patched torch post grad

patched_torch_post_grad.py

# mypy: allow-untyped-decorators
# mypy: allow-untyped-defs
import functools
import itertools
import logging
import operator
from collections import Counter, defaultdict
from typing import Any, Callable, Optional, TypeVar, Union
from typing_extensions import ParamSpec

import torch
import torch._inductor as inductor
import torch.utils._pytree as pytree
from torch import fx
from torch._decomp import register_decomposition
from torch._dynamo.utils import counters
from torch._inductor import comms
from torch._inductor.virtualized import ops
from torch._logging import trace_structured
from torch._prims_common import is_boolean_dtype, is_expandable_to, is_integer_dtype
from torch.fx.experimental.symbolic_shapes import statically_known_true, sym_eq
from torch.utils._ordered_set import OrderedSet

from .. import config, ir, pattern_matcher
from ..comms import remove_fsdp2_unsharded_param_graph_input_usage
from ..fx_utils import FakeTensorUpdater, get_fake_args_kwargs, get_node_storage
from ..lowering import lowerings as L
from ..pattern_matcher import (
    _return_true,
    Arg,
    CallFunction,
    CallFunctionVarArgs,
    filter_nodes,
    fwd_only,
    get_arg_value,
    get_mutation_region_id,
    Ignored,
    init_once_fakemode,
    KeywordArg,
    ListOf,
    Match,
    MultiOutputPattern,
    MULTIPLE,
    PatternMatcherPass,
    register_graph_pattern,
    register_replacement,
    stable_topological_sort,
)
from ..utils import decode_device, get_gpu_type, is_gpu, is_pointwise_use
from ..virtualized import V
from .b2b_gemm import B2B_GEMM_PASS
from .ddp_fusion import fuse_ddp_communication
from .group_batch_fusion import group_batch_fusion_passes, POST_GRAD_FUSIONS
from .micro_pipeline_tp import micro_pipeline_tp_pass
from .pre_grad import is_same_dict, save_inductor_dict
from .reinplace import reinplace_inplaceable_ops
from .split_cat import POST_GRAD_PATTERNS


_T = TypeVar("_T")
_P = ParamSpec("_P")

log = logging.getLogger(__name__)
aten = torch.ops.aten
prims = torch.ops.prims

# First pass_patterns[0] are applied, then [1], then [2]
pass_patterns = [
    PatternMatcherPass(),
    PatternMatcherPass(),
    PatternMatcherPass(),
]


def post_grad_passes(gm: torch.fx.GraphModule, is_inference: bool):
    """
    Passes that run on after grad.  This is called once on the forwards
    graph and once on the backwards graph.

    The IR here has been normalized and functionalized.
    """
    GraphTransformObserver = functools.partial(
        torch.fx.passes.graph_transform_observer.GraphTransformObserver,
        subsystem="post_grad_passes",
    )

    if not torch._dynamo.config.skip_fsdp_hooks:
        remove_fsdp2_unsharded_param_graph_input_usage(gm.graph)

    if config.dce:
        # has some issues with mutation in inference mode
        gm.graph.eliminate_dead_code()

    if is_inference and config.reorder_for_locality:
        GraphTransformObserver(gm, "reorder_for_locality").apply_graph_pass(
            reorder_for_locality
        )

    fake_tensor_updater = FakeTensorUpdater(gm.graph)

    if post_grad_custom_pre_pass := config.post_grad_custom_pre_pass:
        GraphTransformObserver(gm, "post_grad_custom_pre_pass").apply_graph_pass(
            post_grad_custom_pre_pass
        )

    if (
        config.cpp.enable_grouped_gemm_template
        and config.max_autotune
        and "CPP" in config.max_autotune_gemm_backends
        and torch._C._has_mkldnn
    ):
        from .mkldnn_fusion import grouped_gemm_pass

        grouped_gemm_pass(gm.graph)

    if config.pattern_matcher:
        lazy_init()
        trace_structured(
            "artifact",
            metadata_fn=lambda: {
                "name": "before_recompile_post_grad",
                "encoding": "string",
            },
            payload_fn=lambda: gm.print_readable(
                print_output=False, include_stride=True, include_device=True
            ),
        )
        GraphTransformObserver(gm, "post_grad_custom_pre_pass").apply_graph_pass(
            functools.partial(group_batch_fusion_passes, pre_grad=False)
        )
        GraphTransformObserver(gm, "remove_noop_ops").apply_graph_pass(remove_noop_ops)
        GraphTransformObserver(gm, "remove_assert_ops").apply_graph_pass(
            remove_assert_ops
        )
        for i, patterns in enumerate(pass_patterns):
            GraphTransformObserver(gm, f"pass_pattern_{i}").apply_graph_pass(
                patterns.apply
            )
        for pass_name in config.post_grad_fusion_options:
            # skip all patterns for group batch fusions
            if pass_name in POST_GRAD_FUSIONS:
                continue
            pattern_matcher_pass = POST_GRAD_PATTERNS[pass_name]
            inductor_before_change = save_inductor_dict(
                [pattern_matcher_pass.pass_name]
            )
            GraphTransformObserver(gm, pass_name).apply_graph_pass(
                pattern_matcher_pass.apply
            )
            if not is_same_dict(counters["inductor"], inductor_before_change):
                trace_structured(
                    "artifact",
                    metadata_fn=lambda: {
                        "name": f"{pattern_matcher_pass.pass_name}_post_grad",
                        "encoding": "string",
                    },
                    payload_fn=lambda: gm.print_readable(
                        print_output=False, include_stride=True, include_device=True
                    ),
                )
        if config.b2b_gemm_pass:
            B2B_GEMM_PASS.apply(gm.graph)  # type: ignore[arg-type]

    if config._micro_pipeline_tp:
        micro_pipeline_tp_pass(gm.graph)

    if config._fuse_ddp_communication:
        GraphTransformObserver(gm, "fuse_ddp_communication").apply_graph_pass(
            lambda graph: fuse_ddp_communication(
                graph,
                config._fuse_ddp_communication_passes,
                config._fuse_ddp_bucket_size,
            )
        )

    if post_grad_custom_post_pass := config.post_grad_custom_post_pass:
        GraphTransformObserver(gm, "post_grad_custom_post_pass").apply_graph_pass(
            post_grad_custom_post_pass
        )

    GraphTransformObserver(gm, "stable_sort").apply_graph_pass(stable_topological_sort)

    GraphTransformObserver(gm, "move_constructors_to_cuda").apply_graph_pass(
        move_constructors_to_gpu
    )

    fake_tensor_updater.incremental_update()

    # Keep these last, since they introduces mutation. Look at
    # ./fx_passes/README.md for a discussion of mutation invariants.
    GraphTransformObserver(gm, "reinplace_inplaceable_ops").apply_graph_pass(
        reinplace_inplaceable_ops
    )
    GraphTransformObserver(
        gm, "decompose_triton_kernel_wrapper_functional"
    ).apply_graph_pass(decompose_triton_kernel_wrapper_functional)
    GraphTransformObserver(gm, "decompose_auto_functionalized").apply_graph_pass(
        decompose_auto_functionalized
    )
    GraphTransformObserver(gm, "reinplace_fsdp_all_gather").apply_graph_pass(
        comms.reinplace_fsdp_all_gather
    )

    gm.recompile()
    trace_structured(
        "artifact",
        metadata_fn=lambda: {
            "name": "after_recompile_post_grad",
            "encoding": "string",
        },
        payload_fn=lambda: gm.print_readable(
            print_output=False, include_stride=True, include_device=True
        ),
    )
    gm.graph.lint()


def prepare_softmax_pattern(x, dim):
    xmax = x.amax(dim=dim, keepdim=True)
    xsub = x - xmax
    xexp = xsub.exp()
    xsum = xexp.sum(dim=dim, keepdim=True)
    return xmax, xsum, xsub, xexp


def prepare_softmax_replacement(x, dim):
    """
    Return xsub since otherwise log-softmax can not be matched
    due to a use of this intermediate node. Same reason to return
    xsub.exp() for softmax.
    """
    from torch._inductor.inductor_prims import prepare_softmax_online

    xmax, xsum = prepare_softmax_online(x, dim)
    xsub = x - xmax
    return xmax, xsum, xsub, xsub.exp()


def prepare_softmax_extra_check(match):
    """
    We only have triton online softmax kernels currently.
    """
    return (
        config.online_softmax
        and match.kwargs["x"].meta["val"].device.type == "cuda"
        and config.cuda_backend == "triton"
    )


@init_once_fakemode
def lazy_init():
    if torch._C._has_mkldnn:
        from . import decompose_mem_bound_mm  # noqa: F401
        from .mkldnn_fusion import _mkldnn_fusion_init

        _mkldnn_fusion_init()

    # Put this patterns in post-grad pass rather than joint-graph
    # pass since otherwise there will be perf/peak-memory regression:
    # https://github.com/pytorch/pytorch/issues/148141
    register_replacement(
        prepare_softmax_pattern,
        prepare_softmax_replacement,
        [torch.empty(4, 8)],
        scalar_workaround=dict(dim=-1),
        trace_fn=fwd_only,
        pass_dicts=pass_patterns[1],
        extra_check=prepare_softmax_extra_check,
    )


def reorder_for_locality(graph: torch.fx.Graph):
    def visit(other_node):
        if (
            other_node.op == "call_function"
            and other_node.target != operator.getitem
            and all((n in seen_nodes) for n in other_node.users)
            and get_mutation_region_id(graph, node)
            == get_mutation_region_id(graph, other_node)
        ):
            # move node's producers right before it
            node.prepend(other_node)

    seen_nodes = OrderedSet[torch.fx.Node]()

    # only reorder nodes before the first copy_ in the graph.
    # copy_ will appear at the end of functionalized graphs when there is mutation on inputs,
    # and this reordering doesnt work well with mutation
    first_copy = next(
        iter(graph.find_nodes(op="call_function", target=torch.ops.aten.copy_.default)),
        None,
    )
    past_mutating_epilogue = True if first_copy is None else False

    for node in reversed(graph.nodes):
        seen_nodes.add(node)
        if not past_mutating_epilogue:
            past_mutating_epilogue = node is first_copy
            continue

        torch.fx.map_arg((node.args, node.kwargs), visit)


def register_lowering_pattern(
    pattern, extra_check=_return_true, pass_number=1
) -> Callable[[Callable[_P, _T]], Callable[_P, _T]]:
    """
    Register an aten to inductor IR replacement pattern
    """
    return pattern_matcher.register_lowering_pattern(
        pattern, extra_check, pass_dict=pass_patterns[pass_number]
    )


################################################################################
# Actual patterns below this point.
# Priority of patterns is:
#   - later output nodes first
#   - order patterns are defined in
################################################################################


def is_valid_mm_plus_mm(match: Match):
    if not torch._inductor.utils.use_max_autotune():
        return False

    *_b1, m1, k1 = match.kwargs["mat1"].meta.get("tensor_meta").shape
    *_b2, k2, n1 = match.kwargs["mat2"].meta.get("tensor_meta").shape
    if k1 != k2:
        return False

    *_b1, m2, k3 = match.kwargs["mat3"].meta.get("tensor_meta").shape
    *_b2, k4, n2 = match.kwargs["mat4"].meta.get("tensor_meta").shape
    if k3 != k4:
        return False

    if m1 != m2 or n1 != n2:
        return False

    return True


def scatter_upon_const_tensor_extra_check(m):
    if not config.optimize_scatter_upon_const_tensor:
        return False
    full_shape = m.kwargs["shape"]
    selector = m.kwargs["selector"]
    dim = m.kwargs["dim"]
    if dim < 0:
        dim += len(full_shape)

    selector_ft = selector.meta["val"]
    assert selector_ft.dim() == len(full_shape)

    for idx, select_sz, full_sz in zip(
        itertools.count(), selector_ft.shape, full_shape
    ):
        if idx == dim:
            continue

        # TODO: the pattern can be updated to support the case that index tensor
        # is shorter. But that will need a more complex condition expression
        # especially for multi-dimensional tensors.
        # Skip it for now.
        if isinstance(full_sz, fx.Node):
            full_sz = full_sz.meta["val"]
        if select_sz < full_sz:
            return False

    # Actually we can support small size larger than 1. It would be a bit
    # tedius. E.g., we load all the index values (not many) and compare
    # them with the position in tensor to decide what value to return.
    return selector_ft.size(dim) == 1


@register_lowering_pattern(
    CallFunction(
        aten.scatter.value,
        CallFunction(
            aten.full,
            KeywordArg("shape"),
            KeywordArg("background_val"),
            dtype=KeywordArg("dtype"),
        ),
        KeywordArg("dim"),
        KeywordArg("selector"),
        KeywordArg("val"),  # scalar value
    ),
    extra_check=scatter_upon_const_tensor_extra_check,
)
def scatter_upon_const_tensor(
    match: Match, shape, background_val, dtype, dim, selector, val
):
    """
    Match the pattern of full+scatter into a pointwise.

    TODO: Right now the scatter value must be a scalar. But we could support it
    when it is a tensor as well.
    """
    from torch._inductor import metrics

    metrics.num_matches_for_scatter_upon_const_tensor += 1

    selector_loader = selector.make_loader()

    def inner_fn(idx):
        selector_idx = list(idx)
        selector_idx[dim] = 0

        selector = selector_loader(selector_idx)
        return ops.where(
            selector == ops.index_expr(idx[dim], torch.int64),
            ops.constant(val, dtype),
            ops.constant(background_val, dtype),
        )

    return ir.Pointwise.create(
        device=selector.get_device(),
        dtype=dtype,
        inner_fn=inner_fn,
        ranges=shape,
    )


@register_lowering_pattern(
    CallFunction(
        aten.add,
        CallFunction(aten.mm, KeywordArg("mat1"), KeywordArg("mat2")),
        CallFunction(aten.mm, KeywordArg("mat3"), KeywordArg("mat4")),
    ),
    extra_check=is_valid_mm_plus_mm,
)
def mm_plus_mm(match: Match, mat1, mat2, mat3, mat4):
    return inductor.kernel.mm_plus_mm.tuned_mm_plus_mm(mat1, mat2, mat3, mat4)


@register_graph_pattern(
    CallFunction(
        aten.cumsum.default,
        CallFunction(
            torch.ops.aten.full.default,
            KeywordArg("shape"),
            KeywordArg("fill_value"),
            dtype=KeywordArg("dtype"),
            layout=Ignored(),
            device=KeywordArg("device"),
            pin_memory=False,
            _users=MULTIPLE,
        ),
        KeywordArg("dim"),
        _users=MULTIPLE,
    ),
    pass_dict=pass_patterns[1],
)
def pointless_cumsum_replacement(match: Match, shape, fill_value, device, dtype, dim):
    """Based on a pattern in OPTForCausalLM"""

    if is_integer_dtype(dtype) or is_boolean_dtype(dtype):
        # cumsum promotes all integral types to int64
        dtype = torch.int64

    def repl(*shape):
        dim_size = shape[dim]
        idx = torch.arange(1, dim_size + 1, device=device, dtype=dtype)

        inter_shape = [1] * len(shape)
        inter_shape[dim] = dim_size
        return (idx * fill_value).view(inter_shape).expand(shape)

    # only replace the output node, not all nodes
    match.nodes = [match.output_node()]
    match.replace_by_example(repl, list(shape))


_cat_1 = CallFunction(aten.cat, Arg(), 1, _users=2)


@register_lowering_pattern(
    CallFunction(
        aten.cat,
        [
            _cat_1,
            CallFunction(
                aten.slice,
                _cat_1,
                1,
                0,
                KeywordArg("size"),
            ),
        ],
        1,
    )
)
def cat_slice_cat(match, cat_input, size, dim=1):
    """
    This is an example of a more complex pattern where cat_1 is used
    multiple times inside the pattern.  We fold 2 calls to cat into one.

    Matches:
        cat_1: f32[1024, 4077] = torch.ops.aten.cat.default([add_26, primals_217], 1)
        slice_1: f32[1024, 4077] = torch.ops.aten.slice.Tensor(cat_1, 0, 0, 9223372036854775807)
        slice_2: f32[1024, 19] = torch.ops.aten.slice.Tensor(slice_1, 1, 0, 19)
        cat_2: f32[1024, 4096] = torch.ops.aten.cat.default([cat_1, slice_2], 1)


    Rewrite to:
        slice_2 = torch.ops.aten.slice.Tensor(add_26, 1, 0, 19)
        cat_2 = torch.ops.aten.cat.default([add_26, primals_217, slice2], 1)
    """
    first, *rest = cat_input
    # Optimization is optional, because we can just not fold the cat
    # size should be within first.get_size()[dim] such that the optimization is valid.
    # For negative `end`, we currently fallback to not optimizing.
    if size >= 0 and V.graph.sizevars.statically_known_leq(size, first.get_size()[dim]):
        # fold 2 cats into 1 cat
        return L[aten.cat](
            [
                first,
                *rest,
                L[aten.slice](first, dim, 0, size),
            ],
            dim,
        )
    else:
        # don't expect to hit this case, just fall back
        tmp = L[aten.cat](cat_input, dim)
        return L[aten.cat](
            [
                tmp,
                L[aten.slice](tmp, dim, 0, size),
            ],
            dim,
        )


def is_valid_splitwithsizes_cat(match):
    split_nodes = filter_nodes(match.nodes, aten.split_with_sizes)
    cat_nodes = filter_nodes(match.nodes, aten.cat)
    get_item_nodes = filter_nodes(match.nodes, operator.getitem)
    if len(split_nodes) != 1 or len(cat_nodes) != 1:
        return False
    split_node, cat_node = split_nodes[0], cat_nodes[0]
    # The dim of split and cat should match for passthrough
    if get_arg_value(split_node, 2, "dim") != get_arg_value(cat_node, 1, "dim"):
        return False
    get_item_args = OrderedSet(
        get_arg_value(get_item_node, 1) for get_item_node in get_item_nodes
    )
    assert None not in get_item_args
    split_sizes = get_arg_value(split_node, 1, "split_sizes")
    # All parts of split should be included in the cat
    if get_item_args != OrderedSet(range(len(split_sizes))):
        return False
    # The order of get_item_args should same with cat_node used.
    # For example, if the split_node like split_with_sizes(input, [2, 2, 3], 1),
    # the cat node should be like cat([get_item(0), get_item(1), get_item(2)], 1).
    cat_items_args_order = [
        get_arg_value(item_node, 1) for item_node in get_arg_value(cat_node, 0)
    ]
    if cat_items_args_order != list(range(len(split_sizes))):
        return False

    return True


def same_meta(node1: torch.fx.Node, node2: torch.fx.Node):
    """True if two nodes have the same metadata"""
    val1 = node1.meta.get("val")
    val2 = node2.meta.get("val")
    return (
        issubclass(type(val1), torch.Tensor)
        and issubclass(type(val2), torch.Tensor)
        and statically_known_true(sym_eq(val1.size(), val2.size()))
        and val1.layout == val2.layout
        and val1.dtype == val2.dtype
        and val1.device == val2.device
        and (
            val1.layout != torch.strided
            or statically_known_true(sym_eq(val1.stride(), val2.stride()))
        )
    )


noop_registry: dict[Any, Any] = {}


def register_noop_decomp(targets, nop_arg=0):
    def register_fun(cond):
        register_decomposition(targets, registry=noop_registry, unsafe=True)(
            (cond, nop_arg)  # type: ignore[arg-type]
        )
        return cond

    return register_fun


@register_noop_decomp(aten.slice)
def slice_noop(self, dim=0, start=None, end=None, step=1):
    if start is None or end is None:
        return False
    if (
        statically_known_true(sym_eq(start, 0))
        and statically_known_true(end >= 2**63 - 1)
        and statically_known_true(sym_eq(step, 1))
    ):
        return True
    return False


@register_noop_decomp(aten.slice_scatter, 1)
def slice_scatter_noop(self, src, dim=0, start=None, end=None, step=1):
    if start is None:
        start = 0
    if end is None:
        end = 2**63 - 1
    if start == 0 and end >= 2**63 - 1 and step == 1:
        return True
    return False


@register_noop_decomp(aten.repeat)
def repeat_noop(self, repeats):
    return all(r == 1 for r in repeats)


@register_noop_decomp(aten.constant_pad_nd)
def constant_pad_nd(x, padding, fill_value=0):
    return all(p == 0 for p in padding)


@register_noop_decomp(torch.ops.prims.convert_element_type)
def convert_element_type_noop(x, dtype: torch.dtype):
    return x.dtype == dtype


@register_noop_decomp(torch.ops.prims.device_put)
def device_put_noop(x, device, non_blocking=True):
    return x.device == decode_device(device)


@register_noop_decomp([aten.ceil, aten.floor, aten.round, aten.trunc])
def int_noop(x):
    return is_integer_dtype(x.dtype)


@register_noop_decomp([aten.pow])
def pow_noop(a, b):
    return isinstance(b, int) and b == 1


@register_noop_decomp([aten.cat], lambda args: args[0][0])
def cat_noop(inputs, dim=0):
    return len(inputs) == 1


@register_noop_decomp(aten.view)
def view_noop(arg, size):
    return arg.shape == size


# Note, we also always have a check for identical metadata, which is why these
# are safe
@register_noop_decomp([aten.copy], nop_arg=1)
@register_noop_decomp([aten.alias, aten.clone])
def true_noop(*args, **kwargs):
    return True


def remove_noop_ops(graph: torch.fx.Graph):
    """
    Removes both operations that are essentially aten.clone and operations that are essentially aten.alias from the graph.
    """
    inputs = OrderedSet[torch.fx.Node]()
    input_storages = OrderedSet[Union[int, None]]()
    output_storages = OrderedSet[Union[int, None]]()

    for node in graph.find_nodes(op="placeholder"):
        inputs.add(node)
        input_storages.add(get_node_storage(node))

    output_node = next(iter(reversed(graph.nodes)))
    assert output_node.op == "output"
    outputs = output_node.args[0]
    if not isinstance(outputs, (list, tuple)):
        # nested subgraphs can have singleton outputs
        outputs = (outputs,)
    for out in outputs:
        if isinstance(out, torch.fx.Node):
            output_storages.add(get_node_storage(out))

    for node in graph.nodes:
        if node.target in noop_registry:
            cond, src_index = noop_registry[node.target]
            if isinstance(src_index, int):
                src = node.args[src_index]
            else:
                src = src_index(node.args)
            if not isinstance(src, torch.fx.Node):
                continue
            # Don't introduce new aliasing between inputs and outputs.
            # See fx_passes/README.md for a discussion of why this is
            # necessary.
            node_storage = get_node_storage(node)
            src_storage = get_node_storage(src)
            node_is_view = node_storage == src_storage
            if (
                not node_is_view
                and node_storage in output_storages
                and (src_storage in input_storages or src_storage in output_storages)
            ):
                continue

            # Even if input and outputs are expected to alias,
            # don't make "node is src" True
            if (
                node_is_view
                and node in output_node.args
                and (src in inputs or src in output_node.args)
            ):
                continue

            is_valid, args, kwargs = get_fake_args_kwargs(node)
            if not is_valid:
                continue
            if same_meta(node, src) and cond(*args, **kwargs):
                node.replace_all_uses_with(src)
                graph.erase_node(node)


def remove_assert_ops(graph: torch.fx.Graph):
    """
    Removes aten._assert_tensor_metadata.default op because
    1) it will be lowered to a no-op in inductor
    2) it can block fusion, such as unfuse_bias_add_to_pointwise fusion.

    This op could come from aten.to functionalization in export.

    For example, if we have a graph like below

    %addmm = aten.addmm.default(%linear_bias, %arg3_1, %permute)
    %_assert_tensor_metadata = aten._assert_tensor_metadata.default(%addmm, None, None, torch.float16)
    %convert_element_type_3 = prims.convert_element_type.default(%addmm, torch.float32)
    %pow_1 = aten.pow.Tensor_Scalar(%convert_element_type_3, 2)

    We still want to fuse add from addmm with pow, instead of fusing add with mm, according to unfuse_bias_add_to_pointwise fusion.

    However, aten._assert_tensor_metadata.default is not a pointwise op, and would fail the should_prefer_unfused_addmm check.

    We remove this op so it doesn't block fusion decisions. It's safe because this op is lowered to a no-op with @register_lowering.

    """
    for node in graph.find_nodes(
        op="call_function", target=torch.ops.aten._assert_tensor_metadata.default
    ):
        graph.erase_node(node)


def decompose_triton_kernel_wrapper_functional(graph):
    """Decomposes triton_kernel_wrapper_functional nodes into clones and the underlying
    mutation node.

    We assume that the reinplacing pass runs before this; the reinplacing pass
    tells us (via rewriting the arguments or .meta to those nodes) which
    Tensors we should clone and which Tensors are safe to reinplace.
    """
    graph_pass = PatternMatcherPass()

    @register_graph_pattern(
        CallFunctionVarArgs(torch.ops.higher_order.triton_kernel_wrapper_functional),
        pass_dict=graph_pass,
    )
    def _(match: Match, *args, **kwargs):
        from torch._higher_order_ops.triton_kernel_wrap import (
            triton_kernel_wrapper_functional_dense,
        )

        flat_args, spec = pytree.tree_flatten((args, kwargs))

        # NB: we combine (args, kwargs) into flat args for replacing.
        # This is replace_by_example uses make_fx which does not support
        # tracing a function with kwargs.
        def decomp(*flat_args):
            args, kwargs = pytree.tree_unflatten(flat_args, spec)
            return (triton_kernel_wrapper_functional_dense(*args, **kwargs),)

        match.replace_by_example(decomp, flat_args, run_functional_passes=False)

    graph_pass.apply(graph)

    for node in graph.find_nodes(
        op="call_function",
        target=torch.ops.higher_order.triton_kernel_wrapper_functional,
    ):
        raise AssertionError("triton_kernel_wrapper_functional was not removed")


def decompose_auto_functionalized(graph):
    """Decomposes auto_functionalized nodes into clones and the underlying
    mutation node.

    We assume that the reinplacing pass runs before this; the reinplacing pass
    tells us (via rewriting the arguments or .meta to those nodes) which
    Tensors we should clone and which Tensors are safe to reinplace.
    """
    graph_pass = PatternMatcherPass()

    @register_graph_pattern(
        CallFunctionVarArgs(torch.ops.higher_order.auto_functionalized),
        pass_dict=graph_pass,
    )
    def _(match: Match, *args, **kwargs):
        from torch._higher_order_ops.auto_functionalize import auto_functionalized_dense

        only_clone_these_tensors = tuple(
            match.nodes[0].meta.get("only_clone_these_tensors", [])
        )

        flat_args, spec = pytree.tree_flatten((args, kwargs))

        # NB: we combine (args, kwargs) into flat args for replacing.
        # This is replace_by_example uses make_fx which does not support
        # tracing a function with kwargs.
        def decomp(*flat_args):
            args, kwargs = pytree.tree_unflatten(flat_args, spec)
            assert len(args) == 1
            mode = args[0]
            return auto_functionalized_dense(mode, only_clone_these_tensors, **kwargs)

        match.replace_by_example(decomp, flat_args, run_functional_passes=False)

    @register_graph_pattern(
        CallFunctionVarArgs(torch.ops.higher_order.auto_functionalized_v2),
        pass_dict=graph_pass,
    )
    def _(match: Match, *args, **kwargs):
        from torch._higher_order_ops.auto_functionalize import (
            auto_functionalized_v2_dense,
        )

        only_clone_these_bases = tuple(
            match.nodes[0].meta.get("only_clone_these_tensors", [])
        )

        flat_args, spec = pytree.tree_flatten((args, kwargs))

        # NB: we combine (args, kwargs) into flat args for replacing.
        # This is replace_by_example uses make_fx which does not support
        # tracing a function with kwargs.
        def decomp(*flat_args):
            args, kwargs = pytree.tree_unflatten(flat_args, spec)
            assert len(args) == 1
            mutable_op = args[0]
            return auto_functionalized_v2_dense(
                mutable_op, only_clone_these_bases, **kwargs
            )

        match.replace_by_example(decomp, flat_args, run_functional_passes=False)

    graph_pass.apply(graph)

    for _ in graph.find_nodes(
        op="call_function", target=torch.ops.higher_order.auto_functionalized
    ):
        raise AssertionError("auto_functionalized was not removed")

    for _ in graph.find_nodes(
        op="call_function", target=torch.ops.higher_order.auto_functionalized_v2
    ):
        raise AssertionError("auto_functionalized_v2 was not removed")


@register_lowering_pattern(
    CallFunction(
        aten.cat,
        ListOf(
            CallFunction(
                operator.getitem,
                CallFunction(
                    aten.split_with_sizes,
                    KeywordArg("input_"),
                    Ignored(),
                    Ignored(),
                    _users=MULTIPLE,
                ),
                Ignored(),
            ),
        ),
        Ignored(),
    ),
    pass_number=2,
    extra_check=is_valid_splitwithsizes_cat,
)
def splitwithsizes_cat_replace(match, input_):
    return input_


def is_valid_cat_splitwithsizes(match):
    cat_nodes = filter_nodes(match.nodes, aten.cat)
    split_nodes = filter_nodes(match.nodes, aten.split_with_sizes)
    if len(split_nodes) != 1 or len(cat_nodes) != 1:
        return False
    split_node, cat_node = split_nodes[0], cat_nodes[0]

    # the cat node has other users: can't eliminate
    if len(cat_node.users) > 1:
        return False

    # the dim of the cat and split should match
    dim = get_arg_value(split_node, 2, "dim")
    if dim != get_arg_value(cat_node, 1, "dim"):
        return False

    cat_inputs = list(get_arg_value(cat_node, 0))
    split_sizes = get_arg_value(split_node, 1, "split_sizes")
    # the number of input tensors in cat and the
    # length of the split sizes should match
    if len(cat_inputs) != len(split_sizes):
        return False

    for cat_input, split_size in zip(cat_inputs, split_sizes):
        # each cat input tensor's size along dim
        # should match the corresponding split size
        if "val" not in cat_input.meta:
            return False
        cat_input_size = cat_input.meta["val"].size(dim)
        if cat_input_size != split_size:
            return False

    return True


@register_lowering_pattern(
    CallFunction(
        aten.split_with_sizes,
        CallFunction(
            aten.cat,
            KeywordArg("input_"),
            Ignored(),
            _users=MULTIPLE,
        ),
        Ignored(),
        Ignored(),
    ),
    pass_number=2,
    extra_check=is_valid_cat_splitwithsizes,
)
def cat_splitwithsizes_replace(match, input_):
    return input_


def view_to_reshape(gm):
    """
    Replace view ops in the GraphModule to reshape ops.
    """
    for nd in gm.graph.find_nodes(
        op="call_function", target=torch.ops.aten.view.default
    ):
        nd.target = torch.ops.aten.reshape.default


def should_prefer_unfused_addmm(match):
    inp = match.kwargs["inp"]
    if not is_gpu(inp.meta["val"].device.type):
        return False

    output = match.output_node()
    return all(is_pointwise_use(use) for use in output.users)


@register_graph_pattern(
    CallFunction(aten.addmm, KeywordArg("inp"), Arg(), Arg()),
    pass_dict=pass_patterns[2],
    extra_check=should_prefer_unfused_addmm,
)
def unfuse_bias_add_to_pointwise(match: Match, mat1, mat2, *, inp):
    def repl(inp, x1, x2):
        return x1 @ x2 + inp

    match.replace_by_example(repl, [inp, mat1, mat2])


def is_valid_addmm_fusion(match):
    mat1, mat2 = match.args
    inp = match.kwargs["inp"]

    if not (
        isinstance(inp, torch.fx.Node) and isinstance(inp.meta["val"], torch.Tensor)
    ):
        return False  # Input is a number

    in_shape = inp.meta["val"].shape
    mm_shape = mat1.meta["val"].shape[0], mat2.meta["val"].shape[1]
    matched = is_expandable_to(in_shape, mm_shape)
    if not matched:
        return False  # Shape mismatch

    return not should_prefer_unfused_addmm(match)


@register_graph_pattern(
    CallFunction(
        aten.add,
        CallFunction(aten.mm, Arg(), Arg()),
        KeywordArg("inp"),
    ),
    pass_dict=pass_patterns[2],
    extra_check=is_valid_addmm_fusion,
)
@register_graph_pattern(
    CallFunction(
        aten.add,
        KeywordArg("inp"),
        CallFunction(aten.mm, Arg(), Arg()),
    ),
    pass_dict=pass_patterns[2],
    extra_check=is_valid_addmm_fusion,
)
def addmm(match, mat1, mat2, *, inp):
    def repl(inp, mat1, mat2):
        return aten.addmm(inp, mat1, mat2)

    match.replace_by_example(repl, [inp, mat1, mat2])


def register_partial_reduction_pattern():
    "Reuse partial reductions in complete reductions"

    # post grad equivalents
    equiv_red = {
        aten.amax.default: aten.max.default,
        aten.amin.default: aten.min.default,
    }

    # TODO: to support other reductions like sum, would need to skip
    # lower precision reductions since partial output would need to be kept at fp32.
    for red_op in (aten.amax.default, aten.amin.default):
        inp = KeywordArg("input")
        partial_reduc = CallFunction(
            red_op, inp, KeywordArg("reduced_dims"), KeywordArg("keepdim")
        )
        full_reduc = CallFunction([red_op, equiv_red[red_op]], inp)

        @register_graph_pattern(
            MultiOutputPattern([partial_reduc, full_reduc]), pass_dict=pass_patterns[2]
        )
        def reuse_partial(match, input, reduced_dims, keepdim):
            partial_red, full_red = match.output_nodes()

            # if theyre small, reuse not worth it
            if not statically_known_true(input.meta["val"].numel() >= 4096):
                return True

            def replacement(inp: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
                partial = partial_red.target(inp, reduced_dims, keepdim)
                complete = full_red.target(partial)
                return (partial, complete)

            counters["inductor"]["partial_reduction_reuse"] += 1
            match.replace_by_example(replacement, [input])


register_partial_reduction_pattern()


def check_shape_cuda_and_fused_int_mm_mul_enabled(match):
    return (
        config.force_fuse_int_mm_with_mul
        and len(getattr(match.args[2].meta.get("val"), "shape", [])) == 2
        and getattr(match.args[2].meta.get("val"), "is_cuda", False)
    )


def is_index_put_and_requires_h2d_sync_for_gpu_value(node):
    from torch.fx.operator_schemas import normalize_function

    if node.target not in [
        torch.ops.aten.index_put.default,
        torch.ops.aten.index_put_.default,
    ]:
        return False
    # Inductor falls back to aten.index_put_.
    # index_put_ will will call nonzero() and perform a H2D sync if
    # any of its indices are bool/byte tensors
    # However, it will short-circuit this H2D sync and run mask_fill_
    # if the value we are putting is a cpu scalar.
    # Therefore, when inductor sees an index_put_ with byte tensor indices,
    # it should *not* convert the cpu scalar value into a gpu tensor.
    args_, _kwargs = normalize_function(node.target, node.args, node.kwargs)  # type: ignore[misc]
    any_byte_bool_indices = False
    indices = args_[1]
    for i in indices:
        if i is not None and i.meta["val"].dtype in [torch.bool, torch.int8]:
            any_byte_bool_indices = True

    val = args_[2].meta["val"]
    val_is_cpu_scalar = val.device.type == "cpu" and val.numel() == 1
    # If both these conditions hold, then converting the val
    # to a gpu tensor will incur a H2D sync when inductor calls aten.index_put_
    return any_byte_bool_indices and val_is_cpu_scalar


class ConstructorMoverPass:
    def __init__(self, target: str, allow_outputs: bool = False) -> None:
        """
        Move constructors from cpu to the target_device.

        Sweeps through the module, looking for constructor nodes that can be moved
        to the target_device.

        A constructor node can be moved to the target_device iff all of its users
        can also be moved (tested by cannot_be_moved). Otherwise, all dependent
        constructor nodes won't be moved.

        - target: target device type
        - allow_outputs: allow outputs to be moved
        """

        self.target = target
        self.allow_outputs = allow_outputs

        assert isinstance(target, str), (
            "target should be a string representing the device type. "
            f"Got: {type(target).__name__}"
        )

    def allow_cpu_device(self, node: fx.Node) -> bool:
        """
        Returns whether a node that returns a tensor on the target device may have
        cpu tensors as input.
        """
        return node.target in (
            torch.ops.aten.index.Tensor,
            torch.ops.aten.index_put.default,
            torch.ops.aten.index_put_.default,
            torch.ops.aten.copy.default,
            torch.ops.aten.copy_.default,
            torch.ops.aten.slice_scatter.default,
        )

    def cannot_be_moved(self, node: fx.Node) -> bool:
        """
        Returns whether a node can be moved to the target device.

        If this function returns False, it means that this node and all of its users
        won't be moved into the target device.
        """
        if node.target == "output":
            return not self.allow_outputs

        if not (
            isinstance(node.target, torch._ops.OpOverload)
            and node.target.namespace in ("prims", "aten")
        ):
            return True
        if is_index_put_and_requires_h2d_sync_for_gpu_value(node):
            return True

        return False

    def get_node_device(self, node: fx.Node) -> Optional[torch.device]:
        """
        Get the device of a node.
        """
        ten = node.meta.get("val")
        return None if not isinstance(ten, torch.Tensor) else ten.device

    def get_cpu_indeg_count(self, graph: fx.Graph) -> dict[fx.Node, int]:
        """
        Get the number of cpu inputs to a node
        """
        cpu_indeg: dict[fx.Node, int] = Counter()

        for node in graph.nodes:
            cpu_count = 0

            def add_cpu_inp(node):
                nonlocal cpu_count
                device = self.get_node_device(node)
                cpu_count += device is not None and device.type == "cpu"

            pytree.tree_map_only(fx.Node, add_cpu_inp, (node.args, node.kwargs))

            if cpu_count:
                cpu_indeg[node] = cpu_count

        return cpu_indeg

    def __call__(self, graph: fx.Graph) -> None:
        target_devices = OrderedSet[torch.device]()
        constructors = []

        for node in graph.nodes:
            device = self.get_node_device(node)
            if device and device.type == self.target:
                target_devices.add(device)

            if not (
                isinstance(node.target, torch._ops.OpOverload)
                and node.target.namespace in ("prims", "aten")
            ):
                continue

            if not torch._subclasses.fake_tensor._is_tensor_constructor(node.target):
                continue

            if not node.kwargs.get("device") == torch.device("cpu"):
                continue

            constructors.append(node)

        # not handling multiple target devices initially
        if not constructors or len(target_devices) != 1:
            return

        movable_constructors = self.find_movable_constructors(graph, constructors)

        for node in movable_constructors:
            kwargs = node.kwargs.copy()
            kwargs["device"] = next(iter(target_devices))
            node.kwargs = kwargs

    def find_movable_constructors(
        self, graph: fx.Graph, constructors: list[fx.Node]
    ) -> OrderedSet[fx.Node]:
        """
        Starting from the cpu constructors, iterate through the graph and test that all of their
        downstream uses can safely be moved to cpu.
        """
        cpu_indeg: dict[fx.Node, int] = self.get_cpu_indeg_count(graph)

        # which constructors cannot be moved to gpu
        cannot_move_to_gpu = OrderedSet[fx.Node]()

        # For any node in the graph, which constructors does it have a dependency on
        constructor_dependencies: dict[fx.Node, OrderedSet[fx.Node]] = defaultdict(
            OrderedSet
        )

        # if a cpu node has a dependency on two different cpu constructors,
        # then if either constructor cannot be moved to gpu, the other cannot as well.
        # In this case any node with a dependency on one will have a dependency on the other
        equal_constructor_sets: dict[fx.Node, OrderedSet[fx.Node]] = {
            c: OrderedSet([c]) for c in constructors
        }

        def make_dependencies_equivalent(
            set1: OrderedSet[fx.Node], set2: OrderedSet[fx.Node]
        ) -> OrderedSet[fx.Node]:
            # could use union find but not worth complexity here
            set1.update(set2)
            for obj in set1:
                equal_constructor_sets[obj] = set1
            return set1

        queue: list[fx.Node] = list(constructors)

        for c in queue:
            constructor_dependencies[c].add(c)

        while queue:
            node = queue.pop()
            dependencies = constructor_dependencies[node]

            for user in node.users:
                if self.cannot_be_moved(user):
                    cannot_move_to_gpu.update(dependencies)
                    break

                # this node was used on a op which takes in multiple devices and output a gpu
                # tensor. we can convert its cpu input to gpu without making further changes
                node_device = self.get_node_device(user)
                if (
                    self.allow_cpu_device(user)
                    and node_device
                    and node_device.type == self.target
                ):
                    del cpu_indeg[user]
                else:
                    # otherwise, we should continue look at its downstream uses
                    cpu_indeg[user] -= 1
                    if cpu_indeg[user] == 0:
                        del cpu_indeg[user]
                        queue.append(user)

                unioned_set = make_dependencies_equivalent(
                    dependencies, constructor_dependencies[user]
                )
                constructor_dependencies[user] = unioned_set

        for node in cpu_indeg:
            if constructor_dependencies[node]:
                cannot_move_to_gpu.update(constructor_dependencies[node])

        all_cannot_move_to_gpu = cannot_move_to_gpu.copy()
        for constructor in cannot_move_to_gpu:
            all_cannot_move_to_gpu.update(equal_constructor_sets[constructor])

        return OrderedSet(constructors) - all_cannot_move_to_gpu


def move_constructors_to_gpu(graph: fx.Graph) -> None:
    """
    Moves intermediary tensors which are constructed on the cpu to gpu when safe
    """
    ConstructorMoverPass(get_gpu_type())(graph)