o Ei<@sddlZddlZddlmZddlZddlmZmZm Z ddl mZ ej ddddddddd d d Z ejd d ddZejjej dddddd iddedejdededeejedfdede de jfddZdS)N)util)CallableUnionMappingcfddS)Ncs"|fdd|D|fi|S)Ncg|]}|qSr .0xtr I/home/ubuntu/veenaModal/venv/lib/python3.10/site-packages/einx/op/vmap.py .....r )exprs_in tensors_in exprs_outkwargscrr r s ..r rrr rrr r)traceF)flatbackendoprverbosec s duri tt|krtdtdt|tt|D]}|D]} t| tjjjr8tdq*q$fddt |D} |} ret dt dddDt d d d|Dt j |d \} }t |} td d | DsJtdd | DsJ rt dt ddd| Dt d dd| DddDdd|Ddd| Ddd| D rt dst dddDt d ddDt dddDt dddDtjddDd|d f d d! }d"d| D} d#d$| Dfd%d& d'd(} g}t| D]}|D] | rA j|vrA| jq.q* rOt d)|t| t| D]!}|D] j|vpf | krutd* jd+q[qW fd,d| D}d-d|D} rt d.d/d|Dt d0| t d1|g}td2d | D}td3d |D}t|D] t fd4d | D}t fd5d |D} rt d6 d7|d8|t || D]\}}|durtd9 d:|d;q|||||fdd t ||D}tfd?d t ||D}| |D]} |vr4|  |vr>| q( rMt d@| dA|qt|D]\}}}}j|||||dB}qS rrt dCdDd|Dtjj||tdEd |DdF} rt ||D] \}} t dG|j| qfdHdt ||| D} rt dIt || D]\}} t dJtj|| qt j| ||d } rt dKdLd|D||fS)MN Expected z input tensor(s), got zConcatenation not allowedcs$g|]\}}tjj||jdqS)r)einxtracer call_factoryshape)r tensorexprr#r rr#szvmap_stage3..z Expressions:z IN:cSg|]}t|qSr strr er r rr+rz OUT:cSr*r r+r-r r rr,rr#cs|] }tjj|VqdSNr$r)stage3is_flatr r)r r r 1zvmap_stage3..csr/r0r1r4r r rr52r6zFlat expressions:cSr*r r+r-r r rr6rcSr*r r+r-r r rr7rcSg|] }tjj|qSr r$r)r2 get_markedr4r r rr:cSr7r r8r4r r rr;r:cSr7r r8r4r r rr<r:cSr7r r8r4r r rr=r:zExpressions used in op:cSr*r r+r-r r rrBrcSr*r r+r-r r rrCrz IN_FLAT:cSr*r r+r-r r rrDrz OUT_FLAT:cSr*r r+r-r r rrErcSg|] }tj|jqSr r$r%Tensorr'r4r r rrGr:)args)r c sH rtddd|Dtddd|Dt|tks JsBtj|d} r7tddd|Dt|tksAJn|}sHnt|trUt|}nt|tjj se|fd d }||i }t|t t fsv|f}t|tkrt d td t|t d d|Drt ddd|D rtdt|D] \}}td||jqtt|D]!\}\}}tj||jkrt d|jd|dtj|qstj|d\}} rtddd|D|ks Jdd|DdddD r tddd|Dt |S)Nz&Flat input tensors that arrived in op:cSg|] }ttj|qSr r,r$r% get_shaper ar r rrLz+vmap_stage3..op..z Input types to vmapped function:cSr*r typer rr r rrNrr#z Unflattened input tensors in op:cSr?r r@rBr r rrXrDcs@tjj||tdkrddDdStjdjdS)NrcSr;r r<r4r r rrgr:z=vmap_stage3..op....r)r>routput)r$r%applylenr=r')r>r concrete_opexprs_out_expectedr rrcs  z)vmap_stage3..op..r"z1 output tensor(s) from vmapped function, but got css |] }t|tjj VqdSr0) isinstancer$r%r=rGr r rr5usz*vmap_stage3..op..z0Expected tensors from vmapped function, but got cSr*r rErGr r rrxrz!Unflattened output tensors in op: zExpected output shape z from z5-th (zero-based) output of vmapped function, but got zFlattened output tensors in op:cSr?r r@rBr r rrrDcSr*r r+r sr r rrrz != cSr*r r+rPr r rrrz&Returning types from vmapped function:cSr*r rErGr r rrr)printrJr unflattenrNr,getattrr$r%Tracertuplelist ValueErroranyzipr' enumeraterAflatten)r tensors_in_flatr tensors_outexpr_out tensor_outiexprs_out_funcargs_flat2) rrexprs_in_funcargsexprs_in_funcargs_flatexprs_out_funcargsexprs_out_funcargs_flatrrr!rKrr Gs           zvmap_stage3..opcSg|] }dd|DqS)cSg|]}|jqSr namerBr r rr*vmap_stage3...r r rootr r rrr:cSsh|] }|D]}|jqqSr ri)r rnrCr r r rDzvmap_stage3..cs*t|tjjjo|jvotjj| Sr0)rNr$r)r2Axisrj is_markedr))axes_names_in_setr ris_broadcast_axiss z&vmap_stage3..is_broadcast_axiscSstjj| Sr0)r$r)r2rqrrr r r is_vmappedszvmap_stage3..is_vmappedzVmapping the following axes: zAxis z( appears both as vmapped and non-vmappedcsg|] }tjj|qSr )r$r)r2remover4)rtr rrscSrg)cSrhr rirBr r rrrkrlr rmr r rrsz*Flat output expressions without broadcast:cSr*r