#section init_code_struct
prev_algo.algo = PARAMS->conv_algo;
prev_algo.mathType = CUDNN_DEFAULT_MATH;
prev_algo.dataType = CUDNN_DATA_FLOAT;
reuse_algo = 0;
use_cached = 0;
hash_prefix = std::string("GW|GPU#");

#section support_code_struct
#line 11 "dnn_gw.c"
int     reuse_algo;
bool    use_cached;
AlgoRec prev_algo;
std::string hash_prefix;

int
APPLY_SPECIFIC(conv_gw)(PyGpuArrayObject *input, PyGpuArrayObject *output,
                        PyGpuArrayObject *km,
                        cudnnConvolutionDescriptor_t desc,
                        double alpha, double beta, PyGpuArrayObject **kerns,
                        PARAMS_TYPE* params) {
  PyGpuContextObject *c = input->context;
  void *alpha_p;
  void *beta_p;
  float af = alpha, bf = beta;
  cudnnStatus_t err = CUDNN_STATUS_SUCCESS;

  if (PyGpuArray_DIMS(input)[1] != PyGpuArray_DIMS(km)[1] * params->num_groups) {
    PyErr_SetString(PyExc_ValueError,
                    "GpuDnnConv images and kernel must have the same stack size");
    return 1;
  }
  if ((PyGpuArray_DIMS(output)[1] % params->num_groups) != 0) {
    PyErr_SetString(PyExc_ValueError,
		    "Number of output channels must be divisible by number of groups");
    return 1;
  }

  switch (input->ga.typecode) {
  case GA_DOUBLE:
    alpha_p = (void *)&alpha;
    beta_p = (void *)&beta;
    break;
  case GA_FLOAT:
  case GA_HALF:
    alpha_p = (void *)&af;
    beta_p = (void *)&bf;
    break;
  default:
    PyErr_SetString(PyExc_TypeError, "Unsupported type in convolution");
    return 1;
  }

  if (params->inplace) {
    Py_XDECREF(*kerns);
    *kerns = km;
    Py_INCREF(*kerns);
  } else {
    if (theano_prep_output(kerns, PyGpuArray_NDIM(km), PyGpuArray_DIMS(km),
                           km->ga.typecode, GA_C_ORDER, c) != 0)
      return 1;
    if (beta != 0.0 && pygpu_move(*kerns, km))
      return 1;
  }

  if (PyGpuArray_DIMS(input)[0] == 0 || PyGpuArray_DIMS(km)[0] == 0 || PyGpuArray_DIMS(km)[1] == 0) {
    int err2 = GpuArray_memset(&(*kerns)->ga, 0);
    if (err2 != GA_NO_ERROR) {
        PyErr_Format(PyExc_RuntimeError,
                     "GpuDnnConv grad wrt. weights could not fill the output with zeros: %d", err2);
        return 1;
    }
    return 0;
  }

  int groups = c_check_groups_for_conv(desc, params->num_groups);
  if (groups == -1)
    return 1;
  if (c_set_tensor_for_conv(input, APPLY_SPECIFIC(input), groups) == -1)
    return 1;
  if (c_set_tensor_for_conv(output, APPLY_SPECIFIC(output), groups) == -1)
    return 1;
  if (c_set_filter(*kerns, APPLY_SPECIFIC(kerns), groups) == -1)
    return 1;

  size_t input_offset = PyGpuArray_STRIDE(input, 0) / groups;
  size_t kern_offset = PyGpuArray_STRIDE(*kerns, 0) * PyGpuArray_DIM(*kerns, 0) / groups;
  size_t output_offset = PyGpuArray_STRIDE(output, 0) / groups;

  cudnnConvolutionBwdFilterAlgo_t algo = params->conv_algo;
  #ifdef DEBUG
  char algorithm_name[128];
  #endif
  size_t worksize = 0;
  cudnnMathType_t mathtype = CUDNN_DEFAULT_MATH;
  std::string hashkey ;

  size_t free = c_get_largest_free_block_size(c);
  if (PyErr_Occurred()) return 1;

  cuda_enter(c->ctx);

