pytorch/torch/_inductor/utils.py

import collections
import contextlib
import dataclasses
import functools
import inspect
import itertools
import logging
import math
import operator
import os
import shutil
import sys
import tempfile
import textwrap
import time
from collections import defaultdict
from io import StringIO
from typing import Any, Dict, List, NamedTuple, Optional, Union
from unittest import mock

import sympy

import torch
from torch.autograd import DeviceType
from torch.fx.immutable_collections import immutable_dict, immutable_list

from . import config
from .cuda_properties import current_device, get_device_capability

log = logging.getLogger(__name__)

VarRanges = Dict[sympy.Expr, sympy.Expr]


def do_bench(*args, **kwargs):
    @functools.lru_cache(None)
    def load_triton():
        try:
            # NB: Lazily load triton, as importing triton is slow
            # see https://github.com/openai/triton/issues/1599
            from triton.testing import do_bench as triton_do_bench
        except ImportError:
            raise NotImplementedError("requires Triton")

        # triton PR https://github.com/openai/triton/pull/1513 change the
        # quantile fields name from 'percentiles' to 'quantiles'
        # and change the default value from (0.5, 0.2, 0.8) to None.
        # This may break inductor since a caller expects a tuple may get a item.
        #
        # Add a wrapper to maintain the same behavior for inductor.
        # Maybe we should have own implementation of this function?
        return triton_do_bench, (
            "quantiles"
            if inspect.signature(triton_do_bench).parameters.get("quantiles")
            is not None
            else "percentiles"
        )

    triton_do_bench, quantile_field_name = load_triton()

    if quantile_field_name not in kwargs:
        kwargs[quantile_field_name] = (0.5, 0.2, 0.8)
    return triton_do_bench(*args, **kwargs)[0]


@functools.lru_cache(None)
def has_triton():
    if not torch.cuda.is_available():
        return False
    try:
        import triton

        return triton is not None and get_device_capability() >= (7, 0)
    except ImportError:
        return False


@functools.lru_cache(None)
def has_torchvision_roi_align():
    try:
        from torchvision.ops import roi_align  # noqa: F401

        return roi_align is not None and hasattr(
            getattr(torch.ops, "torchvision", None), "roi_align"
        )
    except ImportError:
        return False


def conditional_product(*args):
    return functools.reduce(operator.mul, [x for x in args if x])


def decode_device(device):
    if device is None:
        return torch.tensor(0.0).device  # default device
    if isinstance(device, str):
        device = torch.device(device)
    if device.type == "cuda" and device.index is None:
        return torch.device("cuda", index=current_device())
    return device


def sympy_product(it):
    return functools.reduce(operator.mul, it, sympy.Integer(1))


def sympy_dot(seq1, seq2):
    assert len(seq1) == len(seq2)
    return sympy.expand(sum(a * b for a, b in zip(seq1, seq2)))


def unique(it):
    return {id(x): x for x in it}.values()


def ceildiv(numer: int, denom: int):
    # TODO: There is a bug in a call to this function, to repro:
    # python benchmarks/dynamo/huggingface.py --inductor -d cuda --accuracy
    # --amp --only YituTechConvBert --dynamic-shapes
    assert isinstance(numer, int) and isinstance(
        denom, int
    ), f"{numer}: {type(numer)}, {denom}: {type(denom)}"
    return -(numer // -denom)


def next_power_of_2(n):
    """Return the smallest power of 2 greater than or equal to n"""
    assert n <= 2**32, "32-bit only"
    n -= 1
    n |= n >> 1
    n |= n >> 2
    n |= n >> 4
    n |= n >> 8
    n |= n >> 16
    n += 1
    return n


def convert_shape_to_inductor(lst: List[Union[int, torch.SymInt]]) -> List[sympy.Expr]:
    """
    Gets the shape and stride of a tensor. For non-symbolic tensors, this is
    trivial. But for symbolic tensors, we need to map from SymIntNode into
    sympy.Expr.
    """
    return [
        i.node.expr if isinstance(i, torch.SymInt) else sympy.Integer(i) for i in lst
    ]


