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H H H H H H H$ nd # # + + +^'w// 4 4a $ :dD,=>> q'C ???dj#(.1144B B # #4 < $rv,!$rv,33333 4 ) )D ) ) ) ) )))) 9 9Sw// 9 9 99rw"list[pe.AbstractedAxesSpec] | NonecD|dS|rtd}t|||S)Nct|tr!t|p%t|tot|dS)Nc |duSrrxrws rurfz3_flat_axes_specs..ax_leaf..s Q$Yrw)r rrUrr)ls ruax_leafz!_flat_axes_specs..ax_leaf sP q$   :Jqxxzz$:$: H q% GZ3F3F%G%GIrw)NotImplementedErrorrT)rrrrs rurrs>TT && &III /4 9 99rwceZdZdZddZdS) JitWrappedct)zSee ``jax.eval_shape``.rrrrs rurzJitWrapped.eval_shapes rwrrctrr"r#s rurzJitWrapped.traces rwNr)rrrrrrxrwrur r s7rwr Fc :t||||||||| | || d S)u &Makes ``fun`` compiled and automatically partitioned across multiple devices. NOTE: This function is now equivalent to jax.jit please use that instead. The returned function has semantics equivalent to those of ``fun``, but is compiled to an XLA computation that runs across multiple devices (e.g. multiple GPUs or multiple TPU cores). This can be useful if the jitted version of ``fun`` would not fit in a single device's memory, or to speed up ``fun`` by running each operation in parallel across multiple devices. The partitioning over devices happens automatically based on the propagation of the input partitioning specified in ``in_shardings`` and the output partitioning specified in ``out_shardings``. The resources specified in those two arguments must refer to mesh axes, as defined by the :py:func:`jax.sharding.Mesh` context manager. Note that the mesh definition at :func:`~pjit` application time is ignored, and the returned function will use the mesh definition available at each call site. Inputs to a :func:`~pjit`'d function will be automatically partitioned across devices if they're not already correctly partitioned based on ``in_shardings``. In some scenarios, ensuring that the inputs are already correctly pre-partitioned can increase performance. For example, if passing the output of one :func:`~pjit`'d function to another :func:`~pjit`’d function (or the same :func:`~pjit`’d function in a loop), make sure the relevant ``out_shardings`` match the corresponding ``in_shardings``. .. note:: **Multi-process platforms:** On multi-process platforms such as TPU pods, :func:`~pjit` can be used to run computations across all available devices across processes. To achieve this, :func:`~pjit` is designed to be used in SPMD Python programs, where every process is running the same Python code such that all processes run the same :func:`~pjit`'d function in the same order. When running in this configuration, the mesh should contain devices across all processes. All inputs arguments must be globally shaped. ``fun`` will still be executed across *all* devices in the mesh, including those from other processes, and will be given a global view of the data spread across multiple processes as a single array. The SPMD model also requires that the same multi-process :func:`~pjit`'d functions must be run in the same order on all processes, but they can be interspersed with arbitrary operations running in a single process. Args: fun: Function to be compiled. Should be a pure function, as side-effects may only be executed once. Its arguments and return value should be arrays, scalars, or (nested) standard Python containers (tuple/list/dict) thereof. Positional arguments indicated by ``static_argnums`` can be anything at all, provided they are hashable and have an equality operation defined. Static arguments are included as part of a compilation cache key, which is why hash and equality operators must be defined. in_shardings: Pytree of structure matching that of arguments to ``fun``, with all actual arguments replaced by resource assignment specifications. It is also valid to specify a pytree prefix (e.g. one value in place of a whole subtree), in which case the leaves get broadcast