add prelu trt converter test case

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

@@ -34,11 +34,7 @@ class PReluOpConverter : public OpConverter {
     auto* input = engine_->GetITensor(op_desc.Input("X")[0]);
     // Get attrs
     std::string mode = BOOST_GET_CONST(std::string, op_desc.GetAttr("mode"));
-    //
     auto* alpha_var = scope.FindVar(op_desc.Input("Alpha")[0]);
-    PADDLE_ENFORCE_NOT_NULL(
-        alpha_var, platform::errors::NotFound(
-                       "Variable Alpha of prelu TRT converter is not found."));
     auto* alpha_tensor = alpha_var->GetMutable<framework::LoDTensor>();
 
     platform::CPUPlace cpu_place;
@@ -50,15 +46,9 @@ class PReluOpConverter : public OpConverter {
 
     nvinfer1::ILayer* layer = nullptr;
     if (engine_->with_dynamic_shape()) {
-#if IS_TRT_VERSION_GE(6000)
       plugin::PReluPluginDynamic* plugin = new plugin::PReluPluginDynamic(
           alpha_data, alpha_tensor_temp->numel(), mode);
       layer = engine_->AddDynamicPlugin(&input, input_num, plugin);
-#else
-      PADDLE_THROW(platform::errors::Fatal(
-          "You are running the TRT Dynamic Shape mode, need to confirm that "
-          "your TRT version is no less than 6.0"));
-#endif
     } else {
 #if IS_TRT_VERSION_GE(7000)
       float* alpha_weight_data = engine_->GetWeightCPUData(

paddle/fluid/inference/tensorrt/op_teller.cc

@@ -652,6 +652,28 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
                 << desc.Output("Out").size() << ".";
         return false;
       }
+
+      auto* block = desc.Block();
+      auto* var_desc = block->FindVar(desc.Input("Alpha")[0]);
+      if (!var_desc) {
+        VLOG(3) << "Variable Alpha of prelu TRT converter not found.";
+        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() == 1) {
+        VLOG(3) << "prelu op does not support input's dim is 1 in tensorrt.";
+        return false;
+      }
+
+      if (!with_dynamic_shape) {
+        if (x_shape.size() == 2) {
+          VLOG(3) << "prelu op does not support input's dim is 2 in tensorrt.";
+          return false;
+        }
+      }
     }
 
     if (op_type == "roi_align") {

python/paddle/fluid/tests/unittests/ir/inference/CMakeLists.txt

@@ -24,7 +24,7 @@ if(WITH_GPU AND TENSORRT_FOUND)
 
   foreach(target ${TEST_TRT_CONVERTER})
     py_test_modules(${target} MODULES ${target})
-    set_tests_properties(${target} PROPERTIES TIMEOUT 100)
+    set_tests_properties(${target} PROPERTIES TIMEOUT 300)
   endforeach()
 endif()
 

