pytorch/torch/_dynamo/utils.py

import collections
import contextlib
import copy
import cProfile
import dataclasses
import datetime
import dis
import enum
import functools
import gc
import inspect
import itertools
import logging
import math
import operator
import os
import pstats
import sys
import textwrap
import time
import types
import typing
import weakref
from contextlib import contextmanager
from functools import lru_cache, wraps
from typing import Any, Dict, Tuple, Union

import torch._logging
from torch._guards import detect_fake_mode  # noqa: F401
from . import config

try:
    import numpy as np

    HAS_NUMPY = True
except ModuleNotFoundError:
    np = None  # type: ignore[assignment]
    HAS_NUMPY = False

try:
    import torch_np

    HAS_NUMPY_TORCH_INTEROP = True
except ModuleNotFoundError:
    torch_np = None
    HAS_NUMPY_TORCH_INTEROP = False

import importlib

import torch
import torch._functorch.config
import torch.fx.experimental.symbolic_shapes
import torch.utils.checkpoint
from torch import fx
from torch._dispatch.python import enable_python_dispatcher
from torch._subclasses.fake_tensor import FakeTensor
from torch.nn.modules.lazy import LazyModuleMixin
from torch.utils._pytree import tree_map


counters = collections.defaultdict(collections.Counter)
troubleshooting_url = "https://pytorch.org/docs/master/compile/troubleshooting.html"
nnmodule_doc_url = "https://pytorch.org/docs/master/compile/nn-module.html"
nnmodule_doc_url_msg = f"See {nnmodule_doc_url} for more information and limitations."

log = logging.getLogger(__name__)

# profiling compilation time
compilation_metrics = collections.OrderedDict()

timer_counter = itertools.count()


def tabulate(rows, headers):
    try:
        import tabulate

        return tabulate.tabulate(rows, headers=headers)
    except ImportError:
        return "\n".join(
            ", ".join(map(str, row)) for row in itertools.chain([headers], rows)
        )


def dynamo_profiled(func):
    @wraps(func)
    def profile_wrapper(*args, **kwargs):
        global timer_counter
        datafn = (
            func.__name__ + f"{next(timer_counter)}.profile"
        )  # Name the data file sensibly
        prof = cProfile.Profile()
        prof.enable()
        retval = prof.runcall(func, *args, **kwargs)
        prof.disable()
        print(f"### Cprofile for {func.__name__} iter {next(timer_counter)} ###")
        ps = pstats.Stats(prof)
        ps.sort_stats(pstats.SortKey.TIME).print_stats(20)
        ps.sort_stats(pstats.SortKey.CUMULATIVE).print_stats(20)
        prof.dump_stats(datafn)
        return retval

    return profile_wrapper


frame_phase_timing = collections.OrderedDict()

curr_frame = 0


# Note: Called for you by dynamo - you almost never ever want to invoke this yourself.
def increment_frame():
    global curr_frame
    curr_frame = curr_frame + 1


# Note: Called for you by dynamo - you almost never ever want to invoke this yourself.
def reset_frame_count():
    global curr_frame
    frame_phase_timing.clear()
    curr_frame = 0


op_count = 0


def increment_op_count(cnt):
    global op_count
    op_count += cnt


# Print a report of time spent so far
# Ex:
# TIMING:
# entire_frame_compile:8.574629999999999
# backend_compile:5.26806
def print_time_report():
    total = 0
    total_by_key = {}
    for frame, timings in frame_phase_timing.items():
        for key, timing in timings.items():
            total += timing
            if key not in total_by_key:
                total_by_key[key] = timing
            else:
                total_by_key[key] += timing

    out = "TIMING:"
    for key, value in total_by_key.items():
        out = f"{out} {key}:{round(value, 5)}"

    print(out)


# dynamo_timed API works as a function decorator
# By wrapping a function in dynamo_timed, we can store a record in compilation_metrics
# where the key is the functions name.
# For example:
#
#  @dynamo_timed
#  def _foo(...):
#
# Would show up as an entry in our timing dict:
# OrderedDict([('bar.<locals>._foo', [0.083690, 0.23949, 3.1425e-05])])
# This is extremely useful for granular debugging.
#
# For a higher-level mode, pass a phase_name into dynamo_timed
# phase_names record an extra record into a separate compilation timing structure,
# one keyed on frame+name rather than function.
# The frame is incremented outside of this function, in def increment_frame() above.
def dynamo_timed(original_function=None, phase_name=None):
    def dynamo_timed_inner(func):
        @wraps(func)
        def time_wrapper(*args, **kwargs):
            key = func.__qualname__
            if key not in compilation_metrics:
                compilation_metrics[key] = []
            with torch.profiler.record_function(f"{key} (dynamo_timed)"):
                t0 = time.time()
                r = func(*args, **kwargs)
                time_spent = time.time() - t0
            # print(f"Dynamo timer: key={key}, latency={latency:.2f} sec")
            compilation_metrics[key].append(time_spent)
            if phase_name:
                frame_key = str(curr_frame)
                if frame_key not in frame_phase_timing:
                    frame_phase_timing[frame_key] = {}
                assert (
                    phase_name not in frame_phase_timing[frame_key]
                ), f"Duplicate phase name {phase_name} for frame {frame_key}"
                frame_phase_timing[frame_key][phase_name] = time_spent
            return r

        return time_wrapper

    if original_function:
        return dynamo_timed_inner(original_function)
    return dynamo_timed_inner


def compile_times(repr="str", aggregate=False):
    """
    Get metrics about torchdynamo frontend/backend compilation times.

