Eip%SSKJr SSKrSSKrSSKJr SSKJrJrJrJ 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$Sjjr%g)%) annotationsN)Callable)overload TYPE_CHECKING TypeAliasUnion) ParamSpecSelfTypeVar)Tensor) _POOL_HANDLE) _dummy_type)is_current_stream_capturinggraph_pool_handle CUDAGraphgraphmake_graphed_callables_R_P_CudaStreamBase _CUDAGraph_graph_pool_handle_cuda_isCurrentStreamCapturing)rrrc[5$)zReturn True if CUDA graph capture is underway on the current CUDA stream, False otherwise. If a CUDA context does not exist on the current device, returns False without initializing the context. )rN/home/mande/repo/quber/.venv/lib/python3.13/site-packages/torch/cuda/graphs.pyrr-s * ++rcP[RR[55$)zReturn an opaque token representing the id of a graph memory pool. See :ref:`Graph memory management`. .. warning:: This API is in beta and may change in future releases. )torchcudar rrrrrr6s :: " "#5#7 88rc^\rSrSrSrSSU4SjjjrSSU4SjjjrSU4SjjrSU4SjjrSU4Sjjr SU4Sjjr SU4S jjr SU4S jjr SU4S jjr SU4S jjrSU4S jjrSrU=r$)rBaWrapper around a CUDA graph. Arguments: keep_graph (bool, optional): If ``keep_graph=False``, the cudaGraphExec_t will be instantiated on GPU at the end of ``capture_end`` and the underlying cudaGraph_t will be destroyed. Users who want to query or otherwise modify the underlying cudaGraph_t before instantiation can set ``keep_graph=True`` and access it via ``raw_cuda_graph`` after ``capture_end``. Note that the cudaGraphExec_t 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``. It is allowed to modify the raw cudaGraph_t after first calling ``instantiate``, but the user must call ``instantiate`` again manually to make sure the instantiated graph has these changes. Pytorch has no means of tracking these changes. .. warning:: This API is in beta and may change in future releases. c">[TU]X5$N)super__new__)cls keep_graph __class__s rr'CUDAGraph.__new__]sws//rc >[TU]XS9 g)aBegin capturing CUDA work on the current stream. Typically, you shouldn't call ``capture_begin`` yourself. Use :class:`~torch.cuda.graph` or :func:`~torch.cuda.make_graphed_callables`, which call ``capture_begin`` internally. Arguments: pool (optional): Token (returned by :func:`~torch.cuda.graph_pool_handle` or :meth:`other_Graph_instance.pool()`) that hints this graph may share memory with the indicated pool. See :ref:`Graph memory management`. capture_error_mode (str, optional): specifies the cudaStreamCaptureMode for the graph capture stream. Can be "global", "thread_local" or "relaxed". During cuda graph capture, some actions, such as cudaMalloc, may be unsafe. "global" will error on actions in other threads, "thread_local" will only error for actions in the current thread, and "relaxed" will not error on these actions. Do NOT change this setting unless you're familiar with `cudaStreamCaptureMode `_ )poolcapture_error_modeN)r& capture_begin)selfr-r.r*s rr/CUDAGraph.capture_begin`s& 4Orc">[TU]5 g)aEnd CUDA graph capture on the current stream. After ``capture_end``, ``replay`` may be called on this instance. Typically, you shouldn't call ``capture_end`` yourself. Use :class:`~torch.cuda.graph` or :func:`~torch.cuda.make_graphed_callables`, which call ``capture_end`` internally. N)r& capture_endr0r*s rr3CUDAGraph.capture_endus rc">[TU]5 g)zInstantiate the CUDA graph. Will be called by ``capture_end`` if ``keep_graph=False``, or by ``replay`` if ``keep_graph=True`` and ``instantiate`` has not already been explicitly called. Does not destroy the cudaGraph_t returned by ``raw_cuda_graph``. N)r& instantiater4s rr7CUDAGraph.instantiates rc">[TU]5 g)z,Replay the CUDA work captured by this graph.N)r&replayr4s rr:CUDAGraph.replays  rc">[TU]5 g)z1Delete the graph currently held by this instance.N)r&resetr4s rr=CUDAGraph.resets   rc >[TU]5$)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-r4s rr-CUDAGraph.pools w|~rc >[TU]5$)z/Enable debugging mode for CUDAGraph.debug_dump.)r&enable_debug_moder4s rrBCUDAGraph.enable_debug_modesw(**rc">[TU]U5$)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 CUDAGraph.enable_debug_mode() )r& debug_dump)r0 debug_pathr*s rrECUDAGraph.debug_dumpsw!