Python torch.backends.cudnn 模块，descriptor() 实例源码

def flatten_parameters(self):
        """Resets parameter data pointer so that they can use faster code paths.

        Right now, this works only if the module is on the GPU and cuDNN is enabled.
        Otherwise, it's a no-op.
        """
        any_param = next(self.parameters()).data
        if not any_param.is_cuda or not torch.backends.cudnn.is_acceptable(any_param):
            self._data_ptrs = []
            return

        with torch.cuda.device_of(any_param):
            # This is quite ugly, but it allows us to reuse the cuDNN code without larger
            # modifications. It's really a low-level API that doesn't belong in here, but
            # let's make this exception.
            from torch.backends.cudnn import rnn
            from torch.backends import cudnn
            from torch.nn._functions.rnn import CudnnRNN
            handle = cudnn.get_handle()
            with warnings.catch_warnings(record=True):
                fn = CudnnRNN(
                    self.mode,
                    self.input_size,
                    self.hidden_size,
                    num_layers=self.num_layers,
                    batch_first=self.batch_first,
                    dropout=self.dropout,
                    train=self.training,
                    bidirectional=self.bidirectional,
                    dropout_state=self.dropout_state,
                )

            # Initialize descriptors
            fn.datatype = cudnn._typemap[any_param.type()]
            fn.x_descs = cudnn.descriptor(any_param.new(1, self.input_size), 1)
            fn.rnn_desc = rnn.init_rnn_descriptor(fn, handle)

            # Allocate buffer to hold the weights
            self._param_buf_size = rnn.get_num_weights(handle, fn.rnn_desc, fn.x_descs[0], fn.datatype)
            fn.weight_buf = any_param.new(self._param_buf_size).zero_()
            fn.w_desc = rnn.init_weight_descriptor(fn, fn.weight_buf)

            # Slice off views into weight_buf
            params = rnn.get_parameters(fn, handle, fn.weight_buf)
            all_weights = [[p.data for p in l] for l in self.all_weights]

            # Copy weights and update their storage
            rnn._copyParams(all_weights, params)
            for orig_layer_param, new_layer_param in zip(all_weights, params):
                for orig_param, new_param in zip(orig_layer_param, new_layer_param):
                    orig_param.set_(new_param.view_as(orig_param))

            self._data_ptrs = list(p.data.data_ptr() for p in self.parameters())