    Accumulates information from functions tagged with `@dynamo_timed`.

    repr='str' returns a printable string for user interaction, and 'csv'
    returns headers, rows which can be logged for output

    aggregate causes values from multiple compilations (e.g. split graphs)
    to be accumulated into one value.  If false, expect more than one value
    per metric.
    """

    def fmt_fn(values, item_fn=lambda x: x):
        if aggregate:
            return item_fn(sum(values))
        return ", ".join(map(item_fn, values))

    if repr == "str":
        rows = [
            (k, fmt_fn(compilation_metrics[k], item_fn=lambda x: f"{x:.4f}"))
            for k in compilation_metrics
        ]
        out = "TorchDynamo compilation metrics:\n"
        out += tabulate(rows, headers=("Function", "Runtimes (s)"))
        return out
    elif repr == "csv":
        values = [
            fmt_fn(v, item_fn=lambda x: f"{x:.6f}")
            for v in compilation_metrics.values()
        ]
        headers = list(compilation_metrics.keys())
        return headers, values


tensortype_to_dtype = {
    torch.FloatTensor: (torch.float32, torch.float),
    torch.DoubleTensor: (torch.float64, torch.double),
    torch.HalfTensor: (torch.float16, torch.half),
    torch.BFloat16Tensor: (torch.bfloat16,),
    torch.ByteTensor: (torch.uint8,),
    torch.CharTensor: (torch.int8,),
    torch.LongTensor: (torch.int64, torch.long),
    torch.IntTensor: (torch.int32, torch.int),
    torch.ShortTensor: (torch.int16, torch.short),
    torch.BoolTensor: (torch.bool,),
}


class DuplicateWarningChecker:
    def __init__(self, maxsize=4096):
        self.maxsize = maxsize
        self.reset()

    def reset(self):
        self.set = collections.OrderedDict()

    def add(self, key):
        if key in self.set:
            self.set.move_to_end(key, last=True)
            if not config.verbose:
                return False
        else:
            self.set[key] = None
            while len(self.set) > self.maxsize:
                self.set.popitem(last=False)
        return True


graph_break_dup_warning_checker = DuplicateWarningChecker()


def setup_compile_debug():
    compile_debug = os.environ.get("TORCH_COMPILE_DEBUG", "0") == "1"

    if compile_debug:
        torch._logging.set_logs(
            dynamo=logging.DEBUG,
            aot=logging.DEBUG,
            inductor=logging.DEBUG,
            output_code=True,  # this is off by default
        )
        return add_file_handler()

    return contextlib.ExitStack()


def reset_graph_break_dup_checker():
    graph_break_dup_warning_checker.reset()


def add_file_handler():
    log_path = os.path.join(get_debug_dir(), "torchdynamo")
    if not os.path.exists(log_path):
        os.makedirs(log_path)

    log_file_handler = logging.FileHandler(os.path.join(log_path, "debug.log"))
    logger = logging.getLogger("torch._dynamo")
    logger.addHandler(log_file_handler)

    exitstack = contextlib.ExitStack()
    exitstack.callback(lambda: logger.removeHandler(log_file_handler))
    return exitstack


def setup_log_file():
    exitstack = contextlib.ExitStack()
    if config.log_file_name is not None:
        log_file_handler = logging.FileHandler(config.log_file_name)
        for logger in logging.get_loggers():
            logger.addHandler(log_file_handler)
            exitstack.callback(lambda: logger.removeHandler(log_file_handler))
        return exitstack

    return exitstack


def gen_record_file_name(exc, code):
    return f"{get_debug_dir()}/error_recordings/\
{code.co_name}_{type(exc).__name__}_{code.co_firstlineno}.rec"


def write_record_to_file(filename, exec_record):
    try:
        if os.path.exists(filename):
            log.warning(
                "Unable to write execution record %s; file already exists.", filename
            )
        else:
            os.makedirs(os.path.dirname(filename), exist_ok=True)
            with open(filename, "wb") as f:
                exec_record.dump(f)
    except Exception:
        log.error("Unable to write execution record %s", filename, exc_info=1)


def count_calls(g: fx.Graph):
    c = 0
    for n in g.nodes:
        if "call" in n.op:
            c += 1
    return c


def identity(x):
    return x


def nothing(*args, **kwargs):
    pass


class ExactWeakKeyDictionary:
    """Similar to weakref.WeakKeyDictionary, but use `is`/`id` rather than `==` to compare equality"""

    def __init__(self):
        self.values = dict()
        self.refs = dict()

    def __getitem__(self, key):
        return self.values[id(key)]

    def get(self, key, default=None):
        return self.values.get(id(key), default)

    def __contains__(self, key):
        return id(key) in self.values

    def __setitem__(self, key, value):
        idx = id(key)
        if idx not in self.refs:
            self.refs[idx] = weakref.ref(key, lambda ref: self._remove_id(idx))
        self.values[idx] = value

    def _remove_id(self, idx):
        if idx in self.values:
            del self.values[idx]
        if idx in self.refs:
            del self.refs[idx]

    def clear(self):
        self.refs.clear()
        self.values.clear()


def istype(obj, allowed_types):
    """isinstance() without subclasses"""
    if isinstance(allowed_types, (tuple, list, set)):
        return type(obj) in allowed_types
    return type(obj) is allowed_types


def is_typing(value):
    if sys.version_info < (3, 9):
        return isinstance(value, typing._GenericAlias)
    else:
        return isinstance(
            value, (typing._SpecialGenericAlias, typing._UnionGenericAlias)
        )


def is_numpy_int_type(value):
    if HAS_NUMPY:
        return istype(
            value,
            (
                np.int8,
                np.int16,
                np.int32,
                np.int64,
                np.uint8,
                np.uint16,
                np.uint32,
                np.uint64,
            ),
        )
    else:
        return False


def is_numpy_float_type(value):
    if HAS_NUMPY:
        return istype(
            value,
            (
                np.float16,
                np.float32,
                np.float64,
            ),
        )
    else:
        return False


def is_numpy_ndarray(value):
    if HAS_NUMPY:
        return istype(value, np.ndarray)
    else:
        return False


def istensor(obj):
    """Check of obj is a tensor"""
    tensor_list = (
        torch.Tensor,
        torch.nn.Parameter,
        *config.traceable_tensor_subclasses,
    )
    tensor_list = tensor_list + (torch._subclasses.FakeTensor,)
    return istype(obj, tensor_list)


def is_lazy_module(mod):
    return isinstance(mod, LazyModuleMixin)


@functools.lru_cache(4096)
def print_once(*args):
    print(*args)


def make_cell(val=None):
    """Some black magic to create a cell object that usually only exists in a closure"""
    x = val

    def f():
        return x

    assert len(f.__closure__) == 1
    return f.__closure__[0]


def proxy_args_kwargs(args, kwargs):
    try:
        proxy_args = tuple(arg.as_proxy() for arg in args)
        proxy_kwargs = {key: arg.as_proxy() for key, arg in kwargs.items()}
        return proxy_args, proxy_kwargs
    except NotImplementedError as e:
        from .exc import unimplemented
        from .variables.base import typestr

        raise unimplemented(
            f"call_function args: {typestr(*args)} {typestr(*list(kwargs.values()))}"
        ) from e


