[inference][trt] upgrade prelu op (#48528)

* add prelu

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

@@ -31,8 +31,8 @@ class PReluOpConverter : public OpConverter {
 
     framework::OpDesc op_desc(op, nullptr);
     // Declare inputs
-    size_t input_num = op_desc.Input("X").size();
     auto* input = engine_->GetITensor(op_desc.Input("X")[0]);
+    auto input_dims = input->getDimensions();
     // Get attrs
     std::string mode = PADDLE_GET_CONST(std::string, op_desc.GetAttr("mode"));
     std::string data_format = "NCHW";
@@ -40,50 +40,87 @@ class PReluOpConverter : public OpConverter {
       data_format =
           PADDLE_GET_CONST(std::string, op_desc.GetAttr("data_format"));
     }
-    auto* alpha_var = scope.FindVar(op_desc.Input("Alpha")[0]);
-    auto* alpha_tensor = alpha_var->GetMutable<phi::DenseTensor>();
 
-    auto alpha_weight =
-        engine_->GetFp32TrtWeight(op_desc.Input("Alpha")[0], *alpha_tensor);
+    auto* alpha_var = scope.FindVar(op_desc.Input("Alpha")[0]);
+    auto* alpha_weight = alpha_var->GetMutable<phi::DenseTensor>();
+    auto w_dims = alpha_weight->dims();
+    auto alpha_data =
+        engine_->GetFp32TrtWeight(op_desc.Input("Alpha")[0], *alpha_weight);
 
-    platform::CPUPlace cpu_place;
+    nvinfer1::Dims trt_w_dims;
+    trt_w_dims.nbDims = w_dims.size();
+    for (int i = 0; i < trt_w_dims.nbDims; i++) {
+      trt_w_dims.d[i] = w_dims[i];
+    }
 
-    nvinfer1::ILayer* layer = nullptr;
-    if (engine_->with_dynamic_shape()) {
-      plugin::PReluPluginDynamic* plugin = new plugin::PReluPluginDynamic(
-          static_cast<const float*>(alpha_weight.get().values),
-          alpha_tensor->numel(),
-          mode,
-          data_format);
-      layer = engine_->AddDynamicPlugin(&input, input_num, plugin);
-    } else {
-#if IS_TRT_VERSION_GE(7000)
-      nvinfer1::Dims dims;
-      dims.nbDims = 0;
-      // jump batch dim
-      for (int i = 1; i < alpha_tensor->dims().size(); i++) {
-        dims.d[dims.nbDims++] = alpha_tensor->dims()[i];
-      }
-      for (; dims.nbDims < input->getDimensions().nbDims; dims.nbDims++) {
-        dims.d[dims.nbDims] = 1;
+    // The `element` or `channel` mode contains the batch using static shape.
+    if ((mode == "element" || mode == "channel") &&
+        !engine_->with_dynamic_shape() &&
+        (trt_w_dims.nbDims - 1 == input_dims.nbDims)) {
+      trt_w_dims.nbDims--;
+      for (int i = 0; i < trt_w_dims.nbDims; i++) {
+        trt_w_dims.d[i] = trt_w_dims.d[i + 1];
       }
+    }
 