to all values in that subtree. The ``in_shardings`` argument is optional. JAX will infer the shardings from the input :py:class:`jax.Array`'s, and defaults to replicating the input if the sharding cannot be inferred. The valid resource assignment specifications are: - :py:class:`Sharding`, which will decide how the value will be partitioned. With this, using a mesh context manager is not required. - :py:obj:`None` is a special case whose semantics are: - if the mesh context manager is *not* provided, JAX has the freedom to choose whatever sharding it wants. For in_shardings, JAX will mark is as replicated but this behavior can change in the future. For out_shardings, we will rely on the XLA GSPMD partitioner to determine the output shardings. - If the mesh context manager is provided, None will imply that the value will be replicated on all devices of the mesh. - For backwards compatibility, in_shardings still supports ingesting :py:class:`PartitionSpec`. This option can *only* be used with the mesh context manager. - :py:class:`PartitionSpec`, a tuple of length at most equal to the rank of the partitioned value. Each element can be a :py:obj:`None`, a mesh axis or a tuple of mesh axes, and specifies the set of resources assigned to partition the value's dimension matching its position in the spec. The size of every dimension has to be a multiple of the total number of resources assigned to it. out_shardings: Like ``in_shardings``, but specifies resource assignment for function outputs. The ``out_shardings`` argument is optional. If not specified, :py:func:`jax.jit` will use GSPMD's sharding propagation to determine how to shard the outputs. static_argnums: An optional int or collection of ints that specify which positional arguments to treat as static (compile-time constant). Operations that only depend on static arguments will be constant-folded in Python (during tracing), and so the corresponding argument values can be any Python object. Static arguments should be hashable, meaning both ``__hash__`` and ``__eq__`` are implemented, and immutable. Calling the jitted function with different values for these constants will trigger recompilation. Arguments that are not arrays or containers thereof must be marked as static. If ``static_argnums`` is not provided, no arguments are treated as static. static_argnames: An optional string or collection of strings specifying which named arguments to treat as static (compile-time constant). See the comment on ``static_argnums`` for details. If not provided but ``static_argnums`` is set, the default is based on calling ``inspect.signature(fun)`` to find corresponding named arguments. donate_argnums: Specify which positional argument buffers are "donated" to the computation. It is safe to donate argument buffers if you no longer need them once the computation has finished. In some cases XLA can make use of donated buffers to reduce the amount of memory needed to perform a computation, for example recycling one of your input buffers to store a result. You should not reuse buffers that you donate to a computation, JAX will raise an error if you try to. By default, no argument buffers are donated. If neither ``donate_argnums`` nor ``donate_argnames`` is provided, no arguments are donated. If ``donate_argnums`` is not provided but ``donate_argnames`` is, or vice versa, JAX uses :code:`inspect.signature(fun)` to find any positional arguments that correspond to ``donate_argnames`` (or vice versa). If both ``donate_argnums`` and ``donate_argnames`` are provided, ``inspect.signature`` is not used, and only actual parameters listed in either ``donate_argnums`` or ``donate_argnames`` will be donated. For more details on buffer donation see the `FAQ `_. donate_argnames: An optional string or collection of strings specifying which named arguments