import collections
import dataclasses
import functools
import itertools
import logging
import math
import operator
import os
import pprint
import textwrap
from typing import (
    Any,
    Counter,
    DefaultDict,
    Dict,
    Generic,
    List,
    Optional,
    Sequence,
    Set,
    Tuple,
    TypeVar,
    Union,
)

import sympy

import torch
from torch._dynamo.utils import dynamo_timed
from torch._inductor.metrics import get_metric_table, is_metric_table_enabled
from torch.utils._triton import has_triton

from . import comms, config, dependencies, ir, metrics
from .codegen.common import get_scheduling_for_device, Kernel
from .comm_analysis import estimate_nccl_collective_runtime
from .dependencies import Dep, MemoryDep, StarDep, WeakDep
from .ir import ComputedBuffer, MultiOutput, MultiOutputLayout
from .sizevars import SimplifyIndexing
from .utils import (
    cache_on_self,
    cmp,
    free_symbol_has,
    get_device_tflops,
    get_dtype_size,
    get_gpu_dram_gbps,
    green_text,
    is_collective,
    is_wait,
    red_text,
    sympy_product,
)
from .virtualized import V


log = logging.getLogger(__name__)
fusion_log = torch._logging.getArtifactLogger(__name__, "fusion")


class WhyNoFuse:
    # TODO when we drop support for Python < 3.10, we can use
    # @dataclass(slots=True) instead of manually specifying __slots__.
    __slots__ = ["node1", "node2", "reason", "args"]
    reason: str
    args: Tuple[Any, ...]

    def __init__(self, node1: "BaseSchedulerNode", node2: "BaseSchedulerNode"):
        self.node1 = node1
        self.node2 = node2

    def __call__(self, reason, *args):
        self.reason = reason
        self.args = args
        fusion_log.debug(self)

    def __str__(self):
        return f"cannot fuse {self.node1.get_name()} with {self.node2.get_name()}: " + (
            self.reason % self.args
        )


def pformat(obj):
    if isinstance(obj, set):
        # pformat has trouble with sets of sympy exprs
        obj = sorted(obj, key=str)
    result = pprint.pformat(obj, indent=4)
    if "\n" in result:
        return f"\n{textwrap.indent(result, ' '*4)}"
    return result


class OutputNode:
    def __init__(self, dep):
        self.unmet_dependencies = {dep}
        self.inverse_users = []

    def is_reduction(self):
        return False

    def get_alias_names(self):
        return ()

    def get_name(self):
        return "OUTPUT"

    __repr__ = get_name


def _prune_redundant_deps(node, name_to_fused_node):
    """
    Prunes weakdeps intended for mutation ordering
    on an upstream fused node if after fusion there is another dependency
    on the fused upstream node, making the weakdep redundant

    In essence this enforces an ordering on fusions. As fusions occur, weakdeps will
    be incrementally removed, enabling other fusions, ensuring they are fused in order.
    """
    name_to_dep_count: Counter[str] = collections.Counter()

    for dep in node.unmet_dependencies:
        if not isinstance(dep, WeakDep):
            name_to_dep_count[name_to_fused_node[dep.name].get_name()] += 1

    def should_prune(dep):
        if isinstance(dep, WeakDep):
            is_redundant = (
                name_to_dep_count[name_to_fused_node[dep.name].get_name()] > 0
            )
            # These can occur because fused nodes always gather deps from their snodes
            # If B has a weakdep on A
            # B gets fused with C, then any time BC is fused, the weakdep will reappear
            is_self_dep = name_to_fused_node[dep.name] == node
            return is_redundant or is_self_dep
        else:
            return False

    deps_to_prune = {dep for dep in node.unmet_dependencies if should_prune(dep)}

    if deps_to_prune:
        node.unmet_dependencies = node.unmet_dependencies - deps_to_prune
        node.set_read_writes(node.read_writes.remove_reads(deps_to_prune))


# TODO(xmfan): reuse an existing mapping for this if it exists, or formalize this into ir.py:ExternKernel
kernel_name_to_op = {
    "extern_kernels.convolution": torch.ops.aten.convolution,
    "extern_kernels.mm": torch.ops.aten.mm,
    "extern_kernels.bmm": torch.ops.aten.bmm,
    "extern_kernels.addmm": torch.ops.aten.addmm,
}


class BaseSchedulerNode:
    def __init__(self, scheduler: "Scheduler", node: ir.Buffer):
        self.scheduler: Scheduler = scheduler
        self.node: ir.Buffer = node
        self.users: List[NodeUser] = []
        self.inverse_users: List[BaseSchedulerNode] = []
        self.node_users: List[BaseSchedulerNode] = []
        self.set_read_writes(node.get_read_writes())
        self.ancestors: Set[str] = set()
        self.min_order: int
        self.max_order: int
        self.last_usage: Set[
            str
        ] = set()  # buffers that won't be used after this kernel
        self.written = False

    def __repr__(self):
        return f"{type(self).__name__}(name={self.get_name()!r})"

    def debug_str(self) -> str:
        """Longer form printout for trace logs"""
        name = self.get_name()
        lines = [
            f"{name}: {type(self).__name__}({type(getattr(self, 'node', None)).__name__})",
            f"{name}.writes = {pformat(self.read_writes.writes)}",
            f"{name}.unmet_dependencies = {pformat(self.unmet_dependencies)}",
            f"{name}.met_dependencies = {pformat(self.read_writes.reads - self.unmet_dependencies)}",
            f"{name}.users = {self.users}",
        ]
        try:
            lines += [
                self.debug_str_extra(),
            ]
        except Exception:
            log.warning("Ignoring error in debug_str()", exc_info=True)

        return "\n".join(lines).rstrip()

    def debug_str_extra(self) -> str:
        return ""

    def log_details(self):
        log.info(
            "%s: unmet_dependencies = %s, writes = %s",
            self,
            self.unmet_dependencies,
            self.read_writes.writes,
        )

    def update_mutated_names(self, renames: Dict[str, str]):
        self.set_read_writes(self.read_writes.rename(renames))

    def add_mutation_dep(self, dep):
        self.set_read_writes(self.read_writes.with_read(dep))

    def add_fake_dep(self, dep):
        self.set_read_writes(self.read_writes.with_read(dep))

    def set_users(self, users: List["NodeUser"]):
        # deduplicate
        result: Dict[int, NodeUser] = {}
        for use in users:
            if id(use.node) in result:
                result[id(use.node)] = use.merge(result[id(use.node)])
            else:
                result[id(use.node)] = use
        self.users = list(result.values())

    def set_last_usage(
        self, future_used_buffers: Set[str], mutation_real_name: Dict[str, str]
    ):
        used_buffers = self.used_or_aliased_buffer_names()
        used_buffers = {mutation_real_name.get(k, k) for k in used_buffers}
        self.last_usage = used_buffers - future_used_buffers

    def get_aliases(self):
        return self.node.get_alias_names()

    def get_mutations(self):
        return self.node.get_mutation_names()

    def has_aliasing_or_mutation(self):
        return bool(self.get_aliases() or self.get_mutations())

    def set_read_writes(self, rw: dependencies.ReadWrites):
        self.read_writes: dependencies.ReadWrites = rw
        self.unmet_dependencies = self.read_writes.reads
        self.prune_deps()

    def op_counts(self):
        return self.read_writes.op_counts

    def used_buffer_names(self) -> Set[str]:
        return {
            dep.name
            for dep in itertools.chain(self.read_writes.reads, self.read_writes.writes)
        }

    def used_or_aliased_buffer_names(self) -> Set[str]:
        used_names = set()

        for dep in itertools.chain(self.read_writes.reads, self.read_writes.writes):
            used_names.add(dep.name)
            if V.graph.name_to_buffer.get(dep.name):
                layout = V.graph.name_to_buffer[dep.name].get_layout()
                # needed to avoid deallocating aliased buffer
                # if there are still uses of aliases ahead
                if isinstance(layout, ir.AliasedLayout):
                    used_names.add(layout.view.data.get_name())
        return used_names

    def prune_deps(self):
        self.unmet_dependencies = {
            dep
            for dep in self.unmet_dependencies
            if dep.name not in self.scheduler.available_buffer_names
        }

    def prune_weak_deps(self):
        # Prune weak dependencies on buffers that have been removed
        def should_prune(dep):
            return isinstance(dep, WeakDep) and dep.name in V.graph.removed_buffers

        to_remove = {dep for dep in self.read_writes.reads if should_prune(dep)}
        self.set_read_writes(self.read_writes.remove_reads(to_remove))

    def prune_redundant_deps(self, name_to_fused_node):
        _prune_redundant_deps(self, name_to_fused_node)

    def get_name(self) -> str:
        return self.node.get_name()

    def get_first_name(self) -> str:
        return self.get_name()

    def get_names(self) -> Set[str]:
        return {self.get_name()}

    def get_nodes(self) -> Sequence["BaseSchedulerNode"]:
        return [self]

    def get_device(self):
        return self.node.get_device()

    def is_reduction(self):
        return False

    def is_split_scan(self):
        return False

    def is_template(self):
        return False

    def is_extern(self):
        return False

    def is_foreach(self):
        return False

    def can_inplace(self, read_dep: dependencies.MemoryDep):
        return False

    def has_side_effects(self):
        return False

    def decide_inplace_update(self):
        """
        Decide if there should be inplace updates for the node
        and record the decision in the active kernel.
        """
        if not self.node.should_allocate():
            return

        if isinstance(self, (SchedulerNode,)) and (
            self.node.get_alias_names() or self.node.get_mutation_names()
        ):
            return

        if (
            (
                isinstance(self, (SchedulerNode,))
                # o what have i done.  lets make this an api
                or (
                    isinstance(self, ExternKernelSchedulerNode)
                    and isinstance(self.node, (ir.AllReduce, ir.InPlaceHint))
                )
            )
            and config.inplace_buffers
            and (
                not isinstance(V.kernel, torch._inductor.codegen.triton.TritonKernel)
                or getattr(V.kernel, "mutations", None) is not None
            )
        ):
            from .codegen.wrapper import buffer_reuse_key

