AVh 8dZddlZddlmZddlmZddlmZddlm Z ddl m Z ddlmZdd l mZdd l mZdd l mZdd lmZdd lmZddlmZddlmZm Z m!Z!ddl"m#Z#edddddddddddddddd d!d"d#d$d%Z$dhd&e#e!e$fd'e#e!ejJfd(e&d)e&d*e&d+e#e!e$ff d,Z'ed-ejPe'Z)d&e#e!e$fd'e#e!ejJfd(e&d)e&d*e&d+e#e!e$ff d.Z*ed/Z+gdfd0e#e!ejXfd1e#e e!e+ffd2Z-ed3ejPe-Z.d0e#e!ejXfd1e#e e!e+ffd4Z/ed5dddddddddddddd d!d"d#d$d%Z0dhd&e#e!e0fd6e#e!ejJfd+e#e!e0ffd7Z1ed8ejPe1Z2d&e#e!e0fd6e#e!ejJfd+e#e!e0ffd9Z3did=e4d>e4d?e5d@e5dAe5dBe&d+e#e!ejlffdCZ7edDejPe7Z8d=e4d>e4d?e5d@e5dAe5dBe&d+e#e!ejlffdEZ9dhdFe4fdGZ:edHejPe:Z;dFe4fdIZ<edJdddddd#Z=dhd&e#e!e=fd'e#e!ejJfd+e#e!e=ffdKZ>edLejPe>Z?d&e#e!e=fd'e#e!ejJfd+e#e!e=ffdMZ@edNddZAedOddZBedPddZCgdfdQe#e e!eAfdRe#e e!eBfdSe#e e!eCfdTe#e!ejlfdUe#e!ejJff dVZDedWejPeDZEdQe#e e!eAfdRe#e e!eBfdSe#e e!eCfdTe#e!ejlfdUe#e!ejJff dXZFedYddZGedZddZHed[ddZIgggdfd\e#e e!eGfdRe#e e!eHfdSe#e e!eIfdTe#e!ejlfdUe#e!ejJff d]ZJed^ejPeJZKd\e#e e!eGfdRe#e e!eHfdSe#e e!eIfdTe#e!ejlfdUe#e!ejJff d_ZLed`ddZMedaddZNedbddZOdcgdfdQe#e e!eMfdRe#e e!eNfdSe#e e!eOfdTe#e!ejlfdUe&f ddZPedeejPePZQdQe#e e!eMfdRe#e e!eNfdSe#e e!eOfdTe#e!ejlfdUe&f dfZRdjdge#e e!ejJfdTe#e!ejlfdUe&fdhZSediejPeSZTdge#e e!ejJfdTe#e!ejlfdUe&fdjZUedkddZVedlddZWedmddZXdcgggdfd\e#e e!eVfdRe#e e!eWfdSe#e e!eXfdTe#e!ejlfdUe&f dnZYedoejPeYZZd\e#e e!eVfdRe#e e!eWfdSe#e e!eXfdTe#e!ejlfdUe&f dpZ[edqddZ\edrddZ]edsddZ^dcgdfdte#e e!e\fdRe#e e!e]fdSe#e e!e^fdTe#e!ejlfdUe&f duZ_edvejPe_Z`dte#e e!e\fdRe#e e!e]fdSe#e e!e^fdTe#e!ejlfdUe&f dwZaedxddZbedyddZcedzddZddcgggdfdte#e e!ebfdRe#e e!ecfdSe#e e!edfdTe#e!ejlfdUe&f d{Zeed|ejPeeZfdte#e e!ebfdRe#e e!ecfdSe#e e!edfdTe#e!ejlfdUe&f d}Zged~Zhdhdehd+e#e!ehffdZiedejPeiZjdehd+e#e!ehffdZkdhdZledejPelZmdZnedZoggdcdfd&e#e!eofdUe&fdZpedejPepZqd&e#e!eofdUe&fdZrdjd&e#e!ejfdUe&fdZtedejPetZud&e#e!ejfdUe&fdZvgdcdfdUe&fdZwedejPewZxdUe&fdZydkdFe4d+e#e!ejffdZ{edejPe{Z|dFe4d+e#e!ejffdZ}dlde#e!ejfde#e!ejfde#e!ejfde&de&de&de4dFe4fdZedejPeZde#e!ejfde#e!ejfde#e!ejfde&de&de&de4dFe4fdZdlde#e!ejfde#e!ejfde#e!ejfde&de&de&de4dFe4fdZedejPeZde#e!ejfde#e!ejfde#e!ejfde&de&de&de4dFe4fdZdlde#e!ejfde#e!ejfde#e!ejfde&de&de&de4dFe4fdZedejPeZde#e!ejfde#e!ejfde#e!ejfde&de&de&de4dFe4fdZdlde#e!ejfde#e!ejfde&de&de&de4dFe4fdZedejPeZde#e!ejfde#e!ejfde&de&de&de4dFe4fdZdlde#e!ejfde#e!ejfde#e!ejfde#e!ejfde&de&de&de4dFe4fdZedejPeZde#e!ejfde#e!ejfde#e!ejfde#e!ejfde&de&de&de4dFe4fdZdlde#e!ejfde#e!ejfde#e!ejfde&de&de&de4dFe4fdZedejPeZde#e!ejfde#e!ejfde#e!ejfde&de&de&de4dFe4fdZdlde#e!ejfde#e!ejfde&de&de&de4dFe4fdZedejPeZde#e!ejfde#e!ejfde&de&de&de4dFe4fdZdlde#e!ejfde#e!ejfde#e!ejfde#e!ejfde&de&de&de4dFe4fdZedejPeZde#e!ejfde#e!ejfde#e!ejfde#e!ejfde&de&de&de4dFe4fdZdlde#e!ejfde#e!ejfde&de&de&de4dFe4fdZedejPeZde#e!ejfde#e!ejfde&de&de&de4dFe4fdZdlde#e!ejfde#e!ejfde&de&de&de4dFe4fdZedejPeZde#e!ejfde#e!ejfde&de&de&de4dFe4fdZdlde#e!ejfde#e!ejfde#e!ejfde&de&de&de4dFe4fdÄZedīejPeZde#e!ejfde#e!ejfde#e!ejfde&de&de&de4dFe4fdńZdlde#e!ejfde#e!ejfde#e!ejfde&de&de&de4dFe4fdƄZedǫejPeZde#e!ejfde#e!ejfde#e!ejfde&de&de&de4dFe4fdȄZdlde#e!ejfde&de&de&de4dFe4f dɄZedʫejPeZde#e!ejfde&de&de&de4dFe4f d˄Zed̎ZdjdedUe&d+e#e!effd̈́ZedΫejPeZdedUe&d+e#e!effdτZdjdUe&fdЄZedѫejPeZdUe&fd҄ZdhdUe#e!ejJffdӄZedԫejPeZdUe#e!ejJffdՄZed֎ZdhdUe#e!ejJfded+e#e!effdׄZedثejPeZdUe#e!ejJfded+e#e!effdلZedڎZdhd&e#e!effdۄZedܫejPeZd&e#e!effd݄ZdhdބZed߫ejPeZdZedZggdfd&e#e!efd+e#e!ejffdZedejPeZd&e#e!efd+e#e!ejffdZgdfd+e#e!ejffdZedejPeZd+e#e!ejffdZedZgdfd0e#e!ejXfdede&fdZedejPeëZd0e#e!ejXfdede&fdZŐdhde&dFe4fdZedejPeƫZde&dFe4fdZejdgdZʐdlde&de&de&de4dFe4f dZedejPe˫Zde&de&de&de4dFe4f dZejdgdZΐdlde&de&de&de4dFe4f dZedejPeϫZde&de&de&de4dFe4f dZejdgdZҐdlde&de&de&de4dFe4f dZedejPeӫZde&de&de&de4dFe4f dZejdddgZ֐dlde&de&de&de4dFe4f dZedejPe׫Zde&de&de&de4dFe4f dZejdgdZڐdlde&de&de&de4dFe4f dZedejPe۫Zde&de&de&de4dFe4f d Zejd gd Zސdlde&de&de&de4dFe4f d Zed ejPe߫Zde&de&de&de4dFe4f dZejdddgZdlde&de&de&de4dFe4f dZedejPeZde&de&de&de4dFe4f dZejdgdZdlde&de&de&de4dFe4f dZedejPeZde&de&de&de4dFe4f dZejdddgZdlde&de&de&de4dFe4f dZedejPeZde&de&de&de4dFe4f dZejdddgZdlde&de&de&de4dFe4f dZedejPeZde&de&de&de4dFe4f dZejd gd!Zdlde&de&de&de4dFe4f d"Zed#ejPeZde&de&de&de4dFe4f d$Zejd%gd&Zdlde&de&de&de4dFe4f d'Zed(ejPeZde&de&de&de4dFe4f d)Zdlde&de&de&de4dFe4d+e#e!ejff d*Zed+ejPeZde&de&de&de4dFe4d+e#e!ejff d,Zdhd1e#e e!ejfd-e#e e!ejfdFe4fd.Zed/ejPeZd1e#e e!ejfd-e#e e!ejfdFe4fd0Zdhd1Zed2ejPeZd3Zdhd+e#e!ejlffd4Zed5ejPeZd+e#e!ejlffd6Zdhd7e#e!ejfd8e#e!ejfde&d9e&d+e#e!ejff d:Zed;ejPeZd7e#e!ejfd8e#e!ejfde&d9e&d+e#e!ejff d<Zdhd+e#e!ejJffd=Z ed>ejPe Z d+e#e!ejJffd?Z dmdUe#e!ejJfd@e&fdAZ edBejPe Z dUe#e!ejJfd@e&fdCZdDd:d 235F 12M .0F::L L&'3 (' .=  & &- ##At,,  # #    & !4D){  # #   s0 E F E;;FFF**GGzraw_ops.AllToAllctj|d}tj|d}tj|d}tj|g|tjtj tj tjtjtjtjtjtjtjtjtjtj tj"tj$tj&tj(tj*tj,tj.g\}\}t1j2|tj }||g}d|d|d|d|f} tj4dd|| ||} tj6rtj8d|| | | \} | S) Nr*r+r,r2sAllToAllrEattrsr1r0r/)r>r?args_to_matching_eager_dtypesfloat32float64int32uint8int16int8 complex64int64qint8quint8qint32bfloat16qint16quint16uint16 complex128halfuint32uint64boolr9convert_to_tensorrrBrF) r(r)r*r+r,r0r1_attr_TrOrNrIs rPr<r<kse&&'79KL%%o7HI/!!+}=+55ugsW__V]VeVegngtgtv}wDwDFMFSFSU\UaUacjctctv}vCvCELERERT[TbTbdkdrdrt{tDtDFMFTFTV]VeVegnguguw~wIwIKRKWKWY`YgYgipiwiwy@yEyEEHI'8E++,_m[ B&   [!L#)s ?' ""$ L&'3 (' .) TV_AssignVariableXlaConcatND_Trrrr_atypes.Float16rr_atypes.Float8e4m3b11fnuz_atypes.Float8e4m3fn_atypes.Float8e4m3fnuz_atypes.Float8e5m2_atypes.Float8e5m2fnuzrrr _atypes.Int4rrrr r!r"r#_atypes.Resource_atypes.Stringr$r% _atypes.UInt4r&r'_atypes.VariantresourcerEc tjxstj}|j}|jr t j |d|||d|d| }|St|ttfst!d|zt#|} t|ttfst!d|z|D cgc]} t%j&| d}} |g}t|ttfst!d|z|D cgc]} t%j&| d}} t)j*d||||| \} } } } | S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t||||||S#t j$rYUwxYwcc} wcc} w) a5Concats input tensor across all dimensions. An op which merges slices the input tensor based on the given num_splits attribute, strips paddings optionally, and writes the merged tensor without paddings to the resource variable. This op may be generated via the TPU bridge. For example, with `input` tensor: ``` [[0, 1], [4, 5]] [[2, 3], [6, 7]] [[8, 9], [12, 13]] [[10, 11], [14, 15]] ``` `num_splits`: ``` [2, 2] ``` and `paddings`: ``` [1, 1] ``` the expected `outputs` is: ``` [[0, 1, 2], [4, 5, 6], [8, 9, 10]] ``` Args: resource: A `Tensor` of type `resource`. Resource variable for concatenated input tensors across all dimensions. inputs: A list of at least 1 `Tensor` objects with the same type. Input tensor slices in row-major order to merge across all dimensions. All inputs must have the same shape. num_concats: A list of `ints`. Number of ways to merge per dimension. paddings: An optional list of `ints`. Defaults to `[]`. Optional list of right paddings per dimension to strip from the final merged tensor. These paddings must not exceed the dimension size of the merged result prior to stripping paddings. name: A name for the operation (optional). Returns: The created Operation. AssignVariableXlaConcatND num_concatspaddingsNr~rr0r1RExpected list for 'inputs' argument to 'assign_variable_xla_concat_nd' Op, not %r.WExpected list for 'num_concats' argument to 'assign_variable_xla_concat_nd' Op, not %r.TExpected list for 'paddings' argument to 'assign_variable_xla_concat_nd' Op, not %r.)r{rEr~rr0)r3rr4r5rr6r7r8r9r:r;,assign_variable_xla_concat_nd_eager_fallbackr= isinstancelisttuple TypeErrorlenr>r?r@rA)r{rEr~rr0rGrHrIrJ_attr_N_irKrLrMs rPassign_variable_xla_concat_ndrsf    0h..0$ #\\ 11 )46{J:gn FT5M *  68> ? @@ K' K$ /  68C D EEALL"""2}5L+L H HtUm ,  68@ A BB;C CBhJ/ C( C'88#hv1<.6TC!QX *A  & &- ##At,,  # #    9 F h  # #   MDs<E6F8F=F E00F F  FF54F5z!raw_ops.AssignVariableXlaConcatNDct|ttfstd|zt |}t|ttfstd|z|Dcgc]}t j |d}}|g}t|ttfstd|z|Dcgc]}t j |d}}t jt||g\}}tj|tj}|gt|z} d|d|d|d|f} t jdd | | || } d} | Scc}wcc}w) Nrrr~rrr2NsAssignVariableXlaConcatNDrrT) rrrrrr>r?rVr9rlrWr{r) r{rEr~rr0r1rrrmrOrNrIs rPrrss FT5M *  68> ? @@ K' K$ /  68C D EEALL"""2}5L+L H HtUm ,  68@ A BB;C CBhJ/ C( C33DL#rJ/'6  # #Hg.>.> ?(d6l*, #w { h &   91$0C"& (' ' .#MDs E#ETV_CollectivePermute_Tsource_target_pairsctjxstj}|j}|jr t j |d|||}|Stjd|||\}}}} | dd}tj r7d|j#df} |j$} tj&d| | ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t||||S#t j$rYwxYw)a+An Op to permute tensors across replicated TPU instances. Each instance supplies its own input. For example, suppose there are 4 TPU instances: `[A, B, C, D]`. Passing source_target_pairs=`[[0,1],[1,2],[2,3],[3,0]]` gets the outputs: `[D, A, B, C]`. Args: input: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `qint16`, `quint16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. The local input to be permuted. Currently only supports float and bfloat16. source_target_pairs: A `Tensor` of type `int32`. A tensor with shape [num_pairs, 2]. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `input`. CollectivePermuteNr0r1)r(rr0r2)r3rr4r5rr6r7r8r9r:r;!collective_permute_eager_fallbackr=r@rAr>rBrCrErF) r(rr0rGrHrIrJrKrLrMrNrOs rPcollective_permutersE(    0h..0$ #\\ 11 !40CEg n(8851D"&(!QX QK' ""$3%%c* +F::L \67< (' .+  & &- ##At,,  # #    . $4T;;  # #   0CD"C==DDD))D?>D?zraw_ops.CollectivePermutechtj|g|tjtjtj tj tjtjtjtjtjtjtjtjtjtj tj"tj$tj&tj(tj*g\}\}t-j.|tj }||g}d|f}tj0dd||||}tj2rtj4d||||\}|S)Nr2sCollectivePermuterSrTr)r>rVrWrXrYrZr[r\r]r^r_r`rarbrcrdrerfrgrhrirjr9rlrrBrF)r(rr0r1rmrOrNrIs rPrr3s55ugsW__V]VeVegngtgtv}wDwDFMFSFSU\UaUacjctctv}vCvCELERERT[TbTbdkdrdrt{tDtDFMFTFTV]VeVegnguguw~wIwIKRKWKWY`YgYgipiwiwEz{'8E../BGMMR,-, >&   11\#)s ?' ""$ \67< (' .rnFTembedding_configtpu_embedding_configis_global_initenable_whole_mesh_compilations compilation_failure_closes_chipstpu_cancellation_closes_chipsctjxstj}|j}|jr& t j |d|d|d|d|d|d|d|} | S|d }tj|d}|d }tj|d}|d }tj|d}|d }tj|d}|d }tj|d}|d }tj |d}t#j$d|||||||\} } } } | dd} tj&rd| j)dd| j)dd| j+dd| j+dd| j+dd| j-df }| j.}tj0d||| | \} | S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t|||||||| S#t j$rYwxYw)aSets up the centralized structures for a distributed TPU system. Args: embedding_config: An optional `string`. Defaults to `""`. Reserved. Do not use. tpu_embedding_config: An optional `string`. Defaults to `""`. Serialized tensorflow.tpu.TPUEmbeddingConfiguration that describes the embedding lookups of the program. is_global_init: An optional `bool`. Defaults to `False`. Reserved. Do not use. enable_whole_mesh_compilations: An optional `bool`. Defaults to `False`. compilation_failure_closes_chips: An optional `bool`. Defaults to `True`. tpu_cancellation_closes_chips: An optional `int`. Defaults to `0`. name: A name for the operation (optional). Returns: A `Tensor` of type `string`. ConfigureDistributedTPUrrrrrrN)rrrrrrr0r1rFTr)rrrrrrr0)r3rr4r5rr6r7r8r9r:r;(configure_distributed_tpu_eager_fallbackr=r>make_str make_boolr?r@rArBget_attr_get_attr_boolrDrErF)rrrrrrr0rGrHrIrJrKrLrMrNrOs rPconfigure_distributed_tpurAs&    0h..0$ #\\  11 '/A02F.*J&(J(*I% 'gn"&&'79KL!!**+?AWXN%%n6FG.#+%*"#+#5#56TVv#w %-'+$%-%7%78XZ|%}""*$%!"*"3"34QSr"s'88!4D8L2@B`DdA^(,.!QX QK' ""$ #,,/A"B$cll3I&J 2 23C D.  !AB0  !CD- ?@BF::L !<B (' .s  & &- ##At,,  # #     5+3')G+K(E  # #   s0$GH!G<<HHH,,IIzraw_ops.ConfigureDistributedTPUc |d}tj|d}|d}tj|d}|d}tj|d}|d}tj|d}|d}tj|d}|d }tj|d }g}d|d|d|d|d|d |f } tjd d || || } tj rtj d|| | | \} | S)NrrrFrrTrrrsConfigureDistributedTPUrSrTr)r>rrr?rrBrF) rrrrrrr0r1rOrNrIs rPrrsH&&'79KL!!**+?AWXN%%n6FG.#+%*"#+#5#56TVv#w %-'+$%-%7%78XZ|%}""*$%!"*"3"34QSr"s,  02H(."$B$&F!#@  B&   7$0C"& ('""$ !<B (' .rnconfigctjxstj}|j}|jr t j |d|d|}|Stj|d}tj d||\}}}}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t|||S#t j$rYwxYw)a Sets up TPUEmbedding in a distributed TPU system. Args: config: A `string`. Serialized tensorflow.tpu.TPUEmbeddingConfiguration that describes the embedding lookups of the program. name: A name for the operation (optional). Returns: The created Operation. ConfigureTPUEmbeddingrNrr0r1rr0)r3rr4r5rr6r7r8r9r:r;&configure_tpu_embedding_eager_fallbackr=r>rr@rA) rr0rGrHrIrJrKrLrMs rPconfigure_tpu_embeddingrs    0h..0$ #\\ 11 %tXv?g n   VX .&'88T;!QX *  & &- ##At,,  # #    3d..  # #   s0BC#B>>CC C))C?>C?zraw_ops.ConfigureTPUEmbeddingcxtj|d}g}d|f}tjdd||||}d}|S)NrsConfigureTPUEmbeddingrrT)r>rrrr0r1rOrNrIs rPrrsK   VX .&, f &   5q#)s ?' ' .rnTV_CrossReplicaSum_Tctjxstj}|j}|jr t j |d|||}|Stjd|||\}}}} | dd}tj r7d|j#df} |j$} tj&d| | ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t||||S#t j$rYwxYw)aWAn Op to sum inputs across replicated TPU instances. Each instance supplies its own input. For example, suppose there are 8 TPU instances: `[A, B, C, D, E, F, G, H]`. Passing group_assignment=`[[0,2,4,6],[1,3,5,7]]` sets `A, C, E, G` as group 0, and `B, D, F, H` as group 1. Thus we get the outputs: `[A+C+E+G, B+D+F+H, A+C+E+G, B+D+F+H, A+C+E+G, B+D+F+H, A+C+E+G, B+D+F+H]`. Args: input: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`, `int32`, `uint32`. The local input to the sum. group_assignment: A `Tensor` of type `int32`. An int32 tensor with shape [num_groups, num_replicas_per_group]. `group_assignment[i]` represents the replica ids in the ith subgroup. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `input`. CrossReplicaSumNr)r(r)r0r2)r3rr4r5rr6r7r8r9r:r; cross_replica_sum_eager_fallbackr=r@rAr>rBrCrErF) r(r)r0rGrHrIrJrKrLrMrNrOs rPcross_replica_sumrsE*    0h..0$ #\\ 11 u.>@g n(889I $&!QX QK' ""$3%%c* +F::L <: (' .)  & &- ##At,,  # #    - !