@dataclasses.dataclass
class CleanupHook:
    """Remove a global variable when hook is called"""

    scope: Dict[str, Any]
    name: str

    def __call__(self, *args):
        CleanupManager.count -= 1
        del self.scope[self.name]

    @staticmethod
    def create(scope, name, val):
        assert name not in scope
        CleanupManager.count += 1
        scope[name] = val
        return CleanupHook(scope, name)


class CleanupManager(ExactWeakKeyDictionary):
    count = 0

    def _remove_id(self, idx):
        for hook in self.values[idx]:
            hook()
        super()._remove_id(idx)


CleanupManager.instance = CleanupManager()


def clone_tensor(x):
    """Clone the tensor and its gradient"""
    y = x.clone().requires_grad_(x.requires_grad)
    if x.is_leaf and x.grad is not None:
        y.grad = x.grad.clone()
    return y


def clone_input(x, *, dtype=None):
    """copy while preserving strides"""
    # TODO: this is questionable
    if isinstance(x, torch._subclasses.FakeTensor):
        # this func fails on fake tensors in __torch_dispatch__
        return x

    def torch_clone(x):
        y = torch.clone(x)
        if x.is_leaf:
            y.requires_grad_(x.requires_grad)
        if x.is_leaf and x.grad is not None:
            y.grad = clone_input(x.grad, dtype=dtype)
        if hasattr(x, "_dynamo_dynamic_indices"):
            y._dynamo_dynamic_indices = x._dynamo_dynamic_indices.copy()
        return y

    with torch.no_grad():
        if x.device.type == "xla":
            # Access data_ptr() for a xla tensor will cause crash
            return torch_clone(x)

        needed_size = sum(
            (shape - 1) * stride for shape, stride in zip(x.size(), x.stride())
        )
        if x.is_quantized:
            result = torch.empty_quantized((needed_size + 32,), x)
        else:
            result = torch.empty(
                needed_size + 32, dtype=dtype or x.dtype, device=x.device
            )
        cache_line_offset = (
            (x.data_ptr() - result.data_ptr()) % 32
        ) // x.element_size()
        result.as_strided_(x.size(), x.stride(), cache_line_offset)
        try:
            result.copy_(x.clone())
            if x.is_leaf:
                result.requires_grad_(x.requires_grad)
            if x.is_leaf and x.grad is not None:
                result.grad = clone_input(x.grad, dtype=dtype)
        except RuntimeError:
            # RuntimeError: unsupported operation: more than one element of the written-to
            # tensor refers to a single memory location. Please clone() the tensor before
            # performing the operation.
            return torch_clone(x)
        if hasattr(x, "_dynamo_dynamic_indices"):
            result._dynamo_dynamic_indices = x._dynamo_dynamic_indices.copy()
        return result


def clone_inputs(example_inputs):
    if type(example_inputs) is dict:
        res = dict(example_inputs)
        for key, value in res.items():
            if isinstance(value, tuple):
                res[key] = clone_inputs(value)
            else:
                assert isinstance(value, torch.Tensor), type(value)
                res[key] = clone_input(value)
        return res

    res = list(example_inputs)
    for i in range(len(res)):
        if isinstance(res[i], torch.Tensor):
            res[i] = clone_input(res[i])
    return res


@contextmanager
def preserve_rng_state():
    rng = torch.clone(torch.random.get_rng_state())
    if torch.cuda.is_available():
        cuda_rng = torch.clone(torch.cuda.get_rng_state())
    try:
        yield
    finally:
        torch.random.set_rng_state(rng)
        if torch.cuda.is_available():
            torch.cuda.set_rng_state(cuda_rng)


def is_jit_model(model0):
    return isinstance(
        model0,
        (
            torch.jit._trace.TopLevelTracedModule,
            torch.jit._script.RecursiveScriptModule,
            torch.jit.ScriptFunction,
            torch.jit.ScriptModule,
        ),
    )


def torchscript(model, example_inputs, verbose=False):
    if is_jit_model(model):
        # already done?
        return model

    try:
        return torch.jit.trace(model, example_inputs)
    except Exception:
        try:
            return torch.jit.script(model)
        except Exception:
            if verbose:
                log.exception("jit error")
            else:
                log.error("Both torch.jit.trace and torch.jit.script failed")
    return None


def getfile(obj):
    try:
        return inspect.getfile(obj)
    except TypeError:
        return None


def is_namedtuple(obj):
    """Test if an object is a namedtuple or a torch.return_types.* quasi-namedtuple"""
    return is_namedtuple_cls(type(obj))


def is_namedtuple_cls(cls):
    """Test if an object is a namedtuple or a torch.return_types.* quasi-namedtuple"""
    try:
        if issubclass(cls, tuple):
            bases = getattr(cls, "__bases__", []) or [None]
            module = getattr(cls, "__module__", None)
            return module == "torch.return_types" or (
                bases[0] is tuple and hasattr(cls, "_make") and hasattr(cls, "_fields")
            )
    except TypeError:
        pass
    return False


@functools.lru_cache(1)
def namedtuple_fields(cls):
    """Get the fields of a namedtuple or a torch.return_types.* quasi-namedtuple"""
    if cls is slice:
        return ["start", "stop", "step"]

    assert issubclass(cls, tuple)
    if hasattr(cls, "_fields"):
        # normal namedtuples
        return cls._fields

    @dataclasses.dataclass
    class Marker:
        index: int

    # frustrating ones e.g. torch.return_types.max
    assert cls.__module__ == "torch.return_types"
    obj = cls(map(Marker, range(cls.n_fields)))
    fields = [None] * cls.n_fields
    for name in dir(obj):
        if name[0] != "_" and isinstance(getattr(obj, name), Marker):
            fields[getattr(obj, name).index] = name
    return fields


def checkpoint_params(gm):
    with torch.no_grad():
        rng_state = torch.clone(torch.random.get_rng_state())
        if torch.cuda.is_available():
            cuda_rng_state = torch.clone(torch.cuda.get_rng_state())
        saved_state = []
        for param in itertools.chain(gm.parameters(), gm.buffers()):
            saved_state.append((param, param._version, torch.clone(param)))

    def restore():
        with torch.no_grad():
            torch.random.set_rng_state(rng_state)
            if torch.cuda.is_available():
                torch.cuda.set_rng_state(cuda_rng_state)
            for param, version, original_value in saved_state:
                if param._version != version:
                    param.copy_(original_value)

    return restore


def timed(model, example_inputs, times=1):
    if torch.cuda.is_available():
        synchronize = torch.cuda.synchronize
    else:
        synchronize = nothing

    synchronize()
    gc.collect()
    torch.manual_seed(1337)
    t0 = time.perf_counter()
    for _ in range(times):
        result = model(*example_inputs)
        synchronize()
    t1 = time.perf_counter()
    return result, t1 - t0


def check_is_cuda(gm, example_inputs):
    return all(x.is_cuda for x in itertools.chain(example_inputs, gm.parameters(True)))


@lru_cache(32)
def rot_n_helper(n):
    assert n > 1
    vars = [f"v{i}" for i in range(n)]
    rotated = reversed(vars[-1:] + vars[:-1])
    fn = eval(f"lambda {','.join(vars)}: ({','.join(rotated)})")
    fn.__name__ = f"rot_{n}_helper"
    return fn


def is_safe_constant(v):
    if istype(v, (tuple, frozenset)):
        return all(map(is_safe_constant, v))
    return isinstance(v, (enum.Enum, type)) or istype(
        v,
        (
            types.CodeType,
            int,
            float,
            bool,
            str,
            bytes,
            type(None),
            slice,
            type(type),
            torch.device,
            torch.dtype,
        ),
    )


def check_constant_args(args, kwargs):
    return all(x.is_python_constant() for x in itertools.chain(args, kwargs.values()))


def check_unspec_python_args(args, kwargs):
    from .variables.constant import ConstantVariable
    from .variables.tensor import UnspecializedPythonVariable

    unspec_count = 0
    for x in itertools.chain(args, kwargs.values()):
        if isinstance(x, UnspecializedPythonVariable):
            unspec_count += 1
        elif not isinstance(x, (UnspecializedPythonVariable, ConstantVariable)):
            return False
        else:
            pass

    return unspec_count > 0


def specialize_args_kwargs(tx, args, kwargs):
    specialized_args = []
    specialized_kwargs = {}
    for x in args:
        specialized_args.append(x.as_specialized(tx))
    for k, v in kwargs.items():
        specialized_kwargs.update({k: v.as_specialized(tx)})
    return specialized_args, specialized_kwargs


dict_values = type(dict().values())
odict_values = type(collections.OrderedDict().values())
tuple_iterator = type(iter(tuple()))
tuple_iterator_len = tuple_iterator.__length_hint__
object_new = object.__new__


def nn_module_new(cls):
    obj = object_new(cls)
    torch.nn.Module.__init__(obj)
    return obj


def product(it):
    return functools.reduce(operator.mul, it, 1)


def tuple_iterator_getitem(it, index):
    _, (obj,), start = it.__reduce__()
    return obj[start + index]


def enum_repr(value, local):
    enum_name = str(value)

    name, val = enum_name.split(".")
    scope = "L" if local else "G"
    local_name = f'{scope}["{name}"].{val}'
    return local_name


def dict_param_key_ids(value):
    return {
        id(k) for k in value.keys() if isinstance(k, (torch.nn.Parameter, torch.Tensor))
    }


def dict_const_keys(value):
    return {
        k for k in value.keys() if not isinstance(k, (torch.nn.Parameter, torch.Tensor))
    }


def dict_const_keys_repr(const_keys, *, local):
    if any(isinstance(k, enum.Enum) for k in const_keys):
        # To workaround repr(Enum) returning invalid global reference before python 3.11
        # by calling enum_repr and removing quotes to render enum in guard code.
        const_keys_str = f"{ {enum_repr(k, local=local) if isinstance(k, enum.Enum) else repr(k) for k in const_keys} }".replace(
            "'", ""
        )
    else:
        const_keys_str = f"{const_keys!r}"
    return const_keys_str


def global_key_name(key):
    return f"__dict_key_{id(key)}"


from torch._subclasses import (  # noqa: F401
    FakeTensorMode,
    UnsupportedFakeTensorException,
)


def wrap_fake_exception(fn):
    try:
        return fn()
    except UnsupportedFakeTensorException as e:
        from .exc import unimplemented

        msg = f"Unsupported: {e.reason} with fake tensor propagation."
        log.warning(msg)
        raise unimplemented(msg) from e


def deepcopy_to_fake_tensor(obj, fake_mode):
    with torch._subclasses.fake_tensor.FakeCopyMode(fake_mode):
        return wrap_fake_exception(lambda: copy.deepcopy(obj))


def rmse(ref, res):
    """
    Calculate root mean squared error
    """
    return torch.sqrt(torch.mean(torch.square(ref - res)))


def same(
    ref,
    res,
    fp64_ref=None,
    cos_similarity=False,
    tol=1e-4,
    equal_nan=False,
    exact_dtype=True,
    relax_numpy_equality=False,
    ignore_non_fp=False,
    log_error=log.error,
):
    """Check correctness to see if ref and res match"""
    if fp64_ref is None:
        fp64_ref = ref
    if isinstance(ref, (list, tuple, torch.nn.ParameterList, torch.Size)):
        assert isinstance(res, (list, tuple)), f"type mismatch {type(ref)} {type(res)}"
        return len(ref) == len(res) and all(
            same(
                ai,
                bi,
                fp64_refi,
                cos_similarity,
                tol,
                equal_nan,
                exact_dtype,
                relax_numpy_equality,
                ignore_non_fp,
                log_error=log_error,
            )
            for ai, bi, fp64_refi in zip(ref, res, fp64_ref)
        )
    elif isinstance(ref, dict):
        assert isinstance(res, dict)
        assert set(ref.keys()) == set(
            res.keys()
        ), f"keys mismatch {set(ref.keys())} == {set(res.keys())}"
        for k in sorted(ref.keys()):
            if not (
                same(
                    ref[k],
                    res[k],
                    fp64_ref[k],
                    cos_similarity=cos_similarity,
                    tol=tol,
                    equal_nan=equal_nan,
                    exact_dtype=exact_dtype,
                    relax_numpy_equality=relax_numpy_equality,
                    ignore_non_fp=ignore_non_fp,
                    log_error=log_error,
                )
            ):
                log_error("Accuracy failed for key name %s", k)
                return False
        return True
    elif isinstance(ref, torch.Tensor):
        assert not isinstance(ref, torch._subclasses.FakeTensor)
        assert not isinstance(res, torch._subclasses.FakeTensor)