            ordered_reads = sorted(self.read_writes.reads, key=lambda x: x.name)

            for read in ordered_reads:
                input_node: Optional[
                    BaseSchedulerNode
                ] = self.scheduler.name_to_node.get(read.name)
                if input_node and V.graph.wrapper_code.can_reuse(input_node, self):
                    assert input_node.users is not None
                    remaining_uses = [
                        x
                        for x in input_node.users
                        if x.node.get_name()
                        not in self.scheduler.available_buffer_names
                    ]
                    if (
                        len(remaining_uses) == 1
                        and remaining_uses[0].can_inplace
                        and remaining_uses[0].node is self
                        and not isinstance(
                            input_node.node.get_layout(),
                            (
                                ir.MultiOutputLayout,
                                ir.MutationLayout,
                                ir.AliasedLayout,
                            ),
                        )
                        and not (
                            isinstance(
                                input_node.node, (ir.FallbackKernel, ir.MultiOutput)
                            )
                            and len(input_node.node.get_alias_names()) > 0
                        )
                        and buffer_reuse_key(input_node.node)
                        == buffer_reuse_key(self.node)
                    ):
                        # hacky check for if V.kernel is a real kernel or NullHandler
                        if hasattr(V.kernel, "args"):
                            # if there isn't a triton kernel, then we don't need to call triton-specific things.
                            # but TODO this might be a convenient place to signal to the Collective kernels to inplace
                            # (and, can we make "kernel" less generic of a name?)
                            V.kernel.args.make_inplace(
                                input_node.get_name(), self.get_name()
                            )
                            # mutations not tracked in cpp kernels
                            if isinstance(
                                V.kernel, torch._inductor.codegen.triton.TritonKernel
                            ):
                                V.kernel.mutations.add(input_node.get_name())
                                V.kernel.mutations.add(self.get_name())

                            # update last usage of reused node
                            self.last_usage.discard(input_node.get_name())

                            V.kernel.inplace_update_buffers[
                                self.get_name()
                            ] = input_node.get_name()
                        break

    def allocate(self):
        if not self.node.should_allocate():
            return

        if isinstance(self, (SchedulerNode,)) and (
            self.node.get_alias_names() or self.node.get_mutation_names()
        ):
            V.graph.wrapper_code.codegen_allocation(self.node)
            return

        # hacky check for if V.kernel is a real kernel or NullHandler
        if (
            hasattr(V.kernel, "args")
            and self.get_name() in V.kernel.inplace_update_buffers
        ):
            V.graph.wrapper_code.codegen_inplace_reuse(
                self.scheduler.name_to_node[
                    V.kernel.inplace_update_buffers[self.get_name()]
                ].node,
                self.node,
            )
        else:
            V.graph.wrapper_code.codegen_allocation(self.node)

    def can_free(self):
        # There's no real allocated buffer, no need to free it
        if isinstance(self.node.layout, ir.NoneLayout):
            return False
        for use in self.users:
            if isinstance(use.node, OutputNode):
                return False
        return True

    def codegen_originating_info(self, buffer, only_once=True):
        if not config.comment_origin:
            return

        if only_once and self.written:
            return
        origins = self.node.origins
        out_lines = []

        for o in origins:
            if o.op == "output":
                # These are boring and samey
                continue

            out_lines.append("")
            # TODO(voz): Should the pragma be constant somewhere?
            out_lines.append("#pragma CMT ORIGIN:")
            op_info_str = f"#pragma CMT {o.op} {o.target}"
            if "seq_nr" in o.meta:
                op_info_str = op_info_str + f" seq_nr:{o.meta['seq_nr']}"
            out_lines.append(op_info_str)
            if "stack_trace" in o.meta:
                stack_trace = f"{o.meta['stack_trace']}"
                stack_trace_last_line = stack_trace.split("|")[-1]
                out_lines.append(
                    "#pragma CMT "
                    + stack_trace_last_line.replace("{", "{{")
                    .replace("}", "}}")
                    .replace("\n", "\\")
                )
                out_lines.append("#pragma CMT END ORIGIN")
                out_lines.append("")

        if len(out_lines) == 0:
            return

        # TODO(voz): Ostensibly, we should not need this. But there are cases where C++ codegen does
        # not use BracesBuffer, so we have no good indicator of a C++ buffer atm.
        buffer.writelines(out_lines)
        self.written = True

    def get_read_write_buffers_sizes(self) -> int:
        """
        Counting the number of bytes accessed for a kernel is
        surprisingly tricky. In particular, there is a differentiation
        between 'theoretical' memory accesses and practical memory
        accesses. For example, a layernorm kernel may actually access an
        input 3 times, but in theory, it only needs to access its input
        once (and may be optimized to do so through say, persistent
        reductions)

        Another example is that even though a buffer is passed in, we may
        not access the entire buffer. This may occur if we are accessing
        a slice of the buffer. Another tricky case is for indirect
        indexing, where the amount of bytes accessed depends on the
        values of the input.

        What this function aims to compute is the memory accesses for
        worst-case inputs, best-case optimization. What this means is
        that for each buffer we compute the amount of potential accesses in two ways and take the minimum.

        1. Numel in ranges multiplied by number of deps the buffer has
        2. The buffer size
        """
        if isinstance(self, NopKernelSchedulerNode):
            return 0
        if isinstance(self, ExternKernelSchedulerNode) and isinstance(
            self.node, MultiOutput
        ):
            return 0

        if isinstance(self, SchedulerNode):
            node_numel = V.graph.sizevars.size_hint(
                sympy_product(self.get_ranges()[0])
                * sympy_product(self.get_ranges()[1])
            )
        else:
            node_numel = int(1e9)
        buf_accesses = collections.defaultdict(list)
        for dep in self.read_writes.reads | self.read_writes.writes:
            buf_accesses[dep.name].append(dep)

        reads = {dep.name for dep in self.read_writes.reads}
        writes = {dep.name for dep in self.read_writes.writes}

        def is_materialized(buf, snodes):
            users = self.scheduler.name_to_node[buf].users
            buf_uses = {user.node for user in users}
            return len(buf_uses - set(snodes)) > 0

        if isinstance(self, FusedSchedulerNode):
            removed_buffers = {
                dep for dep in writes if not is_materialized(dep, self.snodes)
            }
            writes = writes - removed_buffers
            reads = reads - removed_buffers
        node_bytes = 0

        for buf_name in reads | writes:
            buf_accessed_elems = sum([node_numel for dep in buf_accesses[buf_name]])
            buf: Union[ir.Buffer, ir.TensorBox]
            if buf_name in V.graph.name_to_buffer:
                buf = V.graph.name_to_buffer[buf_name]
            elif buf_name in V.graph.graph_inputs:
                buf = V.graph.graph_inputs[buf_name]
            else:
                continue

            def get_buf_elems(buf):
                return V.graph.sizevars.size_hint(sympy_product(buf.get_size()))

            # Kind of a lazy way to get the MultiOutput nodes corresponding to
            # a MultiOutputLayout
            if isinstance(buf.layout, MultiOutputLayout):
                users = self.scheduler.name_to_node[buf.get_name()].users
                buf_elems = sum(get_buf_elems(user.node.node) for user in users)
            else:
                buf_elems = get_buf_elems(buf)

            node_bytes += min(buf_elems, buf_accessed_elems) * get_dtype_size(
                buf.get_dtype()
            )

        return node_bytes

    def get_estimated_runtime(self) -> float:
        """
        Returns estimated op runtime in nanoseconds (ns)
        """
        layout = None
        dtype = None
        if not hasattr(self, "node") or not self.node:
            assert isinstance(
                self, (FusedSchedulerNode, ForeachKernelSchedulerNode)
            ), f"{type(self)=}"
            assert self.snodes
            if not self.snodes[0].node:
                return 0
            layout = self.snodes[0].node.get_layout()
            dtype = self.snodes[0].node.get_dtype()
        else:
            layout = self.node.get_layout()
            dtype = self.node.get_dtype()

        if "cuda" != layout.device.type:
            # default to no reordering based on runtime
            return 0

        # Collective kernels
        if is_collective(self.node):
            return estimate_nccl_collective_runtime(self.node)
        elif is_wait(self.node):
            # ir.Wait is only used for collective ops.
            # The time needed for the collective op is already estimated and considered
            # when we are processing the collective op IR node, so ir.Wait takes 0 time
            # since it doesn't take extra time to get the result after the collective is completed.
            return 0

        try:
            gpu_memory_bandwidth = get_gpu_dram_gbps()
            gpu_flops = get_device_tflops(dtype) * 10**12
        except Exception:
            return 0

        if isinstance(self, ExternKernelSchedulerNode):
            assert isinstance(self.node, ir.ExternKernel), f"{type(self.node)=}"
            op = kernel_name_to_op.get(
                getattr(self.node, "python_kernel_name", ""), None
            )

            # if there is a resolved op, dry-run using fake mode and record flop count
            if op is not None:
                from torch._subclasses.fake_tensor import FakeTensorMode
                from torch.utils.flop_counter import FlopCounterMode

                with FakeTensorMode(), FlopCounterMode(
                    display=False
                ) as flop_counter_mode:
                    from .ir import ir_node_to_tensor

                    fake_inputs = [
                        ir_node_to_tensor(input, guard_shape=False)
                        for input in self.node.inputs
                    ]
                    cls = self.node.__class__
                    cls.process_kernel(op, *fake_inputs, **self.node.kwargs)

                    # TODO(xmfan): find a better heuristic to model FLOPS/latency relationship
                    factor = 1.0
                    counted_flops = flop_counter_mode.get_total_flops()
                    counted_bytes = self.get_read_write_buffers_sizes()
                    compute_time = (factor * counted_flops / gpu_flops) * 1e9
                    transfer_time = counted_bytes / gpu_memory_bandwidth