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

@@ -0,0 +1,193 @@
+# 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 TrtConvertPreluTest(TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        return True
+
+    def sample_program_configs(self):
+        def generate_input(batch, dim1, dim2, dim3):
+            shape = [batch]
+            if dim1 != 0:
+                shape.append(dim1)
+            if dim2 != 0:
+                shape.append(dim2)
+            if dim3 != 0:
+                shape.append(dim3)
+            return np.random.random(shape).astype(np.float32)
+
+        def generate_alpha(attrs: List[Dict[str, Any]], dim1, dim2, dim3):
+            if attrs[0]["mode"] == "all":
+                return np.random.random(size=(1)).astype(np.float32)
+            elif attrs[0]["mode"] == "channel":
+                shape = [1]
+                if dim1 != 0:
+                    shape.append(dim1)
+                if dim2 != 0:
+                    shape.append(1)
+                if dim3 != 0:
+                    shape.append(1)
+                return np.random.random(size=shape).astype(np.float32)
+            elif attrs[0]["mode"] == "element":
+                shape = [1]
+                if dim1 != 0:
+                    shape.append(dim1)
+                if dim2 != 0:
+                    shape.append(dim2)
+                if dim3 != 0:
+                    shape.append(dim3)
+                return np.random.random(size=shape).astype(np.float32)
+
+        for batch in [1, 4]:
+            for dim1 in [0, 3]:
+                for dim2 in [0, 16]:
+                    for dim3 in [0, 32]:
+                        self.dim1 = dim1
+                        self.dim2 = dim2
+                        self.dim3 = dim3
+
+                        if dim1 == 0 and dim2 != 0:
+                            continue
+                        if dim1 == 0 and dim2 == 0 and dim3 != 0:
+                            continue
+
+                        for mode in ["all", "channel", "element"]:
+                            if mode == "channel" and dim1 == 0:
+                                continue
+                            dics = [{"mode": mode}]
+                            ops_config = [{
+                                "op_type": "prelu",
+                                "op_inputs": {
+                                    "X": ["input_data"],
+                                    "Alpha": ["alpha_weight"]
+                                },
+                                "op_outputs": {
+                                    "Out": ["output_data"]
+                                },
+                                "op_attrs": dics[0]
+                            }]
+                            ops = self.generate_op_config(ops_config)
+
+                            program_config = ProgramConfig(
+                                ops=ops,
+                                weights={
+                                    "alpha_weight": TensorConfig(
+                                        data_gen=partial(generate_alpha, dics,
+                                                         dim1, dim2, dim3))
+                                },
+                                inputs={
+                                    "input_data": TensorConfig(
+                                        data_gen=partial(generate_input, batch,
+                                                         dim1, dim2, dim3)),
+                                },
+                                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.dim1 == 0:
+                self.dynamic_shape.min_input_shape = {"input_data": [1], }
+                self.dynamic_shape.max_input_shape = {"input_data": [4], }
+                self.dynamic_shape.opt_input_shape = {"input_data": [2], }
+            else:
+                if self.dim2 == 0 and self.dim3 == 0:
+                    self.dynamic_shape.min_input_shape = {
+                        "input_data": [1, 1],
+                    }
+                    self.dynamic_shape.max_input_shape = {
+                        "input_data": [4, 64],
+                    }
+                    self.dynamic_shape.opt_input_shape = {
+                        "input_data": [2, 3],
+                    }
+                elif self.dim2 != 0 and self.dim3 != 0:
+                    self.dynamic_shape.min_input_shape = {
+                        "input_data": [1, 1, 1, 1],
+                    }
+                    self.dynamic_shape.max_input_shape = {
+                        "input_data": [4, 64, 128, 128],
+                    }
+                    self.dynamic_shape.opt_input_shape = {
+                        "input_data": [2, 3, 16, 32],
+                    }
+                elif self.dim3 == 0:
+                    self.dynamic_shape.min_input_shape = {
+                        "input_data": [1, 1, 1],
+                    }
+                    self.dynamic_shape.max_input_shape = {
+                        "input_data": [4, 64, 256],
+                    }
+                    self.dynamic_shape.opt_input_shape = {
+                        "input_data": [2, 3, 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 teller1(program_config, predictor_config):
+            if self.dim1 == 0 and self.dim2 == 0 and self.dim3 == 0:
+                return True
+            return False
+
+        self.add_skip_case(teller1, SkipReasons.TRT_NOT_SUPPORT,
+                           "Need to repair the case: the input's dim is 1.")
+
+        def teller2(program_config, predictor_config):
+            if (len(self.dynamic_shape.min_input_shape) == 0):
+                if self.dim1 != 0 and self.dim2 == 0 and self.dim3 == 0:
+                    return True
+            return False
+
+        self.add_skip_case(teller2, SkipReasons.TRT_NOT_SUPPORT,
+                           "Need to repair the case: the input's dim is 2.")
+
+    def test(self):
+        self.add_skip_trt_case()
+        self.run_test()
+
+
+if __name__ == "__main__":
+    unittest.main()