[Paddle-TRT] reshape fill_constant (#44314)

* reshape fill_constant

* commit

* commit

paddle/fluid/inference/api/analysis_predictor.cc

@@ -2089,6 +2089,7 @@ USE_TRT_CONVERTER(top_k)
 USE_TRT_CONVERTER(top_k_v2)
 USE_TRT_CONVERTER(squeeze2)
 USE_TRT_CONVERTER(unsqueeze2)
+USE_TRT_CONVERTER(fill_constant)
 USE_TRT_CONVERTER(fused_token_prune)
 #if PADDLE_WITH_CUSPARSELT && IS_TRT_VERSION_GE(8000)
 USE_TRT_CONVERTER(sparse_fc)

paddle/fluid/inference/tensorrt/convert/CMakeLists.txt

@@ -69,6 +69,7 @@ list(
   top_k_op.cc
   squeeze2_op.cc
   unsqueeze2_op.cc
+  fill_constant_op.cc
   fused_token_prune_op.cc)
 
 if(CUSPARSELT_FOUND AND ${TENSORRT_MAJOR_VERSION} GREATER_EQUAL 8)

paddle/fluid/inference/tensorrt/convert/fill_constant_op.cc

+/* Copyright (c) 2018 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. */
+
+#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
+
+namespace paddle {
+namespace inference {
+namespace tensorrt {
+
+class FillConstantOpConverter : public OpConverter {
+ public:
+  void operator()(const framework::proto::OpDesc& op,
+                  const framework::Scope& scope,
+                  bool test_mode) override {
+    VLOG(4)
+        << "convert a fluid fill_constant op to tensorrt fill_constant layer";
+
+    framework::OpDesc op_desc(op, nullptr);
+    int dtype = BOOST_GET_CONST(int, op_desc.GetAttr("dtype"));
+    std::string str_value =
+        BOOST_GET_CONST(std::string, op_desc.GetAttr("str_value"));
+    std::vector<int64_t> shape =
+        BOOST_GET_CONST(std::vector<int64_t>, op_desc.GetAttr("shape"));
+    std::unique_ptr<framework::Tensor> out_tensor(new framework::Tensor());
+    out_tensor->Resize(phi::make_ddim(shape));
+    nvinfer1::DataType trt_dtype = nvinfer1::DataType::kFLOAT;
+    void* trt_data = nullptr;
+    size_t trt_num;
+    if (dtype == 2 || dtype == 3) {  // int,int64
+      auto* tmp_ptr = out_tensor->mutable_data<int>(platform::CPUPlace());
+      for (int64_t i = 0; i < out_tensor->numel(); i++)
+        tmp_ptr[i] = std::stoi(str_value);
+      trt_dtype = nvinfer1::DataType::kINT32;
+      trt_data = static_cast<void*>(tmp_ptr);
+    } else if (dtype == 5) {  // float
+      auto* tmp_ptr = out_tensor->mutable_data<float>(platform::CPUPlace());
+      for (int64_t i = 0; i < out_tensor->numel(); i++)
+        tmp_ptr[i] = std::stof(str_value);
+      trt_data = static_cast<void*>(tmp_ptr);
+    }
+
+    trt_num = static_cast<size_t>(out_tensor->numel());
+    engine_->SetWeights("fill_constant_value", std::move(out_tensor));
+    TensorRTEngine::Weight weight{trt_dtype, trt_data, trt_num};
+
+    nvinfer1::Dims trt_in_shape;
+    trt_in_shape.nbDims = shape.size();
+    for (size_t i = 0; i < shape.size(); i++) trt_in_shape.d[i] = shape[i];
+    nvinfer1::ILayer* layer =
+        TRT_ENGINE_ADD_LAYER(engine_, Constant, trt_in_shape, weight.get());
+    auto output_name = op_desc.Output("Out")[0];
+    RreplenishLayerAndOutput(layer, "fill_constant", {output_name}, test_mode);
+  }
+};
+
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespace paddle
+
+REGISTER_TRT_OP_CONVERTER(fill_constant, FillConstantOpConverter);

