graph.py

# Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# See LICENSE for license information.

"""Functions for CUDA Graphs support in FP8"""
from collections.abc import Iterable
import contextlib
import gc
import warnings
from typing import Any, Callable, Dict, List, Optional, Tuple, TypeVar, Union

import torch
from torch.utils._pytree import tree_flatten as _tree_flatten
from torch.utils._pytree import tree_unflatten as _tree_unflatten
from torch._C import _graph_pool_handle

from transformer_engine.common.recipe import DelayedScaling, Recipe
from transformer_engine.pytorch.constants import dist_group_type
from .quantization import (
    autocast,
    FP8GlobalStateManager,
    get_default_fp8_recipe,
)
from .distributed import get_all_rng_states, graph_safe_rng_available
from .module.base import TransformerEngineBaseModule
from .ops.op import BasicOperation
from .ops import Sequential
from .ops.fuser import OperationFuser
from .utils import make_weak_ref

__all__ = ["make_graphed_callables"]


_IS_GRAPH_CAPTURING = False

_T = TypeVar("_T")
SingleOrTuple = Union[_T, Tuple[_T, ...]]


def set_capture_start() -> None:
    """Record beginning of `make_graphed_callables`."""
    global _IS_GRAPH_CAPTURING
    _IS_GRAPH_CAPTURING = True


def set_capture_end() -> None:
    """Record end of `make_graphed_callables`."""
    global _IS_GRAPH_CAPTURING
    _IS_GRAPH_CAPTURING = False


def is_graph_capturing() -> bool:
    """Return whether within `make_graphed_callables`."""
    return _IS_GRAPH_CAPTURING


def graph_pool_handle():
    """
    Returns an opaque token representing the id of a graph memory pool.
    """
    return _graph_pool_handle()


@contextlib.contextmanager
def _graph_context_wrapper(*args, **kwargs):
    """Wrapper around `torch.cuda.graph`.

    This wrapper is a temporary workaround for a PyTorch bug:
    automatic garbage collection can destroy a graph while another
    graph is being captured, resulting in a CUDA error. See
    https://github.com/pytorch/pytorch/pull/161037.

    """
    gc_is_enabled = gc.isenabled()
    if gc_is_enabled:
        gc.disable()
    with torch.cuda.graph(*args, **kwargs):
        yield
    if gc_is_enabled:
        gc.enable()


def _make_graphed_callables(
    callables: SingleOrTuple[Callable],
    sample_args: SingleOrTuple[Tuple[torch.Tensor, ...]],
    num_warmup_iters: int = 3,
    allow_unused_input: bool = False,
    cache_quantized_params: bool = False,
    sample_kwargs: Optional[SingleOrTuple[Dict[str, Any]]] = None,
    _order: Optional[List[int]] = None,
    _num_layers_per_chunk: Optional[List[int]] = None,
    pool: Optional[Tuple[int, ...]] = None,
    retain_graph_in_backward: bool = False,
    _reuse_graph_input_output_buffers: bool = False,
) -> SingleOrTuple[Callable]:
    """
    Helper method for `make_graphed_callables`
    """

    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`."
        )

    # Default is to pass no kwargs to callables
    if sample_kwargs is None:
        if isinstance(callables, tuple):
            sample_kwargs = tuple({} for _ in range(len(sample_args)))
        else:
            sample_kwargs = {}

    # Canonicalize args as tuples
    just_one_callable = False
    if not isinstance(callables, tuple):
        just_one_callable = True
        callables = (callables,)
        sample_args = (sample_args,)
        sample_kwargs = (sample_kwargs,)

    # Check training/inference
    is_training = all(c.training for c in callables)
    if not is_training and any(c.training for c in callables):
        assert False, (
            "make_graphed_callables only supports when modules are all in training or all in"
            " inference mode."
        )

    # Check sizes of args
    if _order is None:
        assert len(sample_args) == len(callables)
        assert len(sample_kwargs) == len(callables)
    else:
        # Custom logic for interleaved pipeline parallelism
        # Note: This is tightly coupled with the Megatron-core
        # implementation of interleaved pipeline parallelism at
        # https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/pipeline_parallel/schedules.py.
        # Note: The model is assumed to consist of layers
        # (corresponding to callables) that are grouped into
        # model chunks. _num_layers_per_chunk is a list of integers
        # that indicates the number of layers in each model chunk.
        # _order is a list of chunk indices (1-indexed) that
        # indicates the order in which the layers are evaluated.
        # Positive values indicate forward passes and negative
        # values indicate backward passes. Each
        # entry in sample_args corresponds to one of the forward
        # passes.
        num_model_chunks = max(_order)
        num_microbatches = len(_order) // num_model_chunks // 2
        assert num_model_chunks * num_microbatches * 2 == len(_order)