$88  # #   rzraw_ops.CrossReplicaSumc tj|g|tjtjtj tj tjtjg\}\}tj|tj}||g}d|f}tjdd||||}tjrtjd||||\}|S)Nr2sCrossReplicaSumrSrTr)r>rVrWrhrcrXrYrZrir9rlrrBrF)r(r)r0r1rmrOrNrIs rPrr&s55ugsW\\SZScSceletetv}wFwFHOHUHUW^WeWeEhi'8E++,&   /<#)s ?' ""$ <: (' .rn4TV_DynamicEnqueueTPUEmbeddingArbitraryTensorBatch_T14TV_DynamicEnqueueTPUEmbeddingArbitraryTensorBatch_T24TV_DynamicEnqueueTPUEmbeddingArbitraryTensorBatch_T3sample_indices_or_row_splitsembedding_indicesaggregation_weights mode_overridedevice_ordinalc "tjxstj}|j}|jr! t j |d||||||d| } | St|ttfst!d|zt#|} t|ttfst!d|zt#|| k7rt%dt#|| fzt|ttfst!d|zt#|| k7rt%d t#|| fz|g}t|ttfst!d |z|D cgc]} t'j(| d}} t+j,d||||||| \} } }}|S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t||||||||S#t j$rYwxYwcc} w) a5 Eases the porting of code that uses tf.nn.embedding_lookup_sparse(). embedding_indices[i] and aggregation_weights[i] correspond to the ith feature. The tensors at corresponding positions in the three input lists (sample_indices, embedding_indices and aggregation_weights) must have the same shape, i.e. rank 1 with dim_size() equal to the total number of lookups into the table described by the corresponding feature. Args: sample_indices_or_row_splits: A list of at least 1 `Tensor` objects with the same type in: `int32`, `int64`. A list of rank 2 Tensors specifying the training example to which the corresponding embedding_indices and aggregation_weights values belong. If the size of its first dimension is 0, we assume each embedding_indices belongs to a different sample. Both int32 and int64 are allowed and will be converted to int32 internally. Or a list of rank 1 Tensors specifying the row splits for splitting embedding_indices and aggregation_weights into rows. It corresponds to ids.row_splits in embedding_lookup(), when ids is a RaggedTensor. When enqueuing N-D ragged tensor, only the last dimension is allowed to be ragged. the row splits is 1-D dense tensor. When empty, we assume a dense tensor is passed to the op Both int32 and int64 are allowed and will be converted to int32 internally. embedding_indices: A list with the same length as `sample_indices_or_row_splits` of `Tensor` objects with the same type in: `int32`, `int64`. A list of rank 1 Tensors, indices into the embedding tables. Both int32 and int64 are allowed and will be converted to int32 internally. aggregation_weights: A list with the same length as `sample_indices_or_row_splits` of `Tensor` objects with the same type in: `float32`, `float64`. A list of rank 1 Tensors containing per training example aggregation weights. Both float32 and float64 are allowed and will be converted to float32 internally. mode_override: A `Tensor` of type `string`. A string input that overrides the mode specified in the TPUEmbeddingConfiguration. Supported values are {'unspecified', 'inference', 'training', 'backward_pass_only'}. When set to 'unspecified', the mode set in TPUEmbeddingConfiguration is used, otherwise mode_override is used. device_ordinal: A `Tensor` of type `int32`. The TPU device to use. Should be >= 0 and less than the number of TPU cores in the task on which the node is placed. combiners: An optional list of `strings`. Defaults to `[]`. A list of string scalars, one for each embedding table that specify how to normalize the embedding activations after weighted summation. Supported combiners are 'mean', 'sum', or 'sqrtn'. It is invalid to have the sum of the weights be 0 for 'mean' or the sum of the squared weights be 0 for 'sqrtn'. If combiners isn't passed, the default is to use 'sum' for all tables. name: A name for the operation (optional). Returns: The created Operation. .DynamicEnqueueTPUEmbeddingArbitraryTensorBatch combinersN)rr0r1Expected list for 'sample_indices_or_row_splits' argument to 'dynamic_enqueue_tpu_embedding_arbitrary_tensor_batch' Op, not %r.tExpected list for 'embedding_indices' argument to 'dynamic_enqueue_tpu_embedding_arbitrary_tensor_batch' Op, not %r.List argument 'embedding_indices' to 'dynamic_enqueue_tpu_embedding_arbitrary_tensor_batch' Op with length %d must match length %d of argument 'sample_indices_or_row_splits'.vExpected list for 'aggregation_weights' argument to 'dynamic_enqueue_tpu_embedding_arbitrary_tensor_batch' Op, not %r.List argument 'aggregation_weights' to 'dynamic_enqueue_tpu_embedding_arbitrary_tensor_batch' Op with length %d must match length %d of argument 'sample_indices_or_row_splits'.lExpected list for 'combiners' argument to 'dynamic_enqueue_tpu_embedding_arbitrary_tensor_batch' Op, not %r.rrrrrrr0)r3rr4r5rr6r7r8r9r:r;Cdynamic_enqueue_tpu_embedding_arbitrary_tensor_batch_eager_fallbackr=rrrrr ValueErrorr>rr@rArrrrrrr0rGrHrIrJr_srKrLrMs rP4dynamic_enqueue_tpu_embedding_arbitrary_tensor_batchr8sl    0h..0$ #\\  11 >$&79L~{I?gn 04- @  MOk l mm , -' %e} 5  MO` a bb w&  K  ) * ++ '$ 7  MOb c dd (  K !7+ , --I Ie} -  MOX Y ZZ=FFbx  [1F)F'888WsL]NaHUIWDM?CE!QX *e  & &- ##At,,  # #    P &(9 }nDd44  # #   @Gs6FH G'GGGG22H H z6raw_ops.DynamicEnqueueTPUEmbeddingArbitraryTensorBatchc Bt|ttfstd|zt |}t|ttfstd|zt ||k7rt dt ||fzt|ttfstd|zt ||k7rt dt ||fz|g}t|ttfstd|z|D cgc]} t j| d}} t jt||tjtjgtj\} }t jt||tjtjgtj\} }t jt||tjtjgtj\} }tj|tj }tj|tj}t|t|zt|z||gz} d| d | d | d |d|f }t j"d d | |||}d}|Scc} w)NrrrrrrrT1T2T3rs.DynamicEnqueueTPUEmbeddingArbitraryTensorBatchrrT)rrrrrrr>rrVrWrZr_rXrYr9rlstringrrrrrrrr0r1rr_attr_T1_attr_T2_attr_T3rOrNrIs rPrrs 04- @  MOk l mm , -' %e} 5  MO` a bb w&  K  ) * ++ '$ 7  MOb c dd (  K !7+ , --I Ie} -  MOX Y ZZ=FFbx  [1F)F+3+J+J4PlKmoru|vCvCELERERuUW^WdWd,e(( ( ( ? ?EV@WY\_f_l_lnun{n{^~AHANAN!O( "*"A"A$GZB[]`cjcrcrt{uDuDcGIPIXIX#Y( ((G-)).'--H.23d;L6MMPTUhPiimz}KmLL, (D(D(C y &   N|6s"& (' ' .Gs2J1TV_DynamicEnqueueTPUEmbeddingRaggedTensorBatch_T11TV_DynamicEnqueueTPUEmbeddingRaggedTensorBatch_T21TV_DynamicEnqueueTPUEmbeddingRaggedTensorBatch_T3 sample_splitsc tjxstj} | j} | jr' t j | d| |||||d|d|d|d|} | St|ttfst!d|zt#|}t|ttfst!d |zt#||k7rt%d t#||fzt|ttfst!d |zt#||k7rt%d t#||fzt|ttfst!d |z|Dcgc]}t'j(|d}}|g}t|ttfst!d|z|Dcgc]}t'j*|d}}|g}t|ttfst!d|z|Dcgc]}t'j(|d}}|g}t|ttfst!d|z|Dcgc]}t'j(|d}}t-j.d||||||||||  \}}}}|S#t j$r } tj| | Yd} ~ nd} ~ wt j$rYnwxYw t|||||||||| |  S#t j$rYwxYwcc}wcc}wcc}wcc}w)a'TODO: add doc. Args: sample_splits: A list of at least 1 `Tensor` objects with the same type in: `int32`, `int64`. embedding_indices: A list with the same length as `sample_splits` of `Tensor` objects with the same type in: `int32`, `int64`. aggregation_weights: A list with the same length as `sample_splits` of `Tensor` objects with the same type in: `float32`, `float64`. mode_override: A `Tensor` of type `string`. device_ordinal: A `Tensor` of type `int32`. table_ids: A list of `ints`. combiners: An optional list of `strings`. Defaults to `[]`. max_sequence_lengths: An optional list of `ints`. Defaults to `[]`. num_features: An optional list of `ints`. Defaults to `[]`. name: A name for the operation (optional). Returns: The created Operation. +DynamicEnqueueTPUEmbeddingRaggedTensorBatchr table_idsmax_sequence_lengths num_featuresN)rrrrr0r1mExpected list for 'sample_splits' argument to 'dynamic_enqueue_tpu_embedding_ragged_tensor_batch' Op, not %r.qExpected list for 'embedding_indices' argument to 'dynamic_enqueue_tpu_embedding_ragged_tensor_batch' Op, not %r.List argument 'embedding_indices' to 'dynamic_enqueue_tpu_embedding_ragged_tensor_batch' Op with length %d must match length %d of argument 'sample_splits'.sExpected list for 'aggregation_weights' argument to 'dynamic_enqueue_tpu_embedding_ragged_tensor_batch' Op, not %r.List argument 'aggregation_weights' to 'dynamic_enqueue_tpu_embedding_ragged_tensor_batch' Op with length %d must match length %d of argument 'sample_splits'.iExpected list for 'table_ids' argument to 'dynamic_enqueue_tpu_embedding_ragged_tensor_batch' Op, not %r.iExpected list for 'combiners' argument to 'dynamic_enqueue_tpu_embedding_ragged_tensor_batch' Op, not %r.tExpected list for 'max_sequence_lengths' argument to 'dynamic_enqueue_tpu_embedding_ragged_tensor_batch' Op, not %r.lExpected list for 'num_features' argument to 'dynamic_enqueue_tpu_embedding_ragged_tensor_batch' Op, not %r.) rrrrrrrrrr0)r3rr4r5rr6r7r8r9r:r;@dynamic_enqueue_tpu_embedding_ragged_tensor_batch_eager_fallbackr=rrrrrrr>r?rr@rA)rrrrrrrrrr0rGrHrIrJrrrrKrLrMs rP1dynamic_enqueue_tpu_embedding_ragged_tensor_batchrs$    0h..0$ #\\  11 ;T(*=} Y Y 4n g n MD%= 1  JLY Z [[  ' %e} 5  JL] ^ __ w&  <  ) * ++ '$ 7  JL_ ` aa (  < !7+ , -- Ie} -  JLU V WW=FFbx  [1F)FI Ie} -  JLU V WW=FFbx  [1F)F! (4- 8  JL` a bbSggB(++B0FGggL L4- 0  JLX Y ZZCOOB(##B7O,O'885]IZK^ERFTAJAJL`DP<@ B!QX *S  & &- ##At,,  # #    M *,? 94H#$D ::  # #   <GGhPsH%I:K5K:)K?4L:K J((KKKK21K2z3raw_ops.DynamicEnqueueTPUEmbeddingRaggedTensorBatchc &t|ttfstd|zt |} t|ttfstd|zt || k7rt dt || fzt|ttfstd|zt || k7rt dt || fzt|ttfstd|z|D cgc]} t j| d}} |g}t|ttfstd|z|D cgc]} t j| d }} |g}t|ttfstd |z|D cgc]} t j| d }} |g}t|ttfstd |z|D cgc]} t j| d }} t jt|| tjtjgtj\}}t jt|| tjtjgtj\}}t jt|| tjtjgtj\}}tj |tj"}tj |tj}t|t|zt|z||gz}d|d|d|d| d |d|d |d |f}t j$dd||| | }d}|Scc} wcc} wcc} wcc} w)Nrrrrrrrrrrrrrrrrrs+DynamicEnqueueTPUEmbeddingRaggedTensorBatchrrTrrrrrrr>r?rrVrWrZr_rXrYr9rlrr)rrrrrrrrrr0r1rrrrrrrOrNrIs rPrrIs MD%= 1  JLY Z [[  ' %e} 5  JL] ^ __ w&  <  ) * ++ '$ 7  JL_ ` aa (  < !7+ , -- Ie} -  JLU V WW=FFbx  [1F)FI Ie} -  JLU V WW=FFbx  [1F)F! (4- 8  JL` a bbSggB(++B0FGggL L4- 0  JLX Y ZZCOOB(##B7O,O$;;D Eases the porting of code that uses tf.nn.embedding_lookup_sparse(). embedding_indices[i] and aggregation_weights[i] correspond to the ith feature. The tensors at corresponding positions in the three input lists (sample_indices, embedding_indices and aggregation_weights) must have the same shape, i.e. rank 1 with dim_size() equal to the total number of lookups into the table described by the corresponding feature. Args: sample_indices_or_row_splits: A list of at least 1 `Tensor` objects with the same type in: `int32`, `int64`. A list of rank 2 Tensors specifying the training example to which the corresponding embedding_indices and aggregation_weights values belong. If the size of its first dimension is 0, we assume each embedding_indices belongs to a different sample. Both int32 and int64 are allowed and will be converted to int32 internally. Or a list of rank 1 Tensors specifying the row splits for splitting embedding_indices and aggregation_weights into rows. It corresponds to ids.row_splits in embedding_lookup(), when ids is a RaggedTensor. When enqueuing N-D ragged tensor, only the last dimension is allowed to be ragged. the row splits is 1-D dense tensor. When empty, we assume a dense tensor is passed to the op Both int32 and int64 are allowed and will be converted to int32 internally. embedding_indices: A list with the same length as `sample_indices_or_row_splits` of `Tensor` objects with the same type in: `int32`, `int64`. A list of rank 1 Tensors, indices into the embedding tables. Both int32 and int64 are allowed and will be converted to int32 internally. aggregation_weights: A list with the same length as `sample_indices_or_row_splits` of `Tensor` objects with the same type in: `float32`, `float64`. A list of rank 1 Tensors containing per training example aggregation weights. Both float32 and float64 are allowed and will be converted to float32 internally. mode_override: A `Tensor` of type `string`. A string input that overrides the mode specified in the TPUEmbeddingConfiguration. Supported values are {'unspecified', 'inference', 'training', 'backward_pass_only'}. When set to 'unspecified', the mode set in TPUEmbeddingConfiguration is used, otherwise mode_override is used. device_ordinal: An optional `int`. Defaults to `-1`. The TPU device to use. Should be >= 0 and less than the number of TPU cores in the task on which the node is placed. combiners: An optional list of `strings`. Defaults to `[]`. A list of string scalars, one for each embedding table that specify how to normalize the embedding activations after weighted summation. Supported combiners are 'mean', 'sum', or 'sqrtn'. It is invalid to have the sum of the weights be 0 for 'mean' or the sum of the squared weights be 0 for 'sqrtn'. If combiners isn't passed, the default is to use 'sum' for all tables. name: A name for the operation (optional). Returns: The created Operation. 'EnqueueTPUEmbeddingArbitraryTensorBatchrrNrrr0r1wExpected list for 'sample_indices_or_row_splits' argument to 'enqueue_tpu_embedding_arbitrary_tensor_batch' Op, not %r.lExpected list for 'embedding_indices' argument to 'enqueue_tpu_embedding_arbitrary_tensor_batch' Op, not %r.List argument 'embedding_indices' to 'enqueue_tpu_embedding_arbitrary_tensor_batch' Op with length %d must match length %d of argument 'sample_indices_or_row_splits'.nExpected list for 'aggregation_weights' argument to 'enqueue_tpu_embedding_arbitrary_tensor_batch' Op, not %r.List argument 'aggregation_weights' to 'enqueue_tpu_embedding_arbitrary_tensor_batch' Op with length %d must match length %d of argument 'sample_indices_or_row_splits'.rdExpected list for 'combiners' argument to 'enqueue_tpu_embedding_arbitrary_tensor_batch' Op, not %r.r)r3rr4r5rr6r7r8r9r:r;;enqueue_tpu_embedding_arbitrary_tensor_batch_eager_fallbackr=rrrrrrr>r?rr@rArs rP,enqueue_tpu_embedding_arbitrary_tensor_batchrsl    0h..0$ #\\  11 7$&79L' g n 04- @  EGc d ee , -' %e} 5  EGX Y ZZ w&  K  ) * ++ '$ 7  EGZ [ \\ (  K !7+ , --N$$^5EF.I Ie} -  EGP Q RR=FFbx  [1F)F'881PlEVGZANBP=F8<>!QX *k  & &- ##At,,  # #    H &(9 }^Dd44  # #   FG6 F/,H'/G6GG65G6:H H$#H$z/raw_ops.EnqueueTPUEmbeddingArbitraryTensorBatchc 0t|ttfstd|zt |}t|ttfstd|zt ||k7rt dt ||fzt|ttfstd|zt ||k7rt dt ||fz|d}t j|d}|g}t|ttfstd|z|D cgc]} t j| d }} t jt||tjtjgtj\} }t jt||tjtjgtj\} }t jt||tjtjgtj\} }tj |tj"}t|t|zt|z|gz} d | d | d | d |d|d |f }t j$dd| |||}d}|Scc} w)Nrrrrrrrrrrrrrs'EnqueueTPUEmbeddingArbitraryTensorBatchrrTrrs rPrrs 04- @  EGc d ee , -' %e} 5  EGX Y ZZ w&  K  ) * ++ '$ 7  EGZ [ \\ (  K !7+ , --N$$^5EF.I Ie} -  EGP Q RR=FFbx  [1F)F+3+J+J4PlKmoru|vCvCELERERuUW^WdWd,e(( ( ( ? ?EV@WY\_f_l_lnun{n{^~AHANAN!O( "*"A"A$GZB[]`cjcrcrt{uDuDcGIPIXIX#Y( ((G-23d;L6MMPTUhPiimzl{{, (D(D(CNK <&   G$0C"& (' ' .G Jbatchc vtjxstj}|j}|jr t j |d|||d|}|St|ttfst!d|zt#|}|d}t%j&|d}t)j*d||||\} } } } | S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t|||||S#t j$rYwxYw)aiAn op that enqueues a list of input batch tensors to TPUEmbedding. Args: batch: A list of at least 1 `Tensor` objects with type `int32`. A list of 1D tensors, one for each embedding table, containing the indices into the tables. mode_override: A `Tensor` of type `string`. A string input that overrides the mode specified in the TPUEmbeddingConfiguration. Supported values are {'unspecified', 'inference', 'training', 'backward_pass_only'}. When set to 'unspecified', the mode set in TPUEmbeddingConfiguration is used, otherwise mode_override is used. device_ordinal: An optional `int`. Defaults to `-1`. The TPU device to use. Should be >= 0 and less than the number of TPU cores in the task on which the node is placed. name: A name for the operation (optional). Returns: The created Operation. EnqueueTPUEmbeddingIntegerBatchrNrr0r1WExpected list for 'batch' argument to 'enqueue_tpu_embedding_integer_batch' Op, not %r.r)rrrr0)r3rr4r5rr6r7r8r9r:r;2enqueue_tpu_embedding_integer_batch_eager_fallbackr=rrrrrr>r?r@rA) rrrr0rGrHrIrJrrKrLrMs rP#enqueue_tpu_embedding_integer_batchr8sT(    0h..0$ #\\ 11 /um.