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ckpts/universal/global_step120/zero/10.attention.dense.weight/exp_avg.pt

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ckpts/universal/global_step120/zero/18.mlp.dense_h_to_4h_swiglu.weight/exp_avg.pt

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ckpts/universal/global_step120/zero/18.mlp.dense_h_to_4h_swiglu.weight/exp_avg_sq.pt

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ckpts/universal/global_step120/zero/9.attention.dense.weight/fp32.pt

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+version https://git-lfs.github.com/spec/v1
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venv/lib/python3.10/site-packages/torch/testing/__init__.py

@@ -0,0 +1,3 @@
+from torch._C import FileCheck as FileCheck
+from ._comparison import assert_allclose, assert_close as assert_close
+from ._creation import make_tensor as make_tensor

venv/lib/python3.10/site-packages/torch/testing/_comparison.py

@@ -0,0 +1,1575 @@
+import abc
+import cmath
+import collections.abc
+import contextlib
+import warnings
+from typing import (
+    Any,
+    Callable,
+    Collection,
+    Dict,
+    List,
+    NoReturn,
+    Optional,
+    Sequence,
+    Tuple,
+    Type,
+    Union,
+)
+
+import torch
+
+try:
+    import numpy as np
+
+    NUMPY_AVAILABLE = True
+except ModuleNotFoundError:
+    NUMPY_AVAILABLE = False
+
+
+class ErrorMeta(Exception):
+    """Internal testing exception that makes that carries error metadata."""
+
+    def __init__(
+        self, type: Type[Exception], msg: str, *, id: Tuple[Any, ...] = ()
+    ) -> None:
+        super().__init__(
+            "If you are a user and see this message during normal operation "
+            "please file an issue at https://github.com/pytorch/pytorch/issues. "
+            "If you are a developer and working on the comparison functions, please `raise ErrorMeta().to_error()` "
+            "for user facing errors."
+        )
+        self.type = type
+        self.msg = msg
+        self.id = id
+
+    def to_error(
+        self, msg: Optional[Union[str, Callable[[str], str]]] = None
+    ) -> Exception:
+        if not isinstance(msg, str):
+            generated_msg = self.msg
+            if self.id:
+                generated_msg += f"\n\nThe failure occurred for item {''.join(str([item]) for item in self.id)}"
+
+            msg = msg(generated_msg) if callable(msg) else generated_msg
+
+        return self.type(msg)
+
+
+# Some analysis of tolerance by logging tests from test_torch.py can be found in
+# https://github.com/pytorch/pytorch/pull/32538.
+# {dtype: (rtol, atol)}
+_DTYPE_PRECISIONS = {
+    torch.float16: (0.001, 1e-5),
+    torch.bfloat16: (0.016, 1e-5),
+    torch.float32: (1.3e-6, 1e-5),
+    torch.float64: (1e-7, 1e-7),
+    torch.complex32: (0.001, 1e-5),
+    torch.complex64: (1.3e-6, 1e-5),
+    torch.complex128: (1e-7, 1e-7),
+}
+# The default tolerances of torch.float32 are used for quantized dtypes, because quantized tensors are compared in
+# their dequantized and floating point representation. For more details see `TensorLikePair._compare_quantized_values`
+_DTYPE_PRECISIONS.update(
+    dict.fromkeys(
+        (torch.quint8, torch.quint2x4, torch.quint4x2, torch.qint8, torch.qint32),
+        _DTYPE_PRECISIONS[torch.float32],
+    )
+)
+
+
+def default_tolerances(
+    *inputs: Union[torch.Tensor, torch.dtype],
+    dtype_precisions: Optional[Dict[torch.dtype, Tuple[float, float]]] = None,
+) -> Tuple[float, float]:
+    """Returns the default absolute and relative testing tolerances for a set of inputs based on the dtype.
+
+    See :func:`assert_close` for a table of the default tolerance for each dtype.