        if ref.is_sparse:
            assert res.is_sparse
            ref = ref.to_dense()
            res = res.to_dense()
        assert isinstance(res, torch.Tensor), f"type mismatch {type(ref)} {type(res)}"
        if exact_dtype:
            if ref.dtype != res.dtype:
                log_error("dtype mismatch %s, %s", ref.dtype, res.dtype)
                return False
            if ref.dtype == torch.bool:
                if ignore_non_fp:
                    return True
                # triton stores bool as int8, so add this for more accurate checking
                r = torch.allclose(
                    ref.to(dtype=torch.uint8),
                    res.to(dtype=torch.uint8),
                    atol=tol,
                    rtol=tol,
                    equal_nan=equal_nan,
                )
                if not r:
                    log_error("Accuracy failed: uint8 tensor did not match")
                return r
        if cos_similarity:
            ref = ref.flatten().to(torch.float32)
            res = res.flatten().to(torch.float32)
            if torch.allclose(ref, res, atol=tol, rtol=tol, equal_nan=True):
                # early exit that handles zero/nan better
                # cosine_similarity(zeros(10), zeros(10), dim=0) is 0
                return True
            score = torch.nn.functional.cosine_similarity(ref, res, dim=0, eps=1e-6)
            if score < 0.99:
                log.warning("Similarity score=%s", score.cpu().detach().item())
            return score >= 0.99
        else:
            if not exact_dtype:
                ref = ref.to(res.dtype)

            # First try usual allclose
            if torch.allclose(ref, res, atol=tol, rtol=tol, equal_nan=equal_nan):
                return True

            # Check error from fp64 version
            if fp64_ref.dtype == torch.float64:
                ref_error = rmse(fp64_ref, ref).item()
                res_error = rmse(fp64_ref, res).item()
                multiplier = 2.0

                if (
                    fp64_ref.numel() < 1000
                    or (ref.ndim == 4 and ref.shape[-1] == ref.shape[-2] == 1)
                    # large tol means a benchmark has been specified as REQUIRE_HIGHER_TOLERANCE
                    or tol >= 2 * 1e-2
                ):
                    # In the presence of noise, noise might dominate our error
                    # metric for smaller tensors.
                    # Similary, for 1x1 kernels, there seems to be high noise with amp.
                    multiplier = 3.0

                passes_test = res_error <= (multiplier * ref_error + tol / 10.0)
                if not passes_test:
                    log_error(
                        "RMSE (res-fp64): %.5f, (ref-fp64): %.5f and shape=%s",
                        res_error,
                        ref_error,
                        res.size(),
                    )
                    # import pdb; pdb.set_trace()
                return passes_test

            if ignore_non_fp:
                return True

            log_error("Accuracy failed: allclose not within tol=%s", tol)
            return False
    elif isinstance(ref, (str, int, type(None), bool, torch.device)):
        if ignore_non_fp:
            return True
        r = ref == res
        if not r:
            log_error("Accuracy failed (%s): %s != %s", type(ref), ref, res)
        return r
    elif isinstance(ref, float):
        r = math.isclose(ref, res, rel_tol=tol, abs_tol=tol)
        if not r:
            log_error(
                "Accuracy failed (float): %s != %s (within tol=%s)", ref, res, tol
            )
        return r
    elif is_numpy_int_type(ref) or is_numpy_float_type(ref):
        if relax_numpy_equality and not (
            is_numpy_int_type(res) or is_numpy_float_type(res)
        ):
            ref = ref.item()
        r = (type(ref) is type(res)) and (ref == res)
        if not r:
            log_error("Accuracy failed (numpy): %s != %s", ref, res)
        return r
    elif is_numpy_ndarray(ref):
        return (type(ref) is type(res)) and (ref == res).all()
    elif type(ref).__name__ in (
        "MaskedLMOutput",
        "Seq2SeqLMOutput",
        "CausalLMOutputWithCrossAttentions",
        "LongformerMaskedLMOutput",
        "Instances",
        "SquashedNormal",
        "Boxes",
        "Normal",
        "TanhTransform",
        "Foo",
        "Variable",
    ):
        assert type(ref) is type(res)
        return all(
            same(
                getattr(ref, key),
                getattr(res, key),
                getattr(fp64_ref, key),
                cos_similarity=cos_similarity,
                tol=tol,
                equal_nan=equal_nan,
                exact_dtype=exact_dtype,
                relax_numpy_equality=relax_numpy_equality,
                ignore_non_fp=ignore_non_fp,
                log_error=log_error,
            )
            for key in ref.__dict__.keys()
        )
    else:
        raise RuntimeError(f"unsupported type: {type(ref).__name__}")


def format_func_info(code):
    short_filename = code.co_filename.split("/")[-1]
    return f"'{code.co_name}' ({short_filename}:{code.co_firstlineno})"


@contextlib.contextmanager
def disable_cache_limit():
    prior = config.cache_size_limit
    config.cache_size_limit = sys.maxsize

    try:
        yield
    finally:
        config.cache_size_limit = prior


# map from transformed code back to original user code
orig_code_map = ExactWeakKeyDictionary()

# keep a record of code_obj -> list of guard failure reasons for logging
guard_failures = collections.defaultdict(list)

# Keep a record of graph break reasons for logging
graph_break_reasons = list()

# keep record of compiled code, if we are in "error if recompile"
# to track code that dynamo has compiled previously
seen_code_map = ExactWeakKeyDictionary()


class CompileProfiler:
    """Utility for profiling how and what dynamo would compile.