                    # Return estimated runtime in nanoseconds
                    return max(compute_time, transfer_time)

        elif isinstance(self, FusedSchedulerNode) or isinstance(
            self.node, ComputedBuffer
        ):
            # Return estimated runtime in nanoseconds (bytes / gbps)
            return self.get_read_write_buffers_sizes() / gpu_memory_bandwidth

        return 0


class ExternKernelSchedulerNode(BaseSchedulerNode):
    def debug_str_extra(self) -> str:
        return f"{self.get_name()}.node.kernel = {getattr(self.node, 'python_kernel_name', None)}"

    def is_extern(self):
        return True

    def has_side_effects(self):
        return hasattr(self.node, "has_side_effects") and self.node.has_side_effects()

    def can_inplace(self, read_dep: dependencies.MemoryDep):
        if self.get_aliases() or self.is_template():
            return False

        if read_dep.name not in self.scheduler.name_to_node:
            # don't allow reuse of an 'input' buffer, we don't own it
            # (would this have been fixed if I tracked mutations properly above?)
            return False
        if not isinstance(
            self.node, (torch._inductor.ir.AllReduce, torch._inductor.ir.InPlaceHint)
        ):
            # TODO make this a property of the IR
            return False

        if len(self.read_writes.writes) == 1:
            write_dep = next(iter(self.read_writes.writes))
            numel_diff = read_dep.get_numel() - write_dep.get_numel()
            return V.graph.sizevars.simplify(numel_diff) == 0

        return False


class NopKernelSchedulerNode(BaseSchedulerNode):
    pass


class SchedulerNode(BaseSchedulerNode):
    def __init__(
        self,
        scheduler: "Scheduler",
        node: Union[ir.ComputedBuffer, ir.TemplateBuffer],
    ):
        super().__init__(scheduler, node)
        self._compute_attrs()

    def _compute_attrs(
        self,
        extra_indexing_constraints: Optional[Tuple[Dict[Any, Any], List[Any]]] = None,
    ):
        assert isinstance(self.node, (ir.ComputedBuffer, ir.TemplateBuffer))
        self._sizes, self._body = self.node.simplify_and_reorder(
            extra_indexing_constraints=extra_indexing_constraints
        )

        group_fn = self.scheduler.get_backend(self.node.get_device()).group_fn
        self.group = (self.node.get_device(), group_fn(self._sizes))

        if isinstance(self.node, ir.TemplateBuffer):
            self.set_read_writes(self.node.normalized_read_writes())
        else:
            self.set_read_writes(
                dependencies.extract_read_writes(
                    self._body, *self._sizes, normalize=True
                )
            )

    def recompute_size_and_body(
        self, extra_indexing_constraints: Tuple[Dict[Any, Any], List[Any]]
    ):
        self._compute_attrs(extra_indexing_constraints=extra_indexing_constraints)

    def debug_str_extra(self) -> str:
        name = self.get_name()
        lines = [
            f"{name}.group.device = {self.group[0]}",
            f"{name}.group.iteration = {self.group[1]}",
            f"{name}.sizes = {self._sizes}",
        ]
        if self.get_aliases():
            lines.append(f"{name}.aliases = {pformat(self.get_aliases())}")
        if self.get_mutations():
            lines.append(f"{name}.mutations = {pformat(self.get_mutations())}")
        if isinstance(self._body, ir.LoopBody):
            lines.append(f"class {name}_loop_body:")
            lines.append(textwrap.indent(self._body.debug_str(), "    "))
        return "\n".join(lines)

    def get_ranges(self):
        return self._sizes

    def is_reduction(self):
        assert isinstance(
            self.node, (ir.ComputedBuffer, ir.TemplateBuffer)
        ), f"{type(self.node)=}"
        return bool(self.node.get_reduction_type())

    def is_split_scan(self):
        assert isinstance(
            self.node, (ir.ComputedBuffer, ir.TemplateBuffer)
        ), f"{type(self.node)=}"
        return isinstance(self.node, ir.ComputedBuffer) and isinstance(
            self.node.data, ir.SplitScan
        )

    def is_template(self):
        return isinstance(self.node, ir.TemplateBuffer)

    def get_template_node(self):
        return self.node if self.is_template() else None

    def run(self, *index_vars):
        self.decide_inplace_update()
        self.mark_run()
        self.codegen(index_vars)

    def mark_run(self):
        self.allocate()

    def ranges_from_index_vars(self, index_vars):
        sizes = self._sizes
        assert sum(map(len, sizes)) == sum(map(len, index_vars))
        var_ranges = dict(
            zip(
                itertools.chain.from_iterable(index_vars),
                itertools.chain.from_iterable(sizes),
            )
        )
        return var_ranges

    def codegen(self, index_vars):
        var_ranges = self.ranges_from_index_vars(index_vars)
        try:
            with V.set_ops_handler(
                SimplifyIndexing(V.get_ops_handler(), var_ranges)
            ), V.kernel.set_current_node(self):
                self._body(*index_vars)
        except Exception:
            log.fatal("Error in codegen for %s", self.node)
            raise

    def pointwise_read_writes(self):
        """
        Get the memory dependencies in the non-reduction axis.
        """
        sizes, reduction_sizes = self._sizes

        def fn(index):
            return self._body(index, [sympy.Integer(0) for _ in reduction_sizes])

        return dependencies.extract_read_writes(fn, sizes)

    def can_inplace(self, read_dep: dependencies.MemoryDep):
        if self.get_aliases() or self.is_template():
            return False
        if len(self.read_writes.writes) == 1 and isinstance(
            read_dep, dependencies.MemoryDep
        ):
            write_dep = next(iter(self.read_writes.writes))
            assert isinstance(write_dep, dependencies.MemoryDep), f"{type(write_dep)=}"
            return read_dep.index == write_dep.index and read_dep.size == write_dep.size
        return False

    @cache_on_self
    def _get_atomic_add_buffers(self) -> Set[str]:
        buffers_store_as_atomic_add = set()
        if isinstance(self._body, ir.LoopBody):
            for node in self._body.get_nodes():
                if (
                    node.op == "call_method"
                    and node.target == "store"
                    and (
                        ("mode" in node.kwargs and node.kwargs["mode"] == "atomic_add")
                        or (len(node.args) == 5 and node.args[4] == "atomic_add")
                    )
                ):
                    buffers_store_as_atomic_add.add(
                        node.kwargs["name"]
                        if "name" in node.kwargs
                        else (node.args[1] if len(node.args) >= 2 else "")
                    )
        return buffers_store_as_atomic_add

    def has_atomic_add(self, check_buf):
        return check_buf in self._get_atomic_add_buffers()


class FusedSchedulerNode(BaseSchedulerNode):
    """
    This is a "fake" scheduler node that represents a group of scheduler nodes
    that are meant to be fused together. The way it does this is by maintaining
    its unmet dependencies as the union of its constituent nodes.
    """

    @classmethod
    def fuse(cls, node1: BaseSchedulerNode, node2: BaseSchedulerNode):
        assert node1.scheduler is node2.scheduler
        assert isinstance(node1, (SchedulerNode, FusedSchedulerNode)) and isinstance(
            node2, (SchedulerNode, FusedSchedulerNode)
        )
        return cls(node1.scheduler, list(node1.get_nodes()) + list(node2.get_nodes()))  # type: ignore[arg-type]

    def __init__(self, scheduler: "Scheduler", snodes: List[SchedulerNode]):
        # NB: No need to call super().__init__() because we don't need to re-use any of its logic.
        self.snodes = snodes
        self.scheduler = scheduler
        self.node: ir.Buffer = None  # type: ignore[assignment]
        self.users: List[NodeUser] = []
        self.inverse_users = []
        self.node_users = []
        self.group = max(snodes, key=lambda x: int(x.is_reduction())).group
        self.ancestors = set.union(
            *[x.ancestors for x in snodes if x.ancestors is not None]
        )

        self.set_read_writes(
            dependencies.ReadWrites.merge_list([x.read_writes for x in snodes])
        )

        self.unmet_dependencies = {
            dep
            for dep in set.union(*[x.unmet_dependencies for x in snodes])
            if dep.name not in self.get_names()
        } - self.read_writes.writes
        self.min_order = min([x.min_order for x in self.snodes])
        self.max_order = max([x.max_order for x in self.snodes])

    @cache_on_self
    def get_name(self) -> str:
        return "_".join([x.get_name() for x in self.snodes])

    def get_first_name(self) -> str:
        return self.snodes[0].get_name()

    @cache_on_self
    def get_names(self) -> Set[str]:
        return set.union(*[x.get_names() for x in self.snodes])

    def debug_str_extra(self) -> str:
        lines = [
            f"{self.get_name()}.snodes[{i}] =\n{node.debug_str()}"
            for i, node in enumerate(self.snodes)
        ]
        return textwrap.indent("\n".join(lines).rstrip(), "    ")

    def set_last_usage(
        self, future_used_buffers: Set[str], mutation_real_name: Dict[str, str]
    ):
        # Set self.last_usage using the global information
        # This will be used for inter-kernel optimisations
        super().set_last_usage(future_used_buffers, mutation_real_name)
        # Set self.last_usage on the snodes
        # This will be used for optimisations within the kernel
        future_used_buffers: Set[str] = set()
        for node in reversed(self.snodes):
            node.set_last_usage(future_used_buffers, mutation_real_name)
            future_used_buffers.update(node.last_usage)  # type: ignore[arg-type]

    @cache_on_self
    def used_buffer_names(self) -> Set[str]:
        return set.union(*[x.used_buffer_names() for x in self.snodes])

    @cache_on_self
    def used_or_aliased_buffer_names(self) -> Set[str]:
        return set.union(*[x.used_or_aliased_buffer_names() for x in self.snodes])

    def get_nodes(self) -> List[SchedulerNode]:
        return self.snodes

    def __repr__(self):
        return f"{type(self).__name__}(nodes={self.get_name()})"

    @cache_on_self
    def is_reduction(self):
        return any(x.is_reduction() for x in self.snodes)

    @cache_on_self
    def is_split_scan(self):
        return any(x.is_split_scan() for x in self.snodes)

    @cache_on_self
    def is_template(self):
        return any(x.is_template() for x in self.snodes)

    @cache_on_self
    def get_template_node(self):
        for node in self.snodes:
            if node.is_template():
                return node
        return None

    def get_device(self):
        return self.group[0]