+
+    Returns:
+        (Tuple[float, float]): Loosest tolerances of all input dtypes.
+    """
+    dtypes = []
+    for input in inputs:
+        if isinstance(input, torch.Tensor):
+            dtypes.append(input.dtype)
+        elif isinstance(input, torch.dtype):
+            dtypes.append(input)
+        else:
+            raise TypeError(
+                f"Expected a torch.Tensor or a torch.dtype, but got {type(input)} instead."
+            )
+    dtype_precisions = dtype_precisions or _DTYPE_PRECISIONS
+    rtols, atols = zip(*[dtype_precisions.get(dtype, (0.0, 0.0)) for dtype in dtypes])
+    return max(rtols), max(atols)
+
+
+def get_tolerances(
+    *inputs: Union[torch.Tensor, torch.dtype],
+    rtol: Optional[float],
+    atol: Optional[float],
+    id: Tuple[Any, ...] = (),
+) -> Tuple[float, float]:
+    """Gets absolute and relative to be used for numeric comparisons.
+
+    If both ``rtol`` and ``atol`` are specified, this is a no-op. If both are not specified, the return value of
+    :func:`default_tolerances` is used.
+
+    Raises:
+        ErrorMeta: With :class:`ValueError`, if only ``rtol`` or ``atol`` is specified.
+
+    Returns:
+        (Tuple[float, float]): Valid absolute and relative tolerances.
+    """
+    if (rtol is None) ^ (atol is None):
+        # We require both tolerance to be omitted or specified, because specifying only one might lead to surprising
+        # results. Imagine setting atol=0.0 and the tensors still match because rtol>0.0.
+        raise ErrorMeta(
+            ValueError,
+            f"Both 'rtol' and 'atol' must be either specified or omitted, "
+            f"but got no {'rtol' if rtol is None else 'atol'}.",
+            id=id,
+        )
+    elif rtol is not None and atol is not None:
+        return rtol, atol
+    else:
+        return default_tolerances(*inputs)
+
+
+def _make_mismatch_msg(
+    *,
+    default_identifier: str,
+    identifier: Optional[Union[str, Callable[[str], str]]] = None,
+    extra: Optional[str] = None,
+    abs_diff: float,
+    abs_diff_idx: Optional[Union[int, Tuple[int, ...]]] = None,
+    atol: float,
+    rel_diff: float,
+    rel_diff_idx: Optional[Union[int, Tuple[int, ...]]] = None,
+    rtol: float,
+) -> str:
+    """Makes a mismatch error message for numeric values.
+
+    Args:
+        default_identifier (str): Default description of the compared values, e.g. "Tensor-likes".
+        identifier (Optional[Union[str, Callable[[str], str]]]): Optional identifier that overrides
+            ``default_identifier``. Can be passed as callable in which case it will be called with
+            ``default_identifier`` to create the description at runtime.
+        extra (Optional[str]): Extra information to be placed after the message header and the mismatch statistics.
+        abs_diff (float): Absolute difference.
+        abs_diff_idx (Optional[Union[int, Tuple[int, ...]]]): Optional index of the absolute difference.
+        atol (float): Allowed absolute tolerance. Will only be added to mismatch statistics if it or ``rtol`` are
+            ``> 0``.
+        rel_diff (float): Relative difference.
+        rel_diff_idx (Optional[Union[int, Tuple[int, ...]]]): Optional index of the relative difference.
+        rtol (float): Allowed relative tolerance. Will only be added to mismatch statistics if it or ``atol`` are
+            ``> 0``.
