def create_filter_descriptor(arr, mode=cudnn.CUDNN_CROSS_CORRELATION):
desc = Descriptor(cudnn.createFilterDescriptor(), cudnn.destroyFilterDescriptor)
data_type = get_data_type(arr.dtype)
if arr.ndim == 4:
cudnn.setFilter4dDescriptor_v3(desc.value, data_type, *arr.shape)
else:
c_shape = _to_ctypes_array(arr.shape)
cudnn.setFilterNdDescriptor_v3(desc.value, data_type, arr.ndim, c_shape.data)
return desc
def create_filter_descriptor(arr, mode=cudnn.CUDNN_CROSS_CORRELATION):
desc = Descriptor(cudnn.createFilterDescriptor(),
cudnn.destroyFilterDescriptor)
data_type = get_data_type(arr.dtype)
if arr.ndim == 4:
cudnn.setFilter4dDescriptor(desc.value, data_type, *arr.shape)
else:
cudnn.setFilterNdDescriptor(desc.value, data_type, arr.ndim,
_to_ctypes_array(arr.shape))
return desc
def create_filter_descriptor(arr, format=cudnn.CUDNN_TENSOR_NCHW):
desc = Descriptor(cudnn.createFilterDescriptor(),
cudnn.destroyFilterDescriptor)
data_type = get_data_type(arr.dtype)
if arr.ndim == 4:
cudnn.setFilter4dDescriptor_v4(desc.value, data_type, format,
*arr.shape)
else:
c_shape = _to_ctypes_array(arr.shape)
cudnn.setFilterNdDescriptor_v4(desc.value, data_type, format, arr.ndim,
c_shape.data)
return desc
def get_rnn_lin_layer_bias_params(
handle, rnn_desc, layer, x_desc, w_desc, w, lin_layer_id):
bias_desc = Descriptor(cudnn.createFilterDescriptor(),
cudnn.destroyFilterDescriptor)
ptr = numpy.array(0, dtype=numpy.intp)
cudnn.getRNNLinLayerBiasParams(
handle, rnn_desc.value, layer, x_desc.value, w_desc.value, w.data.ptr,
lin_layer_id, bias_desc.value, ptr.ctypes.data)
offset = (ptr - w.data.ptr) // 4
_, _, _, dim = cudnn.getFilterNdDescriptor(bias_desc.value, 3)
size = internal.prod(dim)
bias = w[offset: offset + size]
return bias
def get_rnn_lin_layer_bias_params(
handle, rnn_desc, layer, x_desc, w_desc, w, lin_layer_id):
bias_desc = Descriptor(cudnn.createFilterDescriptor(),
cudnn.destroyFilterDescriptor)
ptr = numpy.array(0, dtype=numpy.intp)
cudnn.getRNNLinLayerBiasParams(
handle, rnn_desc.value, layer, x_desc.value, w_desc.value, w.data.ptr,
lin_layer_id, bias_desc.value, ptr.ctypes.data)
offset = (ptr - w.data.ptr) // 4
_, _, _, dim = cudnn.getFilterNdDescriptor(bias_desc.value, 3)
size = numpy.prod(dim)
bias = w[offset: offset + size]
return bias
def get_rnn_lin_layer_matrix_params(
handle, rnn_desc, layer, x_desc, w_desc, w, lin_layer_id):
mat_desc = Descriptor(cudnn.createFilterDescriptor(),
cudnn.destroyFilterDescriptor)
ptr = numpy.array(0, dtype=numpy.intp)
cudnn.getRNNLinLayerMatrixParams(
handle, rnn_desc.value, layer, x_desc.value, w_desc.value, w.data.ptr,
lin_layer_id, mat_desc.value, ptr.ctypes.data)
offset = (ptr - w.data.ptr) // 4
_, _, _, dim = cudnn.getFilterNdDescriptor(mat_desc.value, 3)
size = numpy.prod(dim)
mat = w[offset: offset + size]
return mat
def get_rnn_lin_layer_matrix_params(
handle, rnn_desc, layer, x_desc, w_desc, w, lin_layer_id):
mat_desc = Descriptor(cudnn.createFilterDescriptor(),
cudnn.destroyFilterDescriptor)
ptr = numpy.array(0, dtype=numpy.intp)
cudnn.getRNNLinLayerMatrixParams(
handle, rnn_desc.value, layer, x_desc.value, w_desc.value, w.data.ptr,
lin_layer_id, mat_desc.value, ptr.ctypes.data)
offset = (ptr - w.data.ptr) // 4
_, _, _, dim = cudnn.getFilterNdDescriptor(mat_desc.value, 3)
size = internal.prod(dim)
mat = w[offset: offset + size]
return mat
def create_filter_descriptor(arr, format=cudnn.CUDNN_TENSOR_NCHW):
desc = Descriptor(cudnn.createFilterDescriptor(),
cudnn.destroyFilterDescriptor)
data_type = get_data_type(arr.dtype)
if _cudnn_version >= 4000:
if arr.ndim == 4:
cudnn.setFilter4dDescriptor_v4(desc.value, data_type, format,
*arr.shape)
else:
c_shape = _to_ctypes_array(arr.shape)
cudnn.setFilterNdDescriptor_v4(desc.value, data_type, format,
arr.ndim, c_shape.data)
else:
if arr.ndim == 4:
cudnn.setFilter4dDescriptor_v3(desc.value, data_type, *arr.shape)
else:
c_shape = _to_ctypes_array(arr.shape)
cudnn.setFilterNdDescriptor_v3(desc.value, data_type, arr.ndim,
c_shape.data)
return desc