def convert_shape_to_symint(
    lst: List[Union[int, sympy.Expr]]
) -> List[Union[int, torch.SymInt]]:
    """
    Takes a list of shapes from Inductor and converts them into symints (or just
    ints if all shapes are static).
    """
    from .virtualized import V

    return [
        i
        if isinstance(i, int)
        else int(i)
        if isinstance(i, sympy.Integer)
        else V.graph.sizevars.shape_env.create_symintnode(i, hint=None)
        for i in lst
    ]


def gen_gm_and_inputs(target, args, kwargs):
    g = torch.fx.Graph()
    g_args = []
    a_args = []
    for n, arg in enumerate(args):
        if isinstance(arg, torch.Tensor):
            g_args.append(g.placeholder(f"arg{n}"))
            a_args.append(arg)
        else:
            g_args.append(arg)
    assert all(not isinstance(x, torch.Tensor) for x in kwargs.values())
    node = g.call_function(target, tuple(g_args), kwargs)
    if (
        len(target._schema.returns) == 1
        and str(target._schema.returns[0].type) == "Tensor"
    ):
        node = (node,)
    g.output(node)

    gm = torch.fx.GraphModule({}, g)
    return gm, a_args


def synchronize():
    if torch.cuda.is_available():
        torch.cuda.synchronize()


def timed(model, example_inputs, times=1):
    synchronize()
    torch.manual_seed(1337)
    t0 = time.perf_counter()
    for _ in range(times):
        result = model(*example_inputs)
        synchronize()
    t1 = time.perf_counter()
    # GC the result after timing
    assert result is not None
    return t1 - t0


def print_performance(fn, args=(), times=10, repeat=10, baseline=1.0):
    timings = torch.tensor([timed(fn, args, times) for _ in range(repeat)])
    took = torch.median(timings)
    print(f"{took/baseline:.6f}")
    return took


immutable_dict.__hash__ = lambda self: hash(tuple(self.items()))
immutable_list.__hash__ = lambda self: hash(tuple(self))


def freeze_inputs(f):
    """
    Useful for wrapping lists in tuples for caching purposes
    """

    def freeze_value(x):
        if isinstance(x, (immutable_dict, immutable_list)):
            return x
        if isinstance(x, list):
            return immutable_list(x)
        if isinstance(x, dict):
            return immutable_dict(x)
        return x

    @functools.wraps(f)
    def wrapped(*args):
        args = [freeze_value(x) for x in args]
        return f(*args)

    wrapped.cache_info = f.cache_info
    return wrapped


def precompute_method(obj: Any, method: str):
    """Replace obj.method() with a new method that returns a precomputed constant."""
    result = getattr(obj, method)()
    setattr(obj, method, lambda: result)


def precompute_methods(obj: Any, methods: List[str]):
    """Replace methods with new methods that returns a precomputed constants."""
    for method in methods:
        precompute_method(obj, method)


def cmp(a, b):
    return int(a > b) - int(a < b)


def pad_listlike(x, size):
    if len(x) == 1:
        return type(x)([x[0]]) * size
    else:
        return x


def cache_on_self(fn):
    key = f"__{fn.__name__}_cache"

    @functools.wraps(fn)
    def wrapper(self):
        if not hasattr(self, key):
            setattr(self, key, fn(self))
        return getattr(self, key)

    return wrapper


def get_fused_kernel_name(node_schedule, descriptive_names):
    all_origins = functools.reduce(
        operator.or_,
        [node.node.origins for node in node_schedule if hasattr(node, "node")],
    )
    if descriptive_names == "original_aten":
        # Bases the kernel name off of the top-level aten operator (i.e. pre-decompositions)
        sources = [
            origin.meta["original_aten"]._overloadpacket.__name__
            for origin in all_origins
            if origin.op == "call_function" and "original_aten" in origin.meta
        ]
        sources = sorted(set(sources))
    elif descriptive_names == "torch":
        # Bases the kernel name off of the top-level "torch" operator (i.e. post-dynamo graph)
        sources = []
        for origin in all_origins:
            if origin.op == "call_function" and "source_fn" in origin.meta:
                if isinstance(origin.meta["source_fn"][1], str):
                    sources.append(origin.meta["source_fn"][1])
                else:
                    sources.append(origin.meta["source_fn"][1].__name__)
        sources = sorted(set(sources))
    elif descriptive_names == "inductor_node":
        sources = [
            origin.name for origin in all_origins if origin.op == "call_function"
        ]
    else:
        raise NotImplementedError
    sources = sources
    return "_".join(["fused"] + sources)


