o Ein1@sddlZddlmZddlZddlmZddlmZm Z m Z m Z ddl mZ ddgZejdd d d$d d Zejd dddZejjejdd d dd iddedejdede ejedfdede de jfddZee_ejj  d%dedejde ejedfdede jf ddZejj  d%dedejde ee efde ejedfdede jf ddZejj  d%dedejde ejedfdede jf d d!Zejj  d%dedejde ejedfdede jf d"d#ZdS)&N)util)partial)CallableMappingUnionTuplerollflipcsidffdd S)Ncs|fdd|D|||S)Ncg|]}|qSr .0xtr S/home/ubuntu/veenaModal/venv/lib/python3.10/site-packages/einx/op/vmap_with_axis.py .....r )exprs_in tensors_in exprs_outopkwargsbackendcrr r s..r rrr rrr rr)tracecs|duri}t|t|krtdt|dt|tt|dkr(tdt|t|D]}|D]}t|tjjj rDtdq6q0t|dkrPtdtdd|Dr]td i|}fd d t ||D} |}t j ||d \}}d d|Dfddt |}fdd |Dt|dk} ddfddt|tD} t|tD]}|D]} | j| v| krtd| jdqqdd|D ddD} |  rtd| r' | krtdfdd t ||D} dd | D}dd | D}tdd|DdksJdd|D nt|dks0J|d fd!d" fd#d D| rVt fd$dtd D}nd%d |Dt fd&dttd D}t|d krt|dkr|n|d |d'<t|trt|}nt|tjjs|fd(d)}||i|}t|ttfs|f}t|tkrtdtd*t|fd+d t ||D}t j|||d }||fS),N Expected z input tensor(s), got rz'All output expressions must be the samezConcatenation not allowedzOnly one output tensor allowedcss|] }tj|VqdSN)einxtracer is_scalarrtensorr r r sz(vmap_with_axis_stage3..z.At least one input tensor must be a non-scalarcs$g|]\}}tjj||jdqS)r)r$r% call_factoryshape)rr(exprr*r rr"sz)vmap_with_axis_stage3..r*cSsh|] }|D]}|jqqSr name)rr-axisr r r *sz(vmap_with_axis_stage3..cst|tjjjo |jvSr# isinstancer$r-stage3Axisr/r-) in_axis_namesr ris_broadcast_axis,sz0vmap_with_axis_stage3..is_broadcast_axiscsg|] }tjj|qSr )r$r-r4removerr-)r8r rr0scSstjj| Sr#)r$r-r4 is_markedr6r r r is_vmapped7sz)vmap_with_axis_stage3..is_vmappedcs$h|]}|D] }|r|jqqSr r.)rrootv)r<r rr1:szAxis z( appears both as vmapped and non-vmappedcS>h|]}|D]}t|tjjjrtjj|r|jqqSr allr3r$r-r4r5r;r/rexpr_inr0r r rr1E cSr?r r@)rexpr_outr0r r rr1KrDz8Marked output axes must be a subset of marked input axeszMWhen using multiple input tensors the same axes must be marked in all tensorsc s<g|]\}}tj|r||fn tj||dddqS)rF) broadcastr)r$r%r&rtranspose_broadcast)rrC tensor_in)r exprs_out_flat_without_broadcastr rrZs cSg|]}|dqSrr r r r rrfrcSrJ)rr r r r rrgrcSs h|] }t|dkrt|qSrK)lenr:r r rr1hs cSr?r r@rBr r rr1irDrcspdd|D}g}D] }t|tjjjr/|j|vr/t|jtjjjr*|j}| |qtjjj |S)NcSs"h|] }t|tjjjr|jqSr r2)rr0r r rr1us z>vmap_with_axis_stage3..to_op_output..) rAr3r$r-r4r5r/parentMarkerappendListmaybe)expr_out_flat_wb axis_namesnew_axesr0)rCr r to_op_outputts  z+vmap_with_axis_stage3..to_op_outputcr r r )rrR)rUr rrsc3s"|] \}}|jvr|VqdSr#r.)rir0)marked_input_axesr rr)s cSsg|]}t|qSr )listr:r r rrrc3s,|]tfddDrVqdS)c3s|] }|jvVqdSr#r.)raxes_in)rVrWr rr)sz2vmap_with_axis_stage3...N)any)r)rYrW)rVrr)s r0cs@tjj||tdkrddDdStjdjdS)NrcSsg|] }tj|jqSr )r$r%Tensorr,r:r r rrsz;vmap_with_axis_stage3....r)argsroutput)r$r%applyrLr[r,)r\r) concrete_opexprs_op_outputr rrs  z'vmap_with_axis_stage3..z output tensor(s), got cs(g|]\}}}tj|||ddqS)r*r)rrG)rrC tensor_outrEr*r rrs)rL ValueErrorsetrXrAr3r$r-r4 Concatenationzip all_to_tensorrflattenr/ differencetuple enumeraterangestrgetattrr%Tracer unflatten)rrrrrrr=r-exprs_out_flattranspose_firstvmapped_axis_namesr>marked_output_axesr axis_indices tensors_outr ) rYrr_rCr`rIr7r8r<rWrUrvmap_with_axis_stage3 s                        rvT)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$r-Equation)rrC tensor_shaper r rrs zparse..cSg|]}tj|qSr rx)rrEr r rrcSs4g|]\}}tjj|t|dtjfdddqS).N)depth1depth2)r$r-rynpasarraynewaxis)rkr>r r rrs"F)rw cse_concat)r$rr!_clean_description_and_parametersr-stage1parse_oprLOp __deepcopy__rbsolvereitems) descriptionrw tensor_shapes parametersrexprsrrr r rparses8      * rcsddfdd S)Nr*cs$|gfdd|DRi|S)Ncr r r r rr rrrrr )rrtensorsrrr rrsrr r r rrrr)rrwrrrrrrwrrc OsXt|gdd|DRd|i|\}}t||||||d\}}t|dkr*|dS|S)aApplies a function to the marked axes of the input tensors by passing the ``axis`` argument and relying on implicit broadcasting rules. The function ``op`` must accept input tensors and an ``axis`` argument specifying the indices of the axes along which the operation is applied. When the function is applied on scalars, the ``axis`` argument is not passed. For multiple input tensors, the function must follow `Numpy broadcasting rules `_. Args: description: Description string for the operation in einx notation. tensors: Input tensors or tensor factories matching the description string. op: Backend operation. Is called with ``op(tensor, axis=...)``. If ``op`` is a string, retrieves the attribute of ``backend`` with the same name. kwargs: Additional keyword arguments that are passed to ``op``. 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 operation if ``graph=False``, otherwise the graph representation of the operation. Examples: Reverse order of elements along an axis: >>> x = np.random.uniform(size=(16, 20)) >>> einx.vmap_with_axis("a [b] -> a [b]", x, op=np.flip).shape (16, 20) Roll elements along two axes: >>> x = np.random.uniform(size=(16, 20)) >>> einx.vmap_with_axis( ... "a ([b c]) -> a ([b c])", ... x, ... op=partial(np.roll, shift=(2, 2)), ... b=2, ... ).shape (16, 20) Compute sum along axis: >>> x = np.random.uniform(size=(16, 20)) >>> einx.vmap_with_axis("a ([b] c) -> c a", x, op=np.sum, b=2).shape (16, 20) cSr{r )r$r% get_shaper'r r rr!r|z"vmap_with_axis..rw)rrrrr)rrvrL) rrrrwrrrrrr r rvmap_with_axissA    rr(cKt||fd||d|S)zASpecialization of :func:`einx.vmap_with_axis` with ``op="flip"``.r rrrwrrr(rrwrr r rr , shiftcKs t||fd|d|i|d|S)zjSpecialization of :func:`einx.vmap_with_axis` with ``op="roll"`` and ``kwargs={"shift": shift}``. r r)rrrrwr)rr(rrrwrr r rr 8s cKr)zCSpecialization of :func:`einx.vmap_with_axis` with ``op="softmax"``softmaxrrrr r rrOrrcKr)zGSpecialization of :func:`einx.vmap_with_axis` with ``op="log_softmax"`` log_softmaxrrrr r rr[s r)NN)NT) r$rnumpyr functoolsrtypingrrrr numpy.typingnpt _op_namesjitrv lru_cachertraceback_utilfilterrlr[Backendbool ArrayLikerr intr rrr r r rs    ,# D