o Ei#@s0ddlZddlmmZddlZddlmZddlm Z m Z m Z ddl mZ ejjddddZejjdddZejjd ddZejd Zejjd d d ZddZejdddd"ddZejd#ddZejjejddd  d$dedejde de ejedfdede j dejfd d!Z!ee!_dS)%N)partial)CallableUnionAny PartitionSpecPz jax.sharding)from_ NamedShardingMeshjaxnumpynp)as_cCs2|}|durt|tjjjrdS|j}|dusdS)NTF) isinstanceeinxexprstage3 Compositionparent)rnoderW/home/ubuntu/veenaModal/venv/lib/python3.10/site-packages/einx/experimental/op/shard.py _is_composedsrcdfdd S)Ncs|||SNr)expr_in tensor_inexpr_outbackendctrrs ..rrr!r rrrr"r")tracecCs ddl}||fD]F}|D]?}t|tjjjrtdt|tjjjrM|}|j durMt|j tjjj rEt |j rE||j j durEtd|j }|j dus+qqtj j||j|d}||g\}tj|g|g|d\\}\}tdd|D} |durtttdd| D} t| tdd| Dd }nFt||jjrtd d| D} td d|jD} | | std | d | nt|tdd| D} t| tdd| Dd }tdd|D} dd| D}t|}t ||}t!||}tj"|g|g|g|d\}||fS)NrzConcatenation not allowedzhIf device axes are used within a composition they must appear as the left-most member of the composition)rcss2|]}t|tjjjrtjj|r|VqdSr)rrrrAxis is_marked.0axisrrr :s  zshard_stage3..cs|]}|jVqdSrvaluer*arrrr,Bcsr-rnamer0rrrr,Cr2) axis_namescsr-rr3r0rrrr,Fr2css|]}t|VqdSr)strr0rrrr,Gsz9Marked axes must be subset of mesh axes. Got marked axes z and mesh axes csr-rr.r0rrrr,Nr2csr-rr3r0rrrr,Or2css$|] }t|tjjjr|VqdSr)rrrrr'r)rrrr,Rs"cSs$g|]}tjj|r|jndqSr)rrrr(r4r)rrr Ss$z shard_stage3..)#r allrrrr Concatenation ValueErrorMarkerrListrchildrentracer call_factoryshape all_to_tensorutilflattentupletnparraytjaxdevicesreshapetMeshshardingr setr5issubsettPtNamedSharding device_put unflatten)rrrmeshrr rootrchild marked_axesrH marked_names mesh_namesaxespartition_specrKrrr shard_stage3s\    "   rZTc sddl}tjj|\}tjj|tdkr$t dtfdd}|dur{z|g}Wntjj yz} z5tjj tjj dd} tj | t|g} z|| g}Wn tjj yo| wWYd} ~ nTd} ~ wwt||jjrtjjtjjjdd|jD} tj | |jj} || g}n&t|ttfrtjj tjj dd} tj | t|g} || g}|} || fS)Nrz%Expected exactly one expression, got cs>tjjtjddg|ddDddS)NrcSs4g|]\}}tjj|t|dtjfdddqS).N)depth1depth2)rrEquationr asarraynewaxis)r*kvrrrr7qs"z(parse..solve..)cse)rrsolver^items)eqsrcop parameters tensor_shaperrrdmszparse..solvecSsg|] }tjj|qSr)rrstage1 NamedAxis)r*r4rrrr7szparse..)r rrhrB!_clean_description_and_parametersrrkparse_oplenr:SolveExceptionr get_markedr^rHrrKr r;r<mayber5r@listrD __deepcopy__) descriptionrjrcrR jax_devicesrir rdre expr_meshmesh_eqrrrgrparse`sH         rzcr)Ncs||fd|i|S)NrRr)rutensorrRrkwargsrrrr"sr#NNrr$rrrr"r%rur{rRrrcrireturncKsP|jdkr tdt|tj|f||d|\}}t|||||d\}}|S)a Shards a tensor over a mesh of devices. *This function is currently experimental and will likely change in future versions.* *This function is currently only supported for Jax: A sharding is created based on the given expression, and applied to the tensor using* ``jax.device_put``. The tensor is sharded across the marked axes in the input expression. The marked axes match the axis names and shape of the mesh: >>> x = jnp.ones((2, 4, 128)) >>> x = einx.experimental.shard("[d1 d2] c") >>> x.sharding NamedSharding(mesh=Mesh('d1': 2, 'd2': 4), spec=PartitionSpec('d1', 'd2', None)) Axis compositions can be used to apply the `sharding rules of Jax `_, where tensor axes are evenly divided by the number of shards: >>> x = jnp.ones((128, 640, 480, 3)) >>> x = einx.experimental.shard("([batch] _) ...", x) >>> x.sharding NamedSharding(mesh=Mesh('batch': 8), spec=PartitionSpec('batch',)) If possible, the sharding is created over all devices. ``_`` is a regular axis name, and its value is determined by :doc:`einx's expression solver `. Optionally, an existing mesh can be passed: >>> from jax.sharding import Mesh >>> devices = np.asarray(jax.devices()).reshape(4, 2) >>> mesh = Mesh(devices, axis_names=("d1", "d2")) >>> x = jnp.ones((4, 1024, 1024)) >>> x = einx.experimental.shard("a ([d2] b) ([d1] c)", x, mesh=mesh) >>> x.sharding NamedSharding(mesh=Mesh('d1': 4, 'd2': 2), spec=PartitionSpec(None, 'd2', 'd1')) The array is replicated over all mesh axes that are not part of the expression: >>> x = jnp.ones((1024, 1024)) >>> x = einx.experimental.shard("a ([d1] b)", x, mesh=mesh) >>> x.sharding NamedSharding(mesh=Mesh('d1': 4, 'd2': 2), spec=PartitionSpec(None, 'd1',)) Args: description: Description string in Einstein notation (see above). tensor: Input tensor or tensor factory matching the description string. mesh: Mesh or list of devices to shard the tensor over. If not given, a new mesh over all available devices will be created matching the axes in the given expression. Defaults to ``None``. cse: Whether to apply common subexpression elimination to the expressions. Defaults to True. graph: Whether to return the graph representation of the operation instead of computing the result. Defaults to False. **parameters: Additional parameters that specify values for single axes, e.g. ``a=4``. Returns: The sharded tensor if ``graph=False``, otherwise the graph representation of the operation. r z;einx.experimental.shard is currently only supported for Jax)rRrc)rRr)r4NotImplementedErrorrzrr> get_shaperZ)rur{rRrrcrirrrrrshards J  rr})TNN)Nr T)"r einx.op.utilrhrBr r functoolsrtypingrrr numpy.typingnptr>import_rNrOrJrGrErjitrZ lru_cacherztraceback_utilfilterr6TensorBackendbool ArrayLikerrrrrsR     A 9 M