[ENHANCEMENT] Suggest to insert nvtx Range into cuda generated code.

[LeiWang1999]

Motivation
I'm currently profiling the cuda code generated by nnfusion, to better profile the program, I suggest using NVIDIA Tools Extension, by inserting nvtxRangePush and nvtxRangePop into the start and the end of the function, we can observe which low-level kernel function that cudnn invoked.

// Node name:	Convolution_109
// Description:	Convolution
// Input:
//	- name: Parameter_108_0	type: float	shape: Shape{1, 3, 224, 224}
//	- name: Constant_0_0	type: float	shape: Shape{64, 3, 7, 7}
// Output:
//	- name: Convolution_109_0	type: float	shape: Shape{1, 64, 112, 112}
void Convolution_float_float_float_cuda_lib_Convolution_109(cudnnHandle_t cudnn_handle, float* input0, float* input1, float* input2, float* output0)
{
#ifdef _NVTX_RANGE_DEBUG_
    nvtxRangePush(__FUNCTION__);
#endif
    cudnnTensorDescriptor_t tensor_desc_0;
    CUDNN_SAFE_CALL(cudnnCreateTensorDescriptor(&tensor_desc_0));
    CUDNN_SAFE_CALL(cudnnSetTensor4dDescriptor(tensor_desc_0, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, 3, 224, 224));
    cudnnTensorDescriptor_t tensor_desc_1;
    CUDNN_SAFE_CALL(cudnnCreateTensorDescriptor(&tensor_desc_1));
    CUDNN_SAFE_CALL(cudnnSetTensor4dDescriptor(tensor_desc_1, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, 64, 112, 112));
    cudnnFilterDescriptor_t filter_desc;
    CUDNN_SAFE_CALL(cudnnCreateFilterDescriptor(&filter_desc));
    CUDNN_SAFE_CALL(cudnnSetFilter4dDescriptor(filter_desc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 64, 3, 7, 7));
    cudnnConvolutionDescriptor_t conv_desc;
    CUDNN_SAFE_CALL(cudnnCreateConvolutionDescriptor(&conv_desc));
    CUDNN_SAFE_CALL(cudnnSetConvolution2dDescriptor(conv_desc, 3, 3, 2, 2, 1, 1, CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT));

    static bool selected_algo = false;
    static cudnnConvolutionFwdAlgo_t conv_fwd_algo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM;

    if (!selected_algo) {
        int num_algos;
        int max_algos = 0;
        // cudnnGetConvolutionForwardAlgorithm_v7;
        CUDNN_SAFE_CALL(
            cudnnGetConvolutionForwardAlgorithmMaxCount(cudnn_handle, &max_algos));
        std::vector<cudnnConvolutionFwdAlgoPerf_t> results(max_algos);
        CUDNN_SAFE_CALL(cudnnFindConvolutionForwardAlgorithm(cudnn_handle,
                                                tensor_desc_0,
                                                filter_desc,
                                                conv_desc,
                                                tensor_desc_1,
                                                static_cast<int>(results.size()),
                                                &num_algos,
                                                results.data()));
        results.resize(num_algos);
        for (size_t i = 0; i != results.size(); ++i) {
            cudnnConvolutionFwdAlgoPerf_t const& result = results[i];
            if (result.status == CUDNN_STATUS_SUCCESS) {
                conv_fwd_algo = result.algo;
                break;
            }
        }
        selected_algo = true;
    }
    const float alpha = 1.0;
    const float beta = 0.0;
    static void *workspace_ptr_0 = NULL;
    static size_t workspace_size_in_bytes = 0;
    if (!workspace_ptr_0)
    {
        CUDNN_SAFE_CALL(cudnnGetConvolutionForwardWorkspaceSize(cudnn_handle, tensor_desc_0, filter_desc, conv_desc, tensor_desc_1, conv_fwd_algo, &workspace_size_in_bytes));
        CUDA_SAFE_CALL(cudaMalloc(&workspace_ptr_0, workspace_size_in_bytes));
    }
    const float alpha2 = 0.0;

    cudnnTensorDescriptor_t bias_desc;
    CUDNN_SAFE_CALL(cudnnCreateTensorDescriptor(&bias_desc));
    CUDNN_SAFE_CALL(cudnnSetTensor4dDescriptor(bias_desc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, 64, 1, 1));

    cudnnActivationDescriptor_t relu_desc;
    CUDNN_SAFE_CALL(cudnnCreateActivationDescriptor(&relu_desc));
    CUDNN_SAFE_CALL(cudnnSetActivationDescriptor(relu_desc, CUDNN_ACTIVATION_RELU, CUDNN_PROPAGATE_NAN, 0.0));

