o Üeã@s6dZddlZddlmZddgZd dd„Zdd„ZdS) z1 Determines if a contraction can use BLAS or not éNé)ÚhelpersÚcan_blasÚ tensor_blasc Csºt|ƒdkrdS|\}}t||ƒD]-}| |¡| |¡}}|dks-|dks-||dkr0dS||dt||vƒkr?dSq|dur^|D]}|d| |¡|d| |¡kr]dSqFt|ƒdkrfdSdd„|Dƒ} | d|} | d|} t|ƒ} |d|dkr‡d S| d| dkr‘d S|| d…|d| …kr d S|d| …|| d…kr¯d S|| d…|| d…kr¿d S|d| …|d| …krÍd St| ƒdksÙt| ƒdkrÛd Sd S)aŠ Checks if we can use a BLAS call. Parameters ---------- inputs : list of str Specifies the subscripts for summation. result : str Resulting summation. idx_removed : set Indices that are removed in the summation shapes : sequence of tuple[int], optional If given, check also that none of the indices are broadcast dimensions. Returns ------- type : str or bool The type of BLAS call to be used or False if none. Notes ----- We assume several operations are not efficient such as a transposed DDOT, therefore 'ijk,jki->' should prefer einsum. These return the blas type appended with "/EINSUM" to differentiate when they can still be done with tensordot if required, e.g. when a backend has no einsum. Examples -------- >>> can_blas(['ij', 'jk'], 'ik', set('j')) 'GEMM' >>> can_blas(['ijj', 'jk'], 'ik', set('j')) False >>> can_blas(['ab', 'cd'], 'abcd', set()) 'OUTER/EINSUM' >>> # looks like GEMM but actually 'j' is broadcast: >>> can_blas(['ij', 'jk'], 'ik', set('j'), shapes=[(4, 1), (5, 6)]) False éFrNrz OUTER/EINSUMcSsg|]}t|ƒ‘qS©)Úset©Ú.0Úxrrú?D:\Projects\ConvertPro\env\Lib\site-packages\opt_einsum/blas.pyÚ Uszcan_blas..ÚDOTz DOT/EINSUMZGEMMz GEMV/EINSUMZTDOT)ÚlenrÚcountÚintÚfind) ZinputsÚresultÚ idx_removedZshapesÚ input_leftÚ input_rightÚcÚnlÚnrZsetsÚ keep_leftÚ keep_rightÚrsrrr r sH +ÿ$ÿ   csZt|ƒ}t|ƒ|}t|ƒ|}i‰t||jƒD]\}} | ˆ|<qt||jƒD]\}} | ˆ|<q't|ƒ} t |ˆ¡} t |ˆ¡} t |ˆ¡} ||}|D]} | | d¡}qL||krdt |  ¡|  ¡¡}n—|| d…|d| …kr€t |  | | ¡|  | | ¡¡}n{|d| …|| d…kržt |  | | ¡j |  | | ¡j ¡}n]|| d…|| d…kr¼t |  | | ¡|  | | ¡j ¡}n?|d| …|d| …krØt |  | | ¡j |  | | ¡¡}n#d\}}|D]} ||  | ¡f7}||  | ¡f7}qÞtj ||||fd}t‡fdd„|Dƒƒ}|j|krt|ƒdkr||_nt |¡}||kr+t |d||¡}|S) a Computes the dot product between two tensors, attempts to use np.dot and then tensordot if that fails. Parameters ---------- view_left : array_like The left hand view input_left : str Indices of the left view view_right : array_like The right hand view input_right : str Indices of the right view index_result : str The resulting indices idx_removed : set Indices removed in the contraction Returns ------- type : array The resulting BLAS operation. Notes ----- Interior function for tensor BLAS. This function will attempt to use `np.dot` by the iterating through the four possible transpose cases. If this fails all inner and matrix-vector operations will be handed off to einsum while all matrix-matrix operations will first copy the data, perform the DGEMM, and then copy the data to the required order. Examples -------- >>> a = np.random.rand(4, 4) >>> b = np.random.rand(4, 4) >>> tmp = tensor_blas(a, 'ij', b, 'jk', 'ik', set('j')) >>> np.allclose(tmp, np.dot(a, b)) ÚN)rr)Zaxesc3s|]}ˆ|VqdS©Nrr ©Zdimension_dictrr Ú és€ztensor_blas..rz->)rÚzipÚshaperrZcompute_size_by_dictÚreplaceÚnpÚdotZravelZreshapeÚTrZ tensordotÚtupleZsqueezeZeinsum)Z view_leftrZ view_rightrZ index_resultrrrÚiÚsrZdim_leftZ dim_rightZ dim_removedZ tensor_resultZnew_viewZleft_posZ right_posZ tensor_shaperrr r{sL-       "     r)Ú__doc__Únumpyr$rrÚ__all__rrrrrr Ús    o