are donated to the computation. See the comment on ``donate_argnums`` for details. If not provided but ``donate_argnums`` is set, the default is based on calling ``inspect.signature(fun)`` to find corresponding named arguments. keep_unused: If `False` (the default), arguments that JAX determines to be unused by `fun` *may* be dropped from resulting compiled XLA executables. Such arguments will not be transferred to the device nor provided to the underlying executable. If `True`, unused arguments will not be pruned. device: This argument is deprecated. Please put your arguments on the device you want before passing them to jit. Optional, the Device the jitted function will run on. (Available devices can be retrieved via :py:func:`jax.devices`.) The default is inherited from XLA's DeviceAssignment logic and is usually to use ``jax.devices()[0]``. backend: This argument is deprecated. Please put your arguments on the backend you want before passing them to jit. Optional, a string representing the XLA backend: ``'cpu'``, ``'gpu'``, or ``'tpu'``. Returns: A wrapped version of ``fun``, set up for just-in-time compilation and automatically partitioned by the mesh available at each call site. For example, a convolution operator can be automatically partitioned over an arbitrary set of devices by a single :func:`~pjit` application: >>> import jax >>> import jax.numpy as jnp >>> import numpy as np >>> from jax.sharding import Mesh, PartitionSpec >>> from jax.experimental.pjit import pjit >>> >>> x = jnp.arange(8, dtype=jnp.float32) >>> f = pjit(lambda x: jax.numpy.convolve(x, jnp.asarray([0.5, 1.0, 0.5]), 'same'), ... in_shardings=None, out_shardings=PartitionSpec('devices')) >>> with Mesh(np.array(jax.devices()), ('devices',)): ... print(f(x)) # doctest: +SKIP [ 0.5 2. 4. 6. 8. 10. 12. 10. ] T)r)r) rr<rqrrrrrrrrrs rurrs9l  L-FT 3 3 33rwcvt|\ttfdfS)Nc$tSr)rO)rrsrurfz!hashable_pytree..s.$"?"?rwr)rNrrZ)pytreerrs @@ruhashable_pytreer)sMv&&-$ t$ ?????#*D/ 3 3 33rwc|||jrtSt|tjst |r|S|4d|d}|dkr |d|dz }n|dkr |d|dz }t ||jrt |d|d |d |t|ts J||tj ||ntj ||j |S) Nz2jax.jit only supports `Sharding`s being passed to z_. Looks like you are passing either `PartitionSpec` or `None` which is not allowed in jax.jit. r<z Note that z argument is optional. JAX will infer the shardings from the input jax.Array's and will default to replicating the input if the sharding cannot be inferred.rqz is optional. If not specified, jax.jit will use GSPMD's sharding propagation to figure out what the sharding of the output(s) should be.zL requires a non-empty mesh if you are passing `PartitionSpec`s or `None` to zL! Is a mesh defined at the call site? Alternatively, provide `Sharding`s to z3 and then the mesh context manager is not required.) rr?r r9ShardingrD RuntimeErrorrAr2create_mesh_pspec_sharding user_spec)rrrprrs rurrsgYDLDJL 8$%% )?)B)B H \ 2 2 2 2C ~ ;4;;;|Jttj|d}nt dd|_|S)Nrz Unreachable!T)r<xb get_backendr4r_device_backend)rrrs rurrst  ??? v & &CC >>> r~g66DDFFqI J JCC  ( ((# *rwc  tt||||S#t$rYnwxYwtt |rJ|rNt |tus3t |t |kr|d|d}t |tur|d|dt |dz }nmt |t |kr;|d|dt |dt |d z }t |d kr,t|||f|d |d z }n#t$rYnwxYwt||}t|tg|j z}t||}|r|d }||Jd)N tupled_argsz specification must be a tree prefix of the positional arguments tuple passed to the `pjit`-decorated function. In particular, zj must either be a None, a PartitionSpec, or a tuple of length equal to the number of positional arguments.z But z is not a tuple: got z instead.z4 is the wrong length: got a tuple or list of length z for an args tuple of length rrz> Given the corresponding argument being passed, it looks like z/ might need to be wrapped in a singleton tuple.rFzPlease open a bug report!) rr rrPrQrrchildrenrO PytreeLeafrrV) whatrr}r5r axis_tree dummy_treeerrorsrs ruflatten_axis_resourcesr< sO dD)MMM N NN    D ^I66 7 7777d9ooU22)nnDMMOO(<(<<< M M M M MC  Ie## JT J JY J J JJcc Y3t}}// / / )d))Y))T]]__%%)))*c  4==??q  dYL 1 1 1 &)-&&& ' S//)dZ\\NT_$DEE* J / /& q A !D''M,++++s" //&F FFceZdZdZdS)r7cdS)Nz pytree leafrxrs ru__repr__zPytreeLeaf.