SjVUddlmZddlZddlmZddlmZmZmZddlm Z m Z m Z ddl Z ddl m Z erddlmZdd lmZgd Ze d Ze d Zee j,d s]ede j,j.d<ede j,j.d<ede j,j.d<ddlmZmZmZddZddZGddeZGddZe j@jBede"fzZ#de$d<e d d!dZ%e d d"dZ% d d#dZ%y)$) annotationsN)Callable)overload TYPE_CHECKING TypeAlias) ParamSpecSelfTypeVar)Tensor) _POOL_HANDLE) _dummy_type)is_current_stream_capturinggraph_pool_handleXPUGraphgraphmake_graphed_callables_R_P_XpuStreamBase _XPUGraph_xpu_graph_pool_handle_xpu_isCurrentStreamCapturing)rrrctS)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. )rO/var/www/html/ia-enhanced/venv/lib/python3.12/site-packages/torch/xpu/graphs.pyrr(s ) **rcPtjjtS)zBReturn an opaque token representing the id of a graph memory pool.)torchxpur rrrrrr0s 99 ! !"8": ;;rceZdZdZd dfd Zddfd Zdfd Zdfd Zdfd Zdfd Z dfd Z dfd Z dfd Z dfd Z dfd ZxZS)raWrapper 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$t|||SN)super__new__)cls keep_graph __class__s rr%zXPUGraph.__new__FswsJ//rc&t||y)aBegin capturing XPU work on the current xpu stream. Typically, you shouldn't call ``capture_begin`` yourself. Use :class:`~torch.xpu.graph`, which call ``capture_begin`` internally. Arguments: pool (optional): Token (returned by :func:`~torch.xpu.graph_pool_handle` or :meth:`other_Graph_instance.pool()`) that hints this graph may share memory with the indicated pool. poolN)r$ capture_begin)selfr+r(s rr,zXPUGraph.capture_beginIs 4(rc"t|y)aEnd 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_endr-r(s rr/zXPUGraph.capture_endVs rc"t|y)a/Instantiate 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$ instantiater0s rr2zXPUGraph.instantiate`s rc"t|y)z+Replay the XPU work captured by this graph.N)r$replayr0s rr4zXPUGraph.replayis  rc"t|y)z1Delete the graph currently held by this instance.N)r$resetr0s rr6zXPUGraph.resetms   rc t|S)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$r+r0s rr+z XPUGraph.poolqs w|~rc t|S)z.Enable debugging mode for XPUGraph.debug_dump.)r$enable_debug_moder0s rr9zXPUGraph.enable_debug_modeysw(**rc"t||S)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)r- debug_pathr(s rr;zXPUGraph.debug_dump}sw!*--rc t|S)zReturns the underlying xpuGraph_t. ``keep_graph`` must be True. 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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. Ntorch.xpu.Stream | Nonedefault_capture_streamc>|jj-tjj |j_|dn|f|_||n|jj|_|j td|j |_||_ y)Nrzcapture_stream must not be None) r(rPrr Streamr+capture_streamAssertionError stream_ctx xpu_graph)r-rVr+streams r__init__zgraph.__init__s >> 0 0 849II4D4D4FDNN 1;?`\ 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.c38K|]}|jduyw)FN requires_grad.0bs r z)make_graphed_callables..FsEAq%/EszIn any :class:`~torch.nn.Module` passed to :func:`~make_graphed_callables`, only parameters may be trainable. 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