+    """
+    equality = rtol == 0 and atol == 0
+
+    def make_diff_msg(
+        *,
+        type: str,
+        diff: float,
+        idx: Optional[Union[int, Tuple[int, ...]]],
+        tol: float,
+    ) -> str:
+        if idx is None:
+            msg = f"{type.title()} difference: {diff}"
+        else:
+            msg = f"Greatest {type} difference: {diff} at index {idx}"
+        if not equality:
+            msg += f" (up to {tol} allowed)"
+        return msg + "\n"
+
+    if identifier is None:
+        identifier = default_identifier
+    elif callable(identifier):
+        identifier = identifier(default_identifier)
+
+    msg = f"{identifier} are not {'equal' if equality else 'close'}!\n\n"
+
+    if extra:
+        msg += f"{extra.strip()}\n"
+
+    msg += make_diff_msg(type="absolute", diff=abs_diff, idx=abs_diff_idx, tol=atol)
+    msg += make_diff_msg(type="relative", diff=rel_diff, idx=rel_diff_idx, tol=rtol)
+
+    return msg.strip()
+
+
+def make_scalar_mismatch_msg(
+    actual: Union[bool, int, float, complex],
+    expected: Union[bool, int, float, complex],
+    *,
+    rtol: float,
+    atol: float,
+    identifier: Optional[Union[str, Callable[[str], str]]] = None,
+) -> str:
+    """Makes a mismatch error message for scalars.
+
+    Args:
+        actual (Union[bool, int, float, complex]): Actual scalar.
+        expected (Union[bool, int, float, complex]): Expected scalar.
+        rtol (float): Relative tolerance.
+        atol (float): Absolute tolerance.
+        identifier (Optional[Union[str, Callable[[str], str]]]): Optional description for the scalars. Can be passed
+            as callable in which case it will be called by the default value to create the description at runtime.
+            Defaults to "Scalars".
+    """
+    abs_diff = abs(actual - expected)
+    rel_diff = float("inf") if expected == 0 else abs_diff / abs(expected)
+    return _make_mismatch_msg(
+        default_identifier="Scalars",
+        identifier=identifier,
+        extra=f"Expected {expected} but got {actual}.",
+        abs_diff=abs_diff,
+        atol=atol,
+        rel_diff=rel_diff,
+        rtol=rtol,
+    )
+
+
+def make_tensor_mismatch_msg(
+    actual: torch.Tensor,
+    expected: torch.Tensor,
+    matches: torch.Tensor,
+    *,
+    rtol: float,
+    atol: float,
+    identifier: Optional[Union[str, Callable[[str], str]]] = None,
+):
+    """Makes a mismatch error message for tensors.
+
+    Args:
+        actual (torch.Tensor): Actual tensor.
+        expected (torch.Tensor): Expected tensor.
+        matches (torch.Tensor): Boolean mask of the same shape as ``actual`` and ``expected`` that indicates the
+            location of matches.
+        rtol (float): Relative tolerance.
+        atol (float): Absolute tolerance.
+        identifier (Optional[Union[str, Callable[[str], str]]]): Optional description for the tensors. Can be passed
+            as callable in which case it will be called by the default value to create the description at runtime.
+            Defaults to "Tensor-likes".