__repr__Cs]]rwN)rrrr?rxrwrur7r7Bs*****rwr7i)max_sizetrace_context_in_keyc |st|\}} t||} t| r| ft|z} nt d|| d} t||} | | ft|z} nt d|| d} |o$t|jt|k}t jjs:|s8t| ||dn|jddt| ||dn|jd| | fS) Nzpjit in_shardingsTr4zpjit in_layoutszpjit argumentsFallow_uneven_shardingz jit arguments) rSrOrCrr<rrrr-pjit_check_aval_shardingcheck_aval_layout_compatibility)rrrrrrr(rkwsrorig_in_shardingsrrrrrs rurrFsp +!'**JGQ$%9;NOO%&&K*,s8}}<.W&7TKKK02CDD*!mc(mm3OO,7JDBBBOKZ%9!:!:c(mm!K-   $O]O.%/%7TTZ=Q-ULLLL$" (|S)?NcN|^}\}\}}}}}}}|^}\}\}}||||j|fSr)r)rL transformsrrr(rctxrs ruunpackz*explain_tracing_cache_miss..unpackosDFICJA:J6Q'A:G GZS 88rwctttjdS)N )loggerlogloggingWARNINGjoin)rsrurfz,explain_tracing_cache_miss..ys!GOTYYs^^<<rwzTRACING CACHE MISS at z because:r z defined at r~z never seen function: z id=z~ but seen another function defined on the same line; maybe the function is being re-defined repeatedly, preventing caching?z for c0g|]^}}t|Srx)rS)rkrs rurz.explain_tracing_cache_miss..s%BBBuq1'**BBBrwc$g|] }|gk | Srxrxrt args_tree kwargs_trees rurz.explain_tracing_cache_miss..s2999Q ;777777rwz never seen passing z positional args and z keyword args with keys: z, czg|]7}|gk t|dd8Sr)set node_datar]s rurz.explain_tracing_cache_miss..sM444q9k222adnn&&q)**222rw)rLdefaultz) closest seen is passing no keyword argsz closest seen passes c&g|] ^}}|k |SrxrxrrcrPs rurz.explain_tracing_cache_miss..s"555eq!AHHHHHrwzF tracing context doesn't match, e.g. due to config or context managerc&g|] ^}}|k |Srxrxrgs rurz.explain_tracing_cache_miss..s"777Aa3hh!hhhrwcTtttj|Srrmapopne)rhrPs rurfz,explain_tracing_cache_miss..sCq#4F4F0G0Grw)rLc*g|]\}\}}||k|Srxrx)rrc1c2s rurz.explain_tracing_cache_miss..s% N N N+!Xb"R2XXAXXXrwz7 closest seen context tuple differs at positions: zN compare to tuple returned by config._trace_context() in jax/_src/config.py.c,g|]}|dk|S)rrx)rr[rs rurz.explain_tracing_cache_miss..s"999q1rwLz: z never seen input pytreec$g|] ^}}|k | Srxrx)rr^rrs rurz.explain_tracing_cache_miss..s";;;Aa7ll!lllrwc<t|jjz Sr)absr)r^rs rurfz,explain_tracing_cache_miss..sQ\GDV5V1W1Wrwz closest seen input pytree has z mismatches, including:rrz * at z, seen z but now given z , so c,g|]}|dk|Srbrx)rr[rs rurz.explain_tracing_cache_miss..s";;;q1Q47?????rwz: {}z, c3RK|]"\}}|d|dV#dS): T) short_dtypesN)rm)rrtys rurz-explain_tracing_cache_miss..sV2;2;a 3 3",,D,11 3 32;2;2;2;2;2;rwz! never seen input type signaturec&g|] ^}}}|k |Srxrx)rrr^rs rurz.explain_tracing_cache_miss..s$@@@1q1<.ss3rua3I3I/J/Jrwz( closest seen input type signature has FTz {weak_type=}z, but now given zEwhere weak_type=True often means a Python builtin numeric value, and z"weak_type=False means a jax.Array.zGSee https://jax.readthedocs.io/en/latest/type_promotion.html#weak-typeszNexplanation unavailable! please open an issue at https://github.com/google/jax)*rcheck_tracer_leaksr-rlr summarizecurrentr func_src_infosplitrXrrIaddrlrrSrrdreprminrcsymmetric_differencerrrrrequality_errors_pytreedefidxrWrformatrrrmrnrr ShapedArrayrm weak_type)2rrJrKrLrQr(rrdonecallsitercrrestsrc_info seen_keysargs_kwargs_treesargs_kwargs_matchnum_args kwarg_keys dont_match close_kwargs ctxs_match closest_ctxidxs trees_match in_tree_str closest_treeerrspaththing1thing2 explanationfstbase types_match in_type_str closest_ty num_mismatchadd_weak_type_hintrpty1ty2s1s2r_rPrrr`rs2 @@@@@@ruexplain_tracing_cache_missrks2 $,ff999 /5fSkk+'7J VV# j! < < < <$  '(8(@(B(B C C(!0X000111Q * *( 8*0055KAq4$/HHH %ADXDD2a55D(DDEEE9a =>>> MM( 466MA#x###$$$&%**,,''),G44)[BB BBB99999"3999 #I..