Tensors

pylibwholegraph.torch.WholeMemoryTensor(...)	WholeMemory Tensor
pylibwholegraph.torch.create_wholememory_tensor(...)	Create empty WholeMemory Tensor. Now only support dim = 1 or 2 :param comm: WholeMemoryCommunicator :param memory_type: WholeMemory type, should be continuous, chunked or distributed :param memory_location: WholeMemory location, should be cpu or cuda :param sizes: size of the tensor :param dtype: data type of the tensor :param strides: strides of the tensor :param tensor_entry_partition: rank partition based on entry; tensor_entry_partition[i] determines the entry count of rank i and shoud be a positive integer; the sum of tensor_entry_partition should equal to total entry count; entries will be equally partitioned if None :return: Allocated WholeMemoryTensor.
pylibwholegraph.torch.create_wholememory_tensor_from_filelist(...)	Create WholeMemory Tensor from list of binary files. :param comm: WholeMemoryCommunicator :param memory_type: WholeMemory type, should be continuous, chunked or distributed :param memory_location: WholeMemory location, should be cpu or cuda :param filelist: list of binary files :param dtype: data type of the tensor :param last_dim_size: 0 for create 1-D array, positive value for create matrix column size :param last_dim_strides: stride of last_dim, -1 for same as size of last dim. :param tensor_entry_partition: rank partition based on entry; tensor_entry_partition[i] determines the entry count of rank i and shoud be a positive integer; the sum of tensor_entry_partition should equal to total entry count; entries will be equally partitioned if None :return: WholeMemoryTensor.
pylibwholegraph.torch.destroy_wholememory_tensor(...)	Destroy allocated WholeMemory Tensor :param wm_tensor: WholeMemory Tensor :return: None