    @cache_on_self
    def has_aliasing_or_mutation(self):
        return any(x.has_aliasing_or_mutation() for x in self.snodes)

    @cache_on_self
    def op_counts(self):
        op_counts: Counter[str] = collections.Counter()
        for node in self.snodes:
            op_counts.update(node.op_counts())
        return op_counts

    def has_atomic_add(self, check_buf):
        return any(
            (
                isinstance(sub_schedule_node1, SchedulerNode)
                and sub_schedule_node1.has_atomic_add(check_buf)
            )
            for sub_schedule_node1 in self.get_nodes()
        )

    # None of these need to be implemented, as a FusedSchedulerNode is just an
    # abstraction for scheduling purposes
    def update_mutated_names(self, renames: Dict[str, str]):
        raise NotImplementedError

    def add_mutation_dep(self, name):
        raise NotImplementedError

    def set_users(self, users: List["NodeUser"]):
        raise NotImplementedError

    def get_aliases(self):
        raise NotImplementedError

    def get_mutations(self):
        raise NotImplementedError

    def can_inplace(self, read_dep: dependencies.MemoryDep):
        raise NotImplementedError

    def allocate(self):
        raise NotImplementedError

    def can_free(self):
        raise NotImplementedError

    def debug_str(self) -> str:
        """Longer form printout for trace logs"""
        name = self.get_name()
        node_typestr = ",".join(type(n).__name__ for n in self.snodes)
        lines = [
            f"{name}: {type(self).__name__}({node_typestr})",
            f"{name}.writes = {pformat(self.read_writes.writes)}",
            f"{name}.unmet_dependencies = {pformat(self.unmet_dependencies)}",
            f"{name}.met_dependencies = {pformat(self.read_writes.reads - self.unmet_dependencies)}",
            f"{name}.users = {self.users}",
        ]
        try:
            lines += [
                self.debug_str_extra(),
            ]
        except Exception:
            log.warning("Ignoring error in debug_str()", exc_info=True)

        return "\n".join(lines).rstrip()


class ForeachKernelSchedulerNode(FusedSchedulerNode):
    """Scheduler node which consists of a list of scheduler nodes that each operate on a
    distinct tensor in a list of tensors."""

    def get_consumer_subnode_for(self, producer):
        if producer.get_name() in self.read_to_node:
            return self.read_to_node[producer.get_name()]

        return None

    def get_producer_subnode_for(self, consumer):
        for rd in consumer.read_writes.reads:
            if rd.name in self.name_to_node:
                return self.name_to_node[rd.name]

        return None

    @classmethod
    def can_fuse(cls, producer, consumer):
        why = WhyNoFuse(producer, consumer)
        if producer.is_foreach() and consumer.is_foreach():
            foreach_match = len(producer.snodes) == len(consumer.snodes)
            if not foreach_match:
                why("foreach do not have same length")
            return foreach_match and all(
                producer.scheduler.can_fuse(l, r)
                for l, r in zip(producer.snodes, consumer.snodes)
            )
        elif consumer.is_foreach():
            consumer_subnode = consumer.get_consumer_subnode_for(producer)
            if consumer_subnode is not None:
                return consumer.scheduler.can_fuse(producer, consumer_subnode)

            why("candidate producer is not dep of any foreach consumer")
            return False

        elif producer.is_foreach():
            producer_subnode = producer.get_producer_subnode_for(consumer)
            if producer_subnode is not None:
                return producer.scheduler.can_fuse(producer_subnode, consumer)

            why("candidate consumer has no dep in any foreach producer")
            return False

        raise AssertionError(
            "At least one node passed to ForeachKernelSchedulerNode.can_fuse should be a foreach node"
        )

    @classmethod
    def fuse(cls, producer, consumer):
        assert producer.is_foreach() or consumer.is_foreach()
        prev_node_1 = None
        prev_node_2 = None
        if producer.is_foreach() and consumer.is_foreach():
            fused_nodes = [
                FusedSchedulerNode.fuse(l, r)
                for l, r in zip(producer.snodes, consumer.snodes)
            ]
        elif producer.is_foreach():
            producer_subnode = producer.get_producer_subnode_for(consumer)
            fused_nodes = []
            prev_node_1 = producer
            prev_node_2 = None
            for node in producer.snodes:
                if node is producer_subnode:
                    new_node = FusedSchedulerNode.fuse(node, consumer)
                    prev_node_2 = new_node
                    fused_nodes.append(new_node)
                else:
                    fused_nodes.append(node)

        elif consumer.is_foreach():
            consumer_subnode = consumer.get_consumer_subnode_for(producer)
            fused_nodes = []
            prev_node_1 = consumer
            prev_node_2 = None

            for node in consumer.snodes:
                if node is consumer_subnode:
                    new_node = FusedSchedulerNode.fuse(producer, node)
                    prev_node_2 = new_node
                    fused_nodes.append(new_node)
                else:
                    fused_nodes.append(node)

        return cls(producer.scheduler, fused_nodes, prev_node_1, prev_node_2)  # type: ignore[possibly-undefined]

    def __init__(
        self,
        scheduler: "Scheduler",
        nodes: List[SchedulerNode],
        prev_node_1=None,
        prev_node_2=None,
    ):
        self.read_to_node = {}
        self.name_to_node = {}

        if prev_node_1 is None or prev_node_2 is None:
            super().__init__(scheduler, nodes)

            for node in nodes:
                for read in node.read_writes.reads:
                    self.read_to_node[read.name] = node

                for name in node.get_names():
                    self.name_to_node[name] = node
        else:
            self.scheduler = scheduler
            self.snodes = nodes
            self.node: ir.Buffer = None  # type: ignore[assignment]
            self.users: List[NodeUser] = []

            self.set_read_writes(
                dependencies.ReadWrites.merge_list(
                    [prev_node_1.read_writes, prev_node_2.read_writes]
                )
            )

            self.unmet_dependencies = {
                dep
                for dep in set.union(
                    prev_node_1.unmet_dependencies, prev_node_2.unmet_dependencies
                )
                if dep.name not in self.get_names()
            } - self.read_writes.writes

            self.min_order = min([prev_node_1.min_order, prev_node_2.min_order])
            self.max_order = max([prev_node_1.max_order, prev_node_2.max_order])

            foreach_node = prev_node_1 if prev_node_1.is_foreach() else prev_node_2
            other_node = prev_node_2 if prev_node_1.is_foreach() else prev_node_1

            self.ancestors = foreach_node.ancestors
            self.ancestors.update(other_node.ancestors)

            self.name_to_node = foreach_node.name_to_node
            for name in other_node.get_names():
                self.name_to_node[name] = other_node

        self.group = (nodes[0].get_device(), "foreach")

        self.origins: Set[torch.fx.Node] = set()

    def mark_run(self):
        raise NotImplementedError

    def codegen(self):
        assert isinstance(self.node, ir.ComputedBuffer), f"{type(self.node)=}"
        self.node.get_store_function()(self.node.make_loader()())

    def can_free(self):
        return NotImplementedError

    def is_foreach(self):
        return True

    def get_subkernel_nodes(self):
        """Returns a list of nodes which comprise the foreach kernel, operating on corresponding elements of our input lists.
        These nodes may be vertically fused."""
        return list(self.snodes)

    def get_nodes(self):
        """Returns all nodes contained in this kernel, unpacking fused nodes into their constituent scheduler nodes."""
        return list(itertools.chain.from_iterable(x.get_nodes() for x in self.snodes))

    def get_first_name(self):
        return self.snodes[0].get_first_name()

    def prune_redundant_deps(self, name_to_fused_node):
        _prune_redundant_deps(self, name_to_fused_node)

        for node in self.snodes:
            node.prune_redundant_deps(name_to_fused_node)


def pick_loop_order(stride_lengths, sizes, priority_idx=()):
    """
    A heuristic to decide loop iteration orders.  This has not been well
    tuned and may be something we should autotune.
    """

    @functools.cmp_to_key
    def index_cmp(a, b):
        if sizes[a] == 1 or sizes[b] == 1:
            # 1-sizes don't matter, just move them to the end
            return cmp(sizes[a] == 1, sizes[b] == 1)

        stride_len_a = [sl[a] for sl in stride_lengths]
        stride_len_b = [sl[b] for sl in stride_lengths]

        # equivalent to
        # np.logical_or(stride_lengths[:, b] == 0, stride_lengths[:, a] < stride_lengths[:, b]).all()
        a_first = sum(
            sl_b == 0 or sl_a < sl_b for sl_a, sl_b in zip(stride_len_a, stride_len_b)
        )
        b_first = sum(
            sl_a == 0 or sl_b < sl_a for sl_a, sl_b in zip(stride_len_a, stride_len_b)
        )
        if a_first > b_first:
            return -1
        if b_first > a_first:
            return 1

        # otherwise contiguous
        return cmp(b, a)

    order = list(reversed(range(len(stride_lengths[0]))))
    if len(priority_idx) > 0:
        # if we have priority node, only use that node's order
        stride_lengths = [stride_lengths[pi] for pi in priority_idx]
    if config.pick_loop_orders:
        order.sort(key=index_cmp)
    return order


@dataclasses.dataclass
class NodeUser:
    node: BaseSchedulerNode
    can_inplace: bool = False

    # A weak user must be scheduled after a given node, but doesn't actually
    # use the result
    is_weak: bool = False

    def __hash__(self):
        return hash((self.node.get_name(), self.can_inplace, self.is_weak))

    def __eq__(self, other):
        return (
            self.get_name() == other.get_name()
            and self.can_inplace == other.can_inplace
            and self.is_weak == other.is_weak
        )

    def get_name(self):
        return self.node.get_name()

    def merge(self, other: "NodeUser") -> "NodeUser":
        assert self.node is other.node
        return NodeUser(
            self.node,
            self.can_inplace and other.can_inplace,
            self.is_weak and other.is_weak,
        )


_post_grad_graph_counter = itertools.count()


class Scheduler:
    @dynamo_timed
    def __init__(self, nodes):
        super().__init__()
        self.backends = {}
        self.fuse_cache = {}
        self.post_grad_graph_id = next(_post_grad_graph_counter)

        self.nodes = []
        self.available_buffer_names = {
            *V.graph.graph_inputs.keys(),
            *V.graph.constants.keys(),
        }

        self.nodes = [self.create_scheduler_node(n) for n in nodes]

        # some new constants could have been created above
        self.available_buffer_names.update(V.graph.constants.keys())
        for node in self.nodes:
            node.prune_deps()

        self.name_to_node: Dict[str, BaseSchedulerNode] = {
            n.get_name(): n for n in self.nodes
        }
        self.name_to_fused_node: Dict[
            str, BaseSchedulerNode
        ] = dict()  # set in fuse_nodes()

        # mutation_real_name: Maps back to the original name for codegen
        # Example:
        # If you mutate buf0 inside of buf1's kernel, then:
        # mutation_real_name = {"buf0" : "buf1"}
        # all subsequent uses of buf0 become buf1's usage in dependency graph
        self.mutation_real_name = {}

        # We handle mutation by renaming modified versions of the same
        # buffer in the dependency graph to prevent cycles.
        # mutation_renames: tracks the current name for a given buffer
        #                   (changed once per mutation)
        # Example:
        # If you mutate buf0 inside of buf1's kernel, then:
        # mutation_renames = {"buf1" : "buf0"}
        # in codegen we only use buf0, never buf1
        self.mutation_renames = {}

        self.compute_dependencies()
        self.topological_sort_schedule()
        self.dead_node_elimination()
        if config.reorder_for_compute_comm_overlap:
            comms.decide_global_ordering_of_comms(self.nodes)
        self.compute_ancestors()

        metrics.ir_nodes_pre_fusion += len(self.nodes)
        V.debug.ir_pre_fusion(self.nodes)
        self.num_orig_nodes = len(self.nodes)
        self.name_to_fused_node = {n.get_name(): n for n in self.nodes}
        self.create_foreach_nodes()
        self.topological_sort_schedule()
        self.logged_slow_fusion = set()
        self.fuse_nodes()
        if config.reorder_for_compute_comm_overlap:
            # Refresh node_users and inverse_users to reflect fused nodes
            self.compute_node_users()
            self.nodes = comms.reorder_compute_and_comm_for_overlap(self.nodes)
        self.compute_last_usage()
        V.debug.ir_post_fusion(self.nodes)
        V.debug.graph_diagram(self.nodes)
        self.debug_draw_graph()

        # used during codegen:
        self.current_device: torch.device = None  # type: ignore[assignment]
        self.buffer_names_to_free = set()

        # fx graph node to the position it appears in the graph
        # for debug attribution
        self.origin_to_index = {}

        get_metric_table("graph_stats").add_row(
            lambda: {
                "graph_id": self.post_grad_graph_id,
                "num_nodes_before_fusion": self.num_orig_nodes,
                "num_nodes_after_fusion": len(self.nodes),
            }
        )

    def debug_draw_graph(self):
        """Generate an image of the graph for debugging"""
        if os.environ.get("INDUCTOR_WRITE_SCHEDULER_GRAPH", None) == "1":
            from .debug import draw_buffers

            draw_buffers(self.nodes, print_graph=True)

    def debug_print_nodes(self, label):
        if log.isEnabledFor(logging.INFO):
            log.info("%s:", label)
            for node in self.nodes:
                node.log_details()

    def create_scheduler_node(self, node):
        assert (
            node.origins is not None
        ), "All nodes passed to scheduling must have an origin"
        if node.is_no_op():
            return NopKernelSchedulerNode(self, node)
        elif isinstance(node, (ir.ComputedBuffer, ir.TemplateBuffer)):
            return SchedulerNode(self, node)
        elif isinstance(node, ir.ExternKernel):
            return ExternKernelSchedulerNode(self, node)
        else:
            raise NotImplementedError(node)

    def create_foreach_nodes(self):
        removed_node_names = set()
        fe_nodes = []
        kept_node_names = self.name_to_fused_node.keys()

        for names in V.graph.lists.values():
            names = [
                name
                for name in names
                if name in kept_node_names
                and not isinstance(self.name_to_node[name], NopKernelSchedulerNode)
            ]
            if not names:
                # All nodes eliminated
                continue

            removed_node_names.update(names)
            snodes = [self.name_to_node[name] for name in names]

            fe_node = ForeachKernelSchedulerNode(self, snodes)  # type: ignore[arg-type]

            fe_nodes.append(fe_node)

            for name in names:
                self.name_to_fused_node[name] = fe_node

        self.nodes = [
            node for node in self.nodes if node.get_name() not in removed_node_names
        ] + fe_nodes

    def compute_dependencies(self):
        """
        Create dependency edges between nodes, handling aliasing and
        mutation properly.
        """

        T = TypeVar("T")

        class DedupList(Generic[T]):
            """
            This data structure behaves like a list except it makes sure the
            elements remain unique.
            Normally one could use a set/dict for this purpose however
            the list in question gets elements appended as it is being
            iterated over which means that we need to keep the list
            semantics.
            """

            def __init__(self, items=None, membership=None):
                self.items = items or list()
                self.membership = membership or set()

            def append(self, node_user: T) -> None:
                if node_user in self.membership:
                    return
                self.items.append(node_user)
                self.membership.add(node_user)

            def __add__(self, other: "DedupList[T]") -> "DedupList[T]":
                new_membership = set.union(self.membership, other.membership)
                new_items = self.items + [
                    x for x in other.items if x not in self.membership
                ]
                return DedupList(new_items, new_membership)

        name_to_users: DefaultDict[str, DedupList[NodeUser]] = collections.defaultdict(
            DedupList
        )

        # handle aliasing by using python aliasing in name_to_users
        # if foo aliases bar then we will make name_to_users["foo"] point
        # to the same python list as name_to_users["bar"]
        for node1 in self.nodes:
            node1_name = node1.get_name()
            for node2_name in node1.get_aliases():
                if node1_name in name_to_users and node2_name in name_to_users:
                    # merge the two
                    list1 = name_to_users[node1_name]
                    list2 = name_to_users[node2_name]
                    combined = list1 + list2
                    for key in name_to_users.keys():
                        if name_to_users[key] is list1 or name_to_users[key] is list2:
                            name_to_users[key] = combined
                elif node1_name in name_to_users:
                    name_to_users[node2_name] = name_to_users[node1_name]
                else:
                    name_to_users[node1_name] = name_to_users[node2_name]

        def rename(n):
            if n in self.mutation_renames:
                return rename(self.mutation_renames[n])
            return n

        def dep_closure(node_name):
            reachable_names = {node_name}
            node = self.name_to_node[node_name]
            write_dep = next(iter(node.read_writes.writes))
            for read_dep in node.read_writes.reads:
                if (
                    read_dep.name in self.name_to_node
                    and isinstance(read_dep, dependencies.MemoryDep)
                    and isinstance(write_dep, dependencies.MemoryDep)
                    and read_dep.index == write_dep.index
                    and read_dep.size == write_dep.size
                ):
                    reachable_names.update(dep_closure(read_dep.name))
            return reachable_names

        def add_user(used_by_name, user_node, can_inplace=False, is_weak=False):
            name_to_users[rename(used_by_name)].append(
                NodeUser(user_node, can_inplace, is_weak)
            )

        unbacked_symbol_to_origin_node = {}

        for node in self.nodes:
            log.debug("scheduling %s", node.node)

            # unbacked symbols don't follow ordinary buffer dependencies, so
            # we track their def/uses separately
            unbacked_symbol_defs = sorted(
                node.node.get_unbacked_symbol_defs(), key=lambda x: x.name
            )
            for s in unbacked_symbol_defs:
                assert isinstance(s, sympy.Symbol)
                # Pick the first definer as canonical.  There may be multiple
                # because if a MultiOutputLayout buffer propagates an unbacked
                # symint to multiple outputs, they will all claim to def it.
                if s not in unbacked_symbol_to_origin_node:
                    unbacked_symbol_to_origin_node[s] = node

            unbacked_symbol_uses = sorted(
                node.node.get_unbacked_symbol_uses(), key=lambda x: x.name
            )
            # if a kernel takes unbacked symints, register dependencies
            for s in unbacked_symbol_uses:
                assert (
                    s in unbacked_symbol_to_origin_node
                ), f"{s} not in {unbacked_symbol_to_origin_node}"
                node.add_fake_dep(StarDep(unbacked_symbol_to_origin_node[s].get_name()))

            # a node will mutate either 0 or 1 buffers
            assert len(node.get_mutations()) <= 1
            for alt_name in node.get_mutations():
                alt_name = rename(alt_name)
                # this node must run after the prior writer
                add_user(alt_name, node)
                node.add_mutation_dep(StarDep(alt_name))
                for other_node in name_to_users[alt_name].items:
                    # this node must run after all prior readers
                    other_name = rename(other_node.get_name())
                    known_dep_node_names = dep_closure(node.get_name())
                    if other_name not in known_dep_node_names:
                        # If this node already directly or indirectly depends on other_node,
                        # we don't need to insert an extra dep.
                        node.add_mutation_dep(WeakDep(other_name))
                        add_user(other_name, node, is_weak=True)

            # add normal non-mutation dependencies
            for read in node.read_writes.reads:
                is_weak = isinstance(read, WeakDep)
                add_user(read.name, node, node.can_inplace(read), is_weak)

            node.update_mutated_names(self.mutation_renames)

            # update our renaming scheme for the next iteration
            for alt_name in node.get_mutations():
                self.mutation_renames[rename(alt_name)] = node.get_name()
                self.mutation_renames[alt_name] = node.get_name()
                self.mutation_real_name[node.get_name()] = self.mutation_real_name.get(
                    alt_name, alt_name
                )

        # make sure outputs aren't dead-code-eliminated
        for node_name in V.graph.get_output_names():
            log.debug("scheduling output %s", node_name)
            add_user(node_name, OutputNode(StarDep(node_name)))

        # make sure unbacked symints aren't dead-code-eliminated
        for node in V.graph.graph_outputs:
            for s in node.get_unbacked_symbol_uses():
                assert (
                    s in unbacked_symbol_to_origin_node
                ), f"{s} not in {unbacked_symbol_to_origin_node.keys()}"
                node_name = unbacked_symbol_to_origin_node[s].node.name
                log.debug("scheduling output %s for unbacked symint %s", node_name, s)
                add_user(node_name, OutputNode(StarDep(node_name)))

        # make sure input mutation isn't dead-code-eliminated
        for name in self.mutation_renames:
            if name in V.graph.graph_inputs:
                add_user(name, OutputNode(StarDep(name)))
                V.graph.mutated_inputs.add(name)

        inp_names = {
            name: index for index, name in enumerate(V.graph.graph_inputs.keys())
        }
        V.graph.mutated_input_idxs = [
            inp_names[name] for name in V.graph.mutated_inputs
        ]

        # copy users information onto the nodes
        for node in self.nodes:
            node.set_users(name_to_users[node.get_name()].items)

        # populate inverse_users
        for node in self.nodes:
            for user in node.users:
                user.node.inverse_users.append(node)

    def compute_node_users(self):
        # set up buffer name to (fused)snode mapping
        buf_to_snode = {}
        for node in self.nodes:
            if isinstance(node, FusedSchedulerNode):
                for x in node.snodes:
                    buf_to_snode[x.get_name()] = node
            buf_to_snode[node.get_name()] = node

        for node in self.nodes:
            node.node_users = []
            node.inverse_users = []

        # compute inverse_users
        for node in self.nodes:
            inverse_users = []
            for dep in node.unmet_dependencies:
                assert dep.name in buf_to_snode
                dep_node = buf_to_snode[dep.name]
                inverse_users.append(dep_node)
            node.inverse_users = inverse_users

        # compute node_users
        # TODO: ideally, we should deduplicate .users and .node_users,
        # but currently .users contains extra information that's difficult to
        # extract into a standalone container.
        node_to_users: Dict[BaseSchedulerNode, List[BaseSchedulerNode]] = {}
        for node in self.nodes:
            for inverse_user in node.inverse_users:
                node_to_users.setdefault(inverse_user, []).append(node)
        for node, users in node_to_users.items():
            node.node_users = users

    def dead_node_elimination(self):
        """
        Remove any nodes without users
        """
        again = True  # repeat until a fixed point
        while again:
            updated_nodes = []
            for node in self.nodes:

                def can_eliminate_user(user: NodeUser):
                    return user.is_weak or user.get_name() in V.graph.removed_buffers

                can_eliminate = not node.has_side_effects() and all(
                    can_eliminate_user(u) for u in node.users
                )

                if not can_eliminate:
                    updated_nodes.append(node)
                else:
                    # dead code
                    log.debug("removed dead node: %s", node.get_name())
                    V.graph.removed_buffers.add(node.get_name())

            again = len(self.nodes) > len(updated_nodes)
            self.nodes = updated_nodes

        # Prune any WeakDeps no longer needed
        for node in self.nodes:
            node.prune_weak_deps()

    def topological_sort_schedule(self):
        """
        Ensure self.nodes is in topologically sorted order
        """
        seen: Set[ir.Buffer] = set()
        name_to_node: Dict[str, ir.Buffer] = dict()
        result: List[ir.Buffer] = []

        def visit(n):
            if n not in seen:
                seen.add(n)
                for dep in sorted(n.unmet_dependencies, key=lambda d: d.name):
                    visit(name_to_node[dep.name])
                result.append(n)

        for node in self.nodes:
            for name in node.get_names():
                name_to_node[name] = node
        for node in self.nodes:
            visit(node)
        self.nodes = result

    def compute_ancestors(self):
        """
        Populate each node.ancestors
        """
        # note self.nodes is topologically sorted
        name_to_ancestors: Dict[str, Set[str]] = {}
        for node in self.nodes:
            ancestors = set()
            for dep in node.unmet_dependencies:
                ancestors.add(dep.name)
                ancestors |= name_to_ancestors[dep.name]
            name_to_ancestors[node.get_name()] = ancestors
            node.ancestors = ancestors

        for order, node in enumerate(self.nodes):
            node.min_order = order
            node.max_order = order

    def fuse_nodes(self):
        """
        Mutates self.nodes to combine nodes into FusedSchedulerNodes.
        """
        for i in range(10):
            old_len = len(self.nodes)
            fusion_log.debug(
                "===== attempting fusion (%d/10): %d nodes =====", i + 1, old_len
            )
            self.fuse_nodes_once()
            new_len = len(self.nodes)
            fusion_log.debug(
                "completed fusion round (%d/10): fused %d nodes into %d nodes\n",
                i + 1,
                old_len,
                new_len,
            )
            if new_len == old_len or new_len == 1:
                fusion_log.debug("===== fusion complete (%d iterations) =====", i + 1)
                break

    def benchmark_fused_nodes(self, nodes):
        """
        Benchmark fused list of nodes and return the execution time
        in milliseconds on randomly generated inputs.
        """
        assert len(nodes) > 0
        device = nodes[0].get_device()
        V.graph.scheduler = self
        self.current_device = device
        backend = self.get_backend(device)
        return backend.benchmark_fused_nodes(nodes)

    def speedup_by_fusion(self, node1, node2):
        """
        If config.benchmark_fusion is False, always return True.
        Otherwise, return True if fusion can brings speedup.
        """
        if not config.benchmark_fusion:
            return True

        if (
            node1.is_template()
            and not isinstance(node1.get_template_node(), ir.TritonTemplateBuffer)
            or node1.is_foreach()
            or node2.is_foreach()
        ):
            # TODO support benchmarking epilogue fusion
            return True

        node_list_1 = node1.get_nodes()
        device = node_list_1[0].get_device()

        # don't support benchmark fusion for CPU right now.
        if device.type == "cpu":
            return True

        node_list_2 = node2.get_nodes()
        node_list_fused = node_list_1 + node_list_2

        # We can not accurately benchmark kernel using atomic_add
        # due to how we generate random integer inputs.
        # Skip benchmarking them by allowing fusion.
        if any(
            hasattr(n.node, "data")
            and hasattr(n.node.data, "scatter_mode")
            and n.node.data.scatter_mode == "atomic_add"
            for n in node_list_fused
        ):
            return True

        from triton.compiler.errors import CompilationError

        why = WhyNoFuse(node1, node2)

        try:
            ms1, path1 = self.benchmark_fused_nodes(node_list_1)
            if math.isinf(ms1):
                why("register spilling of the first kernel")
                return False
            ms2, path2 = self.benchmark_fused_nodes(node_list_2)
            if math.isinf(ms2):
                why("register spilling of the second kernel")
                return False
            ms_fused, path_fused = self.benchmark_fused_nodes(node_list_fused)
            if math.isinf(ms_fused):
                why("register spilling of the fused kernel")
                return False
        except CompilationError as e:
            # workaround triton issue: https://github.com/openai/triton/issues/2151
            if "Loop-carried variable" in str(e):
                return True  # allow fusion
            else:
                raise

        if fusion_log.isEnabledFor(logging.DEBUG):
            if ms_fused < ms1 + ms2:
                fusion_log.debug(
                    "can fuse (benchmark): fusing %s with %s cause %sx speedup",
                    node1.get_names(),
                    node2.get_names(),
                    green_text(f"{(ms1 + ms2) / ms_fused:.3f}"),
                )
            else:
                fusion_log.debug(
                    "cannot fuse (benchmark): fusing %s with %s cause %sx slowdown",
                    node1.get_names(),
                    node2.get_names(),
                    red_text(f"{ms_fused / (ms1 + ms2):.3f}"),
                )

        if (
            is_metric_table_enabled("slow_fusion")
            and ms_fused >= ms1 + ms2
            and (path1, path2) not in self.logged_slow_fusion
        ):
            self.logged_slow_fusion.add((path1, path2))
            get_metric_table("slow_fusion").add_row(
                lambda: {
                    "kernel1_path": path1,
                    "kernel1_latency": ms1,
                    "kernel2_path": path2,
                    "kernel2_latency": ms2,
                    "fused_kernel_path": path_fused,
                    "fused_kernel_latency": ms_fused,
                    "slow_down_ratio": ms_fused / (ms1 + ms2),
                }
            )
        return ms_fused < ms1 + ms2

    def fuse_nodes_once(self):
        """
        Mutates self.nodes to combine nodes into FusedSchedulerNodes.

        This relies on two key functions to control the logic:
            - self.can_fuse(): checks if a fusion is legal
            - self.score_fusion(): assigns priority to a given fusion
        """
        fused_nodes = set(self.nodes)
        for node1, node2 in self.get_possible_fusions():
            node1 = self.name_to_fused_node[node1.get_first_name()]
            node2 = self.name_to_fused_node[node2.get_first_name()]
            if self.can_fuse(node1, node2) and not self.will_fusion_create_cycle(
                node1, node2
            ):
                if not self.speedup_by_fusion(node1, node2):
                    continue
                fusion_log.debug(
                    "fusing %s with %s", node1.get_name(), node2.get_name()
                )