//H))++MAzA0h00S__00 YYs4,, - -00111444444E444JJ. s.44At{ # #444444rw)rlog_elapsed_timerJAXPR_TRACE_EVENTrdebug_info_final traced_forrrr-trace_to_jaxpr_dynamic2rannotater rrtrace_to_jaxpr_dynamicr*r, jax.experimental.key_reuse._corerr+ close_jaxprconvert_constvars_jaxpr ClosedJaxpr)rrrr(rrpe_debugrglobal_out_avalsrrr closed_jaxpr final_constss rurPrPs U|8#=??? - -Mb1#z7LMMH "-(*(B +c488 9 9h)P)P)P%e vmm797P w88-8-8-4e v} - - - - - - - - - - - - - - -   $=]= UJ < "".Os[S$)?)?@@rwc dg|],\}}\}}|||tt||f-Srx)rlr$)rrrrrrs rurz _attr_update..PsS = = =*A{T4C(:FF3....BH JE " + , ,+ &&&& s5zz*** """  !I !x ! ! ! ! ! ! " """))#e**55L  # # #*CLQQ!122334 "3uQx$!';';292h22 22=>22*.22EJ!H22*/ 22233 33-33sAB C$B>>Cc 8|dgt|zn|}t|||D]p\}}}|t|tr|rd|nd}|j} || E#t $r} t d||d|d| d} ~ wwxYwdS)Nr~rrz, is incompatible with its layout annotation rz)rrr rIrrr) r|rrrrrrrrprrrs rurFrFs%*]rdS\\!!):w :: " "mdAtyJq*--y26>....BH JE"e$$$$ """  !I !x ! ! ! ! ! ! " """ " "sA.. B8BBrc tj|rdt|zSg}t||||D]=\}}}}tjt |dd|||t |dd} } t |p#tt |ddt} |F| r.| r| d| | | d| rl| sj| htj || sSd} t|trd} td|d | d t|d | | |?t|S) NrlayoutrTr9r~a The layout given to `jax.jit` is `DeviceLocalLayout.AUTO` but the corresponding argument passed is a `jax.Array` with a concrete layout. Consider passing a `jax.ShapeDtypeStruct` instead of `jax.Array` as an argument to the jitted function when using `DeviceLocalLayout.AUTO`.zWLayout passed to jit does not match the layout on the respective arg. Got pjit layout: z, arg layout:  for arg shape: r)r2!check_device_backend_on_shardingsrr\_maybe_get_default_layoutrrCr r=rlis_user_xla_layout_equalrIrr$rmr) rjit_in_layoutsresolved_in_shardingsrresolved_in_layoutsrjit_in_lrsrr arg_layout committedis_pmap_shardingrs ru_resolve_in_layoutsrs  +,ABB) S(( ((!) N18"="=(+(+c8R &wsHd'C'CX')4 1 1\4((J 'r**Q"73 D#A#A<PP )  1  $ $T * * * *  $ $Z 0 0 0 0""4(((( *"*$/*EE% h + + 6 ).6))*4)));3(?(?(I(I(K(K )) ' ))** *   **** " # ##rwpjit_in_shardingsSequence[PjitSharding]pxla.Mesh | Nonecheck_device_assignmentc tj|r|Sg}|D]d}t|dd}|t|tr,t|ddr'||tjjdfe|rtjtj tj |dtj |Ddtj |D||j rdnt|jjg}t#||D]U\} } t%| dr| j| jt| ddfnt(df\}} t+| rt+|r||u| rFt|tr|t(||t-j|r|t(t1dt| t2jr8| js1t9jdkrt=d | jd | t+|s| j |j krAt=d | j d |j d tC| "| rt|ts|tGj$| %| j&|%| j&s7t=d| d|d tC| "|| WtO|S)Nr9rTc3>K|]}|tjjdfVdSr)r2ri IN_SHARDINGros rurz(_resolve_in_shardings..sC = =". 5 = = = = = =rwc3>K|]}|tjjdfVdSr)r2ri OUT_SHARDINGrs rurz(_resolve_in_shardings..sC 9 9"/ 6 9 9 9 9 9 9rwFzFHaving uncommitted Array sharded on multiple devices is not supported.ra~Passing non-trivial shardings for numpy inputs is not allowed. To fix this error, either specify a replicated sharding explicitly or use `jax.experimental.multihost_utils.host_local_array_to_global_array(...)` to convert your host local numpy inputs to a jax.Array which you can pass to pjit. If the numpy input is the same on each process, then you can use `jax.make_array_from_callback(...) to create a `jax.Array` which you can pass to pjit. Please see the jax.Array migration guide for more information https://jax.readthedocs.io/en/latest/jax_array_migration.html#handling-of-host-local-inputs-to-pjit-like-batch-etc. Got arg shape: z , arg value: zgMemory kinds passed to jax.jit does not match memory kind on the respective arg. Got pjit memory kind: z, arg memory kind: rz^Sharding passed to pjit does not match the sharding on the respective arg. Got pjit sharding: z, arg sharding: )(r2rrr r=rlrirj _get_and_check_device_assignmentitchainr stable_uniquerrdevicesflatrrr9r?rCris_single_device_shardingrnpndarrayis_fully_replicatedr0 process_countrr memory_kindr$rmrare_op_shardings_equalrndimr) rrrqrrcommitted_arg_shardingsrarg_srr pjit_in_srs ru_resolve_in_shardingsrsb +,=>>   T Ta Az4 ( (E }%&&q,%%T$$eT->-KT%RSSSZ)   6 7 7 = =():;; = = = 9 9(77 9 9 9  : : "io"4 @Q@V;W;WZZZD"344;.;.nc9#3 3328; 8PwsL$'G'GHH)51E9i  5.   L$$U++++  L | , , 0 ! ( ( 5 5 5 5 ! ( ( / / / /  / 6 6 L ! 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