paddle/fluid/inference/tensorrt/convert/reshape_op.cc

@@ -35,15 +35,30 @@ class ReshapeOpConverter : public OpConverter {
     framework::OpDesc op_desc(op, nullptr);
     // Declare inputs
     auto* input = engine_->GetITensor(op_desc.Input("X")[0]);
+
     std::vector<int> shape =
         BOOST_GET_CONST(std::vector<int>, op_desc.GetAttr("shape"));
     int nbDims_num = shape.size();
     nvinfer1::Dims reshape_dim;
-    if (engine_->with_dynamic_shape()) {  // running the TRT Dynamic Shape mode
+    nvinfer1::ITensor* real_shape_tensor = nullptr;
+    std::vector<nvinfer1::ITensor*> concat_inputs;
+    bool one_input = false;
+    if (engine_->with_dynamic_shape()) {
+      if (op_desc.Inputs().find("ShapeTensor") != op_desc.Inputs().end() &&
+          op_desc.Input("ShapeTensor").size() > 0) {
+        for (auto name : op_desc.Input("ShapeTensor"))
+          concat_inputs.push_back(engine_->GetITensor(name));
+        real_shape_tensor = Concat(concat_inputs);
+      } else if (op_desc.Inputs().find("Shape") != op_desc.Inputs().end() &&
+                 op_desc.Input("Shape").size() > 0) {
+        real_shape_tensor = engine_->GetITensor(op_desc.Input("Shape")[0]);
+      } else {
         reshape_dim.nbDims = nbDims_num;
         for (int i = 0; i < nbDims_num; ++i) {
           reshape_dim.d[i] = shape[i];
         }
+        one_input = true;
+      }
     } else {  // running the TRT Static Shape mode
       reshape_dim.nbDims = nbDims_num - 1;
       for (int i = 0; i < nbDims_num - 1; ++i) {
@@ -51,7 +66,10 @@ class ReshapeOpConverter : public OpConverter {
       }
     }
     auto* layer = TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *input);
+    if (!engine_->with_dynamic_shape() || one_input)
       layer->setReshapeDimensions(reshape_dim);
+    else
+      layer->setInput(1, *real_shape_tensor);
     auto output_name = op_desc.Output("Out")[0];
     RreplenishLayerAndOutput(layer, "reshape", {output_name}, test_mode);
   }

paddle/fluid/inference/tensorrt/op_teller.cc

@@ -169,6 +169,7 @@ struct SimpleOpTypeSetTeller : public Teller {
       "transformer_input_convert",
       "recover_padding",
       "remove_padding",
+      "fill_constant",
       "squeeze2",
       "unsqueeze2"};
   std::unordered_set<std::string> teller_set{
@@ -274,6 +275,7 @@ struct SimpleOpTypeSetTeller : public Teller {
       "transformer_input_convert",
       "recover_padding",
       "remove_padding",
+      "fill_constant",
       "squeeze2",
       "unsqueeze2",
       "fused_token_prune"};
@@ -1448,6 +1450,27 @@ bool OpTeller::Tell(const framework::ir::Node* node,
       }
     }
 
+    if (op_type == "fill_constant") {
+      auto fill_constant_inputs = desc.Inputs();
+      if (fill_constant_inputs.find("ValueTensor") !=
+          fill_constant_inputs.end()) {
+        if (desc.Input("ValueTensor").size()) return false;
+      }
+      if (fill_constant_inputs.find("ShapeTensor") !=
+          fill_constant_inputs.end()) {
+        if (desc.Input("ShapeTensor").size()) return false;
+      }
+      if (fill_constant_inputs.find("ShapeTensorList") !=
+          fill_constant_inputs.end()) {
+        if (desc.Input("ShapeTensorList").size()) return false;
+      }
+      int dtype = BOOST_GET_CONST(int, desc.GetAttr("dtype"));
+      // only support int32, int64, float32
+      if (!(dtype == 2 || dtype == 3 || dtype == 5)) {
+        return false;
+      }
+    }
+
     if (op_type == "instance_norm") {
       if (with_dynamic_shape) {
         VLOG(3) << "trt instance_norm op does not support dynamic shape ";
@@ -1801,6 +1824,9 @@ bool OpTeller::Tell(const framework::ir::Node* node,
     }
 
