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# Source code for torch.cuda.graphs

    import gc

    import torch
    from torch.utils._pytree import (
        tree_flatten as _tree_flatten,
        tree_unflatten as _tree_unflatten,
    )

    from ._utils import _dummy_type

    if not hasattr(torch._C, "_CudaStreamBase"):
        # Define dummy base classes
        torch._C.__dict__["_CUDAGraph"] = _dummy_type("_CUDAGraph")
        torch._C.__dict__["_graph_pool_handle"] = _dummy_type("_graph_pool_handle")
        torch._C.__dict__["_cuda_isCurrentStreamCapturing"] = _dummy_type(
            "_cuda_isCurrentStreamCapturing"
        )

    from torch._C import (  # noqa: F401
        _cuda_isCurrentStreamCapturing,
        _CUDAGraph,
        _graph_pool_handle,
    )

    def is_current_stream_capturing():
        r"""
        Returns 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.
        """
        return _cuda_isCurrentStreamCapturing()

    # Python shim helps Sphinx process docstrings more reliably.
    def graph_pool_handle():
        r"""
        Returns an opaque token representing the id of a graph memory pool.
        See :ref:`Graph memory management<graph-memory-management>`.

        .. warning::
            This API is in beta and may change in future releases.
        """
        return _graph_pool_handle()

    # Python shim helps Sphinx process docstrings more reliably.
    class CUDAGraph(torch._C._CUDAGraph):
        r"""
        Wrapper around a CUDA graph.

        .. warning::
            This API is in beta and may change in future releases.
        """

        def __new__(cls):
            return super().__new__(cls)

        def capture_begin(self, pool=None, capture_error_mode="global"):
            r"""
            Begins 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()<torch.cuda.CUDAGraph.pool>`) that hints this graph may share memory
                    with the indicated pool.  See :ref:`Graph memory management<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 <https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__STREAM.html#group__CUDART__STREAM_1g9d0535d93a214cbf126835257b16ba85>`_
            """  # noqa: B950
            super().capture_begin(pool=pool, capture_error_mode=capture_error_mode)

        def capture_end(self):
            r"""
            Ends 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.
            """
            super().capture_end()

        def replay(self):
            r"""
            Replays the CUDA work captured by this graph.
            """
            super().replay()

        def reset(self):
            r"""
            Deletes the graph currently held by this instance.
            """
            super().reset()

        def pool(self):
            r"""
            Returns 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.
            """
            return super().pool()

        def enable_debug_mode(self):
            r"""
            Enables debugging mode for CUDAGraph.debug_dump.
            """
            return super().enable_debug_mode()

        def debug_dump(self, debug_path):
            r"""
            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()
            """
            return super().debug_dump(debug_path)

    class graph:
        r"""
        Context-manager that captures CUDA work into a :class:`torch.cuda.CUDAGraph`
        object for later replay.

        See :ref:`CUDA Graphs <cuda-graph-semantics>` 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()<torch.cuda.CUDAGraph.pool>`) hinting this graph's capture
                may share memory from the specified pool. See :ref:`Graph memory management<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 <https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__STREAM.html#group__CUDART__STREAM_1g9d0535d93a214cbf126835257b16ba85>`_

        .. 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
        """  # noqa: B950
        default_capture_stream = None

        def __init__(
            self,
            cuda_graph,
            pool=None,
            stream=None,
            capture_error_mode: str = "global",
        ):
            # Lazy-init of default_capture_stream helps avoid circular-import errors.
            # Not thread safe, but graphs already have the general (explicitly documented)
            # restriction that only one capture may be underway at a time in the process.
            if self.__class__.default_capture_stream is None:
                self.__class__.default_capture_stream = torch.cuda.Stream()

            self.pool = () if pool is None else (pool,)
            self.capture_stream = (
                stream if stream is not None else self.__class__.default_capture_stream
            )
            assert self.capture_stream is not None
            self.stream_ctx = torch.cuda.stream(self.capture_stream)
            self.cuda_graph = cuda_graph
            self.capture_error_mode = capture_error_mode

        def __enter__(self):
            # Free as much memory as we can for the graph
            torch.cuda.synchronize()
            gc.collect()
            torch.cuda.empty_cache()

            # Stackoverflow seems comfortable with this pattern
            # https://stackoverflow.com/questions/26635684/calling-enter-and-exit-manually#39172487
            self.stream_ctx.__enter__()

            self.cuda_graph.capture_begin(
                *self.pool, capture_error_mode=self.capture_error_mode
            )

        def __exit__(self, exc_type, exc_value, traceback):
            self.cuda_graph.capture_end()
            self.stream_ctx.__exit__(exc_type, exc_value, traceback)
            # returning None should propagate exceptions from either capture_end or stream_ctx.__exit__()

    def make_graphed_callables(
        callables, sample_args, num_warmup_iters=3, allow_unused_input=False
    ):
        r"""
        Accepts callables (functions or :class:`nn.Module<torch.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<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<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<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.

        .. 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`.
        """
        if torch.is_autocast_enabled() and torch.is_autocast_cache_enabled():
            raise RuntimeError(
                "make_graphed_callables does not support the autocast caching. Please set `cache_enabled=False`."
            )

        just_one_callable = False

        if not isinstance(callables, tuple):
            just_one_callable = True
            callables = (callables,)
            sample_args = (sample_args,)

        flatten_sample_args = []

        for c, args in zip(callables, sample_args):
            if isinstance(c, torch.nn.Module):
                assert (
                    len(c._backward_hooks) == 0
                    and len(c._forward_hooks) == 0
                    and len(c._forward_pre_hooks) == 0
                ), (
                    "Modules 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."
                )
                assert all(b.requires_grad is False for b in c.buffers()), (
                    "In any :class:`~torch.nn.Module` passed to "
                    + ":func:`~make_graphed_callables`, only parameters may be trainable. All buffers must have "
                    + "``requires_grad=False``."
                )
            flatten_arg, _ = _tree_flatten(args)
            flatten_sample_args.append(tuple(flatten_arg))
            assert all(isinstance(arg, torch.Tensor) for arg in flatten_arg), (
                "In the beta API, sample_args "
                + "for each callable must contain only Tensors. Other types are not allowed."
            )

        # If a callable is an nn.Module, its graph's full input surface is the args the user explicitly
        # passes to forward (ie, its sample_args) AND the module's parameter attributes.
        per_callable_len_user_args = [len(args) for args in flatten_sample_args]
        per_callable_module_params = [
            tuple(c.parameters()) if isinstance(c, torch.nn.Module) else ()
            for c in callables
        ]
        per_callable_static_input_surfaces = [
            flatten_sample_args[i] + per_callable_module_params[i]
            for i in range(len(callables))
        ]

        fwd_graphs = [torch.cuda.CUDAGraph() for _ in range(len(callables))]
        bwd_graphs = [torch.cuda.CUDAGraph() for _ in range(len(callables))]

        mempool = graph_pool_handle()

        # Warmup
        # Hopefully prevents cudnn benchmarking and other lazy-initialization cuda work
        # from ending up in any captures.
        torch.cuda.synchronize()
        with torch.cuda.stream(torch.cuda.Stream()):
            for func, args, static_input_surface in zip(
                callables, sample_args, per_callable_static_input_surfaces
            ):
                for _ in range(num_warmup_iters):
                    outputs, _ = _tree_flatten(func(*args))
                    grad_inputs = torch.autograd.grad(
                        outputs=tuple(o for o in outputs if o.requires_grad),
                        inputs=tuple(i for i in static_input_surface if i.requires_grad),
                        grad_outputs=tuple(
                            torch.empty_like(o) for o in outputs if o.requires_grad
                        ),
                        only_inputs=True,
                        allow_unused=allow_unused_input,
                    )
                del outputs, grad_inputs
        torch.cuda.synchronize()

        # All captures here share a mempool. To avoid replays corrupting each other's memory,
        # the safest approach is to capture all passes in the same order they'll run:
        # fwd 1, fwd 2, ... fwd N, then bwd N, bwd N-1, ... bwd 1.

        # Capture forward graphs
        per_callable_static_outputs = []
        per_callable_output_unflatten_spec = []
        for func, args, fwd_graph in zip(callables, sample_args, fwd_graphs):
            with torch.cuda.graph(fwd_graph, pool=mempool):
                outputs = func(*args)

            flatten_outputs, spec = _tree_flatten(outputs)
            per_callable_static_outputs.append(tuple(flatten_outputs))
            per_callable_output_unflatten_spec.append(spec)