*--rc >[TU]5$)amReturns the underlying cudaGraph_t. ``keep_graph`` must be True. See the following for APIs for how to manipulate this object: `Graph Managmement `_ and `cuda-python Graph Management bindings `_ )r&raw_cuda_graphr4s rrICUDAGraph.raw_cuda_graphs w%''rc >[TU]5$)aReturns the underlying cudaGraphExec_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_cuda_graph_exec()``, the previously returned cudaGraphExec_t will be destroyed. It is your responsibility not to use this object after destruction. See the following for APIs for how to manipulate this object: `Graph Execution `_ and `cuda-python Graph Execution bindings `_ )r&raw_cuda_graph_execr4s rrLCUDAGraph.raw_cuda_graph_execs w*,,rr)F)r)boolreturnr )Nglobal)r-_POOL_HANDLE | Noner.strrONonerOrSrOr )rFrRrOrS)rOint)__name__ __module__ __qualname____firstlineno____doc__r'r/r3r7r:r=r-rBrErIrL__static_attributes__ __classcell__)r*s@rrrBsr400KSP'PDGP PP* +.(--rrc^\rSrSr%SrSrS\S'S S SjjrS SjrS Sjr S r g)ramContext-manager that captures CUDA work into a :class:`torch.cuda.CUDAGraph` object for later replay. See :ref:`CUDA Graphs ` for a general introduction, detailed use, and constraints. Arguments: cuda_graph (torch.cuda.CUDAGraph): Graph object used for capture. pool (optional): Opaque token (returned by a call to :func:`~torch.cuda.graph_pool_handle()` or :meth:`other_Graph_instance.pool()`) hinting this graph's capture may share memory from the specified pool. See :ref:`Graph memory management`. stream (torch.cuda.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. capture_error_mode (str, optional): specifies the cudaStreamCaptureMode for the graph capture stream. Can be "global", "thread_local" or "relaxed". During cuda graph capture, some actions, such as cudaMalloc, may be unsafe. "global" will error on actions in other threads, "thread_local" will only error for actions in the current thread, and "relaxed" will not error on actions. Do NOT change this setting unless you're familiar with `cudaStreamCaptureMode `_ .. 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. .. warning:: This API is in beta and may change in future releases. .. _cudaStreamCaptureMode: https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__STREAM.html#group__CUDART__STREAM_1g9d0535d93a214cbf126835257b16ba85 Ntorch.cuda.Stream | Nonedefault_capture_streamcUcDURRc-[RR 5URlUcSOU4UlUbUOURRUlUR c [S5e[RRUR 5Ul Xl X@l g)Nrzcapture_stream must not be None) r*rar r!Streamr-capture_streamAssertionErrorstream stream_ctx cuda_graphr.)r0rhr-rfr.s r__init__graph.__init__s >dnnCCK49JJ4E4E4GDNN 1;?> 3 3 JJL   !!# !!# %% YY  $66  rcjURR5 URR"U6 gr%)rhr3rg__exit__)r0argss rrxgraph.__exit__s$ ##%   $'r)r.rdrhr-rg)NNrP)rhrr-rQrfr`r.rRrT)ryobjectrOrS) rWrXrYrZr[ra__annotations__rirurxr\rrrrrsU:8<4; %)+/"* 55"5) 5  5. 6(rrtorch.nn.Module.r_ModuleOrCallablecgr%r callables sample_argsnum_warmup_itersallow_unused_inputr-s rrrsrcgr%rrs rrrs%(rc 4[R"5(a%[R"5(a [S5eSn[ U[ 5(d+SnU4n[ R"[ [S4U54nO-[ R"[ [ [S4S4U5n/n[X5GHup[ U[RR5(a[UR5S:Xa2[UR5S:Xa[UR5S:Xd [!S5e[#SUR%555(d [!S5e[R&R(R*"U 6n UR-[ U 55 [#S U 55(aGM[!S 5e UV s/sHn [U 5PM n n UVs/sHGn[ U[RR5(a[ UR/55OS PMI n n[1[U55V s/sH n X}X-PM nn [1[U55Vs/sH!n[R2R55PM# nn[1[U55Vs/sH!n[R2R55PM# nnUc [75OUn[R2R95 [R2R;[R2R=55 [XU5Hunn nS unnn[1U5Hn[R&R(R?U"U 65n[ SU55n[U5S:dMR[R@RCU[ SU55[ SU55SUS9nM UUU4HnAM M S S S 5 [R2R95 /n/n[XU5Hunn n2REUUS9 U"U 6nS S S 5 [R&R(RGW5unnUR-[ U55 UR-U5 M /n/n [[IU5[IU5[IU55GHunn!n"[ SU!55n#[ SU!55nS n[U5S:ah[R2REU"US9 [R@RCU[ SU55[ SU#55SUS9nS S S 5 /n$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 CUDA work as a CUDA 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 CUDA 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. See :ref:`Graph memory management` for when this is appropriate. Arguments: callables (torch.nn.Module or Python function, or tuple of these): Callable or callables to graph. See :ref:`Graph memory management` for when passing a tuple of callables is appropriate. 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.cuda.graph_pool_handle` or :meth:`other_Graph_instance.pool()`) that hints this graph may share memory with the indicated pool. See :ref:`Graph memory management`. .. 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.cuda.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.cuda.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.cuda.make_graphed_callables` only with disabled caching. The context manager `torch.cuda.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..sEA%/szIn any :class:`~torch.nn.Module` passed to :func:`~make_graphed_callables`, only parameters may be trainable. 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