o * il@s8ddlZddlmZddlmZgZddZddZdS) N)core)DataToDenseTensorConvertercCst|tjrtt|||St|tr_t|t|gtj j j d}g}|D]}| t|| |q%|g|ks=Jdt|j}|g|jdR}t}||||||St|tjrvt}||||||Std)a Create a DenseTensor from a numpy array, list or existing DenseTensor. The implementation is as follows: 1. Check whether the length-based LoD, i.e., :code:`recursive_seq_lens` is valid. 2. Convert :code:`recursive_seq_lens` to a offset-based LoD. 3. Based on :code:`place` , copy the :code:`data` from a numpy array, list or existing DenseTensor to CPU or GPU device. 4. Set offset-based LoD to the output DenseTensor. Suppose we want to create a DenseTensor to hold data for word sequences, where each word is represented by an integer. If we want to create a DenseTensor to represent two sentences, one of 2 words, and one of 3 words. Then :code:`data` would be a numpy array of integers with shape (5, 1). :code:`recursive_seq_lens` would be [[2, 3]], indicating the word number in each sentence. This length-based :code:`recursive_seq_lens` [[2, 3]] would be converted to offset-based LoD [[0, 2, 5]] inside the function call. Args: data (numpy.ndarray|list|DenseTensor): a numpy array, a list or ad DenseTensor holding the data to be copied. recursive_seq_lens (list[list[int]]): a list of lists indicating the length-based LoD info. place (CPUPlace|CUDAPlace): CPU or GPU place indicating where the data in the created DenseTensor will be stored. Returns: A DenseTensor with tensor data and recursive_seq_lens info. Examples: .. code-block:: python >>> import paddle.base as base >>> import numpy as np >>> t = base.create_lod_tensor(np.ndarray([5, 30]), [[2, 3]], base.CPUPlace()) )placeZ lod_levelshapeZdtypez(data and recursive_seq_lens do not matchrz>> import paddle.base as base >>> t = base.create_random_int_lodtensor(recursive_seq_lens=[[2, 3]], ... base_shape=[30], place=base.CPUPlace(), low=0, high=10) >>> print(t.shape()) [5, 30] zbase_shape should be a listZint64)rr sumr randomZrandom_integersZastyper)rZ base_shaperlowhighZ overall_shaperrrrcreate_random_int_lodtensorps4 r) numpyr rZ data_feederr__all__rrrrrrs    Y