+ &jVa0t$^RIHt^RIt^RIHt^RIHtHtHt^RI H t H t H t ^RI t ^RI Ht]'d^RIHt^RIHt.R!Ot] !R 4t] !R 4t]!] P.R 4'g^]!R4] P.P0R&]!R4] P.P0R&]!R4] P.P0R&^RIHtHtHtRRltRRlt!RR ]4t!RR 4t ] PBPD]R]#3,,t$R]%R&]R"RRll4t&]R"RRll4t&R"RR llt&R#)#) annotationsN)Callable)overload TYPE_CHECKING TypeAlias) ParamSpecSelfTypeVar)Tensor) _POOL_HANDLE) _dummy_typeXPUGraphgraph_R_P_XpuStreamBase _XPUGraph_xpu_graph_pool_handle_xpu_isCurrentStreamCapturing)rrrcV^8dQhRR/#)returnbool)formats"h/Users/jameslopez/projects/CWCArchive/cwc-podcast/.venv/lib/python3.14/site-packages/torch/xpu/graphs.py __annotate__r(s++T+c\4#)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. )rrrris_current_stream_capturingr (s ) **rcV^8dQhRR/#rrr r)rs"rrr0s<<<`) that hints this graph may share memory with the indicated pool. r3N)r- capture_begin)selfr3r0s&&rr8XPUGraph.capture_beginIs 4(rcV^8dQhRR/#rrr5r)rs"rrr*VsTrc $<\SV`4R#)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_endr9r0s&rr>XPUGraph.capture_endVs rcV^8dQhRR/#r<r)rs"rrr*`sTrc $<\SV`4R#)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- instantiater?s&rrCXPUGraph.instantiate`s rcV^8dQhRR/#r<r)rs"rrr*isrc $<\SV`4R#)z+Replay the XPU work captured by this graph.N)r-replayr?s&rrGXPUGraph.replayis  rcV^8dQhRR/#r<r)rs"rrr*mstrc $<\SV`4R#)z1Delete the graph currently held by this instance.N)r-resetr?s&rrKXPUGraph.resetms   rcV^8dQhRR/#r"r)rs"rrr*qslrc <\SV`4#)zReturn an opaque token representing the id of this graph's memory pool. This id can optionally be passed to another graph's ``capture_begin``, which hints the other graph may share the same memory pool. )r-r3r?s&rr3 XPUGraph.poolqs w|~rcV^8dQhRR/#r<r)rs"rrr*ys++4+rc <\SV`4#)z.Enable debugging mode for XPUGraph.debug_dump.)r-enable_debug_moder?s&rrRXPUGraph.enable_debug_modeysw(**rc V^8dQhRRRR/#)r debug_pathstrrr5r)rs"rrr*}s..S.T.rc "<\SV`V4#)z Arguments: debug_path (required): Path to dump the graph to. Calls a debugging function to dump the graph if the debugging is enabled via XPUGraph.enable_debug_mode() )r- debug_dump)r9rUr0s&&rrXXPUGraph.debug_dump}sw!*--rcV^8dQhRR/#rrintr)rs"rrr*s''s'rc <\SV`4#)zuReturns the underlying xpuGraph_t. ``keep_graph`` must be True. XPU doesn't provide APIs to manipulate this object. )r- raw_xpu_graphr?s&rr^XPUGraph.raw_xpu_graphs w$&&rcV^8dQhRR/#r[r)rs"rrr*s,,C,rc <\SV`4#)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_execr?s&rrbXPUGraph.raw_xpu_graph_execs w)++rr)Fr,)__name__ __module__ __qualname____firstlineno____doc__r.r8r>rCrGrKr3rRrXr^rb__static_attributes__ __classcell__)r0s@rrr5sv 00 ) )++..'',,rcR]tRt^t$RtRtR]R&R RRlltRRltR R lt R t R#) 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. 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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. 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