B «»ˆdã@s6dZddlZddlmZddgZd dd„Zdd„ZdS) z1 Determines if a contraction can use BLAS or not éNé)ÚhelpersÚcan_blasÚ tensor_blasc CsÐt|ƒdkrdS|\}}xft||ƒD]V}| |¡| |¡}}|dks\|dks\||dkr`dS||dt||kƒkr&dSq&W|dk r¾x4|D],}|d| |¡|d| |¡krŽdSqŽWt|ƒdkrÎdSdd„|Dƒ} | d|} | d|} t|ƒ} |d|dkrd S| d| dkr(d S|| d…|d| …krHd S|d| …|| d…krhd S|| d…|| d…krŠd S|d| …|d| …kr¨d St| ƒdksÄt| ƒdkrÈd Sd SdS)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.0ÚxrrúA/var/www/html/venv/lib/python3.7/site-packages/opt_einsum/blas.pyú Uszcan_blas..ÚDOTz DOT/EINSUMZGEMMz GEMV/EINSUMZTDOT)ÚlenrÚcountÚintÚfind) ÚinputsÚresultÚ idx_removedZshapesÚ input_leftÚ input_rightÚcÚnlÚnrZsetsÚ keep_leftÚ keep_rightÚrsrrr r sD+  $   csxt|ƒ}t|ƒ|}t|ƒ|}i‰x t||jƒD]\}} | ˆ|<q2Wx t||jƒD]\}} | ˆ|<qTWt|ƒ} t |ˆ¡} t |ˆ¡} t |ˆ¡} ||}x|D]} | | d¡}q¢W||krÖt |  ¡|  ¡¡}n>|| d…|d| …krt |  | | ¡|  | | ¡¡}n|d| …|| d…krPt |  | | ¡j |  | | ¡j ¡}nÄ|| d…|| d…krŽt |  | | ¡|  | | ¡j ¡}n†|d| …|d| …krÈt |  | | ¡j |  | | ¡¡}nLd\}}x.|D]&} ||  | ¡f7}||  | ¡f7}qÖWtj ||||fd}t‡fdd„|Dƒƒ}|j|krVt|ƒdkrL||_n t |¡}||krtt |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)Nr)r r )Údimension_dictrr ú észtensor_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_shaper)rr r{sL-         "      )N)Ú__doc__Únumpyr#rrÚ__all__rrrrrr Ús   o