"EiW%SSKJr SSKrSSKJr SSKJrJrJrJrJ r SSK J r J r J r SSKrSSKJr \(aSSKJr SS KJr /S Qr\ "S 5r\ "S 5r\"\R0S 5(d]\"S5\R0R2S'\"S5\R0R2S'\"S5\R0R2S'SSKJrJrJr SSjrS Sjr"SS\5r "SS5r!\ S\S\"44r#S\$S'\S!S"Sjj5r%\S!S#Sjj5r%S!S$Sjjr%g)%) annotationsN)Callable)Optionaloverload TYPE_CHECKING TypeAliasUnion) ParamSpecSelfTypeVar)Tensor) _POOL_HANDLE) _dummy_type)is_current_stream_capturinggraph_pool_handleXPUGraphgraphmake_graphed_callables_R_P_XpuStreamBase _XPUGraph_xpu_graph_pool_handle_xpu_isCurrentStreamCapturing)rrrc[5$)zReturn True if XPU graph capture is underway on the current XPU stream, False otherwise. If a XPU context does not exist on the current device, returns False without initializing the context. )rM/home/mande/repo/quber/.venv/lib/python3.13/site-packages/torch/xpu/graphs.pyrr)s ) **rcP[RR[55$)zBReturn an opaque token representing the id of a graph memory pool.)torchxpurrrrrrr1s 99 ! !"8": ;;rc^\rSrSrSrSSU4SjjjrSSU4SjjjrSU4SjjrSU4SjjrSU4Sjjr SU4Sjjr SU4S jjr SU4S jjr SU4S jjr SU4S jjrSU4S jjrSrU=r$)r6aWrapper around a XPU graph. Arguments: keep_graph (bool, optional): If ``keep_graph=False``, the executable command graph will be instantiated on GPU at the end of ``capture_end`` and the underlying modifiable command graph will be destroyed. Note that the executable command graph will not be instantiated at the end of ``capture_end`` in this case. Instead, it will be instantiated via an explicit called to ``instantiate`` or automatically on the first call to ``replay`` if ``instantiate`` was not already called. Calling ``instantiate`` manually before ``replay`` is recommended to prevent increased latency on the first call to ``replay``. c">[TU]X5$N)super__new__)cls keep_graph __class__s rr(XPUGraph.__new__Gsws//rc >[TU]US9 g)aBegin capturing XPU work on the current xpu stream. Typically, you shouldn't call ``capture_begin`` yourself. Use :class:`~torch.xpu.graph`, which call ``capture_begin`` internally. Arguments: pool (optional): Token (returned by :func:`~torch.xpu.graph_pool_handle` or :meth:`other_Graph_instance.pool()`) that hints this graph may share memory with the indicated pool. poolN)r' capture_begin)selfr/r+s rr0XPUGraph.capture_beginJs 4(rc">[TU]5 g)zEnd XPU graph capture on the current stream. After ``capture_end``, ``replay`` may be called on this instance. Typically, you shouldn't call ``capture_end`` yourself. Use :class:`~torch.xpu.graph`, which call ``capture_end`` internally. N)r' capture_endr1r+s rr4XPUGraph.capture_endWs rc">[TU]5 g)aInstantiate the XPU graph. Will be called by ``capture_end`` if ``keep_graph=False``, or by ``replay`` if ``keep_graph=True`` and ``instantiate`` has not already been explicitly called. Does not destroy the xpu modify command graph returned by ``raw_xpu_graph``. N)r' instantiater5s rr8XPUGraph.instantiateas rc">[TU]5 g)z+Replay the XPU work captured by this graph.N)r'replayr5s rr;XPUGraph.replayjs  rc">[TU]5 g)z1Delete the graph currently held by this instance.N)r'resetr5s rr>XPUGraph.resetns   rc >[TU]5$)zReturn an opaque token representing the id of this graph's memory pool. This id can optionally be passed to another graph's ``capture_begin``, which hints the other graph may share the same memory pool. )r'r/r5s rr/ XPUGraph.poolrs w|~rc >[TU]5$)z.Enable debugging mode for XPUGraph.debug_dump.)r'enable_debug_moder5s rrCXPUGraph.enable_debug_modezsw(**rc">[TU]U5$)z Arguments: debug_path (required): Path to dump the graph to. Calls a debugging function to dump the graph if the debugging is enabled via XPUGraph.enable_debug_mode() )r' debug_dump)r1 debug_pathr+s rrFXPUGraph.debug_dump~sw!