    CUDNN_SAFE_CALL(cudnnConvolutionBiasActivationForward(cudnn_handle, &alpha, tensor_desc_0, input0, filter_desc, input1, conv_desc, conv_fwd_algo, workspace_ptr_0, workspace_size_in_bytes, &alpha2, tensor_desc_1, output0, bias_desc, input2, relu_desc, tensor_desc_1, output0));
    
    // CUDNN_SAFE_CALL(cudnnConvolutionForward(cudnn_handle, &alpha, tensor_desc_0, input0,filter_desc, input1, conv_desc, conv_fwd_algo, workspace_ptr_0, workspace_size_in_bytes, &beta, tensor_desc_1, output0));
    CUDNN_SAFE_CALL(cudnnDestroyTensorDescriptor(bias_desc));
    CUDNN_SAFE_CALL(cudnnDestroyActivationDescriptor(relu_desc));
    CUDNN_SAFE_CALL(cudnnDestroyTensorDescriptor(tensor_desc_0));
    CUDNN_SAFE_CALL(cudnnDestroyTensorDescriptor(tensor_desc_1));
    CUDNN_SAFE_CALL(cudnnDestroyFilterDescriptor(filter_desc));
    CUDNN_SAFE_CALL(cudnnDestroyConvolutionDescriptor(conv_desc));
#ifdef _NVTX_RANGE_DEBUG_
    nvtxRangePop();
#endif
}

the output will contain kernels used by this op function:

==13690== NVTX result:
==13690==   Thread "<unnamed>" (id = 2074996736)
==13690==     Domain "<unnamed>"
==13690==       Range "Convolution_float_float_float_cuda_lib_Convolution_109"
            Type  Time(%)      Time     Calls       Avg       Min       Max  Name
          Range:  100.00%  897.61ms       105  8.5487ms  47.299us  891.85ms  Convolution_float_float_float_cuda_lib_Convolution_109
 GPU activities:   68.65%  5.0048ms       107  46.773us  46.271us  49.568us  void implicit_convolve_sgemm<float, float, int=128, int=5, int=5, int=3, int=3, int=3, int=1, bool=0, bool=1, bool=1>(int, int, int, float const *, int, float*, float const *, kernel_conv_params, __int64, int, float, float, int, float const *, float const *, bool, int, int)
                   12.93%  942.42us        81  11.634us  11.168us  13.152us  void gemv2N_kernel<int, int, float2, float2, float2, int=128, int=8, int=4, int=4, int=1, bool=0, cublasGemvParams<cublasGemvTensorStridedBatched<float2 const >, cublasGemvTensorStridedBatched<float2>, float2>>(float2 const )
                   10.88%  793.05us        81  9.7900us  9.1840us  14.688us  void fft2d_c2r_32x32<float, bool=1, bool=0, unsigned int=0, bool=0, bool=0>(float*, float2 const *, int, int, int, int, int, int, int, int, int, float, float, cudnn::reduced_divisor, bool, float*, float*, int2, int, int)
                    5.73%  418.05us        81  5.1610us  4.6080us  14.080us  void fft2d_r2c_32x32<float, bool=0, unsigned int=0, bool=0>(float2*, float const *, int, int, int, int, int, int, int, int, int, cudnn::reduced_divisor, bool, int2, int, int)
                    0.59%  42.752us         1  42.752us  42.752us  42.752us  void explicit_convolve_sgemm<float, int, int=1024, int=5, int=5, int=3, int=3, int=3, int=0, bool=0>(int, int, int, float const *, int, float const *, int, float*, kernel_conv_params, __int64, int, __int64, int, float, float, int, float const *, float const *)
                    0.53%  38.720us         1  38.720us  38.720us  38.720us  volta_scudnn_128x32_relu_medium_nn_v1
                    0.39%  28.511us         1  28.511us  28.511us  28.511us  void cudnn::cnn::im2col4d_kernel<float, long>(cudnn::cnn::im2col4d_params, cudnnConvolutionStruct, cudnnTensor4dStruct, float const *, cudnnTensor4dStruct*)
                    0.23%  16.448us         1  16.448us  16.448us  16.448us  void fft2d_r2c_32x32<float, bool=0, unsigned int=1, bool=1>(float2*, float const *, int, int, int, int, int, int, int, int, int, cudnn::reduced_divisor, bool, int2, int, int)
                    0.07%  5.4400us         1  5.4400us  5.4400us  5.4400us  cask_cudnn::computeOffsetsKernel(cask_cudnn::ComputeOffsetsParams)
      API calls:  100.00%  2.4452ms       355  6.8880us  4.0000us  57.998us  cudaLaunchKernel