我们从Python开源项目中，提取了以下15个代码示例，用于说明如何使用torch.backends.cudnn.CUDNN_LSTM。
def get_cudnn_mode(mode): if mode == 'RNN_RELU': return cudnn.CUDNN_RNN_RELU elif mode == 'RNN_TANH': return cudnn.CUDNN_RNN_TANH elif mode == 'LSTM': return cudnn.CUDNN_LSTM elif mode == 'GRU': return cudnn.CUDNN_GRU else: raise Exception("Unknown mode: {}".format(mode))
def _num_linear_layers(fn): if fn.mode == cudnn.CUDNN_LSTM: return 8 elif fn.mode == cudnn.CUDNN_GRU: return 6 elif fn.mode == cudnn.CUDNN_RNN_RELU: return 2 elif fn.mode == cudnn.CUDNN_RNN_TANH: return 2 else: raise RuntimeError('Unknown mode: {}'.format(fn.mode))
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