-      auto alpha_layer =
-          TRT_ENGINE_ADD_LAYER(engine_, Constant, dims, alpha_weight.get());
-      auto alpha_layer_output = alpha_layer->getOutput(0);
-
-      layer = TRT_ENGINE_ADD_LAYER(
-          engine_, ParametricReLU, *input, *alpha_layer_output);
-#else
-      plugin::PReluPlugin* plugin = new plugin::PReluPlugin(
-          static_cast<const float*>(alpha_weight.get().values),
-          alpha_tensor->numel(),
-          mode,
-          data_format);
-      layer = engine_->AddPlugin(&input, input_num, plugin);
-#endif
+    nvinfer1::ITensor* alpha_tensor =
+        TRT_ENGINE_ADD_LAYER(engine_, Constant, trt_w_dims, alpha_data.get())
+            ->getOutput(0);
+
+    auto alpha_dims = alpha_tensor->getDimensions();
+    nvinfer1::ITensor* real_alpha_tensor = alpha_tensor;
+    if (alpha_dims.nbDims != input_dims.nbDims) {
+      auto* reshape_layer =
+          TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *alpha_tensor);
+      int c = alpha_dims.d[0];
+      if (engine_->with_dynamic_shape()) {
+        std::vector<nvinfer1::ITensor*> itensors;
+        auto* n_tensor = Add1DConstantLayer(1);
+        auto* c_tensor = Add1DConstantLayer(c);
+        nvinfer1::ITensor* hw_tensor = nullptr;
+        nvinfer1::ITensor* shape_tensor = nullptr;
+        if (input_dims.nbDims - 2 > 0) {
+          hw_tensor = Add1DConstantLayer(
+              std::vector<int32_t>(input_dims.nbDims - 2, 1));
+        }
+        if (data_format == "NCHW") {
+          if (hw_tensor != nullptr) {
+            shape_tensor = Concat(
+                std::vector<nvinfer1::ITensor*>{n_tensor, c_tensor, hw_tensor});
+
+          } else {
+            shape_tensor =
+                Concat(std::vector<nvinfer1::ITensor*>{n_tensor, c_tensor});
+          }
+        } else {
+          if (hw_tensor != nullptr) {
+            shape_tensor = Concat(
+                std::vector<nvinfer1::ITensor*>{n_tensor, hw_tensor, c_tensor});
+          } else {
+            shape_tensor =
+                Concat(std::vector<nvinfer1::ITensor*>{n_tensor, c_tensor});
+          }
+        }
+        reshape_layer->setInput(1, *shape_tensor);
+      } else {
+        nvinfer1::Dims reshape_dim;
+        reshape_dim.nbDims = input_dims.nbDims;
+        std::fill(reshape_dim.d, reshape_dim.d + input_dims.nbDims, 1);
+        if (data_format == "NCHW") {
+          reshape_dim.d[0] = c;
+        } else if (data_format == "NHWC") {
+          reshape_dim.d[input_dims.nbDims - 1] = c;
+        }
+        reshape_layer->setReshapeDimensions(reshape_dim);
+      }
+      real_alpha_tensor = reshape_layer->getOutput(0);
     }
 
+    nvinfer1::ILayer* layer = nullptr;
+
+    layer = TRT_ENGINE_ADD_LAYER(
+        engine_, ParametricReLU, *input, *real_alpha_tensor);
+
     auto output_name = op_desc.Output("Out")[0];
     RreplenishLayerAndOutput(layer, "prelu", {output_name}, test_mode);
   }

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

@@ -49,22 +49,22 @@ class TrtConvertPreluTest(TrtLayerAutoScanTest):
                 if dim1 != 0:
                     shape.append(dim1)
                 if dim2 != 0:
-                    shape.append(1)
+                    shape.append(dim2)
                 if dim3 != 0:
-                    shape.append(1)
-                return np.random.random(size=shape).astype(np.float32)
+                    shape.append(dim3)
+                return np.random.random(size=shape[1]).astype(np.float32)
             elif (
                 attrs[0]["mode"] == "channel"
                 and attrs[0]["data_format"] == "NHWC"
             ):
                 shape = [1]
                 if dim1 != 0:
-                    shape.append(1)
+                    shape.append(dim1)
                 if dim2 != 0:
-                    shape.append(1)
+                    shape.append(dim2)
                 if dim3 != 0:
                     shape.append(dim3)
-                return np.random.random(size=shape).astype(np.float32)
+                return np.random.random(size=shape[-1]).astype(np.float32)
             elif attrs[0]["mode"] == "element":
                 shape = [1]
                 if dim1 != 0: