o Ei,@sddlZddlmZddlZddlmZddlmZej dddddd Z ej d d d d Z ej jej ddddd ddedejdeejedfdedejdejf ddZe e_ dS)N)util)Unioncsdfdd S)Ncs|fdd|D|S)Ncg|]}|qSr.0xtrH/home/ubuntu/veenaModal/venv/lib/python3.10/site-packages/einx/op/dot.py .....r)exprs_in tensors_inexpr_outbackendcr rr  s..Nrr rrrr r sr)tracecs@t|gD]}|D]}t|tjjjrtdq q|D]}t|tjjjr/tdq!dd|D}D]5}|D].}t|tjjj ro|j |v} tjj |} | rc| sctd|d| so| rotd|dqAq;d d|Dfd d fd d t t |D}|}tj|d\}t|gd} tddDsJtjj| sJifddfddddDdfddDdfdd tjj| D} j| g|R} tjjfdd tjj| D}tj|| | dd} | |j} | |fS)NzConcatenation not allowedz-Brackets in the output expression not allowedcS"h|] }t|tjjjr|jqSr isinstanceeinxexprstage3Axisnamerarrr "zdot_stage3..z Reduced axis z must be markedz Marked axis z# cannot appear in output expressioncSrrrr#rrr r%!r&csfddtDg}g}g}tD]7\}}tfdd|D}tfdd|D}|r?|s?||q|rI|sI||q||qt|t|t|dS)Ncs<h|]\}}|D]}|kr t|tjjjr |jq qSr)allrrrr r!r")riexpr_inr$)idxrr r%$sz/dot_stage3..get_fans..c3*|]}t|tjjjo|jvVqdSrrr#)other_input_axis_namesrr .  z/dot_stage3..get_fans..c3r+rrr#)output_axis_namesrr r-2r.)in_axisout_axis batch_axis) enumerateanyr'appendtuple)r*r0r1r2r(childany_in_other_input any_in_output)rr/)r*r,r get_fans#s,      zdot_stage3..get_fanscs<g|]\}\}}tjj||jfi|dddqS)weightdot)r"init)rtracer call_factoryshape)rr(tensorr)rr:rr r Bs zdot_stage3..rrcss|] }tjj|VqdSr)rrr is_flatrrrrr r-Mszdot_stage3..cs`|vr|Sttdt}t|tdkr*tdtdtddd||<|S)Nr$zzOnly supports up to rz unique input axes)chrordlen ValueError)keyv)einsum_variablesrr get_einsum_variableSs z'dot_stage3..get_einsum_variablecsdfdd|DS)Nc3s|]}|jVqdSrr"r#rMrr r-^sz0dot_stage3..to_einsum..)join)axesrPrr to_einsum]szdot_stage3..to_einsumcSs&h|]}tjj|D]}|jq qSr)rrr get_axesr")rrr$rrr r%`s&,c3s"|] }tjj|VqdSr)rrr rTrD)rSrr r-cs z->csg|] }|jvr|qSrrOr#input_axis_namesrr r escsg|] }|jvr|qSr)r" __deepcopy__r#rVrr r ks  )listr'rrrr ConcatenationrIMarkerr!r" is_markedr3zip all_to_tensorrflattenrCrQrTeinsumListtranspose_broadcastreshaper@)rrrrrootrout_axis_namesr)axisis_reduced_axisr\ expr_out_flat einsum_strrAr)rrLrrMr:rWr/rSr dot_stage3sf             rjT)csec stjj||\}}tjj|}t|ddkrt|dkr|dd|ddfD]$}|D]}t |tjjj rN|j dkrNtjj |rNt d|q1q+t}g}|dd|ddfD]M}|D]F}t |tjjjrtjj |r|j|vr||j|} |} | jdurt | tjjjrtjjj| | jd} | j} | jdus|| qgqatjj|}tjjtjjtjj|dd|gtjjtjj|ddgg}t|dt|krt dt|ddt|t|ddkr t dt|dtjjd d t|d|Dtj|ddgd d |D|d d dt|dd} | dd| d} } tdd| Dsddd| Dfdd | D} | | fS)Nrrz0Cannot mark unnamed non-trivial axes, but found ) ellipsis_idz Expected z input tensor(s), got z&Expected 1 output expression, but got cSsg|] \}}tj||qSr)rrEquation)rr) tensor_shaperrr r s zparse..cSs4g|]\}}tjj|t|dtjfdddqS).N)depth1depth2)rrrnnpasarraynewaxis)rkrKrrr r s"F)rk cse_concatcss,|]}|D] }tjj|VqqdSr)r'rrr r\)rr)rrrr r-s*zparse..cSrrr)rrfrrr r%s zparse..cs$g|]}tjj|fddqS)cst|tjjjo |jvSrr)raxes_names_outrr rs z"parse...)rrr mark)rr)rxrr r s ) ropr!