        # Determine number of layers in each model chunk.
        if _num_layers_per_chunk is None:
            assert len(sample_args) * 2 >= len(_order) and (
                len(sample_args) * 2 % len(_order) == 0
            ), (
                f"{len(sample_args)} * 2 >= {len(_order)} and {len(sample_args)} * 2 %"
                f" {len(_order)} == 0"
            )
            num_layers = len(sample_args) // num_model_chunks // num_microbatches
            _num_layers_per_chunk = [num_layers] * num_model_chunks
        else:
            assert (
                isinstance(_num_layers_per_chunk, int)
                or len(_num_layers_per_chunk) == num_model_chunks
            ), (
                "If _num_layers_per_chunk is provided, it must be an integer or a list of"
                f" {num_model_chunks} integers, but got {_num_layers_per_chunk}."
            )
            if isinstance(_num_layers_per_chunk, int):
                _num_layers_per_chunk = [_num_layers_per_chunk] * num_model_chunks
        total_num_layers = sum(_num_layers_per_chunk)
        assert len(callables) == total_num_layers, (
            f"Callables should have ({total_num_layers}) "
            + f"entries when order input is provided but got {len(callables)}."
        )
        assert len(sample_args) == total_num_layers * num_microbatches, (
            f"Expected {total_num_layers * num_microbatches}"
            + f"args tuple, but got {len(sample_args)}."
        )

        # Calculate the starting index of each chunk in callables for future use.
        _prefix_num_layers = [0]
        for m_chunk in range(num_model_chunks):
            num_layers = _num_layers_per_chunk[m_chunk]
            _prefix_num_layers.append(_prefix_num_layers[-1] + num_layers)

        assert len(sample_kwargs) == len(sample_args)

    # Check reuse graph conditions and reorganize sample_args and sample_kwargs.
    # Note: When capturing a graph, we hold onto the args and kwargs so we have static buffers
    # when the graph is replayed. If two model chunk microbatches have no overlap between their
    # forward and backward, then we can reduce memory usage by reusing the same static buffers.
    if _reuse_graph_input_output_buffers:
        assert (
            _order is not None
        ), "`_order` must be provided when `_reuse_graph_input_output_buffers` is True."
        assert (
            is_training
        ), "`_reuse_graph_input_output_buffers` is only available in training mode."
        assert isinstance(
            sample_args, list
        ), "sample_args must be a list for _reuse_graph_input_output_buffers."

        # Reorganize args and kwargs for input tensor reuse.
        # fwd_sample_qs is keyed by model chunk index. The value is a queue of tuples.
        # Each tuple contains the sample key signature and its fwd_idx. When we finish a backward
        # chunk, we pop the corresponding fwd_idx and push to the consumed_sample_q.
        # consumed_sample_q is keyed by the sample key signature. The value is a queue of the
        # fwd_idx whose backward has been called so that we can reuse the same static buffers.
        # In this way, we can reuse the same static input buffers for the non-overlapping samples
        # with the same input signature.
        fwd_sample_qs = {}
        consumed_sample_q = {}
        fwd_idx = [0] * num_model_chunks
        for c_id in _order:
            m_chunk = abs(c_id) - 1

            if c_id > 0:
                sample_start_idx = (_prefix_num_layers[m_chunk] * num_microbatches) + (
                    fwd_idx[m_chunk] * _num_layers_per_chunk[m_chunk]
                )
                fwd_sample_idx = [
                    sample_start_idx + i for i in range(_num_layers_per_chunk[m_chunk])
                ]
                if m_chunk not in fwd_sample_qs:
                    fwd_sample_qs[m_chunk] = []
                for per_callable_fwd_idx in fwd_sample_idx:
                    sample_args_keys = tuple(
                        (t.shape, t.dtype, t.layout) for t in sample_args[per_callable_fwd_idx]
                    )
                    sample_kwargs_keys = tuple(
                        (k, v.shape, v.dtype, v.layout)
                        for k, v in sorted(sample_kwargs[per_callable_fwd_idx].items())
                    )
                    sample_keys = sample_args_keys + sample_kwargs_keys

                    fwd_sample_qs[m_chunk].append((sample_keys, per_callable_fwd_idx))
                    if consumed_sample_q.get(sample_keys, []):
                        reuse_fwd_idx = consumed_sample_q[sample_keys].pop(0)
                        sample_args[per_callable_fwd_idx] = sample_args[reuse_fwd_idx]
                        sample_kwargs[per_callable_fwd_idx] = sample_kwargs[reuse_fwd_idx]
                fwd_idx[m_chunk] += 1
            else:
                num_consumed_samples = min(
                    len(fwd_sample_qs[m_chunk]), _num_layers_per_chunk[m_chunk]
                )
                for sample_keys, per_callable_fwd_idx in fwd_sample_qs[m_chunk][
                    :num_consumed_samples
                ]:
                    if sample_keys not in consumed_sample_q:
                        consumed_sample_q[sample_keys] = []
                    consumed_sample_q[sample_keys].append(per_callable_fwd_idx)
                fwd_sample_qs[m_chunk] = fwd_sample_qs[m_chunk][num_consumed_samples:]

    if cache_quantized_params:
        # Initialize flag that controls FP8 weight updates
        FP8GlobalStateManager.set_skip_fp8_weight_update_tensor(False)

    # Check callables
    for c in callables:
        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 callable arguments
    per_callable_kwargs_keys = [list(kwargs.keys()) for kwargs in sample_kwargs]
    flatten_sample_args = []
    for args, kwargs, kwargs_keys in zip(sample_args, sample_kwargs, per_callable_kwargs_keys):
        flatten_arg, _ = _tree_flatten(args)
        flatten_kwarg, _ = _tree_flatten([kwargs[key] for key in kwargs_keys])
        flatten_sample_args.append(tuple(flatten_arg + flatten_kwarg))
        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.
    # Note: These per_callable_* variables are not actually
    # per-callable, but per-forward-pass (see description of _order).
    # The names are kept for consistency with
    # torch.cuda.make_graphed_callables.
    per_callable_len_user_args = [len(args) for args in flatten_sample_args]
    if _order is None:
        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))
        ]
    else:
        per_callable_module_params = []
        for m_chunk in range(num_model_chunks):
            for _ in range(num_microbatches):
                for l_no in range(_num_layers_per_chunk[m_chunk]):
                    per_callable_module_params.append(
                        tuple(callables[_prefix_num_layers[m_chunk] + l_no].parameters())
                        if isinstance(
                            callables[_prefix_num_layers[m_chunk] + l_no],
                            torch.nn.Module,
                        )
                        else ()
                    )
        assert len(per_callable_module_params) == len(flatten_sample_args)
        per_callable_static_input_surfaces = [
            flatten_sample_args[i] + per_callable_module_params[i]
            for i in range(len(flatten_sample_args))
        ]

    fwd_graphs = [torch.cuda.CUDAGraph() for _ in range(len(flatten_sample_args))]
    bwd_graphs = [torch.cuda.CUDAGraph() for _ in range(len(flatten_sample_args))]
    bwd_dw_graphs = [torch.cuda.CUDAGraph() for _ in range(len(flatten_sample_args))]
    graph_callables = [None for _ in range(len(flatten_sample_args))]

    # For cases with multiple active RNG states, e.g. TP.
    if graph_safe_rng_available():
        for _, state in get_all_rng_states().items():
            for fwd_graph, bwd_graph, bwd_dw_graph in zip(fwd_graphs, bwd_graphs, bwd_dw_graphs):
                fwd_graph.register_generator_state(state)
                bwd_graph.register_generator_state(state)
                bwd_dw_graph.register_generator_state(state)

    mempool = graph_pool_handle() if pool is None else pool

    # Warmup
    # Hopefully prevents cudnn benchmarking and other lazy-initialization cuda work
    # from ending up in any captures.
    torch.cuda.synchronize()

    # Get warmup func and func_idx.
    warmup_func_idx = []
    warmup_func = []
    if _order is None:
        for func_idx, func in enumerate(callables):
            warmup_func_idx.append(func_idx)
            warmup_func.append(func)
    else:
        fwd_idx = [0] * num_model_chunks
        for c_id in _order:
            if c_id > 0:
                m_chunk = c_id - 1
                for l_no in range(_num_layers_per_chunk[m_chunk]):
                    func = callables[_prefix_num_layers[m_chunk] + l_no]
                    func_idx = (_prefix_num_layers[m_chunk] * num_microbatches) + (
                        fwd_idx[m_chunk] * _num_layers_per_chunk[m_chunk] + l_no
                    )
                    warmup_func_idx.append(func_idx)
                    warmup_func.append(func)
                fwd_idx[m_chunk] += 1
    assert len(warmup_func) == len(
        sample_args
    ), f"Warmup runs {len(warmup_func)} don't match args {len(sample_args)}."
    assert len(warmup_func_idx) == len(
        set(warmup_func_idx)
    ), f"Warmup runs {len(warmup_func)} but only {len(set(warmup_func_idx))} are unique."

    # Filter the TE modules that cudagraph can access.
    visited_te_modules = {}
    need_bwd_dw_graph = {}

    # Run warmup and do the above filtering.
    with torch.cuda.stream(torch.cuda.Stream()):
        for func_idx, func in zip(warmup_func_idx, warmup_func):
            args = sample_args[func_idx]
            kwargs = sample_kwargs[func_idx]
            static_input_surface = per_callable_static_input_surfaces[func_idx]

            def hook_fn(
                module, inputs, outputs, func_idx=func_idx
            ):  # pylint: disable=unused-argument
                modules = set()
                if isinstance(module, TransformerEngineBaseModule):
                    modules.add(module)
                # If forward is called on a BasicOperation directly the hook will run
                elif isinstance(module, BasicOperation):
                    modules.add(module)
                # If forward is called on a te.ops.Sequential it is not called on its constituent ops
                elif isinstance(module, Sequential):
                    assert (
                        module._module_groups is not None
                    ), "Should have been initialized by warmup"
                    for module_group in module._module_groups:
                        if isinstance(module_group, OperationFuser):
                            for basic_op in module_group._basic_ops:
                                modules.add(basic_op)
                if modules:
                    if func_idx not in visited_te_modules:
                        visited_te_modules[func_idx] = modules
                    else:
                        visited_te_modules[func_idx].update(modules)

            for warmup_iter in range(num_warmup_iters):
                hooks = []
                for module in func.modules():
                    hook = module.register_forward_hook(hook_fn)
                    hooks.append(hook)
                outputs, _ = _tree_flatten(func(*args, **kwargs))
                for hook in hooks:
                    hook.remove()
                if is_training:
                    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,
                    )

                    # Filter module params that get None grad from grad_inputs and remove them
                    # from static_input_surface. This is to ensure that the backward hooks
                    # registered to these params are not wrongly triggered.
                    num_required_grad_sample_args = sum(
                        arg.requires_grad for arg in flatten_sample_args[func_idx]
                    )
                    required_grad_input_idx = []
                    for i, arg in enumerate(static_input_surface):
                        if arg.requires_grad:
                            required_grad_input_idx.append(i)
                    module_params_with_grad = []
                    for grad_inputs_idx, inputs_idx in enumerate(required_grad_input_idx):
                        if (
                            grad_inputs[grad_inputs_idx] is not None
                            and grad_inputs_idx >= num_required_grad_sample_args
                        ):
                            module_params_with_grad.append(static_input_surface[inputs_idx])
                    if len(module_params_with_grad) != len(per_callable_module_params[func_idx]):
                        assert warmup_iter == 0, (
                            "no-grad params should only be used as inputs in the first warmup"
                            " iteration"
                        )
                        per_callable_module_params[func_idx] = tuple(module_params_with_grad)
                        static_input_surface = flatten_sample_args[func_idx] + tuple(
                            module_params_with_grad
                        )
                        per_callable_static_input_surfaces[func_idx] = static_input_surface

                    # Run wgrad. This is essential for some TE modules when they have
                    # delay_wgrad_compute enabled.
                    need_backward_dw = False
                    for module in visited_te_modules.get(func_idx, set()):
                        if hasattr(module, "need_backward_dw") and module.need_backward_dw():
                            need_backward_dw = True
                            module.backward_dw()
                    need_bwd_dw_graph[func_idx] = need_backward_dw
                else:
                    grad_inputs = None
                del outputs, grad_inputs
            # The following code is added specifically for MCore's special requirements,
            # aimed at preventing warmup from altering the control flow.
            for module in func.modules():
                if hasattr(module, "is_first_microbatch"):
                    module.is_first_microbatch = True
    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.

    if _order is not None:  # pylint: disable=too-many-nested-blocks
        per_callable_static_outputs = [None] * len(flatten_sample_args)
        per_callable_output_unflatten_spec = [None] * len(flatten_sample_args)
        per_callable_static_grad_outputs = [None] * len(flatten_sample_args)
        per_callable_static_grad_inputs = [None] * len(flatten_sample_args)
        fwd_idx = [0] * num_model_chunks
        bwd_idx = [0] * num_model_chunks
        static_grad_outputs_dict = {}
        previous_chunk_last_callable_bwd_idx = None
        for c_id in _order:
            if c_id > 0:
                # Capture forward graph for model chunk c_id, microbatch fwd_idx[c_id-1]
                m_chunk = c_id - 1
                for l_no in range(_num_layers_per_chunk[m_chunk]):
                    func = callables[_prefix_num_layers[m_chunk] + l_no]
                    per_callable_fwd_idx = (_prefix_num_layers[m_chunk] * num_microbatches) + (
                        fwd_idx[m_chunk] * _num_layers_per_chunk[m_chunk] + l_no
                    )
                    args = sample_args[per_callable_fwd_idx]
                    kwargs = sample_kwargs[per_callable_fwd_idx]
                    fwd_graph = fwd_graphs[per_callable_fwd_idx]
                    with _graph_context_wrapper(fwd_graph, pool=mempool):
                        outputs = func(*args, **kwargs)
                    flatten_outputs, spec = _tree_flatten(outputs)
                    per_callable_static_outputs[per_callable_fwd_idx] = tuple(flatten_outputs)
                    per_callable_output_unflatten_spec[per_callable_fwd_idx] = spec
                    graph_callables[per_callable_fwd_idx] = func
                fwd_idx[m_chunk] += 1
            else:
                # Capture backward graph for model chunk c_id, microbatch bwd_idx[-c_id-1]
                m_chunk = -c_id - 1
                previous_per_callable_bwd_idx = None
                for l_no in list(reversed(range(_num_layers_per_chunk[m_chunk]))):
                    per_callable_bwd_idx = (_prefix_num_layers[m_chunk] * num_microbatches) + (
                        bwd_idx[m_chunk] * _num_layers_per_chunk[m_chunk] + l_no
                    )
                    static_input_surface = per_callable_static_input_surfaces[per_callable_bwd_idx]
                    static_outputs = per_callable_static_outputs[per_callable_bwd_idx]
                    bwd_graph = bwd_graphs[per_callable_bwd_idx]
                    # For now, assumes all static_outputs require grad
                    if _reuse_graph_input_output_buffers:
                        # Note for _reuse_graph_input_output_buffers: grad output is only used
                        # within backward, so we can reuse the same static buffers every time.
                        static_grad_outputs_keys = tuple(
                            (o.shape, o.dtype, o.layout) for o in static_outputs if o.requires_grad
                        )
                        if static_grad_outputs_keys in static_grad_outputs_dict:
                            static_grad_outputs = static_grad_outputs_dict[static_grad_outputs_keys]
                        else:
                            static_grad_outputs = tuple(
                                torch.empty_like(o) if o.requires_grad else None
                                for o in static_outputs
                            )
                            static_grad_outputs_dict[static_grad_outputs_keys] = static_grad_outputs
                    else:
                        static_grad_outputs = tuple(
                            torch.empty_like(o) if o.requires_grad else None for o in static_outputs
                        )
                    if is_training:
                        with _graph_context_wrapper(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,
                                retain_graph=retain_graph_in_backward,
                            )
                        # If no one module needs the backward_dw, the bwd_dw_graph will be empty.
                        # So skip capturing it.
                        if need_bwd_dw_graph[per_callable_bwd_idx]:
                            bwd_dw_graph = bwd_dw_graphs[per_callable_bwd_idx]
                            with _graph_context_wrapper(bwd_dw_graph, pool=mempool):
                                for module in visited_te_modules[per_callable_bwd_idx]:
                                    if (
                                        hasattr(module, "need_backward_dw")
                                        and module.need_backward_dw()
                                    ):
                                        module.backward_dw()
                    # 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 one-liner for this pattern.
                    static_grad_inputs = []
                    grad_idx = 0
                    for arg in static_input_surface:
                        if is_training and isinstance(arg, torch.Tensor) and 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[per_callable_bwd_idx] = static_grad_outputs
                    per_callable_static_grad_inputs[per_callable_bwd_idx] = static_grad_inputs

                    # Weak ref the static outputs and static grad inputs that are no longer needed
                    # in the following steps. These two type of tensors are both in cudagraph
                    # mempool, so we just deallocate them and let PyTorch's memory allocator
                    # reuse them elsewhere.
                    if _reuse_graph_input_output_buffers:
                        # Weak ref the static outputs of the forward pass of this backward. It's
                        # no longer needed after the corresponding backward graph is built up.
                        per_callable_static_outputs[per_callable_bwd_idx] = make_weak_ref(
                            static_outputs
                        )

                        # Weak ref the static grad inputs of the previous backward pass within the
                        # same chunk.
                        if previous_per_callable_bwd_idx is not None:
                            idx = previous_per_callable_bwd_idx
                            per_callable_static_grad_inputs[idx] = make_weak_ref(
                                per_callable_static_grad_inputs[idx]
                            )
                        previous_per_callable_bwd_idx = per_callable_bwd_idx

                        # Weak ref the static grad inputs of the previous chunk's last backward
                        # pass.
                        # Note: After a chunk's backward pass, we assume Mcore will send the grad
                        # input to another pipeline parallel rank and that the communication is
                        # finished before the end of the next chunk's backward pass.
                        if l_no == 0:
                            if previous_chunk_last_callable_bwd_idx is not None:
                                idx = previous_chunk_last_callable_bwd_idx
                                per_callable_static_grad_inputs[idx] = make_weak_ref(
                                    per_callable_static_grad_inputs[idx]
                                )
                            previous_chunk_last_callable_bwd_idx = per_callable_bwd_idx

                bwd_idx[m_chunk] += 1
    else:
        # Capture forward graphs
        per_callable_static_outputs = []
        per_callable_output_unflatten_spec = []
        graph_id = 0
        for func, args, kwargs, fwd_graph in zip(callables, sample_args, sample_kwargs, fwd_graphs):
            with _graph_context_wrapper(fwd_graph, pool=mempool):
                outputs = func(*args, **kwargs)
            graph_callables[graph_id] = func
            graph_id += 1

            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, bwd_dw_graph, bwd_idx in zip(
            reversed(per_callable_static_input_surfaces),
            reversed(per_callable_static_outputs),
            reversed(bwd_graphs),
            reversed(bwd_dw_graphs),
            reversed(range(len(per_callable_static_input_surfaces))),
        ):
            # For now, assumes all static_outputs require grad
            static_grad_outputs = tuple(
                torch.empty_like(o) if o.requires_grad else None for o in static_outputs
            )
            if is_training:
                with _graph_context_wrapper(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,
                        retain_graph=retain_graph_in_backward,
                    )
                if need_bwd_dw_graph[bwd_idx]:
                    with _graph_context_wrapper(bwd_dw_graph, pool=mempool):
                        for module in visited_te_modules[bwd_idx]:
                            if hasattr(module, "need_backward_dw") and module.need_backward_dw():
                                module.backward_dw()
            # 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 is_training and isinstance(arg, torch.Tensor) and 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,
        kwargs_keys,
        len_user_args,
        output_unflatten_spec,
        static_input_surface,
        static_outputs,
        static_grad_outputs,
        static_grad_inputs,
    ):
        class Graphed(torch.autograd.Function):
            """Autograd function for graph replay."""

            @staticmethod
            def forward(ctx, skip_fp8_weight_update, *inputs):
                # pylint: disable=missing-function-docstring

                # Set flag for whether to update FP8 weight updates
                ctx.is_first_module = FP8GlobalStateManager.is_first_fp8_module()
                if ctx.is_first_module and skip_fp8_weight_update is not None:
                    FP8GlobalStateManager.set_skip_fp8_weight_update_tensor(skip_fp8_weight_update)

                # Copy values from new tensors into static tensors
                for i in range(len_user_args):
                    if (
                        isinstance(static_input_surface[i], torch.Tensor)
                        and static_input_surface[i].data_ptr() != inputs[i].data_ptr()
                    ):
                        static_input_surface[i].copy_(inputs[i])

                # Replay forward graph
                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):
                # pylint: disable=missing-function-docstring

                # Replay backward graph
                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()

                # Update FP8 scale factors if needed
                if ctx.is_first_module:
                    FP8GlobalStateManager.reduce_and_update_fp8_tensors(forward=False)

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

        def functionalized(*user_args, **user_kwargs):

            # Decide whether to update FP8 weights
            skip_fp8_weight_update = None
            if cache_quantized_params:
                assert "is_first_microbatch" in user_kwargs and isinstance(
                    user_kwargs["is_first_microbatch"], bool
                ), "`is_first_microbatch` boolean kwarg must be provided for FP8 weight caching."

                skip_fp8_weight_update = not user_kwargs["is_first_microbatch"]

            # Check that required kwargs are provided
            for key in kwargs_keys:
                if key not in user_kwargs:
                    raise TypeError(
                        f"Graphed callable was initialized with kwarg {key} ,"
                        "but it was not provided in graph replay"
                    )

            # 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)
            flatten_user_kwargs, _ = _tree_flatten([user_kwargs[key] for key in kwargs_keys])
            func_args = tuple(flatten_user_args) + tuple(flatten_user_kwargs) + module_params
            out = Graphed.apply(skip_fp8_weight_update, *func_args)
            return _tree_unflatten(out, output_unflatten_spec)

        return functionalized

    def make_graphed_attribute_functions(graph_idx):

        # Attach backward_dw as an attribute to the graphed callable.
        def backward_dw():
            if need_bwd_dw_graph.get(graph_idx, False):
                bwd_dw_graphs[graph_idx].replay()

        # Attach reset as an attribute to the graphed callable.
        def reset():
            fwd_graphs[graph_idx].reset()
            bwd_graphs[graph_idx].reset()
            bwd_dw_graphs[graph_idx].reset()

        return backward_dw, reset

    # Put together the final graphed callables
    ret = []
    for i in range(len(sample_args)):
        graphed = make_graphed_autograd_function(
            fwd_graphs[i],
            bwd_graphs[i],
            per_callable_module_params[i],
            per_callable_kwargs_keys[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],
        )

        func = graph_callables[i]
        te_modules = visited_te_modules.get(i, set())
        if isinstance(func, torch.nn.Module):

            def make_graphed_forward(func, graph_training_state, graphed, orig_fwd, te_modules):
                def new_fwd(*user_args, **user_kwargs):
                    # 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:
                        # Set the FP8 group from global amax reduction.
                        if FP8GlobalStateManager.is_fp8_enabled():
                            fp8_recipe = FP8GlobalStateManager.get_fp8_recipe()
                            for m in func.modules():
                                if m not in te_modules:
                                    # Only Set the FP8 meta for the modules included by forward
                                    continue
                                if isinstance(m, TransformerEngineBaseModule):
                                    from transformer_engine.pytorch.attention.dot_product_attention import (
                                        DotProductAttention,
                                    )

                                    if (
                                        isinstance(m, DotProductAttention)
                                        and not fp8_recipe.fp8_mha
                                        and not fp8_recipe.fp8_dpa
                                    ):
                                        # Don't need to update FP8 meta for non-FP8 DPA
                                        continue
                                    m.fp8_meta["fp8_group"] = FP8GlobalStateManager.get_fp8_group()
                                    m.fp8_meta["recipe"] = FP8GlobalStateManager.get_fp8_recipe()
                                    FP8GlobalStateManager.add_fp8_tensors_to_global_buffer(
                                        m.fp8_meta,
                                    )
                                elif isinstance(m, BasicOperation):
                                    for mode in ("forward", "backward"):
                                        if m.num_quantizers(mode):
                                            m._fp8_metas[mode][
                                                "fp8_group"
                                            ] = FP8GlobalStateManager.get_fp8_group()
                                            m._fp8_metas[mode][
                                                "recipe"
                                            ] = FP8GlobalStateManager.get_fp8_recipe()
                                            FP8GlobalStateManager.add_fp8_tensors_to_global_buffer(
                                                m._fp8_metas[mode],
                                            )
                        return graphed(*user_args, **user_kwargs)
                    return orig_fwd(*user_args, **user_kwargs)

                return new_fwd

            forward = make_graphed_forward(func, func.training, graphed, func.forward, te_modules)
            if _order is None:
                func.forward = forward
                ret.append(func)
            else:
                ret.append(forward)
        else:
            ret.append(graphed)

        backward_dw_func, reset_func = make_graphed_attribute_functions(i)
        setattr(ret[-1], "backward_dw", backward_dw_func)
        setattr(ret[-1], "reset", reset_func)

    if just_one_callable:
        return ret[0]

    return tuple(ret)


def save_fp8_tensors(
    modules: Iterable[torch.nn.Module],
    recipe: Optional[Recipe],
) -> Optional[List[Any]]:
    """
    Returns the FP8 tensors for all modules
    with adjusted amax history sizes.
    """

    if not isinstance(recipe, DelayedScaling):
        return None

    fp8_tensors = []
    for module in modules:
        for m in module.modules():
            module_tensors = None
            if isinstance(m, TransformerEngineBaseModule):
                if m.primary_weights_in_fp8:
                    m.adjust_amax_history_length(recipe.amax_history_len)
                module_tensors = m.get_fp8_meta_tensors()
            elif isinstance(m, BasicOperation):
                m.reset_recipe_state(recipe=recipe)
                module_tensors = m._save_fp8_metas()
            fp8_tensors.append(module_tensors)
    return fp8_tensors


def restore_fp8_tensors(
    modules: Iterable[torch.nn.Module],
    fp8_tensors: Optional[List[Any]],
) -> None:
    """Restore FP8 tensors."""