*gn ED%= )  <>C D EE J'N$$^5EF.'88)9F:H04 6!QX *1  & &- ##At,,  # #    ? ~D  # #   s0CDC55D DD""D87D8z'raw_ops.EnqueueTPUEmbeddingIntegerBatchct|ttfstd|zt |}|d}t j |d}tj|tj}tj|tj}t||gz}d|d|f}t jdd||||}d}|S)NrrrrsEnqueueTPUEmbeddingIntegerBatchrrT)rrrrrr>r?r9convert_n_to_tensorrWrZrlrr) rrrr0r1rrOrNrIs rPrrps ED%= )  <>C D EE J'N$$^5EF.  " "5'-- 8%((G-e ., *N ;&   ?$0C"& (' ' .rn*TV_EnqueueTPUEmbeddingRaggedTensorBatch_T1*TV_EnqueueTPUEmbeddingRaggedTensorBatch_T2*TV_EnqueueTPUEmbeddingRaggedTensorBatch_T3c Htjxstj} | j} | jr( t j | d| ||||d|d|d|d|d|} | St|ttfst!d |zt#|}t|ttfst!d |zt#||k7rt%d t#||fzt|ttfst!d |zt#||k7rt%d t#||fzt|ttfst!d|z|Dcgc]}t'j(|d}}|d}t'j(|d}|g}t|ttfst!d|z|Dcgc]}t'j*|d}}|g}t|ttfst!d|z|Dcgc]}t'j(|d}}|g}t|ttfst!d|z|Dcgc]}t'j(|d}}t-j.d||||||||||  \}}}}|S#t j$r } tj| | Yd} ~ nd} ~ wt j$rYnwxYw t|||||||||| |  S#t j$rYwxYwcc}wcc}wcc}wcc}w)a+ Eases the porting of code that uses tf.nn.embedding_lookup(). sample_splits[i], embedding_indices[i] and aggregation_weights[i] correspond to the ith feature. table_ids[i] indicates which embedding table to look up ith feature. The tensors at corresponding positions in two of the input lists, embedding_indices and aggregation_weights, must have the same shape, i.e. rank 1 with dim_size() equal to the total number of lookups into the table described by the corresponding feature. Args: sample_splits: A list of at least 1 `Tensor` objects with the same type in: `int32`, `int64`. A list of rank 1 Tensors specifying the break points for splitting embedding_indices and aggregation_weights into rows. It corresponds to ids.row_splits in embedding_lookup(), when ids is a RaggedTensor. embedding_indices: A list with the same length as `sample_splits` of `Tensor` objects with the same type in: `int32`, `int64`. A list of rank 1 Tensors, indices into the embedding tables. It corresponds to ids.values in embedding_lookup(), when ids is a RaggedTensor. aggregation_weights: A list with the same length as `sample_splits` of `Tensor` objects with the same type in: `float32`, `float64`. A list of rank 1 Tensors containing per training example aggregation weights. It corresponds to the values field of a RaggedTensor with the same row_splits as ids in embedding_lookup(), when ids is a RaggedTensor. mode_override: A `Tensor` of type `string`. A string input that overrides the mode specified in the TPUEmbeddingConfiguration. Supported values are {'unspecified', 'inference', 'training', 'backward_pass_only'}. When set to 'unspecified', the mode set in TPUEmbeddingConfiguration is used, otherwise mode_override is used. table_ids: A list of `ints`. A list of integers specifying the identifier of the embedding table (offset of TableDescriptor in the TPUEmbeddingConfiguration) to lookup the corresponding input. The ith input is looked up using table_ids[i]. The size of the table_ids list must be equal to that of sample_indices, embedding_indices and aggregation_weights. device_ordinal: An optional `int`. Defaults to `-1`. The TPU device to use. Should be >= 0 and less than the number of TPU cores in the task on which the node is placed. combiners: An optional list of `strings`. Defaults to `[]`. A list of string scalars, one for each embedding table that specify how to normalize the embedding activations after weighted summation. Supported combiners are 'mean', 'sum', or 'sqrtn'. It is invalid to have the sum of the weights be 0 for 'mean' or the sum of the squared weights be 0 for 'sqrtn'. If combiners isn't passed, the default is to use 'sum' for all tables. max_sequence_lengths: An optional list of `ints`. Defaults to `[]`. num_features: An optional list of `ints`. Defaults to `[]`. name: A name for the operation (optional). Returns: The created Operation. $EnqueueTPUEmbeddingRaggedTensorBatchrrrrrNrrrrrr0r1eExpected list for 'sample_splits' argument to 'enqueue_tpu_embedding_ragged_tensor_batch' Op, not %r.iExpected list for 'embedding_indices' argument to 'enqueue_tpu_embedding_ragged_tensor_batch' Op, not %r.List argument 'embedding_indices' to 'enqueue_tpu_embedding_ragged_tensor_batch' Op with length %d must match length %d of argument 'sample_splits'.kExpected list for 'aggregation_weights' argument to 'enqueue_tpu_embedding_ragged_tensor_batch' Op, not %r.List argument 'aggregation_weights' to 'enqueue_tpu_embedding_ragged_tensor_batch' Op with length %d must match length %d of argument 'sample_splits'.aExpected list for 'table_ids' argument to 'enqueue_tpu_embedding_ragged_tensor_batch' Op, not %r.raExpected list for 'combiners' argument to 'enqueue_tpu_embedding_ragged_tensor_batch' Op, not %r.lExpected list for 'max_sequence_lengths' argument to 'enqueue_tpu_embedding_ragged_tensor_batch' Op, not %r.dExpected list for 'num_features' argument to 'enqueue_tpu_embedding_ragged_tensor_batch' Op, not %r.) rrrrrrrrrr0)r3rr4r5rr6r7r8r9r:r;8enqueue_tpu_embedding_ragged_tensor_batch_eager_fallbackr=rrrrrrr>r?rr@rA)rrrrrrrrrr0rGrHrIrJrrrrKrLrMs rP)enqueue_tpu_embedding_ragged_tensor_batchrsl    0h..0$ #\\  11 4dM. .+y+)+? &g n MD%= 1  BDQ R SS  ' %e} 5  BDU V WW w&  <  ) * ++ '$ 7  BDW X YY (  < !7+ , -- Ie} -  BDM N OO=FFbx  [1F)FN$$^5EF.I Ie} -  BDM N OO=FFbx  [1F)F! (4- 8  BDX Y ZZSggB(++B0FGggL L4- 0  BDP Q RRCOOB(##B7O,O'88.mBSDW>K:C?M:CEY=I59 ;!QX *Y  & &- ##At,,  # #    E *,? )4H#$D ::  # #   <GGhPH&JL9LLLK(KKK K66L  L z,raw_ops.EnqueueTPUEmbeddingRaggedTensorBatchc t|ttfstd|zt |} t|ttfstd|zt || k7rt dt || fzt|ttfstd|zt || k7rt dt || fzt|ttfstd|z|D cgc]} t j| d}} |d}t j|d }|g}t|ttfstd |z|D cgc]} t j| d }} |g}t|ttfstd |z|D cgc]} t j| d }} |g}t|ttfstd|z|D cgc]} t j| d}} t jt|| tjtjgtj\}}t jt|| tjtjgtj\}}t jt|| tjtjgtj\}}tj |tj"}t|t|zt|z|gz}d|d|d|d| d |d |d|d |d|f}t j$dd||| | }d}|Scc} wcc} wcc} wcc} w)Nrrrrrrrrrrrrrrrrrrrs$EnqueueTPUEmbeddingRaggedTensorBatchrrTr)rrrrrrrrrr0r1rrrrrrrOrNrIs rPrrs# MD%= 1  BDQ R SS  ' %e} 5  BDU V WW w&  <  ) * ++ '$ 7  BDW X YY (  < !7+ , -- Ie} -  BDM N OO=FFbx  [1F)FN$$^5EF.I Ie} -  BDM N OO=FFbx  [1F)F! (4- 8  BDX Y ZZSggB(++B0FGggL L4- 0  BDP Q RRCOOB(##B7O,O$;;D= 0 and less than the number of TPU cores in the task on which the node is placed. combiners: An optional list of `strings`. Defaults to `[]`. A list of string scalars, one for each embedding table that specify how to normalize the embedding activations after weighted summation. Supported combiners are 'mean', 'sum', or 'sqrtn'. It is invalid to have the sum of the weights be 0 for 'mean' or the sum of the squared weights be 0 for 'sqrtn'. If combiners isn't passed, the default is to use 'sum' for all tables. name: A name for the operation (optional). Returns: The created Operation. EnqueueTPUEmbeddingSparseBatchrrNr_Expected list for 'sample_indices' argument to 'enqueue_tpu_embedding_sparse_batch' Op, not %r.bExpected list for 'embedding_indices' argument to 'enqueue_tpu_embedding_sparse_batch' Op, not %r.List argument 'embedding_indices' to 'enqueue_tpu_embedding_sparse_batch' Op with length %d must match length %d of argument 'sample_indices'.dExpected list for 'aggregation_weights' argument to 'enqueue_tpu_embedding_sparse_batch' Op, not %r.List argument 'aggregation_weights' to 'enqueue_tpu_embedding_sparse_batch' Op with length %d must match length %d of argument 'sample_indices'.rZExpected list for 'combiners' argument to 'enqueue_tpu_embedding_sparse_batch' Op, not %r.)rrrrrrr0)r3rr4r5rr6r7r8r9r:r;1enqueue_tpu_embedding_sparse_batch_eager_fallbackr=rrrrrrr>r?rr@rA)rrrrrrr0rGrHrIrJrrrKrLrMs rP"enqueue_tpu_embedding_sparse_batchr)bsZ    0h..0$ #\\  11 .n. .+yBgn NT5M 2  ;=K L MM  ' %e} 5  ;=N O PP w&  =  ) * ++ '$ 7  ;=P Q RR (  = !7+ , --N$$^5EF.I Ie} -  ;=F G HH=FFbx  [1F)F'88(Q8E9G4=D J!QX *i  & &- ##At,,  # #    > +-@ )  # #   FGrz&raw_ops.EnqueueTPUEmbeddingSparseBatchc 0t|ttfstd|zt |}t|ttfstd|zt ||k7rt dt ||fzt|ttfstd|zt ||k7rt dt ||fz|d}t j|d}|g}t|ttfstd|z|D cgc]} t j| d }} t jt||tjtjgtj\} }t jt||tjtjgtj\} }t jt||tjtjgtj\} }tj |tj"}t|t|zt|z|gz} d | d | d | d |d|d |f }t j$dd| |||}d}|Scc} w)Nr"r#r$r%r&rrr'rrrrrsEnqueueTPUEmbeddingSparseBatchrrTr)rrrrrrr0r1rrrrrrOrNrIs rPr(r(s NT5M 2  ;=K L MM  ' %e} 5  ;=N O PP w&  =  ) * ++ '$ 7  ;=P Q RR (  = !7+ , --N$$^5EF.I Ie} -  ;=F G HH=FFbx  [1F)F%<(??$GZB[[_l^mm, (D(D(CNK <&   >$0C"& (' ' .Gr*TV_EnqueueTPUEmbeddingSparseTensorBatch_T1*TV_EnqueueTPUEmbeddingSparseTensorBatch_T2*TV_EnqueueTPUEmbeddingSparseTensorBatch_T3c Htjxstj} | j} | jr( t j | d| ||||d|d|d|d|d|} | St|ttfst!d |zt#|}t|ttfst!d |zt#||k7rt%d t#||fzt|ttfst!d |zt#||k7rt%d t#||fzt|ttfst!d|z|Dcgc]}t'j(|d}}|d}t'j(|d}|g}t|ttfst!d|z|Dcgc]}t'j*|d}}|g}t|ttfst!d|z|Dcgc]}t'j(|d}}|g}t|ttfst!d|z|Dcgc]}t'j(|d}}t-j.d||||||||||  \}}}}|S#t j$r } tj| | Yd} ~ nd} ~ wt j$rYnwxYw t|||||||||| |  S#t j$rYwxYwcc}wcc}wcc}wcc}w)a Eases the porting of code that uses tf.nn.embedding_lookup_sparse(). sample_indices[i], embedding_indices[i] and aggregation_weights[i] correspond to the ith feature. table_ids[i] indicates which embedding table to look up ith feature. The tensors at corresponding positions in the three input lists (sample_indices, embedding_indices and aggregation_weights) must have the same shape, i.e. rank 1 with dim_size() equal to the total number of lookups into the table described by the corresponding feature. Args: sample_indices: A list of at least 1 `Tensor` objects with the same type in: `int32`, `int64`. A list of rank 1 Tensors specifying the training example to which the corresponding embedding_indices and aggregation_weights values belong. It corresponds to sp_ids.indices[:,0] in embedding_lookup_sparse(). embedding_indices: A list with the same length as `sample_indices` of `Tensor` objects with the same type in: `int32`, `int64`. A list of rank 1 Tensors, indices into the embedding tables. It corresponds to sp_ids.values in embedding_lookup_sparse(). aggregation_weights: A list with the same length as `sample_indices` of `Tensor` objects with the same type in: `float32`, `float64`. A list of rank 1 Tensors containing per training example aggregation weights. It corresponds to sp_weights.values in embedding_lookup_sparse(). mode_override: A `Tensor` of type `string`. A string input that overrides the mode specified in the TPUEmbeddingConfiguration. Supported values are {'unspecified', 'inference', 'training', 'backward_pass_only'}. When set to 'unspecified', the mode set in TPUEmbeddingConfiguration is used, otherwise mode_override is used. table_ids: A list of `ints`. A list of integers specifying the identifier of the embedding table (offset of TableDescriptor in the TPUEmbeddingConfiguration) to lookup the corresponding input. The ith input is looked up using table_ids[i]. The size of the table_ids list must be equal to that of sample_indices, embedding_indices and aggregation_weights. device_ordinal: An optional `int`. Defaults to `-1`. The TPU device to use. Should be >= 0 and less than the number of TPU cores in the task on which the node is placed. combiners: An optional list of `strings`. Defaults to `[]`. A list of string scalars, one for each embedding table that specify how to normalize the embedding activations after weighted summation. Supported combiners are 'mean', 'sum', or 'sqrtn'. It is invalid to have the sum of the weights be 0 for 'mean' or the sum of the squared weights be 0 for 'sqrtn'. If combiners isn't passed, the default is to use 'sum' for all tables. max_sequence_lengths: An optional list of `ints`. Defaults to `[]`. num_features: An optional list of `ints`. Defaults to `[]`. name: A name for the operation (optional). Returns: The created Operation. $EnqueueTPUEmbeddingSparseTensorBatchrrrrrNr fExpected list for 'sample_indices' argument to 'enqueue_tpu_embedding_sparse_tensor_batch' Op, not %r.iExpected list for 'embedding_indices' argument to 'enqueue_tpu_embedding_sparse_tensor_batch' Op, not %r.List argument 'embedding_indices' to 'enqueue_tpu_embedding_sparse_tensor_batch' Op with length %d must match length %d of argument 'sample_indices'.kExpected list for 'aggregation_weights' argument to 'enqueue_tpu_embedding_sparse_tensor_batch' Op, not %r.List argument 'aggregation_weights' to 'enqueue_tpu_embedding_sparse_tensor_batch' Op with length %d must match length %d of argument 'sample_indices'.aExpected list for 'table_ids' argument to 'enqueue_tpu_embedding_sparse_tensor_batch' Op, not %r.raExpected list for 'combiners' argument to 'enqueue_tpu_embedding_sparse_tensor_batch' Op, not %r.lExpected list for 'max_sequence_lengths' argument to 'enqueue_tpu_embedding_sparse_tensor_batch' Op, not %r.dExpected list for 'num_features' argument to 'enqueue_tpu_embedding_sparse_tensor_batch' Op, not %r.) rrrrrrrrrr0)r3rr4r5rr6r7r8r9r:r;8enqueue_tpu_embedding_sparse_tensor_batch_eager_fallbackr=rrrrrrr>r?rr@rA)rrrrrrrrrr0rGrHrIrJrrrrKrLrMs rP)enqueue_tpu_embedding_sparse_tensor_batchr:sh    0h..0$ #\\  11 4dN. .+y+)+? &g n NT5M 2  BDR S TT  ' %e} 5  BDU V WW w&  =  ) * ++ '$ 7  BDW X YY (  = !7+ , -- Ie} -  BDM N OO=FFbx  [1F)FN$$^5EF.I Ie} -  BDM N OO=FFbx  [1F)F! (4- 8  BDX Y ZZSggB(++B0FGggL L4- 0  BDP Q RRCOOB(##B7O,O'88.~BSDW>K:C?M:CEY=I59 ;!QX *Y  & &- ##At,,  # #    E +-@ )4H#$D ::  # #   <GGhPrz,raw_ops.EnqueueTPUEmbeddingSparseTensorBatchc t|ttfstd|zt |} t|ttfstd|zt || k7rt dt || fzt|ttfstd|zt || k7rt dt || fzt|ttfstd|z|D cgc]} t j| d}} |d}t j|d }|g}t|ttfstd |z|D cgc]} t j| d }} |g}t|ttfstd |z|D cgc]} t j| d }} |g}t|ttfstd|z|D cgc]} t j| d}} t jt|| tjtjgtj\}}t jt|| tjtjgtj\}}t jt|| tjtjgtj\}}tj |tj"}t|t|zt|z|gz}d|d|d|d| d |d |d|d |d|f}t j$dd||| | }d}|Scc} wcc} wcc} wcc} w)Nr0r1r2r3r4r5rrrr6rr7rr8rrrrrs$EnqueueTPUEmbeddingSparseTensorBatchrrTr)rrrrrrrrrr0r1rrrrrrrOrNrIs rPr9r9s' NT5M 2  BDR S TT  ' %e} 5  BDU V WW w&  =  ) * ++ '$ 7  BDW X YY (  = !7+ , -- Ie} -  BDM N OO=FFbx  [1F)FN$$^5EF.I Ie} -  BDM N OO=FFbx  [1F)F! (4- 8  BDX Y ZZSggB(++B0FGggL L4- 0  BDP Q RRCOOB(##B7O,O%<(??