|jS2ddlmZddlmZmZddlZddlZddlmZm Z ddl m Z ddl m Z mZmZmZmZddlmZddlmZmZerddlmZdd lmZmZmZmZdd lmZmZm Z d d gZ!d Z"dZ#dZ$dZ% d&d'd Z&d!Z'e d&d(d%Z(dS))) annotations) TYPE_CHECKINGAnyN)_C_opsin_dynamic_mode) convert_dtype)_current_expected_place_get_paddle_placecore dygraph_only in_pir_mode) LayerHelper)max to_tensor)CPUPlaceCUDAPinnedPlace CUDAPlaceTensor) DTypeLikeNumericSequence ShapeLikesparse_coo_tensorsparse_csr_tensorc|rGt|t|jkr"|t|S|S)N)rdtypeastype)datars f/lsinfo/ai/hellotax_ai/data_center/backend/venv/lib/python3.11/site-packages/paddle/sparse/creation.py _handle_dtyper.sF 5   =#<#< < <;;}U3344 4 Kct|jdksJt|d}|dz}|}t|jdkr"t j||jdd}t |S)N)axis)lenshapernumpynpappendlist)indicesvalueslenss r_infer_dense_shaper.5s w}   " " " " wQ   D !8D ::<>> import paddle >>> indices = [[0, 1, 2], [1, 2, 0]] >>> values = [1.0, 2.0, 3.0] >>> dense_shape = [3, 3] >>> coo = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape) >>> print(coo) Tensor(shape=[3, 3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 1, 2], [1, 2, 0]], values=[1., 2., 3.]) NTrr3rBr"z'indices' must be 2-D.r#rzBthe indices and values must have same number of non-zero, but get z and Fzthe minimum shape required is z , but get z7the number of dimensions(len(shape) must be sparse_dim(z) + dense_dim(z ), but get sparse_sparse_coo_tensor)r,r+r&out)typeinputsoutputsattrs)rr4r0r eagerrrr%r&r2r<rr3_equals_copy_torrBr.r*rrHr r)create_sparse_variable_for_type_inference append_op)r+r,r&rr3rBnnz sparse_dim dense_dim min_shapeop_typerKrMhelperrIs rrrSsl>5!!'4:#455 t5G&$*"344 Dvue]CCF w}   " "566 6mA]1% W]+++ &,q/ ! !pUXpp_e_klm_npp  %%) }$$U++ 5&&ue44G|##E** 3__UE22Fvu--,&w77 =EEKKEy   QYQQ%QQ5zzZ)333 KjKKhqKKBCHIIKK.vwFFF .vwFFF-"w77 8 E!H% W%%>>uEE%U     r c|}|}tj|dd|ddkdz}tt ||z |zt |krt d|d|dz}tt ||z dz }|dkr||gS|||gS)NrGr#z-The calculated original matrix batch size is za, but it cannot correctly split the row data. Please carefully check the data or the input shape.)r'r(sumintr%r2r)crowscols crows_numpy cols_numpybatchscolrows r_infer_dense_csr_shapercs++--KJ VK${1226 7 7! ;F C  v %&&/C 4D4DDD fF f f f    ..  Q C c+' ( (1 ,C {{SzS!!r r\;list[int] | tuple[int, ...] | npt.NDArray[np.int_] | Tensorr]ct|}t|tjjst |d|d}t|tjjst |d|d}t|tjjst ||||}t |jt |j|9t|dkr%t|dkrtd|nt||}|t|dz }|j |s| |d}|j |s| |d}|j |s| |d}t||}||_t|jdks0t|jdkst|jdkrtd t|t|krtd t|dkrw|jd |dzkr!td |jd d |d|d d krtd|d|jd krtdn8|jd |dzzd kr!td |jd d|dtj|||||S)aO Constructs a sparse ``paddle.Tensor`` in CSR(Compressed Sparse Row) format according to the ``crows``, ``cols`` and ``values``. Currently, the crows and cols of each batch must be incremented. Args: crows(list|tuple|ndarray|Tensor): 1-D array, each element in the rows represents the starting position of the first non-zero element of each row in values. Can be a list, tuple, numpy\.ndarray, paddle\.Tensor. cols(list|tuple|ndarray|Tensor): 1-D array, the column of non-zero elements. Can be a list, tuple, numpy\.ndarray, paddle\.Tensor. values(list|tuple|ndarray|Tensor): 1-D array, the non-zero elements. Can be a scalar, list, tuple, numpy\.ndarray, paddle\.Tensor. shape(list|tuple, optional): The shape of the sparse tensor also represents the shape of original dense tensor. hold all elements. dtype(str|paddle.dtype|np.dtype, optional): The desired data type of returned tensor. Can be 'bool' , 'float16' , 'float32' , 'float64' , 'int8' , 'int16' , 'int32' , 'int64' , 'uint8', 'complex64' , 'complex128'. Default: None, infers dtype from ``data`` except for python float number which gets dtype from ``get_default_type`` . place(CPUPlace|CUDAPinnedPlace|CUDAPlace|str|None, optional): The place to allocate Tensor. Can be CPUPlace, CUDAPinnedPlace, CUDAPlace. Default: None, means global place. If ``place`` is string, It can be ``cpu``, ``gpu:x`` and ``gpu_pinned``, where ``x`` is the index of the GPUs. stop_gradient(bool, optional): Whether to block the gradient propagation of Autograd. Default: True. Returns: Tensor: A Tensor constructed from ``crows``, ``cols`` and ``values`` . Examples: .. code-block:: python >>> import paddle >>> crows = [0, 2, 3, 5] >>> cols = [1, 3, 2, 0, 1] >>> values = [1, 2, 3, 4, 5] >>> dense_shape = [3, 4] >>> csr = paddle.sparse.sparse_csr_tensor(crows, cols, values, dense_shape) >>> print(csr) Tensor(shape=[3, 4], dtype=paddle.int64, place=Place(cpu), stop_gradient=True, crows=[0, 2, 3, 5], cols=[1, 3, 2, 0, 1], values=[1, 2, 3, 4, 5]) NTrFr"z>SparseCsrTensor only support 2-D or 3-D matrix. but get shape Fr#z-The 'crows', 'cols' and 'values' must be 1-D.z3the length of cols must be same as length of valuesrz The length(z%) of crows must be equal to the rows(z)+1 of matrix.z the 0th value of crows must be 0rGzTAX % %gek!nggSWggg  8q==?@@ @ 9 Q ' 'N  ( ;q>TAX &! + +hek!nhhTXhhh  : ' ' tVUM  r )NNNT)r+r=r,r>r&r?rr@r3rArBrCrDr)r\rdr]rdr,r>r&r?rr@r3rArBrCrDr)) __future__rtypingrrr'r(r6rrpaddle.base.data_feederrpaddle.base.frameworkr r r r r paddle.base.layer_helperr paddle.tensorrr numpy.typingnptrrrrpaddle._typingrrr__all__rr.r4r<rrcrr rrss #"""""%%%%%%%% ********111111100000((((((((ECCCCCCCCCCCCDDDDDDDDDD        #"AEtttttn " " "" #"AEsssssssr