def get_kernel_metadata(node_schedule):
    all_origins = functools.reduce(
        operator.or_,
        [node.node.origins for node in node_schedule if hasattr(node, "node")],
    )
    inductor_nodes = [origin for origin in all_origins if origin.op == "call_function"]
    original_aten_dict = collections.defaultdict(list)
    for node in inductor_nodes:
        if "original_aten" in node.meta:
            original_aten_dict[str(node.meta["original_aten"]._overloadpacket)].append(
                node.name
            )
    metadata = [
        f"# Original ATen: {', '.join(sorted(original_aten_dict.keys()))}\n",
    ]
    for original_aten, nodes in sorted(original_aten_dict.items()):
        metadata.append(f"# {original_aten} => {', '.join(sorted(nodes))}")
    return "\n".join(metadata)


def gather_origins(args, kwargs):
    import itertools

    from . import ir

    def is_unrealized_node(n):
        if isinstance(n, ir.TensorBox):
            return is_unrealized_node(n.data)
        if isinstance(n, ir.StorageBox):
            return is_unrealized_node(n.data)
        return isinstance(n, ir.IRNode) and isinstance(n, ir.Pointwise)

    kwarg_origins = [val.origins for val in kwargs.values() if is_unrealized_node(val)]
    arg_origins = [arg.origins for arg in args if is_unrealized_node(arg)]
    return set(itertools.chain(*arg_origins, *kwarg_origins))


def sympy_str(expr: sympy.Expr):
    """
    Normal sympy str is very slow, this is a lot faster.  The result are
    somewhat worse, as it doesn't do as much simplification.  So don't
    use this for final codegen.
    """
    if isinstance(expr, sympy.Symbol):
        return expr.name
    if isinstance(expr, sympy.Add):
        return " + ".join(map(sympy_str, expr.args))
    if isinstance(expr, sympy.Mul):
        return " * ".join(map(sympy_str, expr.args))

    from .ir import CleanDiv, FloorDiv, ModularIndexing

    if isinstance(expr, (ModularIndexing, CleanDiv, FloorDiv)):
        return f"{expr.func.__name__}({', '.join(map(sympy_str, expr.args))})"
    return str(expr)


def sympy_symbol(name) -> sympy.Symbol:
    # This should never be used for creating shape/stride symbols, as those
    # should all be allocated before Inductor.
    assert name[0] != "s"
    return sympy.Symbol(name, integer=True, positive=True)


def sympy_subs(expr: sympy.Expr, replacements: Dict[Any, Any]) -> sympy.Expr:
    """
    xreplace is faster than subs, but is way more picky
    """

    def promote_strings(key):
        if isinstance(key, str):
            return sympy_symbol(key)
        return key

    return expr.xreplace(
        {promote_strings(k): promote_strings(v) for k, v in replacements.items()}
    )


def free_symbol_startswith(index: sympy.Expr, prefix: str):
    return any(v.name.startswith(prefix) for v in index.free_symbols)


def free_symbol_has(index: sympy.Expr, pattern: str):
    return any(pattern in v.name for v in index.free_symbols)


def has_incompatible_cudagraph_ops(gm):
    forbidden_set = {
        "aten._fused_moving_avg_obs_fq_helper.default",
        "aten._fused_moving_avg_obs_fq_helper_functional.default",
        "fbgemm.dense_to_jagged.default",
        "fbgemm.jagged_to_padded_dense.default",
    }
    if torch.are_deterministic_algorithms_enabled():
        forbidden_set.update(
            {
                "aten._unsafe_index_put.default",
                "aten.index_put.default",
                "aten.index_put_.default",
                "aten.scatter.src",
                "aten.scatter.reduce",
                "aten.scatter.value_reduce",
                "aten.scatter_add_",
                "aten.scatter_add.default",
                "aten.scatter_reduce.two",
                "aten.scatter_reduce_.two",
                "aten.scatter_reduce.two_out",
            }
        )
    for node in gm.graph.nodes:
        if str(node.target) in forbidden_set:
            return True
    return False


instance_descriptor = collections.namedtuple(
    "instance_descriptor", ["divisible_by_16", "equal_to_1"]
)


@contextlib.contextmanager
def fresh_inductor_cache(cache_entries=None):
    """
    Contextmanager that provides a clean tmp cachedir for inductor.

    Optionally, pass a dict as 'cache_entries' to get a list of filenames and sizes
    generated with this cache instance.
    """
    with tempfile.TemporaryDirectory() as inductor_cache_dir:
        with mock.patch.dict(
            os.environ, {"TORCHINDUCTOR_CACHE_DIR": inductor_cache_dir}
        ):
            triton_cache_dir = os.path.join(inductor_cache_dir, "triton")
            with mock.patch.dict(os.environ, {"TRITON_CACHE_DIR": triton_cache_dir}):
                yield
                if isinstance(cache_entries, dict):
                    assert len(cache_entries) == 0, "expected empty cache_entries dict"
                    if os.path.exists(triton_cache_dir):
                        files = os.listdir(triton_cache_dir)
                        cache_entries.update(
                            {
                                f: os.path.getsize(os.path.join(triton_cache_dir, f))
                                for f in files
                                if ".lock" not in f
                            }
                        )


def argsort(seq) -> List[int]:
    # preserve original order for equal strides
    getter = seq.__getitem__
    a_r = range(len(seq))
    return list(reversed(sorted(a_r, key=getter, reverse=True)))  # noqa: C413


@functools.lru_cache(8)
def get_dtype_size(dtype):
    return torch.empty((), dtype=dtype).element_size()


class LineContext(NamedTuple):
    context: Any


class IndentedBuffer:
    tabwidth = 4

    def __init__(self, initial_indent=0):
        self._lines = []
        self._indent = initial_indent

    def getvaluewithlinemap(self):
        buf = StringIO()
        p = 1
        linemap = []
        for line in self._lines:
            if isinstance(line, DeferredLineBase):
                line = line()
                if line is None:
                    continue
            elif isinstance(line, LineContext):
                linemap.append((p, line.context))
                continue
            assert isinstance(line, str)
            buf.write(line)
            buf.write("\n")
            p += 1 + line.count("\n")
        return buf.getvalue(), linemap

    def getvalue(self):
        v, _ = self.getvaluewithlinemap()
        return v

    def getrawvalue(self):
        buf = StringIO()
        for line in self._lines:
            if isinstance(line, DeferredLineBase):
                line = line()
                if line is None:
                    continue
            elif isinstance(line, LineContext):
                continue
            assert isinstance(line, str)
            # backslash implies line continuation
            if line.endswith("\\"):
                buf.write(line[:-1])
            else:
                buf.write(line)
                buf.write("\n")
        return buf.getvalue()

    def clear(self):
        self._lines.clear()

    def __bool__(self):
        return bool(self._lines)

    def prefix(self):
        return " " * (self._indent * self.tabwidth)

    def writeline(self, line):
        if isinstance(line, LineContext):
            self._lines.append(line)
        elif isinstance(line, DeferredLineBase):
            self._lines.append(line.with_prefix(self.prefix()))
        elif line.strip():
            self._lines.append(f"{self.prefix()}{line}")
        else:
            self._lines.append("")

    def writelines(self, lines):
        for line in lines:
            self.writeline(line)

    def indent(self, offset=1):
        @contextlib.contextmanager
        def ctx():
            self._indent += offset
            try:
                yield
            finally:
                self._indent -= offset

        return ctx()

    def splice(self, other_code, strip=False):
        if isinstance(other_code, IndentedBuffer):
            dedent = float("inf")
            for line in other_code._lines:
                if not isinstance(line, LineContext) and line:
                    dedent = min(dedent, len(line) - len(line.lstrip()))
            if math.isinf(dedent):
                dedent = 0
            for line in other_code._lines:
                if isinstance(line, LineContext):
                    self._lines.append(line)
                else:
                    IndentedBuffer.writeline(self, line[dedent:])
        else:
            other_code = textwrap.dedent(other_code)
            if strip:
                other_code = other_code.lstrip()
            if not other_code:
                return
            other_code = other_code.rstrip()
            for line in other_code.split("\n"):
                self.writeline(line)


class DeferredLineBase:
    """A line that can be 'unwritten' at a later time"""

    def __init__(self, line):
        if not line.strip():
            line = ""
        self.line = line

    def __call__(self) -> Optional[str]:
        """Returns either self.line or None to indicate the line has been 'unwritten'"""
        raise NotImplementedError()

    def _new_line(self, line: str) -> "DeferredLineBase":
        """Returns a new deferred line with the same condition"""
        raise NotImplementedError()

    def with_prefix(self, prefix):
        return self._new_line(f"{prefix}{self.line}")

    def lstrip(self):
        return self._new_line(self.line.lstrip())

    def __getitem__(self, index):
        return self._new_line(self.line[index])

    def __bool__(self):
        return bool(self.line)

    def __len__(self):
        return len(self.line)


@functools.lru_cache(None)
def is_big_gpu(index):
    sms = torch.cuda.get_device_properties(index).multi_processor_count
    if sms < 80:  # V100
        log.warning("not enough SMs to use max_autotune_gemm mode")
        return False
    return True


def use_triton_template(layout, *, enable_int32=False):
    layout_dtypes = (torch.float16, torch.bfloat16, torch.float32)
    if enable_int32:
        layout_dtypes = (torch.float16, torch.bfloat16, torch.float32, torch.int32)
    return (
        (
            config.max_autotune
            or config.max_autotune_gemm
            or config.search_autotune_cache
        )
        and layout.device.type == "cuda"
        and layout.dtype in layout_dtypes
        and is_big_gpu(layout.device.index or 0)
    )


class DebugDirManager:
    counter = itertools.count(0)

    def __init__(self):
        self.id = next(DebugDirManager.counter)
        self.prev_debug_name = None

    def __enter__(self):
        self.prev_debug_name = torch._dynamo.config.debug_dir_root
        self.new_name = f"{self.prev_debug_name}_tmp_{self.id}"
        torch._dynamo.config.debug_dir_root = self.new_name

    def __exit__(self, *args):
        shutil.rmtree(self.new_name)
        torch._dynamo.config.debug_dir_root = self.prev_debug_name


def run_and_get_code(fn, *args, **kwargs):
    from .graph import GraphLowering

    compile_to_module = GraphLowering.compile_to_module
    source_codes = []

    def patched_compile_to_module(self):
        mod = compile_to_module(self)
        with open(mod.__file__, "r") as f:
            source_codes.append(f.read())
        return mod

    with mock.patch.object(
        GraphLowering, "compile_to_module", patched_compile_to_module
    ):
        torch._dynamo.reset()
        result = fn(*args, **kwargs)
    return result, source_codes


def run_and_get_triton_code(fn, *args, **kwargs):
    _, source_codes = run_and_get_code(fn, *args, **kwargs)
    assert (
        len(source_codes) == 1
    ), f"expected exactly one code output got {len(source_codes)}"
    return source_codes[0]


def developer_warning(msg):
    """
    Warnings that will be actionable for PyTorch developers, but not
    end users.  Allows us to easily disable them in stable releases but
    keep them on for nightly builds.
    """
    if config.developer_warnings:
        log.warning(msg)
    else:
        log.info(msg)


def get_num_bytes(*args, num_in_out_args=0):
    """
    Return the total number of bytes the arguments of tensor type takes.