$GZB[[_l^mm, (D(D(CNKK #%9> &   Da$0C"& (' ' .MGGhPr) TV_InfeedDequeue_dtyperrrrrprrrqrrrsrtrurrrrvrrrr r!r"r#rwrxr$r%ryr&r'rzdtypec tjxstj}|j}|jr t j |d|d|d|}|Stj|d}tj|d}t!j"d|||\}}}} | dd}tj$rHd|j'dd|j)df} |j*} tj,d| | ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t||||S#t j$rY!wxYw)a1A placeholder op for a value that will be fed into the computation. Args: dtype: A `tf.DType`. The type of elements in the tensor. shape: A `tf.TensorShape` or list of `ints`. The shape of the tensor. name: A name for the operation (optional). Returns: A `Tensor` of type `dtype`. InfeedDequeuer=shapeNr=r@r0r1)r=r@r0)r3rr4r5rr6r7r8r9r:r;infeed_dequeue_eager_fallbackr=r> make_type make_shaper@rArBrCrrErF) r=r@r0rGrHrIrJrKrLrMrNrOs rPinfeed_dequeuerEsv    0h..0$ #\\ 11 otWeWeEg n   UG ,%   eW -%'88uE>!QX QK' ""$s))'2Gll7#%F::L vw8 (' .-  & &- ##At,,  # #    *U499  # #   0DE!D<<EEE((E?>E?zraw_ops.InfeedDequeuectj|d}tj|d}g}d|d|f}tjdd||||}tjrtj d||||\}|S)Nr=r@s InfeedDequeuerSrTr?)r>rCrDrrBrF)r=r@r0r1rOrNrIs rPrBrBs   UG ,%   eW -%, UGU +&   -q#)s ?' ""$ vw8 (' .rnc tjxstj}|j}|jr t j |d|d|d|}|St|ttfst!d|z|Dcgc]}t#j$|d}}t|ttfst!d|z|Dcgc]}t#j&|d}}t)j*d|||\} } } } | dd}|s| St#j,rHd| j/dd| j/df} | j0} t#j2d| | ||S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t||||S#t j$rYwxYwcc}wcc}w) aFetches multiple values from infeed as an XLA tuple. Args: dtypes: A list of `tf.DTypes` that has length `>= 1`. The element types of each element in `outputs`. shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`). The shapes of each tensor in `outputs`. name: A name for the operation (optional). Returns: A list of `Tensor` objects of type `dtypes`. InfeedDequeueTuplershapesNrrJr0r1IExpected list for 'dtypes' argument to 'infeed_dequeue_tuple' Op, not %r.IExpected list for 'shapes' argument to 'infeed_dequeue_tuple' Op, not %r.)rrJr0)r3rr4r5rr6r7r8r9r:r;#infeed_dequeue_tuple_eager_fallbackr=rrrrr>rCrDr@rArBrrErF)rrJr0rGrHrIrJ_trrKrLrMrNrOs rPinfeed_dequeue_tuplerPs    0h..0$ #\\ 11 "D(FHfNg n FT5M *  -/5 6 778> >H  r8 , >& > FT5M *  -/5 6 779? ?"H  H - ?& ?'88VFG!QX QK'  J ""$ X.ll8$&F::L lFG= .?  & &- ##At,,  # #    0Tt==  # #    ? @s<E0G$ G)0F7FF76F7;G G! G!zraw_ops.InfeedDequeueTuplect|ttfstd|z|Dcgc]}t j |d}}t|ttfstd|z|Dcgc]}t j |d}}g}d|d|f}t jdt|||||}t jrt jd||||Scc}wcc}w)NrLrrMrJsInfeedDequeueTuplerTrI) rrrrr>rCrDrrrBrF) rrJr0r1rOrrOrNrIs rPrNrNMs FT5M *  -/5 6 778> >H  r8 , >& > FT5M *  -/5 6 779? ?"H  H - ?& ?, fh /&   2CK$0C"& ('""$ lFG= . ? @s C)0C.) TV_InfeedEnqueue_dtyperrrrrprrrqrrrsrtrurrrrvrrrr r!r"r#rwrxr$r%ryr&r'rzc tjxstj}|j}|jr! t j |d||d|d|d| }|S|g}tj|d}|g}t|t t"fst%d|z|D cgc]} tj&| d}} |d}tj&|d}t)j*d||||| \} } } } | S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t||||||S#t j$rYwxYwcc} w) a1An op which feeds a single Tensor value into the computation. Args: input: A `Tensor`. A tensor that will be provided using the infeed mechanism. shape: An optional `tf.TensorShape` or list of `ints`. Defaults to `[]`. The shape of the tensor. layout: An optional list of `ints`. Defaults to `[]`. A vector holding the requested layout in minor-to-major sequence. If a layout attribute is passed, but its values are all -1, the layout will be computed by the infeed operation. device_ordinal: An optional `int`. Defaults to `-1`. The TPU device to use. This should be -1 when the Op is running on a TPU device, and >= 0 when the Op is running on the CPU device. name: A name for the operation (optional). Returns: The created Operation. InfeedEnqueuer@layoutrN)r@rUrr0r1CExpected list for 'layout' argument to 'infeed_enqueue' Op, not %r.r)r(r@rUrr0)r3rr4r5rr6r7r8r9r:r;infeed_enqueue_eager_fallbackr=r>rDrrrrr?r@rA) r(r@rUrr0rGrHrIrJrrKrLrMs rPinfeed_enqueuerXes*    0h..0$ #\\ 11 otUGUHf.*gn ] E   eW -% ^ F FT5M *  ')/ 0 117= =H  b( + =& =N$$^5EF.'88uE&(6TC!QX *7  & &- ##At,,  # #    * uVN  # #    >s6D&E7ED//EE EE43E4zraw_ops.InfeedEnqueuec|g}tj|d}|g}t|ttfst d|z|Dcgc]}tj |d}}|d}tj |d}tj|g|g\}\}|g}d|d|d|d|f} tjdd|| || } d} | Scc}w) Nr@rVrUrrr=s InfeedEnqueuerrT) r>rDrrrrr?rVr) r(r@rUrr0r1r _attr_dtyperOrNrIs rPrWrWs ] E   eW -% ^ F FT5M *  ')/ 0 117= =H  b( + =& =N$$^5EF."995'3K+x, ['5(FN $&   -q#)s ?' ' . >sC ctjxstj}|j}|jr t j |d||d|}|S|d}tj|d}tj d|||\}}}} |S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t||||S#t j$rYwxYw)aAn op which enqueues prelinearized buffer into TPU infeed. Args: input: A `Tensor` of type `variant`. A variant tensor representing linearized output. device_ordinal: An optional `int`. Defaults to `-1`. The TPU device to use. This should be -1 when the Op is running on a TPU device and = 0 when the Op is running on the CPU device. name: A name for the operation (optional). Returns: The created Operation. InfeedEnqueuePrelinearizedBufferrNrr)r(rr0)r3rr4r5rr6r7r8r9r:r;2infeed_enqueue_prelinearized_buffer_eager_fallbackr=r>r?r@rA) r(rr0rGrHrIrJrKrLrMs rP#infeed_enqueue_prelinearized_bufferr^s    0h..0$ #\\ 11 0$.*gnN$$^5EF.'88*%;I157!QX *#  & &- ##At,,  # #    ? TtEE  # #   s0BC)CCC!C00DDz(raw_ops.InfeedEnqueuePrelinearizedBufferc|d}tj|d}tj|tj }|g}d|f}tj dd||||}d}|S)Nrrs InfeedEnqueuePrelinearizedBufferrrT)r>r?r9rlrWvariantr)r(rr0r1rOrNrIs rPr]r]srN$$^5EF.   8%, n -&   @!$0C"& (' ' .rnc \tjxstj}|j}|jr! t j |d||d|d|d| }|St|ttfst!d|z|D cgc]} t#j$| d}} |g}t|ttfst!d|z|D cgc]} t#j&| d}} |d }t#j&|d}t)j*d||||| \} } } } | S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t||||||S#t j$rY@wxYwcc} wcc} w) aFeeds multiple Tensor values into the computation as an XLA tuple. Args: inputs: A list of `Tensor` objects. A list of tensors that will be provided using the infeed mechanism. shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`). The shapes of each tensor in `inputs`. layouts: An optional list of `ints`. Defaults to `[]`. A vector holding the requested layout in minor-to-major sequence for all the tuple shapes, in the order the shapes appear in the "shapes" input. The layout elements for a sub-shape can be set to -1, in which case the corresponding layout will be computed by the infeed operation. device_ordinal: An optional `int`. Defaults to `-1`. The TPU device to use. This should be -1 when the Op is running on a TPU device, and >= 0 when the Op is running on the CPU device. name: A name for the operation (optional). Returns: The created Operation. InfeedEnqueueTuplerJlayoutsrN)rJrcrr0r1IExpected list for 'shapes' argument to 'infeed_enqueue_tuple' Op, not %r.JExpected list for 'layouts' argument to 'infeed_enqueue_tuple' Op, not %r.r)rErJrcrr0)r3rr4r5rr6r7r8r9r:r;#infeed_enqueue_tuple_eager_fallbackr=rrrrr>rDr?r@rA)rErJrcrr0rGrHrIrJrrrKrLrMs rPinfeed_enqueue_tuplergs,    0h..0$ #\\ 11 "D&(FI!>3gn FT5M *  -/5 6 779? ?"H  H - ?& ? _G GdE] +  -/6 7 889@ @"X  r9 - @' @N$$^5EF.'88VFG-;$H!QX *;  & &- ##At,,  # #    0 'd>>  # #    @ As<D.F$F).E5EE54E59F F! F!zraw_ops.InfeedEnqueueTuplect|ttfstd|z|Dcgc]}t j |d}}|g}t|ttfstd|z|Dcgc]}t j |d}}|d}t j |d}t j||\}}t|} d|d|d|d|f} t jdd | | || } d} | Scc}wcc}w) NrdrJrercrrrsInfeedEnqueueTuplerrT) rrrrr>rDr?convert_to_mixed_eager_tensorsr) rErJrcrr0r1rr _attr_dtypesrOrNrIs rPrfrf1s, FT5M *  -/5 6 779? ?"H  H - ?& ? _G GdE] +  -/6 7 889@ @"X  r9 - @' @N$$^5EF.!@@M,f, lHfiN $&   2Al#)s ?' ' .% @ As C:4C?ctjxstj}|j}|jr t j |d|d|}|S|d}tj|d}tj d||\}}}}|dd}tj"r7d|j%df} |j&} tj(d| | ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t|||S#t j$rYwxYw)zWhether TPU Embedding is initialized in a distributed TPU system. Args: config: An optional `string`. Defaults to `""`. name: A name for the operation (optional). Returns: A `Tensor` of type `bool`. IsTPUEmbeddingInitializedrNrrr)r3rr4r5rr6r7r8r9r:r;+is_tpu_embedding_initialized_eager_fallbackr=r>rr@rArBrrErF) rr0rGrHrIrJrKrLrMrNrOs rPis_tpu_embedding_initializedrnKsV    0h..0$ #\\ 11 )46Cg n ^ F   VX .&'88#F?!QX QK' ""$ X. /F::L #\67D (' .-  & &- ##At,,  # #    8d..  # #   s0C((D/;DD/.D/3 EEEz!raw_ops.IsTPUEmbeddingInitializedc|d}tj|d}g}d|f}tjdd||||}tjrtjd||||\}|S)NrrsIsTPUEmbeddingInitializedrSrTrl)r>rrrBrFrs rPrmrmws~ ^ F   VX .&, f &   91$0C"& ('""$ #\67D (' .rn parametersmomenta velocities num_shardsshard_idtable_id table_namec tjxstj} | j} | jr' t j | d||||d|d|d|d|d|} | Stj|d}tj|d}|d }tj|d}|d }tj|d}|d }tj|d}t!j"d||||||||| \} } }}|S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t||||||||||  S#t j$rYwxYw) aLoad ADAM embedding parameters. An op that loads optimization parameters into HBM for embedding. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to install parameters that are loaded from a checkpoint before a training loop is executed. Args: parameters: A `Tensor` of type `float32`. Value of parameters used in the ADAM optimization algorithm. momenta: A `Tensor` of type `float32`. Value of momenta used in the ADAM optimization algorithm. velocities: A `Tensor` of type `float32`. Value of velocities used in the ADAM optimization algorithm. num_shards: An `int`. shard_id: An `int`. table_id: An optional `int`. Defaults to `-1`. table_name: An optional `string`. Defaults to `""`. config: An optional `string`. Defaults to `""`. name: A name for the operation (optional). Returns: The created Operation. LoadTPUEmbeddingADAMParametersrurvrsrtrNrurvrsrtrr0r1rr) rprqrrrsrtrurvrr0)r3rr4r5rr6r7r8r9r:r;1load_tpu_embedding_adam_parameters_eager_fallbackr=r>r?rr@rA)rprqrrrsrtrurvrr0rGrHrIrJrKrLrMs rP"load_tpu_embedding_adam_parametersr{s4    0h..0$ #\\  11 .j'J, j*h&Jgn  \:*   x 4( H   x 4(J  \:* ^ F   VX .&'88(Z295?5?3;3;5?17dD!QX *A  & &- ##At,,  # #    > gzHJd..  # #   0%DE D44E  E E&&E=<E=z&raw_ops.LoadTPUEmbeddingADAMParametersc .tj|d}tj|d}|d}tj|d}|d}tj|d}|d}tj|d}tj|t j }tj|t j }tj|t j }|||g} d|d|d|d|d|f } tjdd | | | | } d} | S) NrsrtrrurrvrsLoadTPUEmbeddingADAMParametersrrTr>r?rr9rlrWrXr) rprqrrrsrtrurvrr0r1rOrNrIs rPrzrzs  \:*   x 4( H   x 4(J  \:* ^ F   VX .&%%j'//B*  " "7GOO <'%%j'//B*gz2, , L j(Hf 6&   >$0C"& (' ' .rn accumulatorsupdatesc tjxstj} | j} | jr' t j | d||||d|d|d|d|d|} | Stj|d}tj|d}|d }tj|d}|d }tj|d}|d }tj|d}t!j"d||||||||| \} } }}|S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t||||||||||  S#t j$rYwxYw) aLoad Adadelta embedding parameters. An op that loads optimization parameters into HBM for embedding. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to install parameters that are loaded from a checkpoint before a training loop is executed. Args: parameters: A `Tensor` of type `float32`. Value of parameters used in the Adadelta optimization algorithm. accumulators: A `Tensor` of type `float32`. Value of accumulators used in the Adadelta optimization algorithm. updates: A `Tensor` of type `float32`. Value of updates used in the Adadelta optimization algorithm. num_shards: An `int`. shard_id: An `int`. table_id: An optional `int`. Defaults to `-1`. table_name: An optional `string`. Defaults to `""`. config: An optional `string`. Defaults to `""`. name: A name for the operation (optional). Returns: The created Operation. "LoadTPUEmbeddingAdadeltaParametersrurvrsrtrNryrr) rprrrsrtrurvrr0)r3rr4r5rr6r7r8r9r:r;5load_tpu_embedding_adadelta_parameters_eager_fallbackr=r>r?rr@rA)rprrrsrtrurvrr0rGrHrIrJrKrLrMs rP&load_tpu_embedding_adadelta_parametersrs4    0h..0$ #\\  11 2D*gz8\:j*h&Jgn  \:*   x 4( H   x 4(J  \:* ^ F   VX .&'88,;G6=9C7?7?9C5;$H!QX *A  & &- ##At,,  # #    B lGhJd..  # #   r|z*raw_ops.LoadTPUEmbeddingAdadeltaParametersc .tj|d}tj|d}|d}tj|d}|d}tj|d}|d}tj|d}tj|t j }tj|t j }tj|t j }|||g} d|d|d|d|d|f } tjdd | | | | } d} | S) Nrsrtrrurrvrs"LoadTPUEmbeddingAdadeltaParametersrrTr~) rprrrsrtrurvrr0r1rOrNrIs rPrr. s  \:*   x 4( H   x 4(J  \:* ^ F   VX .&%%j'//B*'' gooF,  " "7GOO <'lG4, , L j(Hf 6&   BA$0C"& (' ' .rnc tjxstj} | j} | jr' t j | d||||d|d|d|d|d|} | Stj|d}tj|d}|d }tj|d}|d }tj|d}|d }tj|d}t!j"d||||||||| \} } }}|S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t||||||||||  S#t j$rYwxYw) aLoad Adagrad Momentum embedding parameters. An op that loads optimization parameters into HBM for embedding. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to install parameters that are loaded from a checkpoint before a training loop is executed. Args: parameters: A `Tensor` of type `float32`. Value of parameters used in the Adagrad Momentum optimization algorithm. accumulators: A `Tensor` of type `float32`. Value of accumulators used in the Adagrad Momentum optimization algorithm. momenta: A `Tensor` of type `float32`. Value of momenta used in the Adagrad Momentum optimization algorithm. num_shards: An `int`. shard_id: An `int`. table_id: An optional `int`. Defaults to `-1`. table_name: An optional `string`. Defaults to `""`. config: An optional `string`. Defaults to `""`. name: A name for the operation (optional). Returns: The created Operation. )LoadTPUEmbeddingAdagradMomentumParametersrurvrsrtrNryrr) rprrqrsrtrurvrr0)r3rr4r5rr6r7r8r9r:r;=load_tpu_embedding_adagrad_momentum_parameters_eager_fallbackr=r>r?rr@rA)rprrqrsrtrurvrr0rGrHrIrJrKrLrMs rP.load_tpu_embedding_adagrad_momentum_parametersrG s4    0h..0$ #\\  11 94gz8\:j*h&Jgn  \:*   x 4( H   x 4(J  \:* ^ F   VX .&'883 BN=D@J>F>F@Jr?rr@rArprrsrtrurvrr0rGrHrIrJrKrLrMs rP%load_tpu_embedding_adagrad_parametersr s0    0h..0$ #\\  11 14j(L*j*h&Jgn  \:*   x 4( H   x 4(J  \:* ^ F   VX .&'88+ :F8B6>6>8B4:G!QX *?  & &- ##At,,  # #    A lX*(6  # #   0$DE D22E  E E##E:9E:z)raw_ops.LoadTPUEmbeddingAdagradParametersc tj|d}tj|d}|d}tj|d}|d}tj|d}|d}tj|d}tj|t j }tj|t j }||g} d|d|d|d|d|f } tjdd | | || } d} | S) Nrsrtrrurrvrs!LoadTPUEmbeddingAdagradParametersrrTr~ rprrsrtrurvrr0r1rOrNrIs rPrr s  \:*   x 4( H   x 4(J  \:* ^ F   VX .&%%j'//B*'' gooF,l+, , L j(Hf 6&   A1$0C"& (' ' .rnmsmommgc tjxstj} | j} | jr( t j | d| ||||d|d|d|d|d|} | Stj|d}tj|d}|d }tj|d}|d }tj|d}|d }tj|d}t!j"d|||||||||| \}}}}|S#t j$r } tj| | Yd} ~ nd} ~ wt j$rYnwxYw t|||||||||| |  S#t j$rYwxYw) aTLoad centered RMSProp embedding parameters. An op that loads optimization parameters into HBM for embedding. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to install parameters that are loaded from a checkpoint before a training loop is executed. Args: parameters: A `Tensor` of type `float32`. Value of parameters used in the centered RMSProp optimization algorithm. ms: A `Tensor` of type `float32`. Value of ms used in the centered RMSProp optimization algorithm. mom: A `Tensor` of type `float32`. Value of mom used in the centered RMSProp optimization algorithm. mg: A `Tensor` of type `float32`. Value of mg used in the centered RMSProp optimization algorithm. num_shards: An `int`. shard_id: An `int`. table_id: An optional `int`. Defaults to `-1`. table_name: An optional `string`. Defaults to `""`. config: An optional `string`. Defaults to `""`. name: A name for the operation (optional). Returns: The created Operation. )LoadTPUEmbeddingCenteredRMSPropParametersrurvrsrtrNryrr) rprrrrsrtrurvrr0)r3rr4r5rr6r7r8r9r:r;>load_tpu_embedding_centered_rms_prop_parameters_eager_fallbackr=r>r?rr@rA)rprrrrsrtrurvrr0rGrHrIrJrKrLrMs rP/load_tpu_embedding_centered_rms_prop_parametersr s8    0h..0$ #\\  11 94 CZ<j*h&Jgn  \:*   x 4( H   x 4(J  \:* ^ F   VX .