                # above can_fuse asserts that node2 has the same device
                device = node1.get_device()
                node3 = self.get_backend(device).fuse(node1, node2)
                fused_nodes.remove(node1)
                fused_nodes.remove(node2)
                fused_nodes.add(node3)
                self.name_to_fused_node.update(
                    {n.get_name(): node3 for n in node3.get_nodes()}
                )
        self.nodes = sorted(fused_nodes, key=lambda x: x.min_order)
        self.topological_sort_schedule()
        self.prune_redundant_deps()

    def prune_redundant_deps(self):
        for node in self.nodes:
            node.prune_redundant_deps(self.name_to_fused_node)

    def get_possible_fusions(self):
        """
        Helper to find all legal fusion opportunities, sorted by self.score_fusion()
        """
        possible_fusions = []
        seen = set()

        def check_all_pairs(nodes):
            for node1_index, node1 in enumerate(nodes):
                for node2 in nodes[node1_index + 1 :]:
                    key = (node1, node2)
                    if key in seen:
                        continue
                    seen.add(key)

                    if self.can_fuse(node1, node2):
                        possible_fusions.append(key)
                    elif (node2.is_template() or node2.is_foreach()) and self.can_fuse(
                        node2, node1
                    ):
                        # foreach fusions and epilogue fusions are order dependent
                        possible_fusions.append((node2, node1))

        buffer_names_grouping = collections.defaultdict(list)
        for node in self.nodes:
            for buf in node.used_buffer_names():
                buffer_names_grouping[buf].append(node)
        for node_grouping in buffer_names_grouping.values():
            check_all_pairs(node_grouping)

        if config.aggressive_fusion:
            group_grouping = collections.defaultdict(list)
            for node in self.nodes:
                group = getattr(node, "group", None)
                if group:
                    group_grouping[group].append(node)
            for node_grouping in group_grouping.values():
                check_all_pairs(node_grouping)

        possible_fusions.sort(key=self.score_fusion_key, reverse=True)
        fusion_log.debug("found %d possible fusions", len(possible_fusions))
        return possible_fusions

    def will_fusion_create_cycle(self, node1, node2):
        """
        Finds whether there's a path from node1 to node2 (or vice-versa)
        caused indirectly by other fusions.
        """

        def found_path(node):
            # only fused nodes can introduce new ancestors.
            if isinstance(node, FusedSchedulerNode) and node not in visited:
                visited.add(node)
                if node.get_names().issubset(combined_ancestors):
                    # All fusion outputs are in ancestors of node1 and node2, thus
                    # cannot introduce new path:
                    #
                    # 1. if output is neither descendent of node1 or node2, the
                    #        output cannot introduce a path
                    # 2. due to [can_fuse]: if WLOG output is descendent of node1, it cannot be
                    #        on path(node1->node2), hence it cannot be ancestor of node2
                    # 3. due to [acyclic]: if WLOG output is descendent of node1, it cannot be
                    #        ancestor of node1
                    return False
                else:
                    # continue DFS of new ancestors introduced by the fusion
                    return bool(combined_names & node.ancestors) or any(
                        found_path(self.name_to_fused_node[n])
                        for n in node.ancestors - combined_ancestors
                    )
            return False

        visited = set()
        combined_names = node1.get_names() | node2.get_names()
        combined_ancestors = (node1.ancestors | node2.ancestors) - combined_names
        cycle = any(found_path(self.name_to_fused_node[n]) for n in combined_ancestors)
        if cycle:
            WhyNoFuse(node1, node2)("will create cycle")
        return cycle

    def can_fusion_increase_peak_memory(
        self, node1: BaseSchedulerNode, node2: BaseSchedulerNode
    ):
        """
        This function prevents fusion for nodes that can increase memory
        footprint. This problem is more common in horizontal fusion, where nodes
        that are far apart in the original order get fused, lengthening the live
        intervals of tensors. This is very evident in models with activation
        checkpointing, where the recomputed nodes from different checkpointed
        regions get fused and significantly increase the memory footprint.

        The current attempt is a quick, possibly hacky, heuristic to prevent the
        fusion of nodes that are far away in the original order.

        A better but difficult to implement heurisitic would be to use live
        intervals of the buffers, find region of peak pressure in the original
        program and prevent fusion that crosses that peak region. We might need
        special care or good approximation in this implementation, as fusion of
        node changes live intervals, and re-computing live intervals and peak
        memory after each fusion can introduce large compilation overhead.
        """
        proximity_score = max(
            abs(node1.min_order - node2.max_order),
            abs(node2.min_order - node1.max_order),
        )
        return proximity_score > 64

    def can_fuse(self, node1: BaseSchedulerNode, node2: BaseSchedulerNode):
        """
        Determine if it is possible to combine node1 and node2 into a
        single fused node.
        """

        if node1 is node2:
            return False

        why = WhyNoFuse(node1, node2)

        if (
            isinstance(node1, (ExternKernelSchedulerNode, NopKernelSchedulerNode))
            and not node1.is_template()
        ):
            why("node1 is extern or nop")
            return False
        if (
            isinstance(node2, (ExternKernelSchedulerNode, NopKernelSchedulerNode))
            and not node2.is_template()
        ):
            why("node2 is extern or nop")
            return False

        if node2.get_names() & node1.ancestors:
            why("node1 must go before node2")
            return False

        if (
            isinstance(node1, (FusedSchedulerNode, SchedulerNode))
            and isinstance(node2, SchedulerNode)
            and isinstance(node2._body, ir.LoopBody)
        ):
            # Fix issue: https://github.com/pytorch/pytorch/issues/108963
            # Check:
            #   If node2 reads a buf which is a mutation buf of node1(SchedulerNode) or among nodes in node1(FusedSchedulerNode),
            #   we will get the corresponding mutation buf and check if this mutation buf is stored by atomic_add mode.
            # If True, we will disable the fusion of node1 and node2.
            if any(
                (
                    node2_used_buf in self.mutation_renames
                    and node1.has_atomic_add(self.mutation_renames[node2_used_buf])
                )
                for node2_used_buf in node2._body.reads_name2expr.keys()
            ):
                return False

        if node2.is_template():
            why("templates can only fuse epilogues")
            return False
        if node1.is_template() and (
            node2.has_aliasing_or_mutation()
            or node2.is_reduction()
            or not config.epilogue_fusion
        ):
            why("template epilogue not satisfied")
            return False

        device = node1.get_device()
        device2 = node2.get_device()
        if device != device2:
            why("device mismatch (%s vs %s)", device, device2)
            return False
        del device2

        no_shared_data = self.score_fusion_memory(node1, node2) == 0
        if no_shared_data and (
            not config.aggressive_fusion or node1.is_reduction() or node2.is_reduction()
        ):
            why("no shared data")
            return False  # heuristic not needed for correctness

        if (
            not node1.is_foreach()
            and not node2.is_foreach()
            and len(node1.get_nodes()) + len(node2.get_nodes()) > config.max_fusion_size
        ):
            why("exceeds max fusion")
            return False  # heuristic not needed for correctness

        if node1.get_names() & node2.ancestors:
            # node2 depends on node1 outputs
            if not self.can_fuse_vertical(node1, node2):
                return False
            return self.get_backend(device).can_fuse_vertical(node1, node2)
        else:  # nodes don't depend on each other, but may have common reads
            if self.can_fusion_increase_peak_memory(node1, node2):
                why("will increase peak memory")
                return False
            return self.get_backend(device).can_fuse_horizontal(node1, node2)

    def can_fuse_vertical(self, node1, node2):
        """
        Check if it is legal to fuse a consumer (node2) into a producer (node1).

        We can fuse them if all the reads of node2 either match
        corresponding writes in node1, or are written by nodes that can
        be scheduled before the fusion of node1 and node2.

        We also disable fusion of a write subsequent to a read if the reads
        and writes do not align.
        """
        node1_names = node1.get_names()
        computed_deps = set()
        why = WhyNoFuse(node1, node2)

        # StarDep doesn't match MemoryDep, different indices don't match
        # However, broadcasting sometimes strips dimensions, and if that's the case
        # we still can match unmet dep
        # if there's indirect indexing, don't match it
        def fusable_read_and_write(read: Dep, write: Dep):
            return (
                self.mutation_renames.get(read.name, read.name) == write.name
                and (isinstance(read, MemoryDep) and isinstance(write, MemoryDep))
                and not free_symbol_has(read.index, "tmp")
                and not free_symbol_has(write.index, "tmp")
                and read.index == write.index
                and len(read.size) >= len(write.size)
                and read.size[: len(write.size)] == write.size
            )

        for rd in node2.unmet_dependencies:
            for cd in node1.read_writes.writes:
                if fusable_read_and_write(rd, cd):
                    computed_deps.add(rd)

        remaining_deps = {dep.name for dep in node2.unmet_dependencies - computed_deps}
        if remaining_deps & node1_names:
            # MemoryDeps didn't match and read different locations of the same buffer.
            # Examples here include:
            #   - MemoryDep("foo", x) != MemoryDep("foo", x + 1)
            #   - MemoryDep("foo", x) != StarDep("foo")
            why("memory deps did not match")
            return False
        for name in remaining_deps:
            if node1_names & self.name_to_fused_node[name].ancestors:
                why("intermediate nodes between node1 & node2")
                return False

        # similar to can_inplace, if we are going to fuse a write subsequent to a read
        # require that the indexing and size is the same
        for write in node2.read_writes.writes:
            for read in node1.read_writes.reads:
                if write.name != self.mutation_renames.get(read.name, read.name):
                    continue

                # bail on StarDep
                if not fusable_read_and_write(read=read, write=write):
                    why("fusing a write into a read with different indexing formula")
                    return False

        return True

    def score_fusion(self, node1: BaseSchedulerNode, node2: BaseSchedulerNode):
        """
        Assign a score (higher comes first) to the fusion of node1
        and node2.  When different fusions conflict with each other,
        this is the way we decide what order to run them in.