    For in/out args, tensor sizes are counted twice: once for reading and
    once for writing.

    The first num_in_out_args arguments are in out tensors.
    """
    return sum(
        arg.numel() * arg.element_size() * (1 + int(i < num_in_out_args))
        for i, arg in enumerate(args)
        if isinstance(arg, torch.Tensor)
    )


def create_bandwidth_info_str(ms, num_gb, gb_per_s, prefix="", suffix=""):
    info_str = f"{prefix}{ms:.3f}ms    \t{num_gb:.3f} GB \t {gb_per_s:7.2f}GB/s{suffix}"
    try:
        import colorama

        if ms > 0.012 and gb_per_s < 650:
            info_str = colorama.Fore.RED + info_str + colorama.Fore.RESET
    except ImportError:
        log.warning("Colorama is not installed. Install it if you want colored output")

    return info_str


def get_benchmark_name():
    """
    An experimental API used only when config.benchmark_kernel is true.

    The benchmark name is only available at codegen time. So we can not
    directly call it in benchmark_all_kernels which is run after codegen.

    The function assumes the argument after --only is the benchmark name.
    It works for torchbench.py/hugginface.py/timm_models.py. But for ad-hoc
    scripts, this function may return None.

    There are 2 flavors of --only argument we need handle:
    1. --only model_name
    2. --only=model_name
    """
    try:
        idx = sys.argv.index("--only")
        if (
            idx + 1 < len(sys.argv)
            and len(sys.argv[idx + 1]) > 0
            and sys.argv[idx + 1][0] != "-"
        ):
            return sys.argv[idx + 1]
    except ValueError:
        pass

    for arg in sys.argv:
        if arg.startswith("--only="):
            return arg[len("--only=") :]


_kernel_category_choices = [
    "pointwise",
    "reduction",
    "persistent_reduction",
]


def get_kernel_category(kernel_mod):
    """
    Given the module defining a triton kernel, return the category of the kernel.
    Cateogry can be one of:
    - pointwise
    - reduction
    - persistent_reduction

    Currently we simply decide the category depending on what decorator is imported
    by the kernel.
    """
    choices = [ch for ch in _kernel_category_choices if ch in kernel_mod.__dict__]
    if len(choices) == 1:
        return choices[0]
    else:
        return "unknown"


def get_kernel_category_by_source_code(src_code):
    """
    Similar to get_kernel_category but use the source code. Call this API
    if we have not compile the src_code to module yet.
    """
    choices = [ch for ch in _kernel_category_choices if f"@{ch}" in src_code]
    if len(choices) == 1:
        return choices[0]
    else:
        return "unknown"


def benchmark_all_kernels(benchmark_name, benchmark_all_configs):
    """
    An experimental API used only when config.benchmark_kernel is true.

    Run the kernel benchmarks for all the kernels cached in PyCodeCache.
    Used in the compiled modules.

    Put this method here rather than codegen it for convenience since its implementation
    does not change based on different graph modules being compiled.
    """
    from torch._inductor.codecache import PyCodeCache

    def get_triton_kernel(mod):
        from torch._inductor.triton_heuristics import CachingAutotuner

        cand_list = [
            v
            for k, v in mod.__dict__.items()
            if k.startswith("triton_") and isinstance(v, CachingAutotuner)
        ]
        assert len(cand_list) == 1
        return cand_list[0]

    nfound = 0
    for kernel_key, kernel_mod in PyCodeCache.cache.items():
        if not hasattr(kernel_mod, "get_args") or not hasattr(kernel_mod, "call"):
            continue

        triton_kernel = get_triton_kernel(kernel_mod)
        kernel_category = get_kernel_category(kernel_mod)
        args = kernel_mod.get_args()
        num_in_out_ptrs = len(
            [
                arg_name
                for arg_name in triton_kernel.fn.arg_names
                if arg_name.startswith("in_out_ptr")
            ]
        )
        num_gb = get_num_bytes(*args, num_in_out_args=num_in_out_ptrs) / 1e9

        def get_info_str(ms, n_regs, n_spills, shared, prefix=""):
            if not any(x is None for x in [n_regs, n_spills, shared]):
                kernel_detail_str = (
                    f"  {n_regs:3} regs  {n_spills:3} spills  {shared:8} shared mem"
                )
            else:
                kernel_detail_str = ""

            gb_per_s = num_gb / (ms / 1e3)
            return create_bandwidth_info_str(
                ms, num_gb, gb_per_s, prefix=prefix, suffix=kernel_detail_str
            )

        bench_result = []
        kernel_desc = (
            f"{benchmark_name:20} {kernel_category[:3].upper()} {kernel_key[:10]}"
        )
        if benchmark_all_configs:
            assert hasattr(kernel_mod, "benchmark_all_configs")
            bench_result = kernel_mod.benchmark_all_configs(args)
            print(kernel_desc)
            for launcher, ms in bench_result.items():
                print(
                    f"  {get_info_str(ms, launcher.n_regs, launcher.n_spills, launcher.shared)} @ {launcher.config}"
                )
        else:
            ms = do_bench(lambda: kernel_mod.call(args), rep=40, fast_flush=True)
            assert (
                len(triton_kernel.launchers) == 1
            ), "Autotuner should have selected the best config"
            launcher = triton_kernel.launchers[0]
            print(
                get_info_str(
                    ms,
                    launcher.n_regs,
                    launcher.n_spills,
                    launcher.shared,
                    prefix=f"{kernel_desc} ",
                )
            )

        nfound += 1
    if nfound == 0:
        print(
            "No kernel with benchmark functionality found. Make sure you run inductor with config.benchmark_kernel being True"
        )


def is_ones(items):
    return all(x == 1 for x in items)


def is_zeros(items):
    return all(x == 0 for x in items)


def is_cpu_device(inputs):
    return all(
        item.device == torch.device("cpu")
        for item in inputs
        if isinstance(item, torch.Tensor)
    )


def get_sympy_Expr_dtype(val: sympy.Expr) -> torch.dtype:
    assert isinstance(
        val, sympy.Expr
    ), "only support sympy.Expr as input to get_sympy_Expr_dtype"
    if val.is_integer:
        return torch.int64
    else:
        return torch.float64


@contextlib.contextmanager
def maybe_profile(should_profile, *args, **kwargs):
    if should_profile:
        with torch.profiler.profile(*args, **kwargs) as p:
            yield p
    else:
        yield


@dataclasses.dataclass
class ProfileEvent:
    category: str
    key: str
    self_cuda_time_ms: float
    # the benchmark is run multiple times and we average the count across all the
    # runs. It should be an integer but define a float just in case.
    count: float


def parse_profile_event_list(benchmark_name, event_list, wall_time_ms, nruns):
    def get_self_cuda_time(ev):
        """
        ev.self_cuda_time_total is in microsecond. Convert to millisecond.
        """
        return ev.self_cuda_time_total / 1000 / nruns

    all_events = defaultdict(list)

    def add_event(ev, category):
        profile_ev = ProfileEvent(
            category=category,
            key=ev.key,
            self_cuda_time_ms=get_self_cuda_time(ev),
            count=ev.count / nruns,  # average across all runs
        )
        all_events[category].append(profile_ev)

    for ev in event_list:
        assert not ev.is_legacy, "Don't support the legacy profiler"
        if ev.device_type == DeviceType.CPU:
            # ignore the event on CPU side
            continue

        category = "unknown"
        if ev.key.startswith("triton_"):
            if ev.key.startswith("triton_poi"):
                category = "triton_pointwise"
            elif ev.key.startswith("triton_red"):
                category = "triton_reduction"
            elif ev.key.startswith("triton_per"):
                category = "triton_persistent_reduction"
            else:
                category = "triton_unknown"

        add_event(ev, category)

    def report_category(category, profile_events):
        from tabulate import tabulate