&'883 8:@J>F>F@Jr?rr@rA)rprrrsrtrurvrr0rGrHrIrJrKrLrMs rP"load_tpu_embedding_ftrl_parametersre s4    0h..0$ #\\  11 .jgz8\:j*h&Jgn  \:*   x 4( H   x 4(J  \:* ^ F   VX .&'88(Z7C295?3;3;5?17dD!QX *A  & &- ##At,,  # #    > lGhJd..  # #   r|z&raw_ops.LoadTPUEmbeddingFTRLParametersc .tj|d}tj|d}|d}tj|d}|d}tj|d}|d}tj|d}tj|t j }tj|t j }tj|t j }|||g} d|d|d|d|d|f } tjdd | | | | } d} | S) NrsrtrrurrvrsLoadTPUEmbeddingFTRLParametersrrTr~) rprrrsrtrurvrr0r1rOrNrIs rPrr s  \:*   x 4( H   x 4(J  \:* ^ F   VX .&%%j'//B*'' gooF,  " "7GOO <'lG4, , L j(Hf 6&   >$0C"& (' ' .rn last_hit_stepctjxstj}|j} | jr& t j |d|||d|d|d|d|d|} | Stj|d}tj|d}|d }tj|d}|d }tj|d}|d }tj|d}t!j"d|||||||| \} } } }| S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t||||||||| S#t j$rYwxYw) aLoad frequency estimator embedding parameters. An op that loads optimization parameters into HBM for embedding. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to install parameters that are loaded from a checkpoint before a training loop is executed. Args: parameters: A `Tensor` of type `float32`. Value of parameters used in the frequency estimator optimization algorithm. last_hit_step: A `Tensor` of type `float32`. Value of last_hit_step used in the frequency estimator optimization algorithm. num_shards: An `int`. shard_id: An `int`. table_id: An optional `int`. Defaults to `-1`. table_name: An optional `string`. Defaults to `""`. config: An optional `string`. Defaults to `""`. name: A name for the operation (optional). Returns: The created Operation. ,LoadTPUEmbeddingFrequencyEstimatorParametersrurvrsrtrNryrr)rprrsrtrurvrr0)r3rr4r5rr6r7r8r9r:r;@load_tpu_embedding_frequency_estimator_parameters_eager_fallbackr=r>r?rr@rA)rprrsrtrurvrr0rGrHrIrJrKrLrMs rP1load_tpu_embedding_frequency_estimator_parametersr s0    0h..0$ #\\  11 load_tpu_embedding_mdl_adagrad_light_parameters_eager_fallbackr=r>r?rr@rA)rprrrrsrtrurvrr0rGrHrIrJrKrLrMs rP/load_tpu_embedding_mdl_adagrad_light_parametersr# s8    0h..0$ #\\  11 94gxX|L*j(H g n  \:*   x 4( H   x 4(J  \:* ^ F   VX .&'883 BN=D>F@J>F>F@J(lGX>, , L j(Hf 6&   I1$0C"& (' ' .rnctjxstj}|j} | jr& t j |d|||d|d|d|d|d|} | Stj|d}tj|d}|d }tj|d}|d }tj|d}|d }tj|d}t!j"d|||||||| \} } } }| S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t||||||||| S#t j$rYwxYw) ajLoad Momentum embedding parameters. An op that loads optimization parameters into HBM for embedding. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to install parameters that are loaded from a checkpoint before a training loop is executed. Args: parameters: A `Tensor` of type `float32`. Value of parameters used in the Momentum optimization algorithm. momenta: A `Tensor` of type `float32`. Value of momenta used in the Momentum optimization algorithm. num_shards: An `int`. shard_id: An `int`. table_id: An optional `int`. Defaults to `-1`. table_name: An optional `string`. Defaults to `""`. config: An optional `string`. Defaults to `""`. name: A name for the operation (optional). Returns: The created Operation. "LoadTPUEmbeddingMomentumParametersrurvrsrtrNryrr)rprqrsrtrurvrr0)r3rr4r5rr6r7r8r9r:r;5load_tpu_embedding_momentum_parameters_eager_fallbackr=r>r?rr@rA)rprqrsrtrurvrr0rGrHrIrJrKrLrMs rP&load_tpu_embedding_momentum_parametersr s0    0h..0$ #\\  11 2D*gHlJ J(Fr?rr@rArs rP.load_tpu_embedding_proximal_adagrad_parametersr s0    0h..0$ #\\  11 94j(L*j*h&Jgn  \:*   x 4( H   x 4(J  \:* ^ F   VX .&'883 BN@J>F>F@Jr?rr@rA)rprrrsrtrurvrr0rGrHrIrJrKrLrMs rP+load_tpu_embedding_proximal_yogi_parametersr@ s"    0h..0$ #\\  11 6j! :xz<J(Fr?rr@rA)rprrrsrtrurvrr0rGrHrIrJrKrLrMs rP&load_tpu_embedding_rms_prop_parametersr s4    0h..0$ #\\  11 14RHlJ J(F6>8B4: G!QX *=  & &- ##At,,  # #    B b#Z(6  # #   r|z)raw_ops.LoadTPUEmbeddingRMSPropParametersc .tj|d}tj|d}|d}tj|d}|d}tj|d}|d}tj|d}tj|t j }tj|t j }tj|t j }|||g} d|d|d|d|d|f } tjdd | | | | } d} | S) Nrsrtrrurrvrs!LoadTPUEmbeddingRMSPropParametersrrTr~) rprrrsrtrurvrr0r1rOrNrIs rPrr s  \:*   x 4( H   x 4(J  \:* ^ F   VX .&%%j'//B* b'//2" sGOO4#b#&, , L j(Hf 6&   A1$0C"& (' ' .rnctjxstj}|j}|jr% t j |d||d|d|d|d|d|} | Stj|d}tj|d}|d }tj|d}|d }tj|d}|d }tj|d}t!j"d||||||| \} } } } | S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t||||||||S#t j$rYwxYw) a Load SGD embedding parameters. An op that loads optimization parameters into HBM for embedding. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to install parameters that are loaded from a checkpoint before a training loop is executed. Args: parameters: A `Tensor` of type `float32`. Value of parameters used in the stochastic gradient descent optimization algorithm. num_shards: An `int`. shard_id: An `int`. table_id: An optional `int`. Defaults to `-1`. table_name: An optional `string`. Defaults to `""`. config: An optional `string`. Defaults to `""`. name: A name for the operation (optional). Returns: The created Operation. 3LoadTPUEmbeddingStochasticGradientDescentParametersrurvrsrtrNryrr)rprsrtrurvrr0)r3rr4r5rr6r7r8r9r:r;Hload_tpu_embedding_stochastic_gradient_descent_parameters_eager_fallbackr=r>r?rr@rA)rprsrtrurvrr0rGrHrIrJrKrLrMs rP9load_tpu_embedding_stochastic_gradient_descent_parametersr s,    0h..0$ #\\  11 CTJ, j*h&Jgn  \:*   x 4( H   x 4(J  \:* ^ F   VX .&'88=*JTHPHPJTFLDHJ!QX *?  & &- ##At,,  # #    U xJ(6  # #   s0#DE D00E E  E E76E7z;raw_ops.LoadTPUEmbeddingStochasticGradientDescentParametersc tj|d}tj|d}|d}tj|d}|d}tj|d}|d}tj|d}tj|t j }|g}d|d|d|d|d|f } tjdd || || } d} | S) Nrsrtrrurrvrs3LoadTPUEmbeddingStochasticGradientDescentParametersrrTr~) rprsrtrurvrr0r1rOrNrIs rPrr5 s  \:*   x 4( H   x 4(J  \:* ^ F   VX .&%%j'//B*, , L j(Hf 6&   S|6s"& (' ' .rn) TV_OutfeedDequeue_dtyperrrrrprrrqrrrsrtrurrrrvrrrr r!r"r#rwrxr$r%ryr&r'rzc btjxstj}|j}|jr t j |d|d|d|d| }|Stj|d}tj|d}|d}tj |d}t#j$d||||\}}} } | dd}tj&rYd| j)dd| j+dd| j-df} | j.} tj0d| | ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t|||||S#t j$rYNwxYw) a7Retrieves a single tensor from the computation outfeed. This operation will block indefinitely until data is available. Args: dtype: A `tf.DType`. The type of elements in the tensor. shape: A `tf.TensorShape` or list of `ints`. The shape of the tensor. device_ordinal: An optional `int`. Defaults to `-1`. The TPU device to use. This should be -1 when the Op is running on a TPU device, and >= 0 when the Op is running on the CPU device. name: A name for the operation (optional). Returns: A `Tensor` of type `dtype`. OutfeedDequeuer=r@rN)r=r@rr0r1r)r=r@rr0)r3rr4r5rr6r7r8r9r:r;outfeed_dequeue_eager_fallbackr=r>rCrDr?r@rArBrCrrDrErF) r=r@rr0rGrHrIrJrKrLrMrNrOs rPoutfeed_dequeuerN s"    0h..0$ #\\ 11 gugu.*gn   UG ,%   eW -%N$$^5EF.'88U)7dD!QX QK' ""$s))'2Gll7#%5 013F::L ,9 (' .9  & &- ##At,,  # #    +U>  # #   s0D<<FE**FFFF.-F.zraw_ops.OutfeedDequeuec<tj|d}tj|d}|d}tj|d}g}d|d|d|f}tjdd||||}tj rtj d||||\}|S) Nr=r@rrsOutfeedDequeuerSrTr)r>rCrDr?rrBrF)r=r@rr0r1rOrNrIs rPrr s   UG ,%   eW -%N$$^5EF., UGU,= 1`. The element types of each element in `outputs`. shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`). The shapes of each tensor in `outputs`. device_ordinal: An optional `int`. Defaults to `-1`. The TPU device to use. This should be -1 when the Op is running on a TPU device, and >= 0 when the Op is running on the CPU device. name: A name for the operation (optional). Returns: A list of `Tensor` objects of type `dtypes`. OutfeedDequeueTuplerrJrN)rrJrr0r1JExpected list for 'dtypes' argument to 'outfeed_dequeue_tuple' Op, not %r.JExpected list for 'shapes' argument to 'outfeed_dequeue_tuple' Op, not %r.r)rrJrr0)r3rr4r5rr6r7r8r9r:r;$outfeed_dequeue_tuple_eager_fallbackr=rrrrr>rCrDr?r@rArBrrDrErF)rrJrr0rGrHrIrJrOrrKrLrMrNrOs rPoutfeed_dequeue_tupler s4(    0h..0$ #\\ 11 #T8VXv.*gn FT5M *  .06 7 888> >H  r8 , >& > FT5M *  .06 7 889? ?"H  H - ?& ?N$$^5EF.'88fV.<4I!QX QK'  J ""$ X.ll8$&6 013F::L |VW> .K  & &- ##At,,  # #    1~  # #    ? @s<FHHG%1G  G%$G%)G99HHzraw_ops.OutfeedDequeueTuplect|ttfstd|z|Dcgc]}t j |d}}t|ttfstd|z|Dcgc]}t j |d}}|d}t j|d}g}d|d|d|f}t jdt|||||} t jrt jd ||| | Scc}wcc}w) NrrrrJrrsOutfeedDequeueTuplerTr) rrrrr>rCrDr?rrrBrF) rrJrr0r1rOrrOrNrIs rPrr s' FT5M *  .06 7 888> >H  r8 , >& > FT5M *  .06 7 889? ?"H  H - ?& ?N$$^5EF., fh0@ &   3S[$0C"& ('""$ |VW> .% ? @s D0D c tjxstj}|j}|jr t j |d||d|d|}|St|ttfst!d|z|Dcgc]}t#j$|d}}t|ttfst!d|z|D cgc]} t#j&| d}} t)j*d||||\} } } } | dd}|s| St#j,rHd| j/dd| j/df} | j0}t#j2d|| ||S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t|||||S#t j$rYwxYwcc}wcc} w) a*Retrieve multiple values from the computation outfeed. Device ordinal is a tensor allowing dynamic outfeed. This operation will block indefinitely until data is available. Output `i` corresponds to XLA tuple element `i`. Args: device_ordinal: A `Tensor` of type `int32`. An int scalar tensor, representing the TPU device to use. This should be -1 when the Op is running on a TPU device, and >= 0 when the Op is running on the CPU device. dtypes: A list of `tf.DTypes` that has length `>= 1`. The element types of each element in `outputs`. shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`). The shapes of each tensor in `outputs`. name: A name for the operation (optional). Returns: A list of `Tensor` objects of type `dtypes`. OutfeedDequeueTupleV2rrJNrKMExpected list for 'dtypes' argument to 'outfeed_dequeue_tuple_v2' Op, not %r.MExpected list for 'shapes' argument to 'outfeed_dequeue_tuple_v2' Op, not %r.)rrrJr0)r3rr4r5rr6r7r8r9r:r;'outfeed_dequeue_tuple_v2_eager_fallbackr=rrrrr>rCrDr@rArBrrErF)rrrJr0rGrHrIrJrOrrKrLrMrNrOs rPoutfeed_dequeue_tuple_v2r s*    0h..0$ #\\ 11 %t^Xv&gn FT5M *  139 : ;;8> >H  r8 , >& > FT5M *  139 : ;;9? ?"H  H - ?& ?'88v(.T;!QX QK'  J ""$ X.ll8$&F::L vw@ .A  & &- ##At,,  # #    4 TtMM  # #    ? @s<E2G' G,2F9F  F98F9=G G$#G$zraw_ops.OutfeedDequeueTupleV2c0t|ttfstd|z|Dcgc]}t j |d}}t|ttfstd|z|Dcgc]}t j |d}}tj|tj}|g}d|d|f}t jdt|||||} t jrt jd||| | Scc}wcc}w)NrrrrJsOutfeedDequeueTupleV2rTr)rrrrr>rCrDr9rlrWrZrrrBrF) rrrJr0r1rOrrOrNrIs rPrr;s FT5M *  139 : ;;8> >H  r8 , >& > FT5M *  139 : ;;9? ?"H  H - ?& ?)).'--H. !, fh /&   5s6{$0C"& ('""$ vw@ . ? @s D0D) TV_OutfeedDequeueV2_dtyperrrrrprrrqrrrsrtrurrrrvrrrr r!r"r#rwrxr$r%ryr&r'rzc  tjxstj}|j}|jr t j |d||d|d|}|Stj|d}tj|d}t!j"d||||\}}} } | dd}tj$rHd| j'dd| j)df} | j*} tj,d| | ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t|||||S#t j$rY#wxYw)aRetrieves a single tensor from the computation outfeed. Device ordinal is a tensor allowing dynamic outfeed. This operation will block indefinitely until data is available. Args: device_ordinal: A `Tensor` of type `int32`. An int scalar tensor, representing the TPU device to use. This should be -1 when the Op is running on a TPU device, and >= 0 when the Op is running on the CPU device. dtype: A `tf.DType`. The type of elements in the tensor. shape: A `tf.TensorShape` or list of `ints`. The shape of the tensor. name: A name for the operation (optional). Returns: A `Tensor` of type `dtype`. OutfeedDequeueV2r=r@NrA)rr=r@r0)r3rr4r5rr6r7r8r9r:r;!outfeed_dequeue_v2_eager_fallbackr=r>rCrDr@rArBrCrrErF) rr=r@r0rGrHrIrJrKrLrMrNrOs rPoutfeed_dequeue_v2rTs$    0h..0$ #\\ 11  $gn   UG ,%   eW -%'88>"'d4!QX QK' ""$s))'2Gll7#%F::L L&'; (' ./  & &- ##At,,  # #    . U4II  # #   s0DE#D>>EEE++FFzraw_ops.OutfeedDequeueV2cNtj|d}tj|d}tj|t j }|g}d|d|f}tjdd||||}tjrtjd||||\}|S)Nr=r@sOutfeedDequeueV2rSrTr) r>rCrDr9rlrWrZrrBrF)rr=r@r0r1rOrNrIs rPrrs   UG ,%   eW -%)).'--H. !, UGU +&   0!L#)s ?' ""$ L&'; (' .rn) TV_OutfeedEnqueue_dtyperrrrrprrrqrrrsrtrurrrrvrrrr r!r"r#rwrxr$r%ryr&r'rzctjxstj}|j}|jr t j |d||}|Stjd||\}}}}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t|||S#t j$rYwxYw)zEnqueue a Tensor on the computation outfeed. Args: input: A `Tensor`. A tensor that will be inserted into the outfeed queue. name: A name for the operation (optional). Returns: The created Operation. OutfeedEnqueueNr)r(r0)r3rr4r5rr6r7r8r9r:r;outfeed_enqueue_eager_fallbackr=r@rA) r(r0rGrHrIrJrKrLrMs rPoutfeed_enqueuers    0h..0$ #\\ 11 e-g n(88D2!QX *  & &- ##At,,  # #    + d&&  # #   0A99C B''C?C CC('C(zraw_ops.OutfeedEnqueuectj|g|g\}\}|g}d|f}tjdd||||}d}|S)Nr=sOutfeedEnqueuerrT)r>rVr)r(r0r1rZrOrNrIs rPrrsX"995'3K+x, [ !&   .,#)s ?' ' .rnctjxstj}|j}|jr t j |d||}|Stjd||\}}}}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t|||S#t j$rYwxYw)a Enqueue multiple Tensor values on the computation outfeed. Args: inputs: A list of `Tensor` objects. A list of tensors that will be inserted into the outfeed queue as an XLA tuple. name: A name for the operation (optional). Returns: The created Operation. OutfeedEnqueueTupleNr)rEr0)r3rr4r5rr6r7r8r9r:r;$outfeed_enqueue_tuple_eager_fallbackr=r@rA) rEr0rGrHrIrJrKrLrMs rPoutfeed_enqueue_tuplers    0h..0$ #\\ 11 #T63g n(88f49!QX *  & &- ##At,,  # #    1 t''  # #   rzraw_ops.OutfeedEnqueueTuplectj||\}}t|}d|f}tjdd||||}d}|S)NrsOutfeedEnqueueTuplerrT)r>rirr)rEr0r1rjrOrNrIs rPrrsT!@@M,f, l #&   3Q|#)s ?' ' .rn) TV_Prelinearize_dtyperrrrrprrrqrrrsrtrurrrrvrrrr r!r"r#rwrxr$r%ryr&r'rzc tjxstj}|j}|jr t j |d||d|d|}|S|g}tj|d}|g}t|t t"fst%d|z|Dcgc]}tj&|d}}t)j*d||||\} } } } | dd}tj,rYd| j/dd| j1dd| j1df} | j2} tj4d| | ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t|||||S#t j$rYmwxYwcc}w) aOAn op which linearizes one Tensor value to an opaque variant tensor. Args: input: A `Tensor`. A tensor that will be linearized. shape: An optional `tf.TensorShape` or list of `ints`. Defaults to `[]`. The shape of the tensor. layout: An optional list of `ints`. Defaults to `[]`. A vector holding the requested layout in minor-to-major sequence. If a layout attribute is passed but its values are all -1 the layout will be computed by the infeed operation. name: A name for the operation (optional). Returns: A `Tensor` of type `variant`. Prelinearizer@rUN)r@rUr0r1AExpected list for 'layout' argument to 'prelinearize' Op, not %r.)r(r@rUr0r=)r3rr4r5rr6r7r8r9r:r;prelinearize_eager_fallbackr=r>rDrrrrr?r@rArBrCrrErF)r(r@rUr0rGrHrIrJrrKrLrMrNrOs rP prelinearizers    0h..0$ #\\ 11 ndE7E8VMg n ] E   eW -% ^ F FT5M *  %'- . //7= =H  b( + =& ='88e5dL!QX QK' ""$s))'2Gll7#Xs||H/EGF::L  fg7 (' .=  & &- ##At,,  # #    ( uV$DBB  # #    >s6E$GF!