    Can be used for
     * diagnosing recompilation issues
     * determining an appropriate compile cache limit
     * (TODO)confirming which functions got compiled/skipped
    """

    def __init__(self):
        self.frame_count = 0
        self.op_count = 0
        self.backend_ctx_ctor = lambda: disable_cache_limit()

    def __call__(self, gm: torch.fx.GraphModule, example_inputs):
        self.frame_count += 1
        for node in gm.graph.nodes:
            if "call" in node.op:
                self.op_count += 1
        return gm.forward

    def __enter__(self):
        self.old_report_guard_failure = config.report_guard_failures
        config.report_guard_failures = True
        return self

    def __exit__(self, typ, val, traceback):
        config.report_guard_failures = self.old_report_guard_failure

    def get_metrics(self):
        return {"guard_failures": guard_failures}

    def report(self):
        metrics = self.get_metrics()
        gf = metrics["guard_failures"]

        def num_recompiles(code):
            return len(gf[code])

        def recompile_reasons(code):
            return "\n".join([str(x) for x in gf[code]])

        summarized_gf = [
            [format_func_info(code), num_recompiles(code), recompile_reasons(code)]
            for code in gf
        ]

        def graph_break_report():
            if "graph_break" in counters:
                graph_breaks = counters["graph_break"]
                return tabulate(
                    [[msg, graph_breaks[msg]] for msg in graph_breaks],
                    headers=["Graph Break Reason", "Count"],
                )

        def recompilation_report():
            if len(gf):
                max_recompiles = max([num_recompiles(code) for code in gf])
                recomp_table = tabulate(
                    summarized_gf,
                    headers=["Function", "Recompiles", "Recompile Reasons"],
                )
                return recomp_table + textwrap.dedent(
                    f"""

                    Set torch._dynamo.config.cache_size_limit to {max_recompiles} to avoid being cache limited.
                """
                )

        report = textwrap.dedent(
            """
            Torchdynamo Profiler Report
            ===========================

            Graph Breaks
            ------------
            Graph breaks happen when torchdynamo encounters code it can't safely trace.
            If you want to find out why breaks are happening, check below for each break reason
            You may gain additional insight by passing `fullgraph=True` to torch.compile,
            to stop at the first break.

        """
        )
        report += graph_break_report() or "No graph breaks detected."
        report += textwrap.dedent(
            """

            Recompilation
            -------------
            These subgraphs were recompiled more than once due to guard failures
            Guard failures indicate some condition assumed to be static by the tracer changed,
            making it unsafe to reuse the compiled program.

        """
        )
        report += recompilation_report() or "No recompilation detected.\n"
        return report


# return same dir unless user changes config between calls
@functools.lru_cache(None)
def _get_debug_dir(root_dir):
    dir_name = (
        "run_"
        + datetime.datetime.now().strftime("%Y_%m_%d_%H_%M_%S_%f")
        # use pid to avoid conflicts among ranks
        + "-pid_"
        + str(os.getpid())
    )
    return os.path.join(root_dir, dir_name)


def get_debug_dir():
    debug_root = config.debug_dir_root
    return _get_debug_dir(debug_root)


def get_fake_value(node, tx):
    """
    Run the computation represented by `node` using fake tensors and return the result.
    """
    from .exc import (
        TorchRuntimeError,
        unimplemented,
        Unsupported,
        UserError,
        UserErrorType,
    )

    op = node.op

    def fake_wrapper(e):
        if isinstance(e, torch.Tensor):
            assert isinstance(e, FakeTensor)
        return e

    def visit(n: torch.fx.Node):
        return n.meta["example_value"]

    args, kwargs = torch.fx.node.map_arg((node.args, node.kwargs), visit)
    args = tree_map(fake_wrapper, args)
    kwargs = tree_map(fake_wrapper, kwargs)

    nnmodule = None
    if op == "call_method" and len(args) > 0 and isinstance(args[0], torch.nn.Module):
        # If the first argument is nn.Module, should copy to fake mode.
        args = (deepcopy_to_fake_tensor(args[0], tx.fake_mode),) + tuple(args[1:])

    if op == "call_module":
        nnmodule = tx.output.nn_modules[node.target]

        if is_lazy_module(nnmodule) and hasattr(nnmodule, "_initialize_hook"):
            # In the case of a lazy module, we want to run
            # the pre-hooks which initialize it.
            # Afterwards, lazy module deletes its pre-hooks
            # to avoid treating it as lazy on subsequent recompile.
            nnmodule._infer_parameters(nnmodule, args)

        # no matter it's lazy module or not, we should copy to fake mode.
        nnmodule = deepcopy_to_fake_tensor(nnmodule, tx.fake_mode)

    try:
        with tx.fake_mode, enable_python_dispatcher():
            return wrap_fake_exception(
                lambda: run_node(tx.output, node, args, kwargs, nnmodule)
            )
    except Unsupported:
        raise
    except RuntimeError as e:
        cause = e
        if e.__cause__ is not None:
            cause = e.__cause__

        if isinstance(
            cause, torch._subclasses.fake_tensor.DataDependentOutputException
        ):
            unimplemented(f"data dependent operator: {cause.func}")
        elif isinstance(
            cause, torch._subclasses.fake_tensor.DynamicOutputShapeException
        ):
            unimplemented(f"dynamic shape operator: {cause.func}")
        elif isinstance(
            cause, torch._subclasses.fake_tensor.UnsupportedOperatorException
        ):
            unimplemented(
                f"unsupported operator: {cause.func} (see "
                "https://docs.google.com/document/d/1GgvOe7C8_NVOMLOCwDaYV1mXXyHMXY7ExoewHqooxrs/edit#heading=h.64r4npvq0w0"
                " for how to fix)"
            )
        elif isinstance(
            cause, torch.fx.experimental.symbolic_shapes.GuardOnDataDependentSymNode
        ):
            unimplemented("guard on data-dependent symbolic int/float")
        elif isinstance(cause, torch.utils._sympy.value_ranges.ValueRangeError):
            raise UserError(UserErrorType.CONSTRAIN_VIOLATION, e.args[0]) from e
        raise TorchRuntimeError(str(e)).with_traceback(e.__traceback__) from None


def run_node(tracer, node, args, kwargs, nnmodule):
    """
    Runs a given node, with the given args and kwargs.

    Behavior is dicatated by a node's op.

    run_node is useful for extracting real values out of nodes.
    See get_real_value for more info on common usage.