*--rc >[TU]5$)zuReturns the underlying xpuGraph_t. ``keep_graph`` must be True. XPU doesn't provide APIs to manipulate this object. )r' raw_xpu_graphr5s rrJXPUGraph.raw_xpu_graphs w$&&rc >[TU]5$)aReturns the underlying xpuGraphExec_t. ``instantiate`` must have been called if ``keep_graph`` is True, or ``capture_end`` must have been called if ``keep_graph`` is False. If you call ``instantiate()`` after ``raw_xpu_graph_exec()``, the previously returned xpuGraphExec_t will be destroyed. It is your responsibility not to use this object after destruction. XPU doesn't provide APIs to manipulate this object. )r'raw_xpu_graph_execr5s rrMXPUGraph.raw_xpu_graph_execs w)++rr)F)r*boolreturnr r&)r/Optional[_POOL_HANDLE]rPNonerPrRrPr)rGstrrPrR)rPint)__name__ __module__ __qualname____firstlineno____doc__r(r0r4r8r;r>r/rCrFrJrM__static_attributes__ __classcell__)r+s@rrr6sN 00 ) )+.',,rrcX\rSrSr%SrSrS\S'S S SjjrS SjrS Sjr S r g)ra^Context-manager that captures XPU work into a :class:`torch.xpu.XPUGraph` object for later replay. Arguments: xpu_graph (torch.xpu.XPUGraph): Graph object used for capture. pool (optional): Opaque token (returned by a call to :func:`~torch.xpu.graph_pool_handle()` or :meth:`other_Graph_instance.pool()`) hinting this graph's capture may share memory from the specified pool. stream (torch.xpu.Stream, optional): If supplied, will be set as the current stream in the context. If not supplied, ``graph`` sets its own internal side stream as the current stream in the context. .. note:: For effective memory sharing, if you pass a ``pool`` used by a previous capture and the previous capture used an explicit ``stream`` argument, you should pass the same ``stream`` argument to this capture. NOptional[torch.xpu.Stream]default_capture_streamcDURRc-[RR 5URlUcSOU4UlUbUOURRUlUR c [S5eUR UlXl g)Nrzcapture_stream must not be None) r+rar!r"Streamr/capture_streamAssertionError stream_ctx xpu_graph)r1rgr/streams r__init__graph.__init__s >> 0 0 849II4D4D4FDNN 1,BTG (Fdnn.S.S     & !BC C--"rc[RR5 [RR5 URR 5 UR R"UR6 gr&) r!r" synchronize empty_cacherf __enter__rgr0r/)r1s rrngraph.__enter__sH   !!# $$dii0rcjURR5 URR"U6 gr&)rgr4rf__exit__)r1argss rrqgraph.__exit__s$ ""$   $'r)rdr/rfrg)NN)rgrr/rQrhr`rS)rrobjectrPrR) rWrXrYrZr[ra__annotations__rirnrqr\rrrrrsH :>6= (,-1 ##%#+ #.1(rrtorch.nn.Module.r_ModuleOrCallablecgr&r callables sample_argsnum_warmup_itersallow_unused_inputr/s rrrsrcgr&rrys rrrs%(rc 4[R"5(a%[R"5(a [S5eSn[ U[ 5(d+SnU4n[ R"[ [S4U54nO-[ R"[ [ [S4S4U5n/n[X5GHup[ U[RR5(a[UR5S:Xa2[UR5S:Xa[UR5S:Xd [S5e[!SUR#555(d [S5e[R$R&R("U 6n UR+[ U 55 [!S U 55(aGM[-S 5e UV s/sHn [U 5PM n n UVs/sHGn[ U[RR5(a[ UR/55OS PMI n n[1[U55V s/sH n X}X-PM nn [1[U55Vs/sH!n[R2R55PM# nn[1[U55Vs/sH!n[R2R55PM# nnUc [75OUn[R2R95 [R2R;[R2R=55 [XU5Hunn nS unnn[1U5Hn[R$R&R?U"U 65n[ SU55n[U5S:dMR[R@RCU[ SU55[ SU55SUS9nM UUU4HnAM M S S S 5 [R2R95 /n/n[XU5Hunn n2REUUS9 U"U 6nS S S 5 [R$R&RGW5unnUR+[ U55 UR+U5 M /n/n [[IU5[IU5[IU55GHunn!n"[ SU!55n#[ SU!55nS n[U5S:ah[R2REU"US9 [R@RCU[ SU55[ SU#55SUS9nS S S 5 /n$Sn%UHCn&U&RJ(aUbU$R+UU%5 U%S- n%M2U$R+S 5 ME [ U$5n$UR+U#5 U R+U$5 GM" URM5 U RM5 SSjn'/n([OU5Hun nU'"UU UU XXUU XUU UU U U 5 n)[ U[RR5(aFSSjn*U*"UURPU)URR5Ul)U(R+U5 MU(R+U)5 M U(aU(S$[ U(5$s sn fs snfs sn fs snfs snf!,(df  GNC=f!,(df  GN=f!,(df  GN=f)a Accept callables (functions or :class:`nn.Module`\ s) and returns graphed versions. Each graphed