        # Capture backward graphs in reverse order
        per_callable_static_grad_outputs = []
        per_callable_static_grad_inputs = []
        for static_input_surface, static_outputs, bwd_graph, module_params in zip(
            reversed(per_callable_static_input_surfaces),
            reversed(per_callable_static_outputs),
            reversed(bwd_graphs),
            reversed(per_callable_module_params),
        ):
            # For now, assumes all static_outputs require grad
            # assert all(o.requires_grad for o in static_outputs), "Outputs of graphed callables must require grad."
            static_grad_outputs = tuple(
                torch.empty_like(o) if o.requires_grad else None for o in static_outputs
            )

            with torch.cuda.graph(bwd_graph, pool=mempool):
                grad_inputs = torch.autograd.grad(
                    outputs=tuple(o for o in static_outputs if o.requires_grad),
                    inputs=tuple(i for i in static_input_surface if i.requires_grad),
                    grad_outputs=tuple(o for o in static_grad_outputs if o is not None),
                    only_inputs=True,
                    allow_unused=allow_unused_input,
                )

            # Constructs a tuple suitable for returning from Graphed.backward:
            # Pads out the actually-needed grads with Nones in gradient slots for inputs that don't require grad.
            # I couldn't think of a slick one-liner for this pattern.
            static_grad_inputs = []
            grad_idx = 0
            for arg in static_input_surface:
                if arg.requires_grad:
                    static_grad_inputs.append(grad_inputs[grad_idx])
                    grad_idx += 1
                else:
                    static_grad_inputs.append(None)  # type: ignore[arg-type]
            static_grad_inputs = tuple(static_grad_inputs)  # type: ignore[assignment]

            per_callable_static_grad_outputs.append(static_grad_outputs)
            per_callable_static_grad_inputs.append(static_grad_inputs)

        # Reverses the most recent two lists
        per_callable_static_grad_outputs = list(reversed(per_callable_static_grad_outputs))
        per_callable_static_grad_inputs = list(reversed(per_callable_static_grad_inputs))
        # Now for every per_callable list, per_callable_*[i] holds the stuff for the ith callable.

        def make_graphed_autograd_function(
            fwd_graph,
            bwd_graph,
            module_params,
            len_user_args,
            output_unflatten_spec,
            static_input_surface,
            static_outputs,
            static_grad_outputs,
            static_grad_inputs,
        ):
            class Graphed(torch.autograd.Function):
                @staticmethod
                def forward(ctx, *inputs):
                    # At this stage, only the user args may (potentially) be new tensors.
                    for i in range(len_user_args):
                        if static_input_surface[i].data_ptr() != inputs[i].data_ptr():
                            static_input_surface[i].copy_(inputs[i])
                    fwd_graph.replay()
                    assert isinstance(static_outputs, tuple)
                    return tuple(o.detach() for o in static_outputs)

                @staticmethod
                @torch.autograd.function.once_differentiable
                def backward(ctx, *grads):
                    assert len(grads) == len(static_grad_outputs)
                    for g, grad in zip(static_grad_outputs, grads):
                        if g is not None:
                            # don't copy if autograd gods have been kind and the
                            # incoming grad is already in the right place
                            if g.data_ptr() != grad.data_ptr():
                                g.copy_(grad)
                    bwd_graph.replay()

                    # Input args that didn't require grad expect a None gradient.
                    assert isinstance(static_grad_inputs, tuple)
                    return tuple(
                        b.detach() if b is not None else b for b in static_grad_inputs
                    )

            def functionalized(*user_args):
                # Runs the autograd function with inputs == all inputs to the graph that might require grad
                # (explicit user args + module parameters)
                # Assumes module params didn't change since capture.
                flatten_user_args, _ = _tree_flatten(user_args)
                out = Graphed.apply(*(tuple(flatten_user_args) + module_params))
                return _tree_unflatten(out, output_unflatten_spec)

            return functionalized

        # Put together the final graphed callables
        ret = []
        for i, func in enumerate(callables):
            graphed = make_graphed_autograd_function(
                fwd_graphs[i],
                bwd_graphs[i],
                per_callable_module_params[i],
                per_callable_len_user_args[i],
                per_callable_output_unflatten_spec[i],
                per_callable_static_input_surfaces[i],
                per_callable_static_outputs[i],
                per_callable_static_grad_outputs[i],
                per_callable_static_grad_inputs[i],
            )

            if isinstance(func, torch.nn.Module):

                def make_graphed_forward(func, graph_training_state, graphed, orig_fwd):
                    def new_fwd(*user_args):
                        # If the module's training-or-eval state matches what we graphed,
                        # run the graph, otherwise run the original forward method
                        if func.training == graph_training_state:
                            return graphed(*user_args)
                        else:
                            return orig_fwd(*user_args)

                    return new_fwd

                func.forward = make_graphed_forward(func, func.training, graphed, func.forward)  # type: ignore[assignment]
                ret.append(func)
            else:
                ret.append(graphed)

        if just_one_callable:
            return ret[0]

        return tuple(ret)

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