r+r-r r rrrzGot input axis names:z:Got output axis names (excluding broadcasted output axes):cs|]}|jVqdSr0r'r4r r rr5csrwr0rxr4r r rr5ryc3&|]}|vr |ndVqdSr0indexr axes_namesvr rr5s c3rzr0r{r}rr rr5s  zApplying backend.vmap to axis z, with input axis indices z and output axis indices z.drop_axisc3|] \}}||VqdSr0r r r'rrr rr5r6c3rr0r rrr rr5s zNow has remaining input axes z and output axes )in_axesout_axes input_shapes output_shapesz Sending shapes to backend.vmap:cSsg|]}t|jqSr )r,r'rBr r rrscss|] }tj|jVqdSr0r<r4r r rr5r6)r>rHzGot overall flat tensor_out:cs(g|]\}}}tj|||ddqS)r#r)rtranspose_broadcast)r expr_out_wbr(r_r#r rr sz/Got overall transposed+broadcasted tensors_out:rOz$Got overall unflattened tensors_out:cSr?r r@rBr r rrrD)rJrXrWallrNr$r)r2 ConcatenationrZ all_to_tensorrRrr\rrjappendrVreversedrvvmapr%rIr'rArS)rrrrrr rr!rnr) exprs_in_flatexprs_out_flat axes_names_inru vmapped_axes exprs_out_flat_without_broadcast axes_names_out_without_broadcastvmapsrrrrout_axisr_r~tensorsr(r ) rsrrrrcrdrerfrrtrrr!r vmap_stage3 s4    $R                   rT)csecOstjj||\}}tjj|}t|dkr&tjj|d|d g}t|dt|kr@t dt|ddt|tjj ddt |d|Ddd|dDdd| D|d d dt|dt|d}|dt|d|t|dd}}||fS) Nrrr"z input tensors, but got cSsg|] \}}tj||qSr r$r)Equation)r expr_in tensor_shaper r rr4s zparse..cSg|]}tj|qSr r)r r_r r rr8cSs4g|]\}}tjj|t|dtjfdddqS).N)depth1depth2)r$r)rnpasarraynewaxis)r krr r rr9s"F)r cse_concat)r$r r!_clean_description_and_parametersr)stage1parse_oprJOp __deepcopy__rXsolverZitems) descriptionr tensor_shapes parametersr exprsrrr r rparse!s8      * rcr)Ncs$|gfdd|DRi|S)Ncrr r r r r rrHrrr )rrrrr rrGsrr rr rrrGr)rrrrrrr rrrrrc OsZt|gdd|DRd|i|\}} t||| ||||d\}} t| dkr+|dS|S)aY Vectorizes and applies a function to the input tensors using automatic vectorization. The function ``op`` must accept input tensors and yield output tensors as specified in ``description`` with shapes matching the subexpressions that are marked with ``[]``-brackets. Args: description: Description string for the operation in einx notation. tensors: Input tensors or tensor factories matching the description string. op: Function that will be vectorized. If ``op`` is a string, retrieves the attribute of ``backend`` with the same name. flat: Whether to pass the tensors to ``op`` in flattened form or matching the nested layout in the input expressions. Defaults to False. kwargs: Additional keyword arguments that are passed to ``op``. Defaults to ``{}``. 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 vectorized operation if `graph=False`, otherwise the graph representation of the operation. Examples: Compute the mean along rows of a matrix: >>> x = np.random.uniform(size=(10, 8)) >>> einx.vmap("a [b] -> a", x, op=np.mean) (10,) Vectorize a custom function: >>> x, y = ( ... np.random.uniform(size=(10, 13, 4)), ... np.random.uniform( ... size=( ... 4, ... 9, ... ) ... ), ... ) >>> def op(x, y): # c, d -> 2 >>> return np.stack([np.mean(x), np.max(y)]) >>> einx.vmap("b1 [c] b2, b2 [d] -> b2 [2] b1", x, y, op=op).shape (4, 2, 10) Compute a matrix-matrix multiplication >>> x, y = ( ... np.random.uniform(size=(5, 10)), ... np.random.uniform(size=(10, 3)), ... ) >>> einx.vmap("a [b], [b] c -> a c", x, y, op=np.dot).shape (5, 3) cSrr )r$r%rA)r r(r r rrrzvmap..r)rrr rrr)rrrJ) rr rrrrrrrrr r rrEsJ   r)r$ functoolsrnumpyrtypingrrr numpy.typingnptjitr lru_cachertraceback_utilfilterr,r=boolBackend ArrayLikerr r r rsX   #