+    """
+
+    def unravel_flat_index(flat_index: int) -> Tuple[int, ...]:
+        if not matches.shape:
+            return ()
+
+        inverse_index = []
+        for size in matches.shape[::-1]:
+            div, mod = divmod(flat_index, size)
+            flat_index = div
+            inverse_index.append(mod)
+
+        return tuple(inverse_index[::-1])
+
+    number_of_elements = matches.numel()
+    total_mismatches = number_of_elements - int(torch.sum(matches))
+    extra = (
+        f"Mismatched elements: {total_mismatches} / {number_of_elements} "
+        f"({total_mismatches / number_of_elements:.1%})"
+    )
+
+    actual_flat = actual.flatten()
+    expected_flat = expected.flatten()
+    matches_flat = matches.flatten()
+
+    if not actual.dtype.is_floating_point and not actual.dtype.is_complex:
+        # TODO: Instead of always upcasting to int64, it would be sufficient to cast to the next higher dtype to avoid
+        #  overflow
+        actual_flat = actual_flat.to(torch.int64)
+        expected_flat = expected_flat.to(torch.int64)
+
+    abs_diff = torch.abs(actual_flat - expected_flat)
+    # Ensure that only mismatches are used for the max_abs_diff computation
+    abs_diff[matches_flat] = 0
+    max_abs_diff, max_abs_diff_flat_idx = torch.max(abs_diff, 0)
+
+    rel_diff = abs_diff / torch.abs(expected_flat)
+    # Ensure that only mismatches are used for the max_rel_diff computation
+    rel_diff[matches_flat] = 0
+    max_rel_diff, max_rel_diff_flat_idx = torch.max(rel_diff, 0)
+    return _make_mismatch_msg(
+        default_identifier="Tensor-likes",
+        identifier=identifier,
+        extra=extra,
+        abs_diff=max_abs_diff.item(),
+        abs_diff_idx=unravel_flat_index(int(max_abs_diff_flat_idx)),
+        atol=atol,
+        rel_diff=max_rel_diff.item(),
+        rel_diff_idx=unravel_flat_index(int(max_rel_diff_flat_idx)),
+        rtol=rtol,
+    )
+
+
+class UnsupportedInputs(Exception):  # noqa: B903
+    """Exception to be raised during the construction of a :class:`Pair` in case it doesn't support the inputs."""
+
+
+class Pair(abc.ABC):
+    """ABC for all comparison pairs to be used in conjunction with :func:`assert_equal`.
+
+    Each subclass needs to overwrite :meth:`Pair.compare` that performs the actual comparison.
+
+    Each pair receives **all** options, so select the ones applicable for the subclass and forward the rest to the
+    super class. Raising an :class:`UnsupportedInputs` during constructions indicates that the pair is not able to
+    handle the inputs and the next pair type will be tried.
+
+    All other errors should be raised as :class:`ErrorMeta`. After the instantiation, :meth:`Pair._make_error_meta` can
+    be used to automatically handle overwriting the message with a user supplied one and id handling.
+    """
+
+    def __init__(
+        self,
+        actual: Any,
+        expected: Any,
+        *,
+        id: Tuple[Any, ...] = (),
+        **unknown_parameters: Any,
+    ) -> None:
+        self.actual = actual
+        self.expected = expected
+        self.id = id
+        self._unknown_parameters = unknown_parameters
+
+    @staticmethod
+    def _inputs_not_supported() -> NoReturn:
+        raise UnsupportedInputs()
+
+    @staticmethod
+    def _check_inputs_isinstance(*inputs: Any, cls: Union[Type, Tuple[Type, ...]]):
+        """Checks if all inputs are instances of a given class and raise :class:`UnsupportedInputs` otherwise."""
+        if not all(isinstance(input, cls) for input in inputs):
+            Pair._inputs_not_supported()
+
+    def _fail(
+        self, type: Type[Exception], msg: str, *, id: Tuple[Any, ...] = ()
+    ) -> NoReturn:
+        """Raises an :class:`ErrorMeta` from a given exception type and message and the stored id.
+
+        .. warning::
+
+            If you use this before the ``super().__init__(...)`` call in the constructor, you have to pass the ``id``
+            explicitly.
+        """
+        raise ErrorMeta(type, msg, id=self.id if not id and hasattr(self, "id") else id)
+
+    @abc.abstractmethod
+    def compare(self) -> None:
+        """Compares the inputs and raises an :class`ErrorMeta` in case they mismatch."""
+
+    def extra_repr(self) -> Sequence[Union[str, Tuple[str, Any]]]:
+        """Returns extra information that will be included in the representation.
+
+        Should be overwritten by all subclasses that use additional options. The representation of the object will only
+        be surfaced in case we encounter an unexpected error and thus should help debug the issue. Can be a sequence of
+        key-value-pairs or attribute names.
