#include <vector>
#include <torch/torch.h>
#include <torch/script.h>
#include "ATen/cuda/CUDAContext.h"
#include "triton/driver/stream.h"
#include "triton/dnn/conv.h"

#define CHECK_CUDA(x) AT_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
#define CHECK_CONTIGUOUS(x) AT_CHECK(x.is_contiguous(), #x " must be contiguous")
#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)

torch::Tensor conv_common(
    int32_t B, int32_t C, int32_t D, int32_t H, int32_t W,
    int32_t T, int32_t R, int32_t S, int32_t NF,
    int32_t stride_d, int32_t stride_h, int32_t stride_w,
    int32_t pad_d, int32_t pad_h, int32_t pad_w,
    triton::dnn::conv::type ty,
    torch::Tensor torcha, torch::Tensor torchb, torch::Tensor torchbias,
    bool autotune = false
    ) {
  // Wrap CUDA handles
  c10::DeviceIndex device = torcha.storage().device().index();
  // Get stream
  CUstream custream = (CUstream)at::cuda::getCurrentCUDAStream(device).stream();
  triton::driver::cu_stream stream(custream, false);
  triton::driver::context* ctx = stream.context();
  // Get template
  bool has_bias = torchbias.storage().size() > 0;
  triton::dnn::conv conv(B, C, D, H, W, T, R, S, NF,
                          stride_d, stride_h, stride_w,
                          pad_d, pad_h, pad_w,
                          1, 1, 1,
                          "float", "float", ty, has_bias);
  // Bind memory
  triton::driver::cu_buffer a(ctx, (CUdeviceptr)torcha.storage().data(), false);
  triton::driver::cu_buffer b(ctx, (CUdeviceptr)torchb.storage().data(), false);
  triton::driver::cu_buffer cubias(ctx, (CUdeviceptr)torchbias.storage().data(), false);
  triton::driver::buffer* bias = has_bias ? &cubias : nullptr;
  // Allocate output
  std::vector<int32_t> c_shapes = conv.c_shapes();
  torch::Tensor torchc;
  if(ty == triton::dnn::conv::WGRAD)
    torchc = torch::empty({c_shapes[0], c_shapes[2], c_shapes[3], c_shapes[4]}, torch::kFloat).cuda();
  else
    torchc = torch::empty({c_shapes[0], c_shapes[1], c_shapes[3], c_shapes[4]}, torch::kFloat).cuda();
  triton::driver::cu_buffer c(ctx, (CUdeviceptr)torchc.storage().data(), false);
  // Enqueue
  conv.enqueue(&stream, {&a, &b, &c, bias});
  return torchc;
}

torch::Tensor conv_fprop(
    const torch::Tensor data,
    const torch::Tensor weight,
    const torch::Tensor bias,
    int64_t stride_h, int64_t stride_w,
    int64_t pad_h, int64_t pad_w) {
  // Check
  CHECK_INPUT(data);
  CHECK_INPUT(weight);
  // Unpack data shapes
  const int32_t B  = data.size(0);
  const int32_t Ci = data.size(1);
  const int32_t D = 1;
  const int32_t H  = data.size(2);
  const int32_t W  = data.size(3);
  // Unpack weight shapes
  const int32_t Cf = weight.size(0);
  const int32_t T  = 1;
  const int32_t R  = weight.size(1);
  const int32_t S  = weight.size(2);
  const int32_t NF  = weight.size(3);
  // Configuration
  const int32_t stride_d = 1;
  const int32_t pad_d = 0;
  // Check
  AT_CHECK(Ci == Cf, "Number of channels in data and weights must match");
  return conv_common(B, Ci, D, H, W, T, R, S, NF, stride_d, stride_h, stride_w, pad_d, pad_h, pad_w, triton::dnn::conv::FPROP, data, weight, bias);
}

torch::Tensor conv_bprop(
    const torch::Tensor derror,
    const torch::Tensor weight,
    const torch::Tensor bias,
    int64_t H, int64_t W,
    int64_t stride_h, int64_t stride_w,
    int64_t pad_h, int64_t pad_w){
  // Check
  CHECK_INPUT(derror);
  CHECK_INPUT(weight);
  // Unpack data shapes
  const int32_t B  = derror.size(0);
  const int32_t Ki = derror.size(1);
  const int32_t M = 1;
  const int32_t P  = derror.size(2);
  const int32_t Q  = derror.size(3);
  // Unpack weight shapes
  const int32_t C = weight.size(0);
  const int32_t T  = 1;
  const int32_t R  = weight.size(1);
  const int32_t S  = weight.size(2);
  const int32_t Kw  = weight.size(3);
  // Compute M, P, Q
  const int32_t stride_d = 1;
  int32_t pad_d = 0;
  int32_t D = 1;
  // Check
  AT_CHECK(Ki == Kw, "Number of channels in error and weights must match");
  return conv_common(B, C, D, H, W, T, R, S, Kw, stride_d, stride_h, stride_w, pad_d, pad_h, pad_w, triton::dnn::conv::BPROP, derror, weight, bias);
}

torch::Tensor conv_wgrad(
    const torch::Tensor data,
    const torch::Tensor derror,
    const torch::Tensor bias,
    int64_t R, int64_t S,
    int64_t stride_h, int64_t stride_w,
    int64_t pad_h, int64_t pad_w
    ){
  // Check
  CHECK_INPUT(data);
  CHECK_INPUT(derror);
  // Unpack data shapes
  const int32_t Ba  = data.size(0);
  const int32_t C = data.size(1);
  const int32_t D = 1;
  const int32_t H  = data.size(2);
  const int32_t W  = data.size(3);
  // Unpack error shapes
  const int32_t Bb = derror.size(0);
  const int32_t K  = derror.size(1);
  const int32_t M  = 1;
  const int32_t P  = derror.size(2);
  const int32_t Q  = derror.size(3);
  // Compute M, P, Q
  const int32_t upsample_d = 1, upsample_h = 1, upsample_w = 1;
  const int32_t stride_d = 1;
  const int32_t pad_d = 0;
  const int32_t T = 1;
  // Check
  AT_CHECK(Ba == Bb, "Number of channels in error and weights must match");
  return conv_common(Ba, C, D, H, W, T, R, S, K, stride_d, stride_h, stride_w, pad_d, pad_h, pad_w, triton::dnn::conv::WGRAD, data, derror, bias);
}

static auto registry =
  torch::jit::RegisterOperators("triton::conv_fprop", &conv_fprop)
                            .op("triton::conv_bprop", &conv_bprop)
                            .op("triton::conv_wgrad", &conv_wgrad);