-FF! F!%F55G  G zraw_ops.Prelinearizec|g}tj|d}|g}t|ttfst d|z|Dcgc]}tj |d}}tj|g|g\}\}|g}d|d|d|f}tjdd||||} tjrtjd||| | \} | Scc}w) Nr@rrUr=s PrelinearizerSrTr) r>rDrrrrr?rVrrBrF) r(r@rUr0r1rrZrOrNrIs rPrr/s ] E   eW -% ^ F FT5M *  %'- . //7= =H  b( + =& ="995'3K+x, ['5(F C&   _a #)s ?' ""$  fg7 (' . >sCc  tjxstj}|j}|jr t j |d||d|d|}|St|ttfst!d|z|Dcgc]}t#j$|d}}|g}t|ttfst!d|z|D cgc]} t#j&| d}} t)j*d||||\} } } } | dd}t#j,rYd | j/d d| j/dd| j/df} | j0}t#j2d|| ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t|||||S#t j$rYwxYwcc}wcc} w) aAn op which linearizes multiple Tensor values to an opaque variant tensor. Args: inputs: A list of `Tensor` objects. A list of tensors that will be provided using the infeed mechanism. shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`). The shapes of each tensor in `inputs`. layouts: An optional list of `ints`. Defaults to `[]`. A vector holding the requested layout in minor-to-major sequence for all the tuple shapes in the order the shapes appear in the "shapes" input. The layout elements for a sub-shape can be set to -1 in which case the corresponding layout will be computed by the infeed operation. name: A name for the operation (optional). Returns: A `Tensor` of type `variant`. PrelinearizeTuplerJrcN)rJrcr0r1GExpected list for 'shapes' argument to 'prelinearize_tuple' Op, not %r.HExpected list for 'layouts' argument to 'prelinearize_tuple' Op, not %r.)rErJrcr0r)r3rr4r5rr6r7r8r9r:r;!prelinearize_tuple_eager_fallbackr=rrrrr>rDr?r@rArBrrErF)rErJrcr0rGrHrIrJrrrKrLrMrNrOs rPprelinearize_tuplerFs$    0h..0$ #\\ 11 !469gn FT5M *  +-3 4 559? ?"H  H - ?& ? _G GdE] +  +-4 5 669@ @"X  r9 - @' @'88F67"&(!QX QK' ""$ X.ll8$ii1HJF::L \67< (' .C  & &- ##At,,  # #    . tGG  # #    @ As<FG<HGF55G GG""G98G9zraw_ops.PrelinearizeTuplec,t|ttfstd|z|Dcgc]}t j |d}}|g}t|ttfstd|z|Dcgc]}t j |d}}t j||\}}t|}d|d|d|f} t jdd|| ||} t jrt jd || | | \} | Scc}wcc}w) NrrJrrcrsPrelinearizeTuplerSrTr) rrrrr>rDr?rirrBrF) rErJrcr0r1rrrjrOrNrIs rPrrs, FT5M *  +-3 4 559? ?"H  H - ?& ? _G GdE] +  +-4 5 669@ @"X  r9 - @' @!@@M,f, lHfi I&   11\#)s ?' ""$ \67< (' .# @ As D 4D) TV_ReadVariableXlaSplitND_Trrrrrprrrqrrrsrtrurrrrvrrrr r!r"r#rwrxr$r%ryr&r'rzr2rctjxstj}|j}|jr# t j |d||d|d|d|d| }|Stj|d}tj|d}t!|t"t$fst'd|z|D cgc]} tj| d}} |g}t!|t"t$fst'd |z|D cgc]} tj| d}} t)j*d|||||| \} } } } | dd}|s| Stj,rjd| j/dd| j1dd| j3dd| j3df}| j4}tj6d||||S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t|||||||S#t j$rYwxYwcc} wcc} w) aSplits resource variable input tensor across all dimensions. An op which splits the resource variable input tensor based on the given num_splits attribute, pads slices optionally, and returned the slices. Slices are returned in row-major order. This op may be generated via the TPU bridge. For example, with `input` tensor: ``` [[0, 1, 2], [3, 4, 5], [6, 7, 8]] ``` `num_splits`: ``` [2, 2] ``` and `paddings`: ``` [1, 1] ``` the expected `outputs` is: ``` [[0, 1], [3, 4]] [[2, 0], [5, 0]] [[6, 7], [0, 0]] [[8, 0], [0, 0]] ``` Args: resource: A `Tensor` of type `resource`. Resource variable of input tensor to split across all dimensions. T: A `tf.DType`. N: An `int` that is `>= 1`. num_splits: A list of `ints`. Number of ways to split per dimension. Shape dimensions must be evenly divisible. paddings: An optional list of `ints`. Defaults to `[]`. Optional list of right paddings per dimension of input tensor to apply before splitting. This can be used to make a dimension evenly divisible. name: A name for the operation (optional). Returns: A list of `N` `Tensor` objects with type `T`. ReadVariableXlaSplitNDr2r num_splitsrN)r2rrrr0r1SExpected list for 'num_splits' argument to 'read_variable_xla_split_nd' Op, not %r.QExpected list for 'paddings' argument to 'read_variable_xla_split_nd' Op, not %r.)r{r2rrrr0)r3rr4r5rr6r7r8r9r:r;)read_variable_xla_split_nd_eager_fallbackr=r>rCr?rrrrr@rArBrCrDrrErF)r{r2rrrr0rGrHrIrJrrKrLrMrNrOs rPread_variable_xla_split_ndrshf    0h..0$ #\\ 11 &hQQj*h8gnC !3! Ju .  35? @ AA?II!!"l3I*I H HtUm ,  35= > ??;C CBhJ/ C( C'88 8qA-7('+-!QX QK'  J ""$3%%c*C1B1B31GCLL6 ll:&(F::L  ,A .O  & &- ##At,,  # #    6 a1h  # #   JDs<!G 6II HG88HHH''H>=H>zraw_ops.ReadVariableXlaSplitNDctj|d}tj|d}t|tt fst d|z|Dcgc]}tj|d}}|g}t|tt fst d|z|Dcgc]}tj|d}}tj|tj}|g}d|d|d|d|f} tjd||| ||} tjrtjd || | | Scc}wcc}w) Nr2rrrrrsReadVariableXlaSplitNDrTr)r>rCr?rrrrr9rlrWr{rrBrF) r{r2rrrr0r1rrOrNrIs rPrrsUC !3! Ju .  35? @ AA?II!!"l3I*I H HtUm ,  35= > ??;C CBhJ/ C( C  # #Hg.>.> ?(, CL*j( K&   6$0C"& ('""$  ,A .#JDs D9 D> num_outputsc tjxstj}|j}|jr t j |d|d|d|}|Stj|d}tj|d}t!j"d|||\}}}} | dd}|s|Stj$rHd|j'dd|j)df} |j*} tj,d| | ||S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t||||S#t j$rY!wxYw)aZAn op that receives embedding activations on the TPU. The TPU system performs the embedding lookups and aggregations specified by the arguments to TPUEmbeddingEnqueue(Integer/Sparse/SparseTensor)Batch. The results of these aggregations are visible to the Tensorflow Graph as the outputs of a RecvTPUEmbeddingActivations op. This op returns a list containing one Tensor of activations per table specified in the model. There can be at most one RecvTPUEmbeddingActivations op in the TPU graph. Args: num_outputs: An `int` that is `>= 1`. The number of output activation tensors, equal to the number of embedding tables in the model. config: A `string`. Serialized TPUEmbeddingConfiguration proto. name: A name for the operation (optional). Returns: A list of `num_outputs` `Tensor` objects with type `float32`. RecvTPUEmbeddingActivationsrrN)rrr0r1)rrr0)r3rr4r5rr6r7r8r9r:r;-recv_tpu_embedding_activations_eager_fallbackr=r>r?rr@rArBrDrrErF) rrr0rGrHrIrJrKrLrMrNrOs rPrecv_tpu_embedding_activationsr #s(    0h..0$ #\\ 11 +T=+&gn!!+}=+   VX .&'88%;v,02!QX QK'  J ""$S..}=xll8$&F::L %|VWF .1  & &- ##At,,  # #    :!&tGG  # #   rFz#raw_ops.RecvTPUEmbeddingActivationsctj|d}tj|d}g}d|d|f}tjd|||||}tjrtj d||||S)NrrsRecvTPUEmbeddingActivationsrTr r>r?rrrBrF)rrr0r1rOrNrIs rPr r \s!!+}=+   VX .&, ;& 9&   ;[$0C"& ('""$ %|VWF .rn"RetrieveTPUEmbeddingADAMParameters)rprqrrcjtjxstj}|j}|jr9 t j |d|d|d|d|d|d| }t j|}|Stj |d}tj |d}|d }tj |d}|d }tj"|d}|d }tj"|d}t%j&d|||||| \} } } } | dd}tj(r{d| j+dd| j-dd| j+dd| j+dd| j-df } | j.}tj0d|| |t j|}|S#tj$r } tj| |Yd} ~ nd} ~ wtj$rYnwxYw t|||||||S#tj$rYwxYw) aRetrieve ADAM embedding parameters. An op that retrieves optimization parameters from embedding to host memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to retrieve updated parameters before saving a checkpoint. Args: num_shards: An `int`. shard_id: An `int`. table_id: An optional `int`. Defaults to `-1`. table_name: An optional `string`. Defaults to `""`. config: An optional `string`. Defaults to `""`. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (parameters, momenta, velocities). parameters: A `Tensor` of type `float32`. momenta: A `Tensor` of type `float32`. velocities: A `Tensor` of type `float32`. rrurvrsrtrNryrrrsrtrurvrr0)r3rr4r5rr6)_RetrieveTPUEmbeddingADAMParametersOutput_maker7r8r9r:r;5retrieve_tpu_embedding_adam_parameters_eager_fallbackr=r>r?rr@rArBrDrrErFrsrtrurvrr0rGrHrIrJrKrLrMrNrOs rP&retrieve_tpu_embedding_adam_parametersrn?.    0h..0$ #\\  11 2D*, L*Hh0g:??Hg n  \:*   x 4( H   x 4(J  \:* ^ F   VX .&'88,7?7?9C5;$ H!QX QK' ""$#++J7ll<(, -z +Xs||H7MOF::L ,lFGM 5 ; ;G D' .M  & &- ##At,,  # #    B zF4AA  # #   07F>>HG,,HH HH21H2z*raw_ops.RetrieveTPUEmbeddingADAMParametersc tj|d}tj|d}|d}tj|d}|d}tj|d}|d}tj|d}g}d|d|d|d|d|f }tjdd |||| } tjrtj d ||| t j| } | S) Nrsrtrrurrvrs"RetrieveTPUEmbeddingADAMParametersrTr)r>r?rrrBrFrr rsrtrurvrr0r1rOrNrIs rPrr  \:*   x 4( H   x 4(J  \:* ^ F   VX .&, , L j(Hf 6&   BA$0C"& ('""$ ,lFGM 5 ; ;G D' .rn&RetrieveTPUEmbeddingAdadeltaParameters)rprrcjtjxstj}|j}|jr9 t j |d|d|d|d|d|d| }t j|}|Stj |d}tj |d}|d }tj |d}|d }tj"|d}|d }tj"|d}t%j&d|||||| \} } } } | dd}tj(r{d| j+dd| j-dd| j+dd| j+dd| j-df } | j.}tj0d|| |t j|}|S#tj$r } tj| |Yd} ~ nd} ~ wtj$rYnwxYw t|||||||S#tj$rYwxYw) a&Retrieve Adadelta embedding parameters. An op that retrieves optimization parameters from embedding to host memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to retrieve updated parameters before saving a checkpoint. Args: num_shards: An `int`. shard_id: An `int`. table_id: An optional `int`. Defaults to `-1`. table_name: An optional `string`. Defaults to `""`. config: An optional `string`. Defaults to `""`. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (parameters, accumulators, updates). parameters: A `Tensor` of type `float32`. accumulators: A `Tensor` of type `float32`. updates: A `Tensor` of type `float32`. rrurvrsrtrNryrrr)r3rr4r5rr6-_RetrieveTPUEmbeddingAdadeltaParametersOutputrr7r8r9r:r;9retrieve_tpu_embedding_adadelta_parameters_eager_fallbackr=r>r?rr@rArBrDrrErFrs rP*retrieve_tpu_embedding_adadelta_parametersr!s?.    0h..0$ #\\  11 6j, L*Hh0g>CCGLg n  \:*   x 4( H   x 4(J  \:* ^ F   VX .&'880Z;C;C=G9?d L!QX QK' ""$#++J7ll<(, -z +Xs||H7MOF::L 0,Q 9 ? ? H' .M  & &- ##At,,  # #    F zF4AA  # #   rz.raw_ops.RetrieveTPUEmbeddingAdadeltaParametersc tj|d}tj|d}|d}tj|d}|d}tj|d}|d}tj|d}g}d|d|d|d|d|f }tjdd |||| } tjrtj d ||| t j| } | S) Nrsrtrrurrvrs&RetrieveTPUEmbeddingAdadeltaParametersrrTr)r>r?rrrBrFrrrs rPr r #  \:*   x 4( H   x 4(J  \:* ^ F   VX .&, , L j(Hf 6&   F$0C"& ('""$ 0,Q 9 ? ? H' .rn-RetrieveTPUEmbeddingAdagradMomentumParameters)rprrqcjtjxstj}|j}|jr9 t j |d|d|d|d|d|d| }t j|}|Stj |d}tj |d}|d }tj |d}|d }tj"|d}|d }tj"|d}t%j&d|||||| \} } } } | dd}tj(r{d| j+dd| j-dd| j+dd| j+dd| j-df } | j.}tj0d|| |t j|}|S#tj$r } tj| |Yd} ~ nd} ~ wtj$rYnwxYw t|||||||S#tj$rYwxYw) a.Retrieve Adagrad Momentum embedding parameters. An op that retrieves optimization parameters from embedding to host memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to retrieve updated parameters before saving a checkpoint. Args: num_shards: An `int`. shard_id: An `int`. table_id: An optional `int`. Defaults to `-1`. table_name: An optional `string`. Defaults to `""`. config: An optional `string`. Defaults to `""`. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (parameters, accumulators, momenta). parameters: A `Tensor` of type `float32`. accumulators: A `Tensor` of type `float32`. momenta: A `Tensor` of type `float32`. r$rurvrsrtrNryrrr)r3rr4r5rr64_RetrieveTPUEmbeddingAdagradMomentumParametersOutputrr7r8r9r:r;Aretrieve_tpu_embedding_adagrad_momentum_parameters_eager_fallbackr=r>r?rr@rArBrDrrErFrs rP2retrieve_tpu_embedding_adagrad_momentum_parametersr(@sA.    0h..0$ #\\  11 =tHlJ J(FB D!QX QK' ""$#++J7ll<(, -z +Xs||H7MOF::L 7vwX @ F Fw O' .O  & &- ##At,,  # #    N zF4AA  # #   rz5raw_ops.RetrieveTPUEmbeddingAdagradMomentumParametersc tj|d}tj|d}|d}tj|d}|d}tj|d}|d}tj|d}g}d|d|d|d|d|f }tjdd |||| } tjrtj d ||| t j| } | S) Nrsrtrrurrvrs-RetrieveTPUEmbeddingAdagradMomentumParametersrrTr$)r>r?rrrBrFr&rrs rPr'r'  \:*   x 4( H   x 4(J  \:* ^ F   VX .&, , L j(Hf 6&   M|6s"& ('""$ 7vwX @ F Fw O' .rn%RetrieveTPUEmbeddingAdagradParameterscjtjxstj}|j}|jr9 t j |d|d|d|d|d|d| }t j|}|Stj |d}tj |d}|d }tj |d}|d }tj"|d}|d }tj"|d}t%j&d|||||| \} } } } | dd}tj(r{d| j+dd| j-dd| j+dd| j+dd| j-df } | j.}tj0d|| |t j|}|S#tj$r } tj| |Yd} ~ nd} ~ wtj$rYnwxYw t|||||||S#tj$rYwxYw) aRetrieve Adagrad embedding parameters. An op that retrieves optimization parameters from embedding to host memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to retrieve updated parameters before saving a checkpoint. Args: num_shards: An `int`. shard_id: An `int`. table_id: An optional `int`. Defaults to `-1`. table_name: An optional `string`. Defaults to `""`. config: An optional `string`. Defaults to `""`. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (parameters, accumulators). parameters: A `Tensor` of type `float32`. accumulators: A `Tensor` of type `float32`. r+rurvrsrtrNryrrr)r3rr4r5rr6,_RetrieveTPUEmbeddingAdagradParametersOutputrr7r8r9r:r;8retrieve_tpu_embedding_adagrad_parameters_eager_fallbackr=r>r?rr@rArBrDrrErFrs rP)retrieve_tpu_embedding_adagrad_parametersr/s?,    0h..0$ #\\  11 5tZ, L*Hh0g=BB7Kg n  \:*   x 4( H   x 4(J  \:* ^ F   VX .&'88/J:B:BT K!QX QK' ""$#++J7ll<(, -z +Xs||H7MOF::L /vwP 8 > >w G' .M  & &- ##At,,  # #    E zF4AA  # #   rz-raw_ops.RetrieveTPUEmbeddingAdagradParametersc tj|d}tj|d}|d}tj|d}|d}tj|d}|d}tj|d}g}d|d|d|d|d|f }tjdd |||| } tjrtj d ||| t j| } | S) Nrsrtrrurrvrs%RetrieveTPUEmbeddingAdagradParametersrTr+)r>r?rrrBrFr-rrs rPr.r.  \:*   x 4( H   x 4(J  \:* ^ F   VX .&, , L j(Hf 6&   Eq$0C"& ('""$ /vwP 8 > >w G' .rn-RetrieveTPUEmbeddingCenteredRMSPropParameters)rprrrcjtjxstj}|j}|jr9 t j |d|d|d|d|d|d| }t j|}|Stj |d}tj |d}|d }tj |d}|d }tj"|d}|d }tj"|d}t%j&d|||||| \} } } } | dd}tj(r{d| j+dd| j-dd| j+dd| j+dd| j-df } | j.}tj0d|| |t j|}|S#tj$r } tj| |Yd} ~ nd} ~ wtj$rYnwxYw t|||||||S#tj$rYwxYw) a<Retrieve centered RMSProp embedding parameters. An op that retrieves optimization parameters from embedding to host memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to retrieve updated parameters before saving a checkpoint. Args: num_shards: An `int`. shard_id: An `int`. table_id: An optional `int`. Defaults to `-1`. table_name: An optional `string`. Defaults to `""`. config: An optional `string`. Defaults to `""`. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (parameters, ms, mom, mg). parameters: A `Tensor` of type `float32`. ms: A `Tensor` of type `float32`. mom: A `Tensor` of type `float32`. mg: A `Tensor` of type `float32`. r3rurvrsrtrNryrrr)r3rr4r5rr64_RetrieveTPUEmbeddingCenteredRMSPropParametersOutputrr7r8r9r:r;Bretrieve_tpu_embedding_centered_rms_prop_parameters_eager_fallbackr=r>r?rr@rArBrDrrErFrs rP3retrieve_tpu_embedding_centered_rms_prop_parametersr7A0    0h..0$ #\\  11 =tHlJ J(FB D!QX QK' ""$#++J7ll<(, -z +Xs||H7MOF::L 7vwX @ F Fw O' .O  & &- ##At,,  # #    O zF4AA  # #   rz5raw_ops.RetrieveTPUEmbeddingCenteredRMSPropParametersc tj|d}tj|d}|d}tj|d}|d}tj|d}|d}tj|d}g}d|d|d|d|d|f }tjdd |||| } tjrtj d ||| t j| } | S) Nrsrtrrurrvrs-RetrieveTPUEmbeddingCenteredRMSPropParametersrTr3)r>r?rrrBrFr5rrs rPr6r6`r*rn"RetrieveTPUEmbeddingFTRLParameters)rprrcjtjxstj}|j}|jr9 t j |d|d|d|d|d|d| }t j|}|Stj |d}tj |d}|d }tj |d}|d }tj"|d}|d }tj"|d}t%j&d|||||| \} } } } | dd}tj(r{d| j+dd| j-dd| j+dd| j+dd| j-df } | j.