+        """
+        return []
+
+    def __repr__(self) -> str:
+        head = f"{type(self).__name__}("
+        tail = ")"
+        body = [
+            f"    {name}={value!s},"
+            for name, value in [
+                ("id", self.id),
+                ("actual", self.actual),
+                ("expected", self.expected),
+                *[
+                    (extra, getattr(self, extra)) if isinstance(extra, str) else extra
+                    for extra in self.extra_repr()
+                ],
+            ]
+        ]
+        return "\n".join((head, *body, *tail))
+
+
+class ObjectPair(Pair):
+    """Pair for any type of inputs that will be compared with the `==` operator.
+
+    .. note::
+
+        Since this will instantiate for any kind of inputs, it should only be used as fallback after all other pairs
+        couldn't handle the inputs.
+
+    """
+
+    def compare(self) -> None:
+        try:
+            equal = self.actual == self.expected
+        except Exception as error:
+            # We are not using `self._raise_error_meta` here since we need the exception chaining
+            raise ErrorMeta(
+                ValueError,
+                f"{self.actual} == {self.expected} failed with:\n{error}.",
+                id=self.id,
+            ) from error
+
+        if not equal:
+            self._fail(AssertionError, f"{self.actual} != {self.expected}")
+
+
+class NonePair(Pair):
+    """Pair for ``None`` inputs."""
+
+    def __init__(self, actual: Any, expected: Any, **other_parameters: Any) -> None:
+        if not (actual is None or expected is None):
+            self._inputs_not_supported()
+
+        super().__init__(actual, expected, **other_parameters)
+
+    def compare(self) -> None:
+        if not (self.actual is None and self.expected is None):
+            self._fail(
+                AssertionError, f"None mismatch: {self.actual} is not {self.expected}"
+            )
+
+
+class BooleanPair(Pair):
+    """Pair for :class:`bool` inputs.
+
+    .. note::
+
+        If ``numpy`` is available, also handles :class:`numpy.bool_` inputs.
+
+    """
+
+    def __init__(
+        self,
+        actual: Any,
+        expected: Any,
+        *,
+        id: Tuple[Any, ...],
+        **other_parameters: Any,
+    ) -> None:
+        actual, expected = self._process_inputs(actual, expected, id=id)
+        super().__init__(actual, expected, **other_parameters)
+
+    @property
+    def _supported_types(self) -> Tuple[Type, ...]:
+        cls: List[Type] = [bool]
+        if NUMPY_AVAILABLE:
+            cls.append(np.bool_)
+        return tuple(cls)
+
+    def _process_inputs(
+        self, actual: Any, expected: Any, *, id: Tuple[Any, ...]
+    ) -> Tuple[bool, bool]:
+        self._check_inputs_isinstance(actual, expected, cls=self._supported_types)
+        actual, expected = (
+            self._to_bool(bool_like, id=id) for bool_like in (actual, expected)
+        )
+        return actual, expected
+
+    def _to_bool(self, bool_like: Any, *, id: Tuple[Any, ...]) -> bool:
+        if isinstance(bool_like, bool):
+            return bool_like
+        elif isinstance(bool_like, np.bool_):
+            return bool_like.item()
+        else:
+            raise ErrorMeta(
+                TypeError, f"Unknown boolean type {type(bool_like)}.", id=id
+            )
+
+    def compare(self) -> None:
+        if self.actual is not self.expected:
+            self._fail(
+                AssertionError,
+                f"Booleans mismatch: {self.actual} is not {self.expected}",
+            )
+
+
+class NumberPair(Pair):
+    """Pair for Python number (:class:`int`, :class:`float`, and :class:`complex`) inputs.
+
+    .. note::
+
+        If ``numpy`` is available, also handles :class:`numpy.number` inputs.
+
+    Kwargs:
+        rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default
+            values based on the type are selected with the below table.
+        atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default
+            values based on the type are selected with the below table.
+        equal_nan (bool): If ``True``, two ``NaN`` values are considered equal. Defaults to ``False``.
+        check_dtype (bool): If ``True``, the type of the inputs will be checked for equality. Defaults to ``False``.
+
+    The following table displays correspondence between Python number type and the ``torch.dtype``'s. See
+    :func:`assert_close` for the corresponding tolerances.
+
+    +------------------+-------------------------------+
+    | ``type``         | corresponding ``torch.dtype`` |
+    +==================+===============================+
+    | :class:`int`     | :attr:`~torch.int64`          |
+    +------------------+-------------------------------+
+    | :class:`float`   | :attr:`~torch.float64`        |
+    +------------------+-------------------------------+
+    | :class:`complex` | :attr:`~torch.complex64`      |
+    +------------------+-------------------------------+
+    """
+
+    _TYPE_TO_DTYPE = {
+        int: torch.int64,
+        float: torch.float64,
+        complex: torch.complex128,
+    }
+    _NUMBER_TYPES = tuple(_TYPE_TO_DTYPE.keys())
+
+    def __init__(
+        self,
+        actual: Any,
+        expected: Any,
+        *,
+        id: Tuple[Any, ...] = (),
+        rtol: Optional[float] = None,
+        atol: Optional[float] = None,
+        equal_nan: bool = False,
+        check_dtype: bool = False,
+        **other_parameters: Any,
+    ) -> None:
+        actual, expected = self._process_inputs(actual, expected, id=id)
+        super().__init__(actual, expected, id=id, **other_parameters)
+
+        self.rtol, self.atol = get_tolerances(
+            *[self._TYPE_TO_DTYPE[type(input)] for input in (actual, expected)],
+            rtol=rtol,
+            atol=atol,
+            id=id,
+        )
+        self.equal_nan = equal_nan
+        self.check_dtype = check_dtype
+
+    @property
+    def _supported_types(self) -> Tuple[Type, ...]:
+        cls = list(self._NUMBER_TYPES)
+        if NUMPY_AVAILABLE:
+            cls.append(np.number)
+        return tuple(cls)
+
+    def _process_inputs(
+        self, actual: Any, expected: Any, *, id: Tuple[Any, ...]
+    ) -> Tuple[Union[int, float, complex], Union[int, float, complex]]:
+        self._check_inputs_isinstance(actual, expected, cls=self._supported_types)
+        actual, expected = (
+            self._to_number(number_like, id=id) for number_like in (actual, expected)
+        )
+        return actual, expected
+
+    def _to_number(
+        self, number_like: Any, *, id: Tuple[Any, ...]
+    ) -> Union[int, float, complex]:
+        if NUMPY_AVAILABLE and isinstance(number_like, np.number):
+            return number_like.item()
+        elif isinstance(number_like, self._NUMBER_TYPES):
+            return number_like  # type: ignore[return-value]
+        else:
+            raise ErrorMeta(
+                TypeError, f"Unknown number type {type(number_like)}.", id=id
+            )
+
+    def compare(self) -> None:
+        if self.check_dtype and type(self.actual) is not type(self.expected):
+            self._fail(
+                AssertionError,
+                f"The (d)types do not match: {type(self.actual)} != {type(self.expected)}.",
+            )
+
+        if self.actual == self.expected:
+            return
+
+        if self.equal_nan and cmath.isnan(self.actual) and cmath.isnan(self.expected):
+            return
+
+        abs_diff = abs(self.actual - self.expected)
+        tolerance = self.atol + self.rtol * abs(self.expected)
+
+        if cmath.isfinite(abs_diff) and abs_diff <= tolerance:
+            return
+
+        self._fail(
+            AssertionError,
+            make_scalar_mismatch_msg(
+                self.actual, self.expected, rtol=self.rtol, atol=self.atol
+            ),
+        )
+
+    def extra_repr(self) -> Sequence[str]:
+        return (
+            "rtol",
+            "atol",
+            "equal_nan",
+            "check_dtype",
+        )
+
+
+class TensorLikePair(Pair):
+    """Pair for :class:`torch.Tensor`-like inputs.
+
+    Kwargs:
+        allow_subclasses (bool):
+        rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default
+            values based on the type are selected. See :func:assert_close: for details.