  if (params->choose_algo) {

    if (!reuse_algo) {
      char pci_id[16];
      gpucontext_property(c->ctx, GA_CTX_PROP_PCIBUSID, pci_id);
      hashkey = dnn_conv_shape(APPLY_SPECIFIC(input), input, APPLY_SPECIFIC(kerns), *kerns, desc, output, groups);
      if (hashkey.empty())
        return 1;
      hashkey =  hash_prefix + pci_id + hashkey;
      // check out cache
      const AlgoRec* cached = dnn_conv_check_cache(hashkey);
      if (cached) {
        prev_algo = *cached;
        use_cached = 1;
      }
    }

    if (reuse_algo || use_cached) {
      algo = (cudnnConvolutionBwdFilterAlgo_t)prev_algo.algo;
      worksize = prev_algo.wsSize;
      mathtype = prev_algo.mathType;
    } else {

      if (params->choose_time) {
        int count;
        cudnnConvolutionBwdFilterAlgoPerf_t choice;
        gpudata *tmpmem;

        tmpmem = gpudata_alloc(c->ctx, free, NULL, 0, NULL);
        if (tmpmem == NULL) {
          PyErr_SetString(PyExc_MemoryError, "Could not allocate working GPU memory");
          return -1;
        }

        err = cudnnFindConvolutionBackwardFilterAlgorithmEx(
          params->handle, APPLY_SPECIFIC(input), PyGpuArray_DEV_DATA(input),
          APPLY_SPECIFIC(output), PyGpuArray_DEV_DATA(output), desc,
          APPLY_SPECIFIC(kerns), PyGpuArray_DEV_DATA(*kerns),
          1, &count, &choice, *(void **)tmpmem, free);
        gpudata_release(tmpmem);

        if (err != CUDNN_STATUS_SUCCESS) {
          PyErr_Format(PyExc_RuntimeError,
                       "error selecting convolution algo: %s",
                       cudnnGetErrorString(err));
          cuda_exit(c->ctx);
          return 1;
        }

        #ifdef DEBUG
        if (count == 0) {
            PyErr_SetString(PyExc_RuntimeError, "No best-timed conv gradweight algorithm found");
            return 1;
        } else if (choice.status != CUDNN_STATUS_SUCCESS) {
            PyErr_Format(PyExc_RuntimeError,
                         "error getting best-timed gradweight algo: %s",
                         cudnnGetErrorString(choice.status));
            return 1;
        } // Else, count is necessarly 1 for current implementation.
        #endif

        algo = choice.algo;
        prev_algo.algo = (int)algo;
        prev_algo.wsSize = worksize = choice.memory;
#if CUDNN_MAJOR >= 7
        prev_algo.mathType = mathtype = choice.mathType;
#endif
        // Add to the cache
        dnn_conv_update_cache(hashkey, prev_algo);

      } else {
        err = cudnnGetConvolutionBackwardFilterAlgorithm(
          params->handle, APPLY_SPECIFIC(input), APPLY_SPECIFIC(output),
          desc, APPLY_SPECIFIC(kerns),
          CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT, free, &algo);
        if (err != CUDNN_STATUS_SUCCESS) {
          PyErr_Format(PyExc_RuntimeError,
                       "error selecting convolution algo: %s",
                       cudnnGetErrorString(err));
          cuda_exit(c->ctx);
          return 1;
        }
	    prev_algo.algo = algo;
	    // no tensor_op returned from Get()
	    prev_algo.mathType = mathtype = CUDNN_DEFAULT_MATH;
      }
    }
  } /* choose_algo */