}tj0d|| |t j|}|S#tj$r } tj| |Yd} ~ nd} ~ wtj$rYnwxYw t|||||||S#tj$rYwxYw) a"Retrieve FTRL embedding parameters. An op that retrieves optimization parameters from embedding to host memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to retrieve updated parameters before saving a checkpoint. Args: num_shards: An `int`. shard_id: An `int`. table_id: An optional `int`. Defaults to `-1`. table_name: An optional `string`. Defaults to `""`. config: An optional `string`. Defaults to `""`. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (parameters, accumulators, linears). parameters: A `Tensor` of type `float32`. accumulators: A `Tensor` of type `float32`. linears: A `Tensor` of type `float32`. r;rurvrsrtrNryrrr)r3rr4r5rr6)_RetrieveTPUEmbeddingFTRLParametersOutputrr7r8r9r:r;5retrieve_tpu_embedding_ftrl_parameters_eager_fallbackr=r>r?rr@rArBrDrrErFrs rP&retrieve_tpu_embedding_ftrl_parametersr?}rrz*raw_ops.RetrieveTPUEmbeddingFTRLParametersc tj|d}tj|d}|d}tj|d}|d}tj|d}|d}tj|d}g}d|d|d|d|d|f }tjdd |||| } tjrtj d ||| t j| } | S) Nrsrtrrurrvrs"RetrieveTPUEmbeddingFTRLParametersrrTr;)r>r?rrrBrFr=rrs rPr>r>rrn0RetrieveTPUEmbeddingFrequencyEstimatorParameterscjtjxstj}|j}|jr9 t j |d|d|d|d|d|d| }t j|}|Stj |d}tj |d}|d }tj |d}|d }tj"|d}|d }tj"|d}t%j&d|||||| \} } } } | dd}tj(r{d| j+dd| j-dd| j+dd| j+dd| j-df } | j.}tj0d|| |t j|}|S#tj$r } tj| |Yd} ~ nd} ~ wtj$rYnwxYw t|||||||S#tj$rYwxYw) aRetrieve frequency estimator embedding parameters. An op that retrieves optimization parameters from embedding to host memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to retrieve updated parameters before saving a checkpoint. Args: num_shards: An `int`. shard_id: An `int`. table_id: An optional `int`. Defaults to `-1`. table_name: An optional `string`. Defaults to `""`. config: An optional `string`. Defaults to `""`. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (parameters, last_hit_step). parameters: A `Tensor` of type `float32`. last_hit_step: A `Tensor` of type `float32`. rArurvrsrtrNryrrr)r3rr4r5rr67_RetrieveTPUEmbeddingFrequencyEstimatorParametersOutputrr7r8r9r:r;Dretrieve_tpu_embedding_frequency_estimator_parameters_eager_fallbackr=r>r?rr@rArBrDrrErFrs rP5retrieve_tpu_embedding_frequency_estimator_parametersrEsB,    0h..0$ #\\  11 @$HlJ J(Fr?rrrBrFrCrrs rPrDrD2s  \:*   x 4( H   x 4(J  \:* ^ F   VX .&, , L j(Hf 6&   P|6s"& ('""$ :L&RY[ C I I' R' .rn-RetrieveTPUEmbeddingMDLAdagradLightParameters)rprrrcjtjxstj}|j}|jr9 t j |d|d|d|d|d|d| }t j|}|Stj |d}tj |d}|d }tj |d}|d }tj"|d}|d }tj"|d}t%j&d|||||| \} } } } | dd}tj(r{d| j+dd| j-dd| j+dd| j+dd| j-df } | j.}tj0d|| |t j|}|S#tj$r } tj| |Yd} ~ nd} ~ wtj$rYnwxYw t|||||||S#tj$rYwxYw) aeRetrieve MDL Adagrad Light embedding parameters. An op that retrieves optimization parameters from embedding to host memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to retrieve updated parameters before saving a checkpoint. Args: num_shards: An `int`. shard_id: An `int`. table_id: An optional `int`. Defaults to `-1`. table_name: An optional `string`. Defaults to `""`. config: An optional `string`. Defaults to `""`. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (parameters, accumulators, weights, benefits). parameters: A `Tensor` of type `float32`. accumulators: A `Tensor` of type `float32`. weights: A `Tensor` of type `float32`. benefits: A `Tensor` of type `float32`. rGrurvrsrtrNryrrr)r3rr4r5rr64_RetrieveTPUEmbeddingMDLAdagradLightParametersOutputrr7r8r9r:r;Bretrieve_tpu_embedding_mdl_adagrad_light_parameters_eager_fallbackr=r>r?rr@rArBrDrrErFrs rP3retrieve_tpu_embedding_mdl_adagrad_light_parametersrKOr8rz5raw_ops.RetrieveTPUEmbeddingMDLAdagradLightParametersc tj|d}tj|d}|d}tj|d}|d}tj|d}|d}tj|d}g}d|d|d|d|d|f }tjdd |||| } tjrtj d ||| t j| } | S) Nrsrtrrurrvrs-RetrieveTPUEmbeddingMDLAdagradLightParametersr:rTrG)r>r?rrrBrFrIrrs rPrJrJr*rn&RetrieveTPUEmbeddingMomentumParameterscjtjxstj}|j}|jr9 t j |d|d|d|d|d|d| }t j|}|Stj |d}tj |d}|d }tj |d}|d }tj"|d}|d }tj"|d}t%j&d|||||| \} } } } | dd}tj(r{d| j+dd| j-dd| j+dd| j+dd| j-df } | j.}tj0d|| |t j|}|S#tj$r } tj| |Yd} ~ nd} ~ wtj$rYnwxYw t|||||||S#tj$rYwxYw) aRetrieve Momentum embedding parameters. An op that retrieves optimization parameters from embedding to host memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to retrieve updated parameters before saving a checkpoint. Args: num_shards: An `int`. shard_id: An `int`. table_id: An optional `int`. Defaults to `-1`. table_name: An optional `string`. Defaults to `""`. config: An optional `string`. Defaults to `""`. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (parameters, momenta). parameters: A `Tensor` of type `float32`. momenta: A `Tensor` of type `float32`. rMrurvrsrtrNryrrr)r3rr4r5rr6-_RetrieveTPUEmbeddingMomentumParametersOutputrr7r8r9r:r;9retrieve_tpu_embedding_momentum_parameters_eager_fallbackr=r>r?rr@rArBrDrrErFrs rP*retrieve_tpu_embedding_momentum_parametersrQs?,    0h..0$ #\\  11 6j, L*Hh0g>CCGLg n  \:*   x 4( H   x 4(J  \:* ^ F   VX .&'880Z;C;C=G9?d L!QX QK' ""$#++J7ll<(, -z +Xs||H7MOF::L 0,Q 9 ? ? H' .M  & &- ##At,,  # #    F zF4AA  # #   rz.raw_ops.RetrieveTPUEmbeddingMomentumParametersc tj|d}tj|d}|d}tj|d}|d}tj|d}|d}tj|d}g}d|d|d|d|d|f }tjdd |||| } tjrtj d ||| t j| } | S) Nrsrtrrurrvrs&RetrieveTPUEmbeddingMomentumParametersr1rTrM)r>r?rrrBrFrOrrs rPrPrPr#rn-RetrieveTPUEmbeddingProximalAdagradParameterscjtjxstj}|j}|jr9 t j |d|d|d|d|d|d| }t j|}|Stj |d}tj |d}|d }tj |d}|d }tj"|d}|d }tj"|d}t%j&d|||||| \} } } } | dd}tj(r{d| j+dd| j-dd| j+dd| j+dd| j-df } | j.}tj0d|| |t j|}|S#tj$r } tj| |Yd} ~ nd} ~ wtj$rYnwxYw t|||||||S#tj$rYwxYw) aRetrieve proximal Adagrad embedding parameters. An op that retrieves optimization parameters from embedding to host memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to retrieve updated parameters before saving a checkpoint. Args: num_shards: An `int`. shard_id: An `int`. table_id: An optional `int`. Defaults to `-1`. table_name: An optional `string`. Defaults to `""`. config: An optional `string`. Defaults to `""`. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (parameters, accumulators). parameters: A `Tensor` of type `float32`. accumulators: A `Tensor` of type `float32`. rSrurvrsrtrNryrrr)r3rr4r5rr64_RetrieveTPUEmbeddingProximalAdagradParametersOutputrr7r8r9r:r;Aretrieve_tpu_embedding_proximal_adagrad_parameters_eager_fallbackr=r>r?rr@rArBrDrrErFrs rP2retrieve_tpu_embedding_proximal_adagrad_parametersrW"sA,    0h..0$ #\\  11 =tHlJ J(FB D!QX QK' ""$#++J7ll<(, -z +Xs||H7MOF::L 7vwX @ F Fw O' .O  & &- ##At,,  # #    N zF4AA  # #   rz5raw_ops.RetrieveTPUEmbeddingProximalAdagradParametersc tj|d}tj|d}|d}tj|d}|d}tj|d}|d}tj|d}g}d|d|d|d|d|f }tjdd |||| } tjrtj d ||| t j| } | S) Nrsrtrrurrvrs-RetrieveTPUEmbeddingProximalAdagradParametersr1rTrS)r>r?rrrBrFrUrrs rPrVrVnr*rn*RetrieveTPUEmbeddingProximalYogiParameters)rprrcjtjxstj}|j}|jr9 t j |d|d|d|d|d|d| }t j|}|Stj |d}tj |d}|d }tj |d}|d }tj"|d}|d }tj"|d}t%j&d|||||| \} } } } | dd}tj(r{d| j+dd| j-dd| j+dd| j+dd| j-df } | j.}tj0d|| |t j|}|S#tj$r } tj| |Yd} ~ nd} ~ wtj$rYnwxYw t|||||||S#tj$rYwxYw) aTODO: add doc. Args: num_shards: An `int`. shard_id: An `int`. table_id: An optional `int`. Defaults to `-1`. table_name: An optional `string`. Defaults to `""`. config: An optional `string`. Defaults to `""`. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (parameters, v, m). parameters: A `Tensor` of type `float32`. v: A `Tensor` of type `float32`. m: A `Tensor` of type `float32`. rYrurvrsrtrNryrrr)r3rr4r5rr61_RetrieveTPUEmbeddingProximalYogiParametersOutputrr7r8r9r:r;>retrieve_tpu_embedding_proximal_yogi_parameters_eager_fallbackr=r>r?rr@rArBrDrrErFrs rP/retrieve_tpu_embedding_proximal_yogi_parametersr]sA$    0h..0$ #\\  11 :D*, L*Hh0gBGGPg n  \:*   x 4( H   x 4(J  \:* ^ F   VX .&'884?G?GAK=C;? A!QX QK' ""$#++J7ll<(, -z +Xs||H7MOF::L 4lFGU = C CG L' .O  & &- ##At,,  # #    K zF4AA  # #   rz2raw_ops.RetrieveTPUEmbeddingProximalYogiParametersc tj|d}tj|d}|d}tj|d}|d}tj|d}|d}tj|d}g}d|d|d|d|d|f }tjdd |||| } tjrtj d ||| t j| } | S) Nrsrtrrurrvrs*RetrieveTPUEmbeddingProximalYogiParametersrrTrY)r>r?rrrBrFr[rrs rPr\r\s  \:*   x 4( H   x 4(J  \:* ^ F   VX .&, , L j(Hf 6&   JA$0C"& ('""$ 4lFGU = C CG L' .rn%RetrieveTPUEmbeddingRMSPropParameters)rprrcjtjxstj}|j}|jr9 t j |d|d|d|d|d|d| }t j|}|Stj |d}tj |d}|d }tj |d}|d }tj"|d}|d }tj"|d}t%j&d|||||| \} } } } | dd}tj(r{d| j+dd| j-dd| j+dd| j+dd| j-df } | j.}tj0d|| |t j|}|S#tj$r } tj| |Yd} ~ nd} ~ wtj$rYnwxYw t|||||||S#tj$rYwxYw) a Retrieve RMSProp embedding parameters. An op that retrieves optimization parameters from embedding to host memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to retrieve updated parameters before saving a checkpoint. Args: num_shards: An `int`. shard_id: An `int`. table_id: An optional `int`. Defaults to `-1`. table_name: An optional `string`. Defaults to `""`. config: An optional `string`. Defaults to `""`. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (parameters, ms, mom). parameters: A `Tensor` of type `float32`. ms: A `Tensor` of type `float32`. mom: A `Tensor` of type `float32`. r_rurvrsrtrNryrrr)r3rr4r5rr6,_RetrieveTPUEmbeddingRMSPropParametersOutputrr7r8r9r:r;9retrieve_tpu_embedding_rms_prop_parameters_eager_fallbackr=r>r?rr@rArBrDrrErFrs rP*retrieve_tpu_embedding_rms_prop_parametersrcs?.    0h..0$ #\\  11 5tZ, L*Hh0g=BB7Kg n  \:*   x 4( H   x 4(J  \:* ^ F   VX .&'88/J:B:BT K!QX QK' ""$#++J7ll<(, -z +Xs||H7MOF::L /vwP 8 > >w G' .M  & &- ##At,,  # #    F zF4AA  # #   rz-raw_ops.RetrieveTPUEmbeddingRMSPropParametersc tj|d}tj|d}|d}tj|d}|d}tj|d}|d}tj|d}g}d|d|d|d|d|f }tjdd |||| } tjrtj d ||| t j| } | S) Nrsrtrrurrvrs%RetrieveTPUEmbeddingRMSPropParametersrrTr_)r>r?rrrBrFrarrs rPrbrb<r2rnctjxstj}|j}|jr$ t j |d|d|d|d|d|d| }|Stj|d}tj|d}|d }tj|d}|d }tj|d}|d }tj|d}t!j"d|||||| \} } } } | dd}tj$r{d| j'dd| j)dd| j'dd| j'dd| j)df } | j*}tj,d|| ||\}|S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t|||||||S#t j$rYwxYw) atRetrieve SGD embedding parameters. An op that retrieves optimization parameters from embedding to host memory. Must be preceded by a ConfigureTPUEmbeddingHost op that sets up the correct embedding table configuration. For example, this op is used to retrieve updated parameters before saving a checkpoint. Args: num_shards: An `int`. shard_id: An `int`. table_id: An optional `int`. Defaults to `-1`. table_name: An optional `string`. Defaults to `""`. config: An optional `string`. Defaults to `""`. name: A name for the operation (optional). Returns: A `Tensor` of type `float32`. 7RetrieveTPUEmbeddingStochasticGradientDescentParametersrurvrsrtrNryrrr)r3rr4r5rr6r7r8r9r:r;Lretrieve_tpu_embedding_stochastic_gradient_descent_parameters_eager_fallbackr=r>r?rr@rArBrDrrErFrs rP=retrieve_tpu_embedding_stochastic_gradient_descent_parametersrhUs)&    0h..0$ #\\  11 GHlJ J(Frr@rA) rErjrr0rGrHrIrJr_attr_NNrKrLrMs rPsend_tpu_embedding_gradientsrqsz0    0h..0$ #\\ 11 )4&gn FT5M *  57= > ?? K' NT5M 2  57E F GG (   VX .&'88#F4B,2?!QX *3  & &- ##At,,  # #    8 .dFF  # #   s0C22D9D  D98D9=E E$#E$z!raw_ops.SendTPUEmbeddingGradientsct|ttfstd|zt |}t|ttfstd|zt |}t j |d}tj|tj}tj|tj}t|t|z}d|d|d|f}t jdd||||} d} | S) NrmrnrrNNsSendTPUEmbeddingGradientsrrT) rrrrrr>rr9rrWrXr) rErjrr0r1rrprOrNrIs rProros FT5M *  57= > ?? K' NT5M 2  57E F GG (   VX .&  # #FGOO <&++NGOOL.f^ 44, $(F ;&   91$0C"& (' ' .rnctjxstj}|j}|jr t j |d|}|Stjd|\}}}}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t||S#t j$rYwxYw)zShuts down a running distributed TPU system. The op returns an error if no system is running. Args: name: A name for the operation (optional). Returns: The created Operation. ShutdownDistributedTPUNrr0)r3rr4r5rr6r7r8r9r:r;'shutdown_distributed_tpu_eager_fallbackr=r@rA)r0rGrHrIrJrKrLrMs rPshutdown_distributed_tpurx s    0h..0$ #\\ 11 &.g n(88 t-!QX *  & &- ##At,,  # #    4  # #   s0A77B> B%%B>=B> CC%$C%zraw_ops.ShutdownDistributedTPUcHg}d}tjdd||||}d}|S)NsShutdownDistributedTPUrrT)r>rr0r1rOrNrIs rPrwrw,s7, &   6$0C"& (' ' .rncVtjxstj}|j}|jr t j |d|}|Stjd|\}}}}|dd}tj r&d}|j"} tj$d| |||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t||S#t j$rYwxYw)aIReturns the result of a TPU compilation. This operation returns the result of a TPU compilation as a serialized CompilationResultProto, which holds a status and an error message if an error occurred during compilation. Args: name: A name for the operation (optional). Returns: A `Tensor` of type `string`. TPUCompilationResultNrrv)r3rr4r5rr6r7r8r9r:r;%tpu_compilation_result_eager_fallbackr=r@rAr>rBrErF r0rGrHrIrJrKrLrMrNrOs rPtpu_compilation_resultr6s"    0h..0$ #\\ 11 $d,g n(88T+!QX QK' ""$ F::L  fg? (' .'  & &- ##At,,  # #    2  # #   0B::D C((DD DD('D(zraw_ops.TPUCompilationResultcg}d}tjdd||||}tjrtjd||||\}|S)NsTPUCompilationResultrSrTr|r>rrBrFrzs rPr~r~bsY, &   4a #)s ?' ""$  fg? (' .rnembedding_variablesliced_activations lookup_idc tjxstj}|j}|jr t j |d|||d|d| }|Stj|d}tj|d}tj d|||||\} } } } | dd}tj"rHd| j%dd| j%df} | j&} tj(d| | ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t||||||S#t j$rY%wxYw)aAn op enabling differentiation of TPU Embeddings. This op simply returns its first input, which is assumed to have been sliced from the Tensors returned by TPUEmbeddingDequeueActivations. The presence of this op, and its first argument being a trainable Variable, enables automatic differentiation of graphs containing embeddings via the TPU Embedding Python libraries. Args: embedding_variable: A `Tensor` of type `float32`. A trainable variable, enabling optimizers to find this op. sliced_activations: A `Tensor` of type `float32`. The embedding activations Tensor to return. table_id: An `int` that is `>= 0`. The id of the table in the embedding layer configuration from which these activations were computed. lookup_id: An `int` that is `>= 0`. Identifier of the set of embedding indices which produced these activations. name: A name for the operation (optional). Returns: A `Tensor` of type `float32`. TPUEmbeddingActivationsrurN)rurr0r1)rrrurr0)r3rr4r5rr6r7r8r9r:r;(tpu_embedding_activations_eager_fallbackr=r>r?r@rArBrDrErF)rrrurr0rGrHrIrJrKrLrMrNrOs rPtpu_embedding_activationsrns2    0h..0$ #\\ 11 '/AJ+yJgn  x 4( ;7)'88!6H6H,4 (, .!QX QK' ""$#++J7 ,.F::L !<B (' .5  & &- ##At,,  # #    5 08Dd44  # #   s0DE%EEEE..FFzraw_ops.TPUEmbeddingActivationsctj|d}tj|d}tj|tj }tj|tj }||g}d|d|f}tj dd||||}tjrtjd||||\}|S)NrursTPUEmbeddingActivationsrSrTr) r>r?r9rlrWrXrrBrF) rrrurr0r1rOrNrIs rPrrs   x 4( ;7)--.@'//R--.@'//R$&89, +y 9&   7$0C"& ('""$ !