape = {
+                        "concat_input1": [1, 3, 24],
+                        "concat_input2": [1, 3, 24],
+                        "concat_input3": [1, 3, 24],
+                        "AxisTensor": [1]
+                    }
+                elif self.dims == 2:
+                    self.dynamic_shape.min_input_shape = {
+                        "concat_input1": [1, 24],
+                        "concat_input2": [1, 24],
+                        "concat_input3": [1, 24],
+                        "AxisTensor": [1]
+                    }
+                    self.dynamic_shape.max_input_shape = {
+                        "concat_input1": [4, 48],
+                        "concat_input2": [4, 48],
+                        "concat_input3": [4, 48],
+                        "AxisTensor": [1]
+                    }
+                    self.dynamic_shape.opt_input_shape = {
+                        "concat_input1": [1, 24],
+                        "concat_input2": [1, 24],
+                        "concat_input3": [1, 24],
+                        "AxisTensor": [1]
+                    }
+                elif self.dims == 1:
+                    self.dynamic_shape.min_input_shape = {
+                        "concat_input1": [24],
+                        "concat_input2": [24],
+                        "concat_input3": [24],
+                        "AxisTensor": [0]
+                    }
+                    self.dynamic_shape.max_input_shape = {
+                        "concat_input1": [48],
+                        "concat_input2": [48],
+                        "concat_input3": [48],
+                        "AxisTensor": [0]
+                    }
+                    self.dynamic_shape.opt_input_shape = {
+                        "concat_input1": [24],
+                        "concat_input2": [24],
+                        "concat_input3": [24],
+                        "AxisTensor": [0]
+                    }
+            elif self.num_input == 1:
+                if self.dims == 4:
+                    self.dynamic_shape.min_input_shape = {
+                        "concat_input1": [1, 3, 24, 24],
+                        "concat_input2": [1, 3, 24, 24],
+                        "concat_input3": [1, 3, 24, 24],
+                    }
+                    self.dynamic_shape.max_input_shape = {
+                        "concat_input1": [4, 3, 48, 48],
+                        "concat_input2": [4, 3, 48, 48],
+                        "concat_input3": [4, 3, 48, 48]
+                    }
+                    self.dynamic_shape.opt_input_shape = {
+                        "concat_input1": [1, 3, 24, 24],
+                        "concat_input2": [1, 3, 24, 24],
+                        "concat_input3": [1, 3, 24, 24]
+                    }
+                elif self.dims == 3:
+                    self.dynamic_shape.min_input_shape = {
+                        "concat_input1": [1, 3, 24],
+                        "concat_input2": [1, 3, 24],
+                        "concat_input3": [1, 3, 24]
+                    }
+                    self.dynamic_shape.max_input_shape = {
+                        "concat_input1": [4, 12, 48],
+                        "concat_input2": [4, 12, 48],
+                        "concat_input3": [4, 12, 48]
+                    }
+                    self.dynamic_shape.opt_input_shape = {
+                        "concat_input1": [1, 3, 24],
+                        "concat_input2": [1, 3, 24],
+                        "concat_input3": [1, 3, 24]
+                    }
+                elif self.dims == 2:
+                    self.dynamic_shape.min_input_shape = {
+                        "concat_input1": [1, 24],
+                        "concat_input2": [1, 24],
+                        "concat_input3": [1, 24]
+                    }
+                    self.dynamic_shape.max_input_shape = {
+                        "concat_input1": [4, 48],
+                        "concat_input2": [4, 48],
+                        "concat_input3": [4, 48]
+                    }
+                    self.dynamic_shape.opt_input_shape = {
+                        "concat_input1": [1, 24],
+                        "concat_input2": [1, 24],
+                        "concat_input3": [1, 24]
+                    }
+                elif self.dims == 1:
+                    self.dynamic_shape.min_input_shape = {
+                        "concat_input1": [24],
+                        "concat_input2": [24],
+                        "concat_input3": [24]
+                    }
+                    self.dynamic_shape.max_input_shape = {
+                        "concat_input1": [48],
+                        "concat_input2": [48],
+                        "concat_input3": [48]
+                    }
+                    self.dynamic_shape.opt_input_shape = {
+                        "concat_input1": [24],
+                        "concat_input2": [24],
+                        "concat_input3": [24]
+                    }
+
+        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 == True:
+                if attrs[0]['axis'] >= 0:
+                    return 1, 4
+                else:
+                    return 0, 5
+            else:
+                if attrs[0]['axis'] > 0:
+                    return 1, 4
+                else:
+                    return 0, 5
+
+        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 add_skip_trt_case(self):
+        def teller1(program_config, predictor_config):
+            if len(program_config.inputs) == 4:
+                return True
+            return False
+
+        self.add_skip_case(teller1, SkipReasons.TRT_NOT_SUPPORT,
+                           "INPUT AxisTensor NOT SUPPORT")
+
+    def test(self):
+        self.add_skip_trt_case()
+        self.run_test()
+
+
+if __name__ == "__main__":
+    unittest.main()