    Note: The tracer arg is only used for 'get_attr' ops
    Note: The nnmodule arg is only used for 'call_module' ops

    Nodes that are not call_function, call_method, call_module, or get_attr will
    raise an AssertionError.
    """
    op = node.op
    try:
        if op == "call_function":
            return node.target(*args, **kwargs)
        elif op == "call_method":
            return getattr(args[0], node.target)(*args[1:], **kwargs)
        elif op == "call_module":
            assert nnmodule is not None
            return nnmodule(*args, **kwargs)
        elif op == "get_attr":
            return tracer.get_submodule(node.target)
        elif op == "placeholder":
            assert "example_value" in node.meta
            return node.meta["example_value"]
    except Exception as e:
        fn_str = f"Failed running {op} {node.target}(*{args}, **{kwargs}):\n"
        raise RuntimeError(fn_str + str(e)).with_traceback(e.__traceback__) from e

    raise AssertionError(op)


def get_real_value(node, tracer):
    """
    Run the actual computation represented by `node` and return the result.
    This will execute any dependent nodes in the graph as well.
    """
    from .exc import TorchRuntimeError

    cache = tracer.real_value_cache
    if node in cache:
        return cache[node]

    op = node.op
    args, kwargs = torch.fx.node.map_arg(
        (node.args, node.kwargs),
        lambda n: get_real_value(n, tracer),
    )

    if op == "call_module":
        nn_module = tracer.output_graph.nn_modules[node.target]
        if not is_lazy_module(nn_module):
            nn_module = copy.deepcopy(nn_module)
        else:
            # In the case of a lazy module, we want to run
            # the pre-hooks which initialize it
            nn_module(*args, **kwargs)
    else:
        nn_module = None

    try:
        real_value = run_node(tracer, node, args, kwargs, nn_module)
        cache[node] = real_value
    except RuntimeError as e:
        raise TorchRuntimeError(str(e)).with_traceback(e.__traceback__) from None
    return real_value


def assert_no_fake_params_or_buffers(gm):
    from torch._subclasses.fake_tensor import FakeTensorConfig

    def stack_or_hint(t):
        if FakeTensorConfig.debug:
            import traceback

            return f"FAKE TENSOR CREATION TRACEBACK: \n {traceback.format_list(t._debug_trace)}"
        else:
            return "Enable TORCH_FAKE_TENSOR_DEBUG=1 to get creation stack traces on fake tensors."

    for name, buffer in gm.named_buffers():
        assert not isinstance(
            buffer, torch._subclasses.FakeTensor
        ), f"Unexpected fake buffer {name} {stack_or_hint(buffer)}"
    for name, param in gm.named_parameters():
        assert not isinstance(
            param, torch._subclasses.FakeTensor
        ), f"Unexpected fake param {name} {stack_or_hint(param)}"


def fqn(obj: Any):
    """
    Returns the fully qualified name of the object.
    """
    return f"{obj.__module__}.{obj.__qualname__}"


def ifdyn(count1, count2):
    if torch._dynamo.config.dynamic_shapes:
        return count1
    else:
        return count2


def ifdynstaticdefault(count1, count2):
    if torch._dynamo.config.assume_static_by_default:
        return count1
    else:
        return count2


def ifunspec(count1, count2):
    if torch._dynamo.config.dynamic_shapes and not torch._dynamo.config.specialize_int:
        return count1
    else:
        return count2


def import_submodule(mod: types.ModuleType):
    """
    Ensure all the files in a given submodule are imported
    """
    for filename in sorted(os.listdir(os.path.dirname(mod.__file__))):
        if filename.endswith(".py") and filename[0] != "_":
            importlib.import_module(f"{mod.__name__}.{filename[:-3]}")


def object_has_getattribute(value: Any):
    try:
        if isinstance(
            inspect.getattr_static(type(value), "__getattribute__"),
            types.FunctionType,
        ):
            return True
    except AttributeError:
        pass
    return False


def get_custom_getattr(value: Any):
    try:
        getattr_fn = inspect.getattr_static(type(value), "__getattr__")
    except AttributeError:
        getattr_fn = None
    if getattr_fn is torch.nn.Module.__getattr__:
        # ignore this case of getattr
        getattr_fn = None
    return getattr_fn


class TensorStaticReason(enum.Enum):
    PARAMETER = 2
    CONFIG_NOT_DYN = 3
    NOT_TENSOR = 4
    NN_MODULE_PROPERTY = 5


def tensor_static_reason_to_message(reason: TensorStaticReason):
    if reason == TensorStaticReason.PARAMETER:
        return "mark_dynamic on parameter, parameters are always static today."
    if reason == TensorStaticReason.CONFIG_NOT_DYN:
        return "mark_dynamic usage with dynamic_shapes=False is not yet supported"
    if reason == TensorStaticReason.NOT_TENSOR:
        return "mark_dynamic on a non tensor, how did this happen?"
    if reason == TensorStaticReason.NN_MODULE_PROPERTY:
        return "tensor is static because it is nn module associated."
    raise AssertionError(f"Illegal reason {reason}")


def tensor_always_has_static_shape(
    tensor: Union[torch.Tensor, Any], is_tensor: bool, guard_source: "GuardSource"
) -> Tuple[bool, TensorStaticReason]:
    """
    Given a tensor, source, and is_tensor flag, determine if a shape should be static.

    Args:
    tensor - the real tensor to evaluate, parameters force a static shape.
    is_tensor - internal dynamo check, esentially "is_tensor": target_cls is TensorVariable,
    tensors not in a TensorVariable for whatever reason are forced static.