<B (' .rncVtjxstj}|j}|jr t j |d|}|Stjd|\}}}}|dd}tj r&d}|j"} tj$d| |||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t||S#t j$rYwxYw)a1A TPU core selector Op. This Op produces a set of TPU cores (for warm-up) or a single TPU core (for regular inference) to execute the TPU program on. The output is consumed by TPUPartitionedCall. Args: name: A name for the operation (optional). Returns: A `Tensor` of type `int32`. TPUOrdinalSelectorNrrvr})r3rr4r5rr6r7r8r9r:r;#tpu_ordinal_selector_eager_fallbackr=r@rAr>rBrErFrs rPtpu_ordinal_selectorrs"    0h..0$ #\\ 11 "D*g n(884)!QX QK' ""$ F::L lFG= (' .'  & &- ##At,,  # #    0  # #   rzraw_ops.TPUOrdinalSelectorcg}d}tjdd||||}tjrtjd||||\}|S)NsTPUOrdinalSelectorrSrTrrrzs rPrrsY, &   2Al#)s ?' ""$ lFG= (' .rnautotuner_threshc tjxstj}|j}|jr" t j |d|||d|d|d| }|St|ttfst!d|z|D cgc]} t#j$| d}} |d}t#j&|d}t)j*d|||||| \} } } } | dd}t#j,rjd | j/d d| j/dd| j/dd| j1df}| j2}t#j4d||||S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t|||||||S#t j$rYzwxYwcc} w) aCalls a function placed on a specified TPU device. Args: args: A list of `Tensor` objects. The arguments to the function. device_ordinal: A `Tensor` of type `int32`. The TPU device ordinal to run the function on. Tout: A list of `tf.DTypes`. The types of the outputs of the function. f: A function decorated with @Defun. The function to call. autotuner_thresh: An optional `int`. Defaults to `0`. name: A name for the operation (optional). Returns: A list of `Tensor` objects of type `Tout`. TPUPartitionedCallToutfrN)rrrr0r1GExpected list for 'Tout' argument to 'tpu_partitioned_call' Op, not %r.r)argsrrrrr0Tin)r3rr4r5rr6r7r8r9r:r;#tpu_partitioned_call_eager_fallbackr=rrrrr>rCr?r@rArBrrDrErF)rrrrrr0rGrHrIrJrOrKrLrMrNrOs rPtpu_partitioned_callrs    0h..0$ #\\ 11 "D$ Q"$46gn D4- (  -/3 4 5548 8R(  R ( 8$ 8&&'79KL'884#'1/?dL!QX QK' ""$S\\%(&#,,v2Fll3!3 235F::L lFG= .?  & &- ##At,,  # #    0 TQ+$DBB  # #    9s6 E( G(F/;FF/.F/3GGGzraw_ops.TPUPartitionedCallct|ttfstd|z|Dcgc]}t j |d}}|d}t j |d}t j||\}}tj|tj}t||gz} d|d|d|d|f} t jdt|| | ||} t jrt jd | | | | Scc}w) NrrrrrrsTPUPartitionedCallrTr)rrrrr>rCr?rir9rlrWrZrrrBrF) rrrrrr0r1rO _attr_TinrOrNrIs rPrr2s D4- (  -/3 4 5548 8R(  R ( 8$ 8&&'79KL;;D#F/)T)).'--H.d~.., 9fdC4F &   2CI$0C"& ('""$ lFG= . 9sD rSSTEP_MARK_AT_ENTRY num_replicasnum_cores_per_replicatopologyuse_tpustep_marker_locationallow_soft_placementuse_spmd_for_xla_partitioningtpu_compile_options_protoc Jtjxstj} | j}|jr2 t j | d| d|d|d|d|d|d|d|d |d |d | d | d | }|Stj|d}|d}tj|d}|d}tj|d}|d}tj |d}|g}t#|t$t&fst)d|z|Dcgc]}tj|d}}|g}t#|t$t&fst)d|z|Dcgc]}tj|d}}|g}t#|t$t&fst)d|z|Dcgc]}tj|d}}|g}t#|t$t&fst)d|z|Dcgc]}tj|d }}|d}tj|d }| d} tj | d } | d} tj | d } | d} tj| d } t+j,d|||||||||| | | | \}}}}|S#t j$r }tj|| Yd}~nd}~wt j$rYnwxYw t|||||||||| | | | | S#t j$rYwxYwcc}wcc}wcc}wcc}w)agMetadata indicating how the TPU computation should be replicated. This operation holds the metadata common to operations of a `tpu.replicate()` computation subgraph. Args: num_replicas: An `int` that is `>= 0`. Number of replicas of the computation num_cores_per_replica: An optional `int`. Defaults to `1`. Number of cores per replica. Used for model parallelism. topology: An optional `string`. Defaults to `""`. TopologyProto indicating the topology of the TPU pod slice. use_tpu: An optional `bool`. Defaults to `True`. Whether to place the computation on the TPU. device_assignment: An optional list of `ints`. Defaults to `[]`. The assignment of devices for the computation. computation_shape: An optional list of `ints`. Defaults to `[]`. DEPRECATED. Use num_cores_per_replica instead. host_compute_core: An optional list of `strings`. Defaults to `[]`. padding_map: An optional list of `strings`. Defaults to `[]`. step_marker_location: An optional `string`. Defaults to `"STEP_MARK_AT_ENTRY"`. allow_soft_placement: An optional `bool`. Defaults to `False`. use_spmd_for_xla_partitioning: An optional `bool`. Defaults to `False`. tpu_compile_options_proto: An optional `string`. Defaults to `""`. name: A name for the operation (optional). Returns: The created Operation. TPUReplicateMetadatarrrrdevice_assignmentcomputation_shapehost_compute_core padding_maprrrrN)rrrrrrrrrrrrr0r1rSrTVExpected list for 'device_assignment' argument to 'tpu_replicate_metadata' Op, not %r.VExpected list for 'computation_shape' argument to 'tpu_replicate_metadata' Op, not %r.VExpected list for 'host_compute_core' argument to 'tpu_replicate_metadata' Op, not %r.PExpected list for 'padding_map' argument to 'tpu_replicate_metadata' Op, not %r.rF) rrrrrrrrrrrrr0)r3rr4r5rr6r7r8r9r:r;%tpu_replicate_metadata_eager_fallbackr=r>r?rrrrrrr@rA)rrrrrrrrrrrrr0rGrHrIrJrrrKrLrMs rPtpu_replicate_metadatarIs:    0h..0$ #\\ 11 $dNL!6 H7/1B.0C=+7M46J')F#%>@gn(""<@,""++,ACZ[ H   x 4( _G   w 2' %e} 5  /1B C DDM^^bx((-@A^^ %e} 5  /1B C DDM^^bx((-@A^^ %e} 5  /1B C DDM^^bx((-@A^^K K$ /  /1< = >>ALL"""2}5L+L!/!**+?AWX! !++,@BXY"*$)!"*"4"45RTs"t& "&//0IKfg'88\6K)172C2C2C,75I5I>[:S%) +!QX *e  & &- ##At,,  # #     2# 5->--;33(E$=D    # #   &___MsH0JL LL"L K&KKKK77L Lzraw_ops.TPUReplicateMetadatactj|d}|d}tj|d}|d}tj|d}|d}tj|d}|g}t |t t fstd|z|Dcgc]}tj|d }}|g}t |t t fstd |z|Dcgc]}tj|d }}|g}t |t t fstd |z|Dcgc]}tj|d }}|g}t |t t fstd|z|Dcgc]}tj|d}}|d}tj|d}| d} tj| d} | d} tj| d} | d} tj| d} g}d|d|d|d|d |d |d |d|d|d| d| d| f}tjdd||| | }d}|Scc}wcc}wcc}wcc}w)NrrSrrrTrrrrrrrrrrrFrrrsTPUReplicateMetadatarrT) r>r?rrrrrrr)rrrrrrrrrrrrr0r1rrrOrNrIs rPrrs""<@,""++,ACZ[ H   x 4( _G   w 2' %e} 5  /1B C DDM^^bx((-@A^^ %e} 5  /1B C DDM^^bx((-@A^^ %e} 5  /1B C DDM^^bx((-@A^^K K$ /  /1< = >>ALL"""2}5L+L!/!**+?AWX! !++,@BXY"*$)!"*"4"45RTs"t& "&//0IKfg, L*AXy'(*=(*;] %';.!#@8 :&   4a #)s ?' ' .]___MsH4H9'H>2I) TV_TPUReplicatedInput_Trrrrrprrrqrrrsrtrurrrrvrrrr r!r"r#rwrxr$r%ryr&r'rzis_mirrored_variableindex is_packedc tjxstj}|j}|jr! t j |d||d|d|d| }|St|ttfst!d|zt#|} |d}t%j&|d}|d }t%j(|d}|d}t%j&|d}t+j,d||||| \} } } } | dd}t%j.r{d | j1d d | j3d d| j5dd| j1dd| j5df } | j6}t%j8d|| ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t||||||S#t j$rYwxYw) a-Connects N inputs to an N-way replicated TPU computation. This operation holds a replicated input to a `tpu.replicate()` computation subgraph. Each replicated input has the same shape and type alongside the output. For example: ``` %a = "tf.opA"() %b = "tf.opB"() %replicated_input = "tf.TPUReplicatedInput"(%a, %b) %computation = "tf.Computation"(%replicated_input) ``` The above computation has a replicated input of two replicas. Args: inputs: A list of at least 1 `Tensor` objects with the same type. is_mirrored_variable: An optional `bool`. Defaults to `False`. index: An optional `int`. Defaults to `-1`. is_packed: An optional `bool`. Defaults to `False`. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `inputs`. TPUReplicatedInputrrrN)rrrr0r1IExpected list for 'inputs' argument to 'tpu_replicated_input' Op, not %r.Fr)rErrrr0rr2)r3rr4r5rr6r7r8r9r:r;#tpu_replicated_input_eager_fallbackr=rrrrrr>rr?r@rArBrDrCrrErF)rErrrr0rGrHrIrJrrKrLrMrNrOs rPtpu_replicated_inputrs12    0h..0$ #\\ 11 "D&2Hguk9Fgn FT5M *  -/5 6 77 K'! !++,@BXY ] E   E7 +%I  K8)'88V3G$)YTK!QX QK' ""$3$$S)30B0B30G$  !78'(+  - /F ::L lFG= (' .Q  & &- ##At,,  # #    0 ';5Dd44  # #   s0FG*GGG"G33H  H zraw_ops.TPUReplicatedInputc  t|ttfstd|zt |}|d}t j |d}|d}t j|d}|d}t j |d}t jt||g\}}t|}d|d|d|d|d|f } t jd d || || } t jrt jd || | | \} | S) NrFrrrrrr2sTPUReplicatedInputrSrTr) rrrrrr>rr?rVrrBrF) rErrrr0r1rrmrOrNrIs rPrr\s  FT5M *  -/5 6 77 K'! !++,@BXY ] E   E7 +%I  K8)33DL#rJ/'6f, #w(> Y @&   2Al#)s ?' ""$ lFG= (' .rn) TV_TPUReplicatedOutput_Trrrrrprrrqrrrsrtrurrrrvrrrr r!r"r#rwrxr$r%ryr&r'rzctjxstj}|j}|jr t j |d||d|}|Stj|d}tj d|||\}}}} | dd}tj"rHd|j%dd|j'df} |j(} tj*d| | ||S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t||||S#t j$rYwxYw)axConnects N outputs from an N-way replicated TPU computation. This operation holds a replicated output from a `tpu.replicate()` computation subgraph. Each replicated output has the same shape and type alongside the input. For example: ``` %computation = "tf.Computation"() %replicated_output:2 = "tf.TPUReplicatedOutput"(%computation) ``` The above computation has a replicated output of two replicas. Args: input: A `Tensor`. num_replicas: An `int` that is `>= 1`. name: A name for the operation (optional). Returns: A list of `num_replicas` `Tensor` objects with the same type as `input`. TPUReplicatedOutputrN)rr0r1)r(rr0r2)r3rr4r5rr6r7r8r9r:r;$tpu_replicated_output_eager_fallbackr=r>r?r@rArBrDrCrErF) r(rr0rGrHrIrJrKrLrMrNrOs rPtpu_replicated_outputrzse*    0h..0$ #\\ 11 #T5.gn""<@,'88U$(*!QX QK' ""$c//?  %'F::L |VW> .+  & &- ##At,,  # #    1 l4AA  # #   s0C33D:D!!D:9D:>E E$#E$zraw_ops.TPUReplicatedOutputc tj|d}tj|g|g\}\}|g}d|d|f}tjd|||||}tjrtj d||||S)Nrr2sTPUReplicatedOutputrTr)r>r?rVrrBrF)r(rr0r1rmrOrNrIs rPrrs""<@,55ugsBG'8E, L#w 7&   3\$0C"& ('""$ |VW> .rnrequestc\tjxstj}|j}|jr t j |d||}|Stjd||\}}}}|dd}tj r&d} |j"} tj$d| | ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t|||S#t j$rYwxYw)aWorker heartbeat op. Heartbeats may be sent periodically to indicate the coordinator is still active, to retrieve the current worker status and to expedite shutdown when necessary. Args: request: A `Tensor` of type `string`. A string tensor containing a serialized WorkerHeartbeatRequest name: A name for the operation (optional). Returns: A `Tensor` of type `string`. WorkerHeartbeatNr)rr0r})r3rr4r5rr6r7r8r9r:r;worker_heartbeat_eager_fallbackr=r@rAr>rBrErF) rr0rGrHrIrJrKrLrMrNrOs rPworker_heartbeatrs(    0h..0$ #\\ 11 w0g n(8877!QX QK' ""$ F::L <: (' .'  & &- ##At,,  # #    , $((  # #   s0B<<DC**DD DD+*D+zraw_ops.WorkerHeartbeatctj|tj}|g}d}t j dd||||}t j rt jd||||\}|S)NsWorkerHeartbeatrSrTr)r9rlrWrr>rrBrF)rr0r1rOrNrIs rPrrsq  " "7GNN ;', &   /<#)s ?' ""$ <: (' .rn) TV_XlaConcatND_Trrrrrprrrqrrrsrtrurrrrvrrrr r!r"r#rwrxr$r%ryr&r'rzc tjxstj}|j}|jr t j |d||d|d|}|St|ttfst!d|zt#|}t|ttfst!d|z|D cgc]} t%j&| d}} |g}t|ttfst!d|z|D cgc]} t%j&| d}} t)j*d|||| \} } } } | dd}t%j,rjd | j/d d | j1d d| j3dd| j3df} | j4}t%j6d|| ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t|||||S#t j$rYwxYwcc} wcc} w) aConcats input tensor across all dimensions. An op which merges slices the input tensor based on the given num_splits attribute, strips paddings optionally, and returns the merged tensor without paddings. This op may be generated via the TPU bridge. For example, with `input` tensor: ``` [[0, 1], [4, 5]] [[2, 3], [6, 7]] [[8, 9], [12, 13]] [[10, 11], [14, 15]] ``` `num_splits`: ``` [2, 2] ``` and `paddings`: ``` [1, 1] ``` the expected `outputs` is: ``` [[0, 1, 2], [4, 5, 6], [8, 9, 10]] ``` Args: inputs: A list of at least 1 `Tensor` objects with the same type. Input tensor slices in row-major order to merge across all dimensions. All inputs must have the same shape. num_concats: A list of `ints`. Number of ways to merge per dimension. paddings: An optional list of `ints`. Defaults to `[]`. Optional list of right paddings per dimension to strip from the final merged tensor. These paddings must not exceed the dimension size of the merged result prior to stripping paddings. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `inputs`. XlaConcatNDr~rNrBExpected list for 'inputs' argument to 'xla_concat_nd' Op, not %r.GExpected list for 'num_concats' argument to 'xla_concat_nd' Op, not %r.DExpected list for 'paddings' argument to 'xla_concat_nd' Op, not %r.)rEr~rr0r2r)r3rr4r5rr6r7r8r9r:r;xla_concat_nd_eager_fallbackr=rrrrrr>r?r@rArBrCrDrrErF)rEr~rr0rGrHrIrJrrrKrLrMrNrOs rP xla_concat_ndrsab    0h..0$ #\\ 11 mT6=+Hgn FT5M *  &(. / 00 K' K$ /  &(3 4 55ALL"""2}5L+L H HtUm ,  &(0 1 22;C CBhJ/ C( C'88f+ (t5!QX QK' ""$3%%c*C1B1B31GS\\-8*ll:&(F::L |VW6 (' .Q  & &- ##At,,  # #    ) kH4  # #   MDs<G5H<IHG55H HH""H98H9zraw_ops.XlaConcatNDct|ttfstd|zt |}t|ttfstd|z|Dcgc]}t j |d}}|g}t|ttfstd|z|Dcgc]}t j |d}}t jt||g\}}t|}d|d|d|d|f} t jdd || || } t jrt jd || | | \} | Scc}wcc}w) Nrrr~rrr2rs XlaConcatNDrSrTr) rrrrrr>r?rVrrBrF) rEr~rr0r1rrrmrOrNrIs rPrrasm FT5M *  &(. / 00 K' K$ /  &(3 4 55ALL"""2}5L+L H HtUm ,  &(0 1 22;C CBhJ/ C( C33DL#rJ/'6f, #w { h &   ^Q|#)s ?' ""$ |VW6 (' .%MDs E#E ) TV_XlaSplitND_Trrrrrprrrqrrrsrtrurrrrvrrrr r!r"r#rwrxr$r%ryr&r'rzc Ztjxstj}|j}|jr! t j |d||d|d|d| }|Stj|d}t|t t"fst%d|z|D cgc]} tj| d}} |g}t|t t"fst%d|z|D cgc]} tj| d}} t'j(d||||| \} } } } | dd}tj*rjd | j-d d| j/dd| j1dd| j1df} | j2}tj4d|| ||S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t||||||S#t j$rYwxYwcc} wcc} w) atSplits input tensor across all dimensions. An op which slices the input tensor based on the given num_splits attribute, pads slices optionally, and returned the slices. Slices are returned in row-major order. This op may be generated via the TPU bridge. For example, with `input` tensor: ``` [[0, 1, 2], [3, 4, 5], [6, 7, 8]] ``` `num_splits`: ``` [2, 2] ``` and `paddings`: ``` [1, 1] ``` the expected `outputs` is: ``` [[0, 1], [3, 4]] [[2, 0], [5, 0]] [[6, 7], [0, 0]] [[8, 0], [0, 0]] ``` Args: input: A `Tensor`. Input tensor to split across all dimensions. N: An `int` that is `>= 1`. num_splits: A list of `ints`. Number of ways to split per dimension. Shape dimensions must be evenly divisible. paddings: An optional list of `ints`. Defaults to `[]`. Optional list of right paddings per dimension of input tensor to apply before splitting. This can be used to make a dimension evenly divisible. name: A name for the operation (optional). Returns: A list of `N` `Tensor` objects with the same type as `input`. XlaSplitNDrrrN)rrrr0r1EExpected list for 'num_splits' argument to 'xla_split_nd' Op, not %r.CExpected list for 'paddings' argument to 'xla_split_nd' Op, not %r.)r(rrrr0r2)r3rr4r5rr6r7r8r9r:r;xla_split_nd_eager_fallbackr=r>r?rrrrr@rArBrCrDrrErF)r(rrrr0rGrHrIrJrrKrLrMrNrOs rP xla_split_ndrs@b    0h..0$ #\\ 11 lD%azHgn3! Ju .  %'1 2 33?II!!"l3I*I H HtUm ,  %'/ 0 11;C CBhJ/ C( C'88EQ:'d4!QX QK' ""$3%%c*C1B1B31GCLL6 ll:&(F::L lFG5 .G  & &- ##At,,  # #    ( 1hT  # #   JDs<F-H#)H(-G4GG43G48H H H zraw_ops.XlaSplitNDcLtj|d}t|ttfst d|z|Dcgc]}tj|d}}|g}t|ttfst d|z|Dcgc]}tj|d}}tj |g|g\}\}|g}d|d|d|d|f} tjd||| ||} tjrtjd || | | Scc}wcc}w) Nrrrrrr2s XlaSplitNDrTr) r>r?rrrrrVrrBrF) r(rrrr0r1rrmrOrNrIs rPrrsC3! Ju .  %'1 2 33?II!!"l3I*I H HtUm ,  %'/ 0 11;C CBhJ/ C( C55ugsBG'8E, #q, J  &   ]Al#)s ?' 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