        Our current score is based on:
        - Estimate of the saved memory operations
        - Fusions closer together in original order
        """
        memory_score = self.score_fusion_memory(node1, node2)
        proximity_score = -max(
            abs(node1.min_order - node2.max_order),
            abs(node2.min_order - node1.max_order),
        )
        return (
            node1.is_template() == config.epilogue_fusion_first and memory_score > 0,
            node1.is_reduction() == node2.is_reduction() and memory_score > 0,
            memory_score,
            proximity_score,
        )

    def score_fusion_memory(self, node1, node2):
        """
        The first term in our fusion score that estimates number of saved memory operations.
        """
        common_memory_deps = (node1.read_writes.reads | node1.read_writes.writes) & (
            node2.read_writes.reads | node2.read_writes.writes
        )
        common_memory_deps = {
            dep for dep in common_memory_deps if not dep.has_unbacked_symbols()
        }
        return sum(dep.numbytes_hint() for dep in common_memory_deps)

    def score_fusion_key(self, nodes):
        """
        Shim for list.sort(key=...)
        """
        node1, node2 = nodes
        return self.score_fusion(node1, node2)

    def compute_last_usage(self):
        """
        Populate node.last_usage recursively (also for the nodes within a FusedSchedulerNode)
        """

        future_used_buffers = set()
        for node_name in V.graph.get_output_names():
            future_used_buffers.add(node_name)

        for node in reversed(self.nodes):
            node.set_last_usage(future_used_buffers, self.mutation_real_name)
            future_used_buffers.update(node.last_usage)

    def free_buffers(self):
        """Free any buffers that are no longer needed"""
        for name in sorted(
            self.buffer_names_to_free
            - V.graph.removed_buffers
            - V.graph.wrapper_code.freed
        ):
            if name in self.name_to_node:
                node = self.name_to_node[name]
                if node.can_free():
                    V.graph.wrapper_code.codegen_free(node.node)
            elif name in V.graph.graph_inputs:
                storage = V.graph.graph_inputs[name].data
                assert isinstance(storage, ir.StorageBox) and storage.is_input_buffer()
                V.graph.wrapper_code.codegen_free(storage.data)

        self.buffer_names_to_free.clear()

    def remove_kernel_local_buffers(self):
        """
        Any buffers that are both created and have a last use in the
        same kernel can be removed.
        """

        # V.kernel.store_buffer_names should represent the set of nodes
        # get fused
        fused_node_names = V.kernel.store_buffer_names
        names_to_remove = []
        for out_buf in V.kernel.store_buffer_names:
            users = self.name_to_node[out_buf].users
            assert users is not None
            users = {user.get_name() for user in users if not user.is_weak}
            if users.issubset(fused_node_names):
                names_to_remove.append(out_buf)

        def remove_filter(n):
            return (
                n not in V.kernel.must_keep_buffers
                and n not in V.kernel.args.input_buffers
                and n not in self.mutation_renames
                and n not in self.mutation_real_name
            )

        names_to_remove = list(filter(remove_filter, names_to_remove))

        for name in names_to_remove:
            if name in V.kernel.args.inplace_buffers:
                buf = V.kernel.args.inplace_buffers[name]
                if isinstance(buf, str) and buf.startswith("REMOVED"):
                    continue
                remove = all(n in names_to_remove for n in buf.other_names)
                if remove:
                    self.remove_inplace_buffer(name)
                V.kernel.inplaced_to_remove.add(name)
            else:
                self.remove_buffer(name)

    def remove_buffer(self, name):
        # Assign a special value instead of deleting the entry
        # because we still rely on output_buffers's length to
        # generate unique arg name.
        log.debug("remove_buffer(%r)", name)
        V.kernel.args.output_buffers[name] = "REMOVED"
        V.kernel.removed_buffers.add(name)

    def remove_inplace_buffer(self, name):
        log.debug("removing_inplace_buffer(%r)", name)
        inner_name = V.kernel.args.inplace_buffers[name].inner_name
        V.kernel.args.inplace_buffers[name] = inner_name.replace(
            "in_out_ptr", "REMOVED"
        )
        V.kernel.removed_buffers.add(name)

    def flush(self):
        for backend in self.backends.values():
            backend.flush()
        self.free_buffers()

    def codegen_extern_call(self, scheduler_node: ExternKernelSchedulerNode):
        assert isinstance(scheduler_node, ExternKernelSchedulerNode)
        # 'decide_inplace_update' stores the inplace update decisions in
        # the current kernel from where 'allocate' retrieve those decisions.
        # We have to make sure there is a non-NULL kernel handler to store
        # those inplace update decisions.
        with V.set_kernel_handler(Kernel(increase_kernel_count=False)):
            scheduler_node.decide_inplace_update()
            scheduler_node.allocate()
        node = scheduler_node.node
        assert isinstance(node, ir.ExternKernel), f"{type(node)=}"
        node.codegen(V.graph.wrapper_code)
        self.free_buffers()

    def create_backend(self, device: torch.device):
        assert (
            device.type != "cuda" or device.index is not None
        ), f"{device} should have been normalized in lowering"
        V.graph.add_device_info(device)

        device_scheduling = get_scheduling_for_device(device.type)
        if device_scheduling is None:
            raise RuntimeError(f"Unsupported device type: {device.type}")

        if device.type == "cuda" and not has_triton():
            device_props = torch.cuda.get_device_properties(device)
            if device_props.major < 7:
                raise RuntimeError(
                    f"Found {device_props.name} which is too old to be supported by the triton GPU compiler, which is used as the backend. Triton only supports devices of CUDA Capability >= 7.0, but your device is of CUDA capability {device_props.major}.{device_props.minor}"  # noqa: B950
                )
            else:
                raise RuntimeError(
                    "Cannot find a working triton installation. More information on installing Triton can be found at https://github.com/openai/triton"  # noqa: B950
                )

        return device_scheduling(self)

    def get_backend(self, device: torch.device):
        if device not in self.backends:
            self.backends[device] = self.create_backend(device)
        return self.backends[device]

    def enter_context(self, node):
        def get_order(n):
            if n not in self.origin_to_index:
                self.origin_to_index.update({n: i for i, n in enumerate(n.graph.nodes)})
            return self.origin_to_index[n]

        # Use a dict to have ordering
        origins = {
            (get_order(e), e): None for n in node.get_nodes() for e in n.node.origins
        }
        origins = list(origins.keys())
        if origins:
            _, last = max(origins, key=operator.itemgetter(0))
            V.graph.wrapper_code.enter_context(last)

    @dynamo_timed
    def codegen(self):
        for node in self.nodes:
            try:
                log.debug(
                    "Generating code for node %s with estimated runtime %f",
                    node.get_name(),
                    node.get_estimated_runtime(),
                )
            except Exception as e:
                log.debug(
                    "Generating code for node %s with estimated runtime 0.0",
                    node.get_name(),
                )

            self.enter_context(node)

            if not isinstance(node, NopKernelSchedulerNode):
                device = node.get_device()
                if (
                    device != self.current_device
                    or node.is_extern()
                    or node.is_template()
                ):
                    self.flush()
                if device != self.current_device:
                    if device.type == "cuda":
                        if self.current_device and self.current_device.type == "cuda":
                            V.graph.wrapper_code.codegen_device_guard_exit()
                        assert device.index is not None, "device should have an index"
                        V.graph.wrapper_code.codegen_device_guard_enter(device.index)
                    elif self.current_device and self.current_device.type == "cuda":
                        V.graph.wrapper_code.codegen_device_guard_exit()
                    self.current_device = device

            self.buffer_names_to_free.update(node.last_usage)

            if node.is_template():
                node, *epilogue = node.get_nodes()
                self.get_backend(device).codegen_template(node, epilogue)  # type: ignore[possibly-undefined]
            elif node.is_extern():
                self.codegen_extern_call(node)
            elif node.is_foreach():
                self.get_backend(device).codegen_foreach(node)  # type: ignore[possibly-undefined]
            elif isinstance(node, (FusedSchedulerNode, SchedulerNode)):
                self.get_backend(device).codegen_nodes(node.get_nodes())  # type: ignore[possibly-undefined]
            else:
                assert isinstance(node, NopKernelSchedulerNode)
                node.allocate()

            if config.debug_check_inf_and_nan:
                V.graph.wrapper_code.generate_inf_and_nan_checker(node)

            if config.triton.debug_sync_kernel:
                self.get_backend(device).codegen_sync()  # type: ignore[possibly-undefined]

            self.available_buffer_names.update(node.get_names())

            if not isinstance(node, NopKernelSchedulerNode):
                device = node.get_device()
                if self.get_backend(device).ready_to_flush():
                    self.flush()

        if self.current_device and self.current_device.type == "cuda":
            # exit the outermost CUDA device guard. this is
            # important for nested indentation codegen-ing.
            V.graph.wrapper_code.codegen_device_guard_exit()

        self.flush()

    def is_unaligned_buffer(self, buf_name):
        if buf_name in V.graph.graph_inputs or buf_name in V.graph.constants:
            # all graph inputs or constants are assumed to be aligned
            return False
        node = self.name_to_node[buf_name]
        layout = node.node.get_layout()
        if isinstance(layout, ir.AliasedLayout):
            return not layout.maybe_guard_aligned()
        else:
            return False


class BaseScheduling:
    def can_fuse_vertical(self, node1: BaseSchedulerNode, node2: BaseSchedulerNode):
        """
        Check whether node1 and node2 can be vertically fused or not.
        """
        raise NotImplementedError()

    def can_fuse_horizontal(self, node1: BaseSchedulerNode, node2: BaseSchedulerNode):
        """
        Check whether node1 and node2 can be horizontally fused or not.
        """
        raise NotImplementedError()

    def fuse(self, node1: BaseSchedulerNode, node2: BaseSchedulerNode):
        """
        Fuse two nodes
        """
        if node1.is_foreach() or node2.is_foreach():
            return ForeachKernelSchedulerNode.fuse(node1, node2)
        else:
            return FusedSchedulerNode.fuse(node1, node2)

    def group_fn(self, sizes):
        """
        Process the iteration sizes in case a transformation needs to be applied.
        """
        raise NotImplementedError()

    def codegen_template(
        self, template_node: SchedulerNode, epilogue_nodes: List[SchedulerNode]
    ):
        """
        Given a template node, generate a kernel.

        This function is only available for triton now. If the third-party backend behaves as a sub-class
        of TritonScheduling, it can override it or reuse it.
        """
        raise NotImplementedError()

    def codegen_nodes(self, nodes: List[SchedulerNode]):
        """
        Generate a kernel given a list of pre-fused nodes.
        """
        raise NotImplementedError()

    def codegen_sync(self):
        """
        Generate synchronization code for the kernel. This method depends on the hardware characteristics.
        """
        raise NotImplementedError()

    def ready_to_flush(self) -> bool:
        """
        Check whether the backend is requesting the scheduler to flush the generated kernel.
        If not supported, please return False.
        """
        return False

    def flush(self):
        """
        Flush the generated kernel and python wrapper code to the source code file.
        """
        raise NotImplementedError()

    def benchmark_fused_nodes(self, nodes):
        """
        Benchmark fused list of nodes and return the execution time
        in milliseconds on randomly generated inputs.
        """
        raise NotImplementedError()