     if (op_type == "reshape" || op_type == "reshape2") {
+      if (with_dynamic_shape) {
+        return true;
+      }
       if (!desc.HasAttr("shape")) {
         return false;
       }

python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_fill_constant.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 unittest
+import numpy as np
+import paddle.inference as paddle_infer
+from functools import partial
+from typing import Optional, List, Callable, Dict, Any, Set
+
+
+class TrtConvertSplitTest(TrtLayerAutoScanTest):
+
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        return True
+
+    def sample_program_configs(self):
+
+        def generate_value_data(attrs: List[Dict[str, Any]]):
+            return np.array([1]).astype(np.int32)
+
+        def generate_shape_data(attrs: List[Dict[str, Any]]):
+            return np.array([4, 23]).astype(np.int32)
+
+        def generate_shapelist_data(attrs: List[Dict[str, Any]]):
+            return np.array([4]).astype(np.int32)
+
+        for shape in [[2, 3, 4]]:
+            for num_input in [0, 1, 2, 3]:
+                for dtype in [5, 2, 3]:
+                    for str_value in ["2", "23", "-1"]:
+                        self.num_input = num_input
+                        dics = [{
+                            "str_value": str_value,
+                            "shape": shape,
+                            "dtype": dtype
+                        }, {
+                            "axis": -1
+                        }]
+                        dics_intput = [{
+                            "ValueTensor": ["value_data"]
+                        }, {
+                            "ShapeTensor": ["shape_data"],
+                        }, {
+                            "ShapeTensorList": ["shapeT1_data", "shapeT2_data"],
+                        }, {}]
+                        ops_config = [
+                            {
+                                "op_type": "fill_constant",
+                                "op_inputs": dics_intput[num_input],
+                                "op_outputs": {
+                                    "Out": ["out_data"],
+                                },
+                                "op_attrs": dics[0]
+                            },
+                        ]
+
+                        def generate_input():
+                            return np.random.random([1, 1]).astype(np.float32)
+
+                        ops = self.generate_op_config(ops_config)
+                        program_config = ProgramConfig(
+                            ops=ops,
+                            weights={},
+                            inputs={
+                                "value_data":
+                                TensorConfig(data_gen=partial(
+                                    generate_value_data, dics)),
+                                "shape_data":
+                                TensorConfig(data_gen=partial(
+                                    generate_shape_data, dics)),
+                                "shapeT1_data":
+                                TensorConfig(data_gen=partial(
+                                    generate_shapelist_data, dics)),
+                                "shapeT2_data":
+                                TensorConfig(data_gen=partial(
+                                    generate_shapelist_data, dics)),
+                            },
+                            outputs=["out_data"])
+
+                        yield program_config
+
+    def sample_predictor_configs(
+            self, program_config) -> (paddle_infer.Config, List[int], float):
+
+        def generate_dynamic_shape(attrs):
+            self.input_shape = [1, 1]
+            max_shape = list(self.input_shape)
+            min_shape = list(self.input_shape)
+            opt_shape = list(self.input_shape)
+            for i in range(len(self.input_shape)):
+                max_shape[i] = max_shape[i] + 1
+            self.dynamic_shape.min_input_shape = {"Y_data": min_shape}
+            self.dynamic_shape.max_input_shape = {"Y_data": max_shape}
+            self.dynamic_shape.opt_input_shape = {"Y_data": opt_shape}
+
+        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 (self.num_input < 3):
+                return 0, 6
+            return 1, 5
+
+        attrs = [
+            program_config.ops[i].attrs for i in range(len(program_config.ops))
+        ]
+        # Don't test static shape
+
+        # 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):
+        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_reshape.py

@@ -48,12 +48,16 @@ class TrtConvertReshapeTest(TrtLayerAutoScanTest):
 
         def generate_input1(attrs: List[Dict[str, Any]]):
             if self.dims == 4:
+                self.input_shape = [1, 2, 4, 6]
                 return np.ones([1, 2, 4, 6]).astype(np.float32)
             elif self.dims == 3:
+                self.input_shape = [1, 8, 6]
                 return np.ones([1, 8, 6]).astype(np.float32)
             elif self.dims == 2:
+                self.input_shape = [1, 48]
                 return np.ones([1, 48]).astype(np.float32)
             elif self.dims == 1:
+                self.input_shape = [48]
                 return np.ones([48]).astype(np.float32)
 
         def generate_weight1(attrs: List[Dict[str, Any]]):
@@ -66,52 +70,20 @@ class TrtConvertReshapeTest(TrtLayerAutoScanTest):
             return np.array([24]).astype(np.int32)
 
         for dims in [4, 3, 2, 1]:
-            for num_input in [0, 1, 2, 3]:
             for shape in [[1, 6, 8], [1, 2, 4, 6], [1, 1, 0, 12], [1, 0, 6],
-                              [1, -1, 12], [2, -1], [3, 16], [3, 4, 4], [48]]:
-                    dics = [{
+                          [1, -1, 12], [2, -1], [3, 16], [3, 4, 4], [48],
+                          [-1, 48]]:
+                dics = [
+                    {
                         "shape": shape,
-                    }, {}]
-                    self.num_input = num_input
+                    },
+                ]
                 self.dims = dims
-                    dics_intput = [{
-                        "X": ["reshape_input"],
-                        "Shape": ["shape_data"],
-                        "ShapeTensor": ["shapeT1_data", "shapeT2_data"],
-                    }, {
-                        "X": ["reshape_input"],
-                        "Shape": ["shape_data"],
-                    }, {
-                        "X": ["reshape_input"],
-                        "ShapeTensor": ["shapeT1_data", "shapeT2_data"],
-                    }, {
-                        "X": ["reshape_input"]
-                    }]
-
-                    dics_weight = [{
-                        "shape_data":
-                        TensorConfig(data_gen=partial(generate_weight1, dics)),
-                        "shapeT1_data":
-                        TensorConfig(
-                            data_gen=partial(generate_shapeT1_data, dics)),
-                        "shapeT2_data":
-                        TensorConfig(
-                            data_gen=partial(generate_shapeT2_data, dics))
-                    }, {
-                        "shape_data":
-                        TensorConfig(data_gen=partial(generate_weight1, dics))
-                    }, {
-                        "shapeT1_data":
-                        TensorConfig(
-                            data_gen=partial(generate_shapeT1_data, dics)),
-                        "shapeT2_data":
-                        TensorConfig(
-                            data_gen=partial(generate_shapeT2_data, dics))
-                    }, {}]
+                dics_intput = [{"X": ["reshape_input"]}]
 
                 ops_config = [{
                     "op_type": "reshape",
-                        "op_inputs": dics_intput[num_input],
+                    "op_inputs": dics_intput[0],
                     "op_outputs": {
                         "Out": ["reshape_out"]
                     },
@@ -120,11 +92,10 @@ class TrtConvertReshapeTest(TrtLayerAutoScanTest):
                 ops = self.generate_op_config(ops_config)
                 program_config = ProgramConfig(
                     ops=ops,
-                        weights=dics_weight[num_input],
+                    weights={},
                     inputs={
                         "reshape_input":
-                            TensorConfig(
-                                data_gen=partial(generate_input1, dics))
+                        TensorConfig(data_gen=partial(generate_input1, dics))
                     },
                     outputs=["reshape_out"])
 
@@ -169,14 +140,23 @@ class TrtConvertReshapeTest(TrtLayerAutoScanTest):
             self.dynamic_shape.opt_input_shape = {}
 
         def generate_trt_nodes_num(attrs, dynamic_shape):
+            # in static shape mode, here is consistent with op_teller.cc
+            if (not dynamic_shape):
+                if (attrs[0]['shape'][0] == 0):
+                    return 1, 2
+                elif (len(attrs[0]['shape']) == 1):
+                    return 0, 3
+                elif (np.prod(attrs[0]['shape'][1:]) == np.prod(
+                        self.input_shape[1:])):
+                    return 1, 2
+                else:
+                    return 0, 3
             return 1, 2
 
         attrs = [
             program_config.ops[i].attrs for i in range(len(program_config.ops))
         ]
-        if attrs[0]['shape'][0] > 1 and len(attrs[0]['shape']) > 1:
-            pass
-        else:
+
         # for static_shape
         clear_dynamic_shape()
         self.trt_param.precision = paddle_infer.PrecisionType.Float32
@@ -196,14 +176,243 @@ class TrtConvertReshapeTest(TrtLayerAutoScanTest):
             attrs, True), 1e-5
 
     def add_skip_trt_case(self):
+        pass
+
+    def test(self):
+        self.add_skip_trt_case()
+        self.run_test()
 
-        def teller1(program_config, predictor_config):
-            if len(program_config.weights) >= 1:
+
+# reshape having three inputs.
+class TrtConvertReshapeTest2(TrtLayerAutoScanTest):
+
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
         return True
-            return False
 
-        self.add_skip_case(teller1, SkipReasons.TRT_NOT_SUPPORT,
-                           "INPUT ShapeTensor and Shape NOT SUPPORT")
+    def sample_program_configs(self):
+
+        def generate_input1(attrs: List[Dict[str, Any]]):
+            if self.dims == 4:
+                return np.random.random([1, 2, 4, 6]).astype(np.float32)
+            elif self.dims == 3:
+                return np.random.random([1, 8, 6]).astype(np.float32)
+            elif self.dims == 2:
+                return np.random.random([1, 48]).astype(np.float32)
+            elif self.dims == 1:
+                return np.random.random([48]).astype(np.float32)
+
+        for dims in [4, 3, 2, 1]:
+            for shape in [[-1, 48]]:
+                dics = [{
+                    "shape": shape,
+                }, {}]
+                self.dims = dims
+                dics_intput = [
+                    {
+                        "X": ["reshape_input"],
+                        "ShapeTensor": ["shapeT1_data", "shapeT2_data"],
+                    },
+                ]
+                ops_config = [
+                    {
+                        "op_type": "fill_constant",
+                        "op_inputs": {},
+                        "op_outputs": {
+                            "Out": ["shapeT1_data"]
+                        },
+                        "op_attrs": {
+                            "dtype": 2,
+                            "str_value": "2",
+                            "shape": [1],
+                        },
+                    },
+                    {
+                        "op_type": "fill_constant",
+                        "op_inputs": {},
+                        "op_outputs": {
+                            "Out": ["shapeT2_data"]
+                        },
+                        "op_attrs": {
+                            "dtype": 2,
+                            "str_value": "24",
+                            "shape": [1],
+                        },
+                    },
+                    {
+                        "op_type": "reshape",
+                        "op_inputs": dics_intput[0],
+                        "op_outputs": {
+                            "Out": ["reshape_out"]
+                        },
+                        "op_attrs": dics[0]
+                    },
+                ]
+                ops = self.generate_op_config(ops_config)
+                program_config = ProgramConfig(
+                    ops=ops,
+                    weights={},
+                    inputs={
+                        "reshape_input":
+                        TensorConfig(data_gen=partial(generate_input1, dics))
+                    },
+                    outputs=["reshape_out"])
+
+                yield program_config
+
+    def sample_predictor_configs(
+            self, program_config) -> (paddle_infer.Config, List[int], float):
+
+        def generate_dynamic_shape():
+            if self.dims == 4:
+                self.dynamic_shape.min_input_shape = {
+                    "reshape_input": [1, 2, 4, 6]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "reshape_input": [4, 2, 4, 6]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "reshape_input": [1, 2, 4, 6]
+                }
+            elif self.dims == 3:
+                self.dynamic_shape.min_input_shape = {
+                    "reshape_input": [1, 8, 6]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "reshape_input": [4, 8, 6]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "reshape_input": [1, 8, 6]
+                }
+            elif self.dims == 2:
+                self.dynamic_shape.min_input_shape = {"reshape_input": [1, 48]}
+                self.dynamic_shape.max_input_shape = {"reshape_input": [4, 48]}
+                self.dynamic_shape.opt_input_shape = {"reshape_input": [1, 48]}
+            elif self.dims == 1:
+                self.dynamic_shape.min_input_shape = {"reshape_input": [48]}
+                self.dynamic_shape.max_input_shape = {"reshape_input": [48]}
+                self.dynamic_shape.opt_input_shape = {"reshape_input": [48]}
+
+        # for dynamic_shape
+        generate_dynamic_shape()