        profile_events.sort(key=lambda ev: ev.self_cuda_time_ms, reverse=True)

        rows = []
        total_time = 0.0
        print(f"\n  == {category} category kernels == ")
        for ev in profile_events:
            total_time += ev.self_cuda_time_ms
            percent = f"{ev.self_cuda_time_ms / wall_time_ms * 100:.2f}%"
            rows.append([ev.key[:120], ev.self_cuda_time_ms, ev.count, percent])
        rows.append(
            ["Total", total_time, "", f"{total_time / wall_time_ms * 100:.2f}%"]
        )
        print(
            tabulate(
                rows, headers=["Kernel", "Self CUDA TIME (ms)", "Count", "Percent"]
            )
        )
        return total_time

    def report():
        category_list = [
            "triton_pointwise",
            "triton_reduction",
            "triton_persistent_reduction",
            "triton_unknown",
            "unknown",
        ]
        assert set(all_events.keys()).issubset(
            set(category_list)
        ), f"{list(all_events.keys())}"

        per_category_wall_time = {}
        total_cuda_ms = 0.0
        for category in category_list:
            if category in all_events:
                _time = report_category(category, all_events[category])
                per_category_wall_time[category] = _time
                total_cuda_ms += _time

        gpu_busy_percent = f"{total_cuda_ms / wall_time_ms * 100:.2f}%"
        print(f"\nPercent of time when GPU is busy: {gpu_busy_percent}")
        print(f"Total wall time {wall_time_ms:.3f} ms")

        # output such a line so we can gather such line from all compiled modules from all
        # benchmarks and tabulate it!
        # Columns: benchmark_name, pointwise_percent, reduction_percent, persistent_reduction_percent,
        #   unknown_category_percent, GPU_busy_percent, wall_time_ms
        tabulate_line = f"Output for tabulate: {benchmark_name}"
        for category in category_list:
            percent = (
                f"{per_category_wall_time.get(category, 0.0) / wall_time_ms * 100:.2f}%"
            )
            tabulate_line += f", {percent}"
        tabulate_line += f", {gpu_busy_percent}, {wall_time_ms:.3f}ms"

        print(tabulate_line)

    report()


def compiled_module_main(benchmark_name, benchmark_compiled_module_fn):
    """
    This is the function called in __main__ block of a compiled module.
    """
    import argparse

    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--benchmark-kernels",
        "-k",
        action="store_true",
        help="Whether to benchmark each individual kernels",
    )
    parser.add_argument(
        "--benchmark-all-configs",
        "-c",
        action="store_true",
        help="Whether to benchmark each individual config for a kernel",
    )
    parser.add_argument(
        "--profile",
        "-p",
        action="store_true",
        help="Whether to profile the compiled module",
    )
    args = parser.parse_args()

    if args.benchmark_kernels:
        benchmark_all_kernels(benchmark_name, args.benchmark_all_configs)
    else:
        times = 10
        repeat = 10
        wall_time_ms = (
            benchmark_compiled_module_fn(times=times, repeat=repeat) / times * 1000
        )

        if not args.profile:
            return

        with torch.profiler.profile(record_shapes=True) as p:
            benchmark_compiled_module_fn(times=times, repeat=repeat)

        path = f"{tempfile.gettempdir()}/compiled_module_profile.json"
        p.export_chrome_trace(path)
        print(f"Profiling result for a compiled module of benchmark {benchmark_name}:")
        print(f"Chrome trace for the profile is written to {path}")
        event_list = p.key_averages(group_by_input_shape=True)
        print(event_list.table(sort_by="self_cuda_time_total", row_limit=10))
        parse_profile_event_list(
            benchmark_name, event_list, wall_time_ms, times * repeat
        )


def triton_config_to_hashable(cfg):
    """
    Convert triton config to a tuple that can uniquely identify it. We can use
    the return value as a dictionary key.
    """
    items = sorted(cfg.kwargs.items())
    items.append(("num_warps", cfg.num_warps))
    items.append(("num_stages", cfg.num_stages))
    return tuple(items)