    if fp8_tensors is None:
        return

    for module in modules:
        for m in module.modules():
            module_tensors = fp8_tensors.pop(0)
            if isinstance(m, TransformerEngineBaseModule):
                m.reset_fp8_meta_tensors(module_tensors)
            elif isinstance(m, BasicOperation):
                m._load_fp8_metas(module_tensors)
    if len(fp8_tensors) != 0:
        raise RuntimeError(
            f"Got FP8 state for {len(fp8_tensors)} more modules than expected. "
            "There is probably a discrepancy with `save_fp8_tensors`."
        )


def make_graphed_callables(
    modules: SingleOrTuple[Callable],
    sample_args: SingleOrTuple[Tuple[torch.Tensor, ...]],
    num_warmup_iters: int = 3,
    allow_unused_input: bool = False,
    sample_kwargs: Optional[SingleOrTuple[Dict[str, Any]]] = None,
    fp8_enabled: Optional[SingleOrTuple[bool]] = None,
    fp8_calibrating: Optional[bool] = None,
    fp8_recipe: Optional[Recipe] = None,
    fp8_group: Optional[dist_group_type] = None,
    fp8_weight_caching: Optional[bool] = None,
    enabled: Optional[SingleOrTuple[bool]] = None,
    calibrating: Optional[bool] = None,
    recipe: Optional[Recipe] = None,
    amax_reduction_group: Optional[dist_group_type] = None,
    cache_quantized_params: Optional[bool] = None,
    _order: Optional[List[int]] = None,
    _num_layers_per_chunk: Optional[List[int]] = None,
    pool: Optional[Tuple[int, ...]] = None,
    retain_graph_in_backward: bool = False,
    _reuse_graph_input_output_buffers: bool = False,
) -> Union[Callable, Tuple[Callable, ...]]:
    """
    Make CUDA graph version of Transformer Engine modules

    A variation of PyTorch's `make_graphed_callables` utility function
    with support for Transformer Engine modules and FP8. Please see
    the
    `original PyTorch implementation <https://pytorch.org/docs/stable/generated/torch.cuda.make_graphed_callables.html>`_
    for more documentation.

    .. warning::

       Arguments 'fp8_enabled', 'fp8_calibrating', 'fp8_recipe', 'fp8_group', and 'fp8_weight_caching' are deprecated.
       Use arguments 'enabled', 'calibrating', 'recipe', 'amax_reduction_group', and 'cache_quantized_params' instead.

    Graphing parameters
    -------------------
    modules: (tuple of) callable
             Callable or callables to graph.
    sample_args: (tuple of) tuple of torch.Tensor
                 Positional arguments to callable(s).
    num_warmup_iters: int, default = 3
                      Number of warmup iterations.
    allow_unused_input: bool, default = `False`
                        Whether to handle case where callable inputs
                        and outputs are disconnected in compute graph.
    sample_kwargs: (tuple of) dict, optional
                   Keyword arguments to callable(s)
    pool: (tuple of) int, default = `None`, optional
          An instance returned from function `torch.cuda.graph_pool_handle` that hints
          this graph may share memory with the indicated pool.
    retain_graph_in_backward: bool, default = `False`
                              Whether to set retain_graph=True in backward graph capture.
    _reuse_graph_input_output_buffers: bool, default = `False`
        Reduce memory usage by reusing input/output data buffers between
        graphs. Only supported with Mcore interleaved pipeline parallelism, i.e.
        when `_order` is provided. All callables in `modules` are assumed to have
        inputs and outputs with the same dtype and shape.

    Quantization related parameters
    ----------------------
    enabled: (tuple of) bool, default = `False`
             whether or not to enable low precision quantization (FP8/FP4).
             If tuple, the length must match the number of modules.
    calibrating: bool, default = `False`
                 calibration mode allows collecting statistics such as amax and scale
                 data of quantized tensors even when executing without quantization enabled.
                 This is useful for saving an inference ready checkpoint while training
                 using a higher precision.
    recipe: recipe.Recipe, default = `None`
            recipe used for low precision quantization.
    amax_reduction_group: torch._C._distributed_c10d.ProcessGroup, default = `None`
                          distributed group over which amaxes for the quantized tensors
                          are reduced at the end of each training step.
    cache_quantized_params: bool, default = `False`
                            Whether or not to cache quantized weights across microbatches. if set to `True`,
                            the `is_first_microbatch` boolean argument must be passed into the forward
                            method for TransformerEngine modules. When storing primary weights in low precision
                            using TE's `quantized_model_init` API and using an quantization aware optimizer,
                            this arg must be set to `False` if calculating weight transposes' outside TE, e.g.,
                            in the optimizer step.

