clean some IS_TRT_VERSION_GE(7200) (PaddlePaddle#75864)

Author: co63oc

paddle/fluid/inference/tensorrt/plugin/anchor_generator_op_plugin.cu

@@ -549,11 +549,7 @@ bool AnchorGeneratorPluginDynamic::supportsFormatCombination(
   // anchor generator doesn't read input raw data, only need the shape info
   auto type = inOut[pos].type;
   auto format = inOut[pos].format;
-#if IS_TRT_VERSION_GE(7234)
   if (pos == 0) return true;
-#else
-  if (pos == 0) return format == nvinfer1::TensorFormat::kLINEAR;
-#endif
   return (type == nvinfer1::DataType::kFLOAT &&
           format == nvinfer1::TensorFormat::kLINEAR);
 }
@@ -850,11 +846,7 @@ bool PIRAnchorGeneratorPluginDynamic::supportsFormatCombination(
   // anchor generator doesn't read input raw data, only need the shape info
   auto type = inOut[pos].type;
   auto format = inOut[pos].format;
-#if IS_TRT_VERSION_GE(7234)
   if (pos == 0) return true;
-#else
-  if (pos == 0) return format == nvinfer1::TensorFormat::kLINEAR;
-#endif
   return (type == nvinfer1::DataType::kFLOAT &&
           format == nvinfer1::TensorFormat::kLINEAR);
 }

paddle/fluid/inference/tensorrt/plugin/custom_generic_plugin.cu

@@ -37,12 +37,8 @@ void validate(const std::string& op_type,
       "float32", "float16", "int8", "int32"};
   std::unordered_set<std::string> supports_tensor_formats = {
       "LINEAR", "CHW32", "CHW2", "HWC8", "CHW4"};
-#if IS_TRT_VERSION_GE(7200)
   supports_tensor_formats.insert("DHWC8");
-#endif
-#if IS_TRT_VERSION_GE(8000)
   supports_tensor_formats.insert("HWC16");
-#endif
   // refer to
   // https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#ipluginv2
   PADDLE_ENFORCE_GE(supports_dtypes.count(datatype),
@@ -76,12 +72,8 @@ void validate(const std::string& op_type,
   if (datatype == "float16") {
     std::unordered_set<std::string> supports_formats_tmp = {
         "LINEAR", "CHW2", "HWC8", "CHW4"};
-#if IS_TRT_VERSION_GE(7200)
     supports_formats_tmp.insert("DHWC8");
-#endif
-#if IS_TRT_VERSION_GE(8000)
     supports_formats_tmp.insert("HWC16");
-#endif
     PADDLE_ENFORCE_GE(supports_formats_tmp.count(tensor_format),
                       0,
                       common::errors::InvalidArgument(
@@ -180,14 +172,10 @@ nvinfer1::TensorFormat getTrtTensorFormat(std::string tensor_format) {
     return nvinfer1::TensorFormat::kHWC8;
   } else if (tensor_format == "CHW4") {
     return nvinfer1::TensorFormat::kCHW4;
-#if IS_TRT_VERSION_GE(7200)
   } else if (tensor_format == "DHWC8") {
     return nvinfer1::TensorFormat::kDHWC8;
-#endif
-#if IS_TRT_VERSION_GE(8000)
   } else if (tensor_format == "HWC16") {
     return nvinfer1::TensorFormat::kHWC16;
-#endif
   } else {
     PADDLE_THROW(common::errors::Unimplemented("Unsupported tensor format [%s]",
                                                tensor_format));

paddle/fluid/inference/tensorrt/plugin/elementwiseadd_transpose_op_plugin.cu

@@ -86,14 +86,8 @@ bool ElementwiseAddTransposePluginDynamic::supportsFormatCombination(
   }
   // output 0
   if (pos == 2) {
-    // 7.0.0.11 test_pcpvt_base_trt_fp16.py failed if support C8.
-    // Only support linear format in lower versions of TRT
-#if IS_TRT_VERSION_GE(7100)
     bool support_format = in.format == nvinfer1::TensorFormat::kLINEAR ||
                           in.format == nvinfer1::TensorFormat::kHWC8;
-#else
-    bool support_format = in.format == nvinfer1::TensorFormat::kLINEAR;
-#endif
 
     return (in.type == in_out[0].type) && (support_format);
   }