+        self.trt_param.precision = paddle_infer.PrecisionType.Float32
+        yield self.create_inference_config(), (1, 2), 1e-5
+        self.trt_param.precision = paddle_infer.PrecisionType.Half
+        yield self.create_inference_config(), (1, 2), 1e-5
+
+    def add_skip_trt_case(self):
+        pass
+
+    def test(self):
+        self.add_skip_trt_case()
+        self.run_test()
+
+
+# reshape having 2 inputs.
+class TrtConvertReshapeTest3(TrtLayerAutoScanTest):
+
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        return True
+
+    def sample_program_configs(self):
+
+        def generate_input1(attrs: List[Dict[str, Any]]):
+            if self.dims == 4:
+                return np.random.random([1, 2, 12, 6]).astype(np.float32)
+            elif self.dims == 3:
+                return np.random.random([1, 8, 18]).astype(np.float32)
+            elif self.dims == 2:
+                return np.random.random([1, 144]).astype(np.float32)
+            elif self.dims == 1:
+                return np.random.random([144]).astype(np.float32)
+
+        for dims in [4, 3, 2, 1]:
+            for shape in [[-1, 144]]:
+                dics = [{
+                    "shape": shape,
+                }, {}]
+                self.dims = dims
+                dics_intput = [
+                    {
+                        "X": ["reshape_input"],
+                        "shape_data": ["shape_data"],
+                    },
+                ]
+                ops_config = [
+                    {
+                        "op_type": "fill_constant",
+                        "op_inputs": {},
+                        "op_outputs": {
+                            "Out": ["shape_data"]
+                        },
+                        "op_attrs": {
+                            "dtype": 2,
+                            "str_value": "12",
+                            "shape": [2],
+                        },
+                    },
+                    {
+                        "op_type": "reshape",
+                        "op_inputs": dics_intput[0],
+                        "op_outputs": {
+                            "Out": ["reshape_out"]
+                        },
+                        "op_attrs": dics[0]
+                    },
+                ]
+                ops = self.generate_op_config(ops_config)
+                program_config = ProgramConfig(
+                    ops=ops,
+                    weights={},
+                    inputs={
+                        "reshape_input":
+                        TensorConfig(data_gen=partial(generate_input1, dics))
+                    },
+                    outputs=["reshape_out"])
+
+                yield program_config
+
+    def sample_predictor_configs(
+            self, program_config) -> (paddle_infer.Config, List[int], float):
+
+        def generate_dynamic_shape():
+            if self.dims == 4:
+                self.dynamic_shape.min_input_shape = {
+                    "reshape_input": [1, 2, 12, 6]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "reshape_input": [4, 2, 12, 6]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "reshape_input": [1, 2, 12, 6]
+                }
+            elif self.dims == 3:
+                self.dynamic_shape.min_input_shape = {
+                    "reshape_input": [1, 8, 18]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "reshape_input": [4, 8, 18]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "reshape_input": [1, 8, 18]
+                }
+            elif self.dims == 2:
+                self.dynamic_shape.min_input_shape = {"reshape_input": [1, 144]}
+                self.dynamic_shape.max_input_shape = {"reshape_input": [4, 144]}
+                self.dynamic_shape.opt_input_shape = {"reshape_input": [1, 144]}
+            elif self.dims == 1:
+                self.dynamic_shape.min_input_shape = {"reshape_input": [144]}
+                self.dynamic_shape.max_input_shape = {"reshape_input": [144]}
+                self.dynamic_shape.opt_input_shape = {"reshape_input": [144]}
+
+        # for dynamic_shape
+        generate_dynamic_shape()
+        self.trt_param.precision = paddle_infer.PrecisionType.Float32
+        yield self.create_inference_config(), (1, 2), 1e-5
+        self.trt_param.precision = paddle_infer.PrecisionType.Half
+        yield self.create_inference_config(), (1, 2), 1e-5
+
+    def add_skip_trt_case(self):
+        pass
 
     def test(self):
         self.add_skip_trt_case()