我们从Python开源项目中，提取了以下11个代码示例，用于说明如何使用torch.backends.cudnn.lib()。
def init_dropout_descriptor(fn, handle): dropout_desc = cudnn.DropoutDescriptor() dropout_states_size = ctypes.c_long() check_error(cudnn.lib.cudnnDropoutGetStatesSize( handle, ctypes.byref(dropout_states_size))) dropout_states = torch.cuda.ByteTensor(dropout_states_size.value) dropout_desc.set( handle, fn.dropout, dropout_states, fn.seed ) return dropout_desc
def get_num_weights(handle, rnn_desc, x_desc, datatype): weight_size = ctypes.c_long() check_error(cudnn.lib.cudnnGetRNNParamsSize( handle, rnn_desc, x_desc, ctypes.byref(weight_size), datatype )) elem_size = cudnn._sizeofmap[datatype] assert(weight_size.value % elem_size == 0) return weight_size.value // elem_size
def get_num_weights(handle, rnn_desc, x_desc, datatype): weight_size = ctypes.c_long() check_error(cudnn.lib.cudnnGetRNNParamsSize( handle, rnn_desc, x_desc, ctypes.byref(weight_size), datatype )) elem_size = cudnn._sizeofmap[datatype] assert weight_size.value % elem_size == 0 return weight_size.value // elem_size
def backward_weight(fn, input, hx, output, weight, grad_weight): with torch.cuda.device_of(input): handle = cudnn.get_handle() if fn.mode == cudnn.CUDNN_LSTM: hx, cx = hx else: cx = None if fn.batch_first: input = input.transpose(1, 2) output = output.transpose(1, 2) input_size = _input_size(fn) hidden_size = _hidden_size(fn) if not fn.train: raise RuntimeError('backward_weight can only be called when training!') if fn.dropout != 0 and lib.version < 5103: raise RuntimeError('dropout supported only in cudnn v 5.1 and above') if tuple(input.size()) != input_size: raise RuntimeError('Expected input size {}, got {}'.format( input_size, tuple(input.size()))) if not fn.train: raise RuntimeError('backward_weight can only be called when training!') if tuple(hx.size()) != hidden_size: raise RuntimeError('Expected input size {}, got {}'.format( hidden_size, hx.size())) x = input.contiguous() y = output dw = fn.weight_buf.new().resize_as_(fn.weight_buf).zero_() check_error(cudnn.lib.cudnnRNNBackwardWeights( handle, fn.rnn_desc, fn.seq_length, fn.x_descs, ctypes.c_void_p(x.data_ptr()), fn.hx_desc, ctypes.c_void_p(hx.data_ptr()), fn.y_descs, ctypes.c_void_p(y.data_ptr()), ctypes.c_void_p(fn.workspace.data_ptr()), fn.workspace.size(0), fn.w_desc, ctypes.c_void_p(dw.data_ptr()), ctypes.c_void_p(fn.reserve.data_ptr()), fn.reserve.size(0) )) # copy the weights from the weight_buf into grad_weight grad_params = get_parameters(fn, handle, dw) _copyParams(grad_params, grad_weight) return grad_weight
def backward_weight(fn, input, hx, output, weight, grad_weight): with torch.cuda.device_of(input): is_input_packed = fn.batch_sizes is not None handle = cudnn.get_handle() if fn.mode == cudnn.CUDNN_LSTM: hx, cx = hx else: cx = None if fn.batch_first and not is_input_packed: input = input.transpose(0, 1) output = output.transpose(0, 1) input_size = _input_size(fn, input) hidden_size = _hidden_size(fn) if not fn.requires_grad: raise RuntimeError('backward_weight can only be called when the function requires grad!') if fn.dropout != 0 and cudnn.version() < 5103: raise RuntimeError('dropout supported only in cudnn v 5.1 and above') if tuple(input.size()) != input_size: raise RuntimeError('Expected input size {}, got {}'.format( input_size, tuple(input.size()))) if tuple(hx.size()) != hidden_size: raise RuntimeError('Expected input size {}, got {}'.format( hidden_size, hx.size())) assert hx.is_contiguous() assert cx is None or cx.is_contiguous() x = input.contiguous() y = output dw = fn.weight_buf.new().resize_as_(fn.weight_buf).zero_() check_error(cudnn.lib.cudnnRNNBackwardWeights( handle, fn.rnn_desc, fn.seq_length, fn.x_descs, ctypes.c_void_p(x.data_ptr()), fn.hx_desc, ctypes.c_void_p(hx.data_ptr()), fn.y_descs, ctypes.c_void_p(y.data_ptr()), ctypes.c_void_p(fn.workspace.data_ptr()), fn.workspace.size(0), fn.w_desc, ctypes.c_void_p(dw.data_ptr()), ctypes.c_void_p(fn.reserve.data_ptr()), fn.reserve.size(0) )) # copy the weights from the weight_buf into grad_weight grad_params = get_parameters(fn, handle, dw) _copyParams(grad_params, grad_weight) return grad_weight
def backward_weight(fn, input, hx, output, weight, grad_weight): with torch.cuda.device_of(input): is_input_packed = fn.batch_sizes is not None handle = cudnn.get_handle() if fn.mode == cudnn.CUDNN_LSTM: hx, cx = hx else: cx = None if fn.batch_first and not is_input_packed: input = input.transpose(0, 1) output = output.transpose(0, 1) input_size = _input_size(fn, input) hidden_size = _hidden_size(fn) if not fn.requires_grad: raise RuntimeError('backward_weight can only be called when the function requires grad!') if fn.dropout != 0 and cudnn.version() < 5103: raise RuntimeError('dropout supported only in cudnn v 5.1 and above') if tuple(input.size()) != input_size: raise RuntimeError('Expected input size {}, got {}'.format( input_size, tuple(input.size()))) if tuple(hx.size()) != hidden_size: raise RuntimeError('Expected input size {}, got {}'.format( hidden_size, hx.size())) assert hx.is_contiguous() assert cx is None or cx.is_contiguous() x = input.contiguous() y = output dw = fn.weight_buf.new().resize_as_(fn.weight_buf).zero_() with torch.cuda.device_of(input): workspace = torch.cuda.ByteTensor(fn.workspace_size) check_error(cudnn.lib.cudnnRNNBackwardWeights( handle, fn.rnn_desc, fn.seq_length, fn.x_descs, ctypes.c_void_p(x.data_ptr()), fn.hx_desc, ctypes.c_void_p(hx.data_ptr()), fn.y_descs, ctypes.c_void_p(y.data_ptr()), ctypes.c_void_p(workspace.data_ptr()), workspace.size(0), fn.w_desc, ctypes.c_void_p(dw.data_ptr()), ctypes.c_void_p(fn.reserve.data_ptr()), fn.reserve.size(0) )) # copy the weights from the weight_buf into grad_weight grad_params = get_parameters(fn, handle, dw) _copyParams(grad_params, grad_weight) return grad_weight