    """

    # Handle deprecated args. If old kwargs are set, they are prioritized with warning.
    if fp8_enabled is not None:
        if enabled is not None:
            raise ValueError(
                "make_graphed_callables has deprecated `fp8_enabled` kwarg "
                "in favor of `enabled`, but both kwargs are set."
            )
        warnings.warn(
            "make_graphed_callables has deprecated `fp8_enabled` kwarg in favor of `enabled`. "
            "`fp8_enabled` will be removed in a future release.",
            category=DeprecationWarning,
            stacklevel=2,
        )
        enabled = fp8_enabled
    if enabled is None:
        enabled = False

    if fp8_calibrating is not None:
        if calibrating is not None:
            raise ValueError(
                "make_graphed_callables has deprecated `fp8_calibrating` kwarg "
                "in favor of `calibrating`, but both kwargs are set."
            )
        warnings.warn(
            "make_graphed_callables has deprecated `fp8_calibrating` kwarg in favor of "
            "`calibrating`. `fp8_calibrating` will be removed in a future release.",
            category=DeprecationWarning,
            stacklevel=2,
        )
        calibrating = fp8_calibrating
    if calibrating is None:
        calibrating = False

    if fp8_recipe is not None:
        if recipe is None:
            warnings.warn(
                "make_graphed_callables has deprecated `fp8_recipe` kwarg in favor of "
                "`recipe`. `fp8_recipe` will be removed in a future release.",
                category=DeprecationWarning,
                stacklevel=2,
            )
        else:
            raise ValueError(
                "make_graphed_callables has deprecated `fp8_recipe` kwarg "
                "in favor of `recipe`, but both kwargs are set."
            )
        recipe = fp8_recipe

    if fp8_group is not None:
        if amax_reduction_group is None:
            warnings.warn(
                "make_graphed_callables has deprecated `fp8_group` kwarg in favor of "
                "`amax_reduction_group`. `fp8_group` will be removed in a future release.",
                category=DeprecationWarning,
                stacklevel=2,
            )
        else:
            raise ValueError(
                "make_graphed_callables has deprecated `fp8_group` kwarg "
                "in favor of `amax_reduction_group`, but both kwargs are set."
            )
        amax_reduction_group = fp8_group

    if fp8_weight_caching is not None:
        if cache_quantized_params is not None:
            raise ValueError(
                "make_graphed_callables has deprecated `fp8_weight_caching` kwarg "
                "in favor of `cache_quantized_params`, but both kwargs are set."
            )
        warnings.warn(
            "make_graphed_callables has deprecated `fp8_weight_caching` kwarg in favor of "
            "`cache_quantized_params`. `fp8_weight_caching` will be removed in a future release.",
            category=DeprecationWarning,
            stacklevel=2,
        )
        cache_quantized_params = fp8_weight_caching
    if cache_quantized_params is None:
        cache_quantized_params = False

    set_capture_start()

    # Handle single module.
    just_one_callable = False
    if not isinstance(modules, tuple):
        just_one_callable = True
        modules = (modules,)

    if not isinstance(enabled, tuple):
        assert isinstance(enabled, bool), "enabled must be a bool or a tuple of bools"
        enabled = (enabled,) * len(modules)
    else:
        assert len(enabled) == len(
            modules
        ), f"enabled length ({len(enabled)}) must match modules length ({len(modules)})"
    if any(enabled) and recipe is None:
        recipe = get_default_fp8_recipe()
    elif not any(enabled):
        recipe = None
    module_uses_fp8 = dict(zip((id(m) for m in modules), enabled))

    # Store FP8 tensors to reset later.
    saved_fp8_tensors = save_fp8_tensors(modules, recipe=recipe)

    # FP8 wrapper.
    old_call_funcs = {}

    def wrap_autocast(block):
        block_cls = type(block)
        if block_cls in old_call_funcs:
            return

        old_call_funcs[block_cls] = block_cls.__call__

        # Wrap the original call function of the module class.
        def call_func(self, *args, **kwargs):
            with autocast(
                enabled=module_uses_fp8.get(id(self), False),
                calibrating=calibrating,
                recipe=recipe,
                amax_reduction_group=amax_reduction_group,
                _graph=True,
            ):
                outputs = old_call_funcs[block_cls](self, *args, **kwargs)
            return outputs

        block_cls.__call__ = call_func

    forward_funcs = []
    for module in modules:
        assert isinstance(module, torch.nn.Module), f"Graphing for {type(module)} is not supported."
        wrap_autocast(module)
        forward_funcs.append(module)

    if just_one_callable:
        forward_funcs = forward_funcs[0]
    else:
        forward_funcs = tuple(forward_funcs)

    # Save RNG state.
    if graph_safe_rng_available():
        generators = [
            torch.cuda.default_generators[torch.cuda.current_device()],
            *get_all_rng_states().values(),
        ]
        original_rng_states = [state.get_state() for state in generators]
    else:
        original_rng_states = torch.cuda.get_rng_state()

    graphed_callables = _make_graphed_callables(
        forward_funcs,
        sample_args,
        num_warmup_iters=num_warmup_iters,
        allow_unused_input=allow_unused_input,
        cache_quantized_params=cache_quantized_params,
        sample_kwargs=sample_kwargs,
        _order=_order,
        _num_layers_per_chunk=_num_layers_per_chunk,
        pool=pool,
        retain_graph_in_backward=retain_graph_in_backward,
        _reuse_graph_input_output_buffers=_reuse_graph_input_output_buffers,
    )

    # Ensures warmup does not affect numerics for ops such as dropout.
    if graph_safe_rng_available():
        for gen, state in zip(generators, original_rng_states):
            gen.set_state(state)
    else:
        torch.cuda.set_rng_state(original_rng_states)

    # Remove FP8 wrapper.
    for module_cls, old_call in old_call_funcs.items():
        module_cls.__call__ = old_call

    # Restore FP8 state.
    restore_fp8_tensors(modules, saved_fp8_tensors)

    set_capture_end()
    return graphed_callables