  // if FindEx was used (choose_time), workspace size is set.
  if (!(reuse_algo || use_cached || params->choose_time))
    {

      err = cudnnGetConvolutionBackwardFilterWorkspaceSize(
        params->handle, APPLY_SPECIFIC(input), APPLY_SPECIFIC(output), desc,
        APPLY_SPECIFIC(kerns), algo, &worksize);

      if (err != CUDNN_STATUS_SUCCESS) {
#ifdef DEBUG
        if (0 != theano_enum_to_string_cudnnConvolutionBwdFilterAlgo_t(algo, algorithm_name))
          return 1;
        fprintf(stderr, "(%s error getting worksize:%s, falling back to CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0",
                algorithm_name, cudnnGetErrorString(err));
#endif
        algo = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0;
        err = cudnnGetConvolutionBackwardFilterWorkspaceSize(
          params->handle, APPLY_SPECIFIC(input), APPLY_SPECIFIC(output), desc,
          APPLY_SPECIFIC(kerns), algo, &worksize);

        if (err != CUDNN_STATUS_SUCCESS) {
          PyErr_Format(PyExc_RuntimeError, "error getting worksize: %s",
                       cudnnGetErrorString(err));


          cuda_exit(c->ctx);
          return 1;
        }
      }

      // save for next time/cache
      prev_algo.wsSize = worksize;
      prev_algo.algo = algo;

      // Add to the cache
      if (params->choose_algo)
        dnn_conv_update_cache(hashkey, prev_algo);
    }

#ifdef DEBUG
  if (params->choose_algo) {
    if (0 != theano_enum_to_string_cudnnConvolutionBwdFilterAlgo_t(algo, algorithm_name))
      return 1;
    // NB: This is printed only when algorithm is chosen at runtime.
    fprintf(stderr, "(using %s %s%s%s%s, ws:%ld, hash:%s)\n",
            algorithm_name,
            params->choose_time ? "(timed)": "" ,
            reuse_algo ? "(reused)" : "",
            use_cached ? "(cache)": "",
            mathtype == CUDNN_TENSOR_OP_MATH ? "(tensor op)" : "",
            worksize,
            hashkey.c_str()
     );
  }
#endif

    if (params->choose_once) {
      reuse_algo = 1;
    }

    gpudata *workspace = 0;
#if CUDNN_MAJOR >= 7
    // CUDNN7: need to set math type
    err = cudnnSetConvolutionMathType(desc, mathtype);
    if (err != CUDNN_STATUS_SUCCESS) {
      PyErr_Format(PyExc_RuntimeError,
                   "error setting math type for convolution : %s",
                   cudnnGetErrorString(err));
      cuda_exit(c->ctx);
      return 1;
    }
#endif
  if (worksize != 0) {
    workspace = gpudata_alloc(c->ctx, worksize, NULL, 0, NULL);
    if (workspace == NULL) {
      PyErr_SetString(PyExc_RuntimeError, "Could not allocate working memory");
      cuda_exit(c->ctx);
      return 1;
    }
  }

  cuda_wait(input->ga.data, GPUARRAY_CUDA_WAIT_READ);
  cuda_wait(output->ga.data, GPUARRAY_CUDA_WAIT_READ);
  cuda_wait((*kerns)->ga.data, GPUARRAY_CUDA_WAIT_WRITE);

  for ( int g = 0; g < groups; g++)
  {

    err = cudnnConvolutionBackwardFilter(
      params->handle,
      alpha_p,
      APPLY_SPECIFIC(input), ((char *)PyGpuArray_DEV_DATA(input)) + input_offset * g ,
      APPLY_SPECIFIC(output), ((char *)PyGpuArray_DEV_DATA(output)) + output_offset * g,
      desc, algo, worksize == 0 ? NULL : *(void **)workspace, worksize,
      beta_p,
      APPLY_SPECIFIC(kerns), ((char *)PyGpuArray_DEV_DATA(*kerns)) + kern_offset * g);
  }

  if (worksize != 0)
    gpudata_release(workspace);

  cuda_record(input->ga.data, GPUARRAY_CUDA_WAIT_READ);
  cuda_record(output->ga.data, GPUARRAY_CUDA_WAIT_READ);
  cuda_record((*kerns)->ga.data, GPUARRAY_CUDA_WAIT_WRITE);

  cuda_exit(c->ctx);

  if (err != CUDNN_STATUS_SUCCESS) {
    PyErr_Format(PyExc_RuntimeError, "error doing operation: %s",
                 cudnnGetErrorString(err));
    return 1;
  }
  return 0;
}