callable's forward pass runs its source callable's forward XPU work as a XPU graph inside a single autograd node. The graphed callable's forward pass also appends a backward node to the autograd graph. During backward, this node runs the callable's backward work as a XPU graph. Therefore, each graphed callable should be a drop-in replacement for its source callable in an autograd-enabled training loop. See :ref:`Partial-network capture` for detailed use and constraints. If you pass a tuple of several callables, their captures will use the same memory pool. Arguments: callables (torch.nn.Module or Python function, or tuple of these): Callable or callables to graph. If you pass a tuple of callables, their order in the tuple must be the same order they'll run in the live workload. sample_args (tuple of Tensors, or tuple of tuples of Tensors): Samples args for each callable. If a single callable was passed, ``sample_args`` must be a single tuple of argument Tensors. If a tuple of callables was passed, ``sample_args`` must be tuple of tuples of argument Tensors. num_warmup_iters (int): The number of warmup iterations. Currently, ``DataDistributedParallel`` needs 11 iterations for warm up. Default: ``3``. allow_unused_input (bool): If False, specifying inputs that were not used when computing outputs (and therefore their grad is always zero) is an error. Defaults to False. pool (optional): Token (returned by :func:`~torch.xpu.graph_pool_handle` or :meth:`other_Graph_instance.pool()`) that hints this graph may share memory with the indicated pool. .. note:: The ``requires_grad`` state of each Tensor in ``sample_args`` must match the state that's expected for the corresponding real input in the training loop. .. warning:: This API is in beta and may change in future releases. .. warning:: ``sample_args`` for each callable must contain only Tensors. Other types are not allowed. .. warning:: Returned callables do not support higher order differentiation (e.g., double backward). .. warning:: In any :class:`~torch.nn.Module` passed to :func:`~make_graphed_callables`, only parameters may be trainable. Buffers must have ``requires_grad=False``. .. warning:: After you pass a :class:`torch.nn.Module` through :func:`~make_graphed_callables`, you may not add or remove any of that Module's parameters or buffers. .. warning:: :class:`torch.nn.Module`\s passed to :func:`~torch.xpu.make_graphed_callables` must not have module hooks registered on them at the time they are passed. However, registering hooks on modules *after* passing them through :func:`~torch.xpu.make_graphed_callables` is allowed. .. warning:: When running a graphed callable, you must pass its arguments in the same order and format they appeared in that callable's ``sample_args``. .. warning:: The automatic mixed precision is supported in :func:`~torch.xpu.make_graphed_callables` only with disabled caching. The context manager `torch.amp.autocast()` must have `cache_enabled=False`. z_make_graphed_callables does not support the autocast caching. Please set `cache_enabled=False`.FT.rzModules must not have hooks registered at the time they are passed. However, registering hooks on modules after passing them through make_graphed_callables is allowed.c3<# UHoRSLv M g7f)FN requires_grad.0bs r )make_graphed_callables..IsEA%/szIn any :class:`~torch.nn.Module` passed to :func:`~make_graphed_callables`, only parameters may be trainable. 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