    Returns a tuple, where the first element is the bool of whether or not this tensor should have a static shape.
    The second element is a TensorStaticReason, useful for passing to tensor_static_reason_to_message if needed.
    """
    if type(tensor) is torch.nn.Parameter:
        return True, TensorStaticReason.PARAMETER
    if config.dynamic_shapes is False:
        return True, TensorStaticReason.CONFIG_NOT_DYN
    if not is_tensor:
        return True, TensorStaticReason.NOT_TENSOR
    if guard_source.is_nn_module():
        return True, TensorStaticReason.NN_MODULE_PROPERTY
    return False, None


class LazyString:
    def __init__(self, func, *args, **kwargs):
        self.func = func
        self.args = args
        self.kwargs = kwargs

    def __str__(self):
        return self.func(*self.args, **self.kwargs)


def lazy_format_graph_code(name, gm, maybe_id=None):
    def format_name():
        if maybe_id is not None:
            return f"{name} {maybe_id}"
        else:
            return name

    return LazyString(
        lambda: _format_graph_code(
            f"===== {format_name()} =====\n",
            gm.forward.__code__.co_filename,
            gm.print_readable(print_output=False),
        )
    )


def _format_graph_code(name, filename, graph_str):
    return f"TRACED GRAPH\n {name} {filename} {graph_str}\n"


def lazy_format_graph_tabular(fn_name, gm):
    def inner():
        try:
            from tabulate import tabulate  # TODO: Check that this is installed
        except ImportError:
            return (
                "Tabulate module missing, please install tabulate to log the graph in tabular format, logging code instead:\n"
                + str(lazy_format_graph_code(fn_name, gm))
            )

        node_specs = [
            [n.op, n.name, n.target, n.args, n.kwargs] for n in gm.graph.nodes
        ]
        graph_str = tabulate(
            node_specs, headers=["opcode", "name", "target", "args", "kwargs"]
        )
        return _format_graph_code(fn_name, gm.forward.__code__.co_filename, graph_str)

    return LazyString(inner)


def format_bytecode(prefix, name, filename, line_no, code):
    return f"{prefix} {name} {filename} line {line_no} \n{dis.Bytecode(code).dis()}\n"


forward_hook_names = ["_forward_pre_hooks", "_forward_hooks"]
backward_hook_names = ["_backward_pre_hooks", "_backward_hooks"]
state_dict_hook_names = [
    "_state_dict_pre_hooks",
    "_state_dict_hooks",
    "_load_state_dict_pre_hooks",
    "_load_state_dict_post_hooks",
]
all_hook_names = forward_hook_names + backward_hook_names + state_dict_hook_names


def nnmodule_has_hooks(
    mod,
    check_forward_hooks=False,
    check_backward_hooks=False,
    check_state_dict_hooks=False,
):
    """
    Sometimes its useful to differentiate between types of hooks such as forward/backward/pre
    hooks executed during module.__call__, and state_dict hooks which are executed separately.
    """
    hook_dicts_to_check = []
    check_all_hooks = (
        not check_forward_hooks
        and not check_backward_hooks
        and not check_state_dict_hooks
    )
    if check_forward_hooks or check_all_hooks:
        hook_dicts_to_check.extend(forward_hook_names)
    if check_backward_hooks or check_all_hooks:
        hook_dicts_to_check.extend(backward_hook_names)
    if check_state_dict_hooks:
        hook_dicts_to_check.extend(state_dict_hook_names)
    return any(len(getattr(mod, x)) > 0 for x in hook_dicts_to_check if hasattr(mod, x))


def to_numpy_helper(value):
    """Convert tensor and torch_np.ndarray to numpy.ndarray."""
    if isinstance(value, torch_np.ndarray):
        return value.tensor.numpy()
    elif isinstance(value, torch.Tensor):
        return value.numpy()
    elif isinstance(value, (tuple, list)):
        return type(value)(to_numpy_helper(obj) for obj in value)
    else:
        return value


def numpy_to_tensor(value):
    """Convert torch_np.ndarray to tensor, leave other types intact. If a list/tuple, loop through it to convert."""
    if isinstance(value, torch_np.ndarray):
        return value.tensor
    elif isinstance(value, (tuple, list)):
        return type(value)(numpy_to_tensor(obj) for obj in value)
    else:
        return value


class numpy_to_tensor_wrapper:
    def __init__(self, f):
        self.f = f
        self.__name__ = "wrapped_" + self.f.__name__

    def __repr__(self):
        return f"<Wrapped function <original {self.f.__name__}>>"

    def __call__(self, *args, **kwargs):
        out = self.f(*args, **kwargs)
        return numpy_to_tensor(out)


def numpy_attr_wrapper(obj, name):
    if isinstance(obj, torch_np.ndarray):
        out = getattr(obj, name)
        return numpy_to_tensor(out)
    elif isinstance(obj, torch.Tensor):
        out = getattr(torch_np.ndarray(obj), name)
        return numpy_to_tensor(out)


def defake(x):
    if not isinstance(x, FakeTensor):
        return x
    if x._has_symbolic_sizes_strides:
        size = [
            s.node.shape_env.size_hint(s.node.expr)
            if isinstance(s, torch.SymInt)
            else s
            for s in x.size()
        ]
        stride = [
            s.node.shape_env.size_hint(s.node.expr)
            if isinstance(s, torch.SymInt)
            else s
            for s in x.stride()
        ]
    else:
        size = x.size()
        stride = x.stride()
    y = torch.empty_strided(
        size,
        stride,
        dtype=x.dtype,
        device=x.device,
        requires_grad=x.requires_grad,
    )
    y.zero_()
    return y


# NB: The dictionary has to be created lazily after TorchPatcher is called so
# that we pick up the disabled torch.utils.checkpoint wrapper. Therefore, it is
# sitting in a separate function.
@functools.lru_cache(None)
def higher_order_op_converter():
    import torch._higher_order_ops.wrap

    return {
        torch.utils.checkpoint.checkpoint: torch._higher_order_ops.wrap.wrap_activation_checkpoint,
    }


def requires_higher_order_op(obj):
    return obj in higher_order_op_converter()


def get_higher_order_op(obj):
    if (
        obj is torch.utils.checkpoint.checkpoint
        and not torch._functorch.config.functionalize_rng_ops
    ):
        from .exc import unimplemented

        # TODO - functionalize_rng_ops flags cannot be turned ON by default
        # because 1) Performance concerns - seed and offset are read and passed
        # to each AOT graph 2) Inductor has rand-specific optimizations and
        # there is work remaining to compose them together with
        # functionalization.
        #
        # Until we make it ON by default, we will have to ask users to turn on
        # this flag manually.  TODO - Revisit if there is a simpler way to
        # resolve this problem.
        torch._logging.warning_once(
            log,
            "torch.compile on activation checkpointing is an experimental feature. "
            "Please manually set torch._functorch.config.functionalize_rng_ops=True "
            "to run torch.compile with activation checkpointing. Without this flag, "
            "checkpointed function will not get compiled and fallback to eager.",
        )
        unimplemented(
            "torch.compile requires functioanlization of rng ops to be turned on"
        )
    return higher_order_op_converter().get(obj)