add hard_sigmoid trt converter test cases (#35908)

paddle/fluid/inference/tensorrt/op_teller.cc

@@ -1265,6 +1265,24 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
       }
     }
 
+    if (op_type == "hard_sigmoid") {
+      if (!with_dynamic_shape) {
+        auto* block = desc.Block();
+        if (block == nullptr) {
+          VLOG(3) << "The block is null.";
+          return false;
+        }
+        auto x_var_name = desc.Input("X")[0];
+        auto* x_var_desc = block->FindVar(x_var_name);
+        const auto x_shape = x_var_desc->GetShape();
+        if (x_shape.size() <= 2) {
+          VLOG(3) << "hard_sigmoid op does not support input's dim less than 3 "
+                     "in tensorrt.";
+          return false;
+        }
+      }
+    }
+
     if ((*teller)(op_type, desc, use_no_calib_int8)) return true;
   }
 

python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_hard_sigmoid.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 TrtConvertHardSigmoidTest_dim_2(TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        return True
+
+    def sample_program_configs(self):
+        def generate_input(shape):
+            return np.random.random(shape).astype(np.float32)
+
+        for batch in [1, 2, 4]:
+            for shape in [[batch, 64], [batch, 32, 64], [batch, 64, 32, 128]]:
+                self.input_dim = len(shape)
+                for slope in [0.1, 0.5]:
+                    for offset in [0.2, 0.7]:
+                        dics = [{"slope": slope, "offset": offset}]
+                        ops_config = [{
+                            "op_type": "hard_sigmoid",
+                            "op_inputs": {
+                                "X": ["input_data"],
+                            },
+                            "op_outputs": {
+                                "Out": ["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_input, shape))
+                            },
+                            outputs=["output_data"])
+
+                        yield program_config
+
+    def sample_predictor_configs(
+            self, program_config) -> (paddle_infer.Config, List[int], float):
+        def generate_dynamic_shape(attrs):
+            if self.input_dim == 2:
+                self.dynamic_shape.min_input_shape = {"input_data": [1, 8]}
+                self.dynamic_shape.max_input_shape = {"input_data": [64, 128]}
+                self.dynamic_shape.opt_input_shape = {"input_data": [2, 16]}
+            elif self.input_dim == 3:
+                self.dynamic_shape.min_input_shape = {"input_data": [1, 8, 8]}
+                self.dynamic_shape.max_input_shape = {
+                    "input_data": [64, 128, 256]
+                }
+                self.dynamic_shape.opt_input_shape = {"input_data": [2, 16, 64]}
+            elif self.input_dim == 4:
+                self.dynamic_shape.min_input_shape = {
+                    "input_data": [1, 8, 8, 4]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "input_data": [64, 128, 256, 512]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "input_data": [2, 16, 64, 128]
+                }
+
+        def clear_dynamic_shape():
+            self.dynamic_shape.max_input_shape = {}
+            self.dynamic_shape.min_input_shape = {}
+            self.dynamic_shape.opt_input_shape = {}
+
+        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(), (1, 2), 1e-5
+        self.trt_param.precision = paddle_infer.PrecisionType.Half
+        yield self.create_inference_config(), (1, 2), 1e-5
+
+        # for dynamic_shape
+        generate_dynamic_shape(attrs)
+        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):
+        def teller(program_config, predictor_config):
+            if len(self.dynamic_shape.
+                   min_input_shape) == 0 and self.input_dim == 2:
+                return True
+            return False
+
+        self.add_skip_case(
+            teller, SkipReasons.TRT_NOT_SUPPORT,
+            "Need to repair the case: the output of trt and GPU has diff when inputs' dims is 2 in static shape mode."
+        )
+
+    def test(self):
+        self.add_skip_trt_case()
+        self.run_test()
+
+
+if __name__ == "__main__":
+    unittest.main()