_clean_description_and_parametersrstage1parse_oprHr'r UnnamedAxisvaluer\rIset NamedAxisr"addrXparentEllipsisrmr5raOpArgsdemarksolver]rnitemsr4) descriptionrk tensor_shapes parametersr{rdrnamesexpr_in2expr2rexprsrrrrxr parsezs                 rcsddfdd S)NrBcs$|gfdd|DRi|S)Ncrrrrr rr r rrr)rrtensorskwargsrrr rsrrrrrr rr)rrkrrrrkrreturnc Os@t|gdd|DRd|i|\}}t||||d\}}|S)a Computes a general dot-product of the input tensors. The following shorthand notation is supported: * When no brackets are found, brackets are placed implicitly around all axes that do not appear in the output. Example: ``a b, b c -> a c`` expands to ``a [b], [b] c -> a c`` * When given two input tensors, the expression of the second input is determined implicitly from the marked axes in the input and output expression. Example: ``a [b] -> a [c]`` expands to ``a b, b c -> a c`` Axes marked multiple times appear only once in the implicit second input expression. Example: ``[a b] -> [a c]`` expands to ``a b, a b c -> a c`` The function additionally passes the ``in_axes``, ``out_axes`` and ``batch_axes`` arguments to tensor factories that can be used to determine the fan-in and fan-out of a neural network layer and initialize weights accordingly (see e.g. `jax.nn.initializers.lecun_normal `_) Args: description: Description string for the operation in einx notation. tensors: Input tensors or tensor factories matching the description string. backend: Backend to use for all operations. If None, determines the backend from the input tensors. Defaults to None. cse: Whether to apply common subexpression elimination to the expressions. Defaults to True. graph: Whether to return the graph representation of the operation instead of computing the result. Defaults to False. **parameters: Additional parameters that specify values for single axes, e.g. ``a=4``. Returns: The result of the dot-product operation if ``graph=False``, otherwise the graph representation of the operation. Examples: Compute an inner product between two vectors: >>> a, b = np.random.uniform(size=(10,)), np.random.uniform(size=(10,)) >>> einx.dot("a, a ->", a, b).shape () Compute a matrix-vector product: >>> a, b = np.random.uniform(size=(10, 10)), np.random.uniform(size=(10,)) >>> einx.dot("a b, b -> a", a, b).shape (10,) >>> einx.dot("a [b] -> a", a, b).shape (10,) >>> einx.dot("a [b->]", a, b).shape (10,) Compute a vector-matrix product: >>> a, b = np.random.uniform(size=(10,)), np.random.uniform(size=(10, 10)) >>> einx.dot("a, a b -> b", a, b).shape (10,) >>> einx.dot("[a] -> [b]", a, b).shape (10,) >>> einx.dot("[a->b]", a, b).shape (10,) Multiply a tensor with a weight matrix: >>> x, w = ( ... np.random.uniform(size=(4, 16, 16, 64)), ... np.random.uniform( ... size=( ... 64, ... 32, ... ) ... ), ... ) >>> einx.dot("b... [c1->c2]", x, w).shape (4, 16, 16, 32) cSsg|]}tj|qSr)rr> get_shape)rrArrr r 3szdot..rkrB)rrj) rrrkrrrrrA_exprrrr r<s\  r<r)rrNrnumpyrrtypingr numpy.typingnptjitrj lru_cachertraceback_utilfilterstrTensorBackendbool ArrayLiker<rrrr s>    m[ ]