+        atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default
+            values based on the type are selected. See :func:assert_close: for details.
+        equal_nan (bool): If ``True``, two ``NaN`` values are considered equal. Defaults to ``False``.
+        check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same
+            :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different
+            :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared.
+        check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this
+            check is disabled, tensors with different ``dtype``'s are promoted  to a common ``dtype`` (according to
+            :func:`torch.promote_types`) before being compared.
+        check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this
+            check is disabled, tensors with different ``layout``'s are converted to strided tensors before being
+            compared.
+        check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride.
+    """
+
+    def __init__(
+        self,
+        actual: Any,
+        expected: Any,
+        *,
+        id: Tuple[Any, ...] = (),
+        allow_subclasses: bool = True,
+        rtol: Optional[float] = None,
+        atol: Optional[float] = None,
+        equal_nan: bool = False,
+        check_device: bool = True,
+        check_dtype: bool = True,
+        check_layout: bool = True,
+        check_stride: bool = False,
+        **other_parameters: Any,
+    ):
+        actual, expected = self._process_inputs(
+            actual, expected, id=id, allow_subclasses=allow_subclasses
+        )
+        super().__init__(actual, expected, id=id, **other_parameters)
+
+        self.rtol, self.atol = get_tolerances(
+            actual, expected, rtol=rtol, atol=atol, id=self.id
+        )
+        self.equal_nan = equal_nan
+        self.check_device = check_device
+        self.check_dtype = check_dtype
+        self.check_layout = check_layout
+        self.check_stride = check_stride
+
+    def _process_inputs(
+        self, actual: Any, expected: Any, *, id: Tuple[Any, ...], allow_subclasses: bool
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        directly_related = isinstance(actual, type(expected)) or isinstance(
+            expected, type(actual)
+        )
+        if not directly_related:
+            self._inputs_not_supported()
+
+        if not allow_subclasses and type(actual) is not type(expected):
+            self._inputs_not_supported()
+
+        actual, expected = (self._to_tensor(input) for input in (actual, expected))
+        for tensor in (actual, expected):
+            self._check_supported(tensor, id=id)
+        return actual, expected
+
+    def _to_tensor(self, tensor_like: Any) -> torch.Tensor:
+        if isinstance(tensor_like, torch.Tensor):
+            return tensor_like
+
+        try:
+            return torch.as_tensor(tensor_like)
+        except Exception:
+            self._inputs_not_supported()
+
+    def _check_supported(self, tensor: torch.Tensor, *, id: Tuple[Any, ...]) -> None:
+        if tensor.layout not in {
+            torch.strided,
+            torch.sparse_coo,
+            torch.sparse_csr,
+            torch.sparse_csc,
+            torch.sparse_bsr,
+            torch.sparse_bsc,
+        }:
+            raise ErrorMeta(
+                ValueError, f"Unsupported tensor layout {tensor.layout}", id=id
+            )
+
+    def compare(self) -> None:
+        actual, expected = self.actual, self.expected
+
+        self._compare_attributes(actual, expected)
+        if any(input.device.type == "meta" for input in (actual, expected)):
+            return
+
+        actual, expected = self._equalize_attributes(actual, expected)
+        self._compare_values(actual, expected)
+
+    def _compare_attributes(
+        self,
+        actual: torch.Tensor,
+        expected: torch.Tensor,
+    ) -> None:
+        """Checks if the attributes of two tensors match.
+
+        Always checks
+
+        - the :attr:`~torch.Tensor.shape`,
+        - whether both inputs are quantized or not,
+        - and if they use the same quantization scheme.
+
+        Checks for
+
+        - :attr:`~torch.Tensor.layout`,
+        - :meth:`~torch.Tensor.stride`,
+        - :attr:`~torch.Tensor.device`, and
+        - :attr:`~torch.Tensor.dtype`
+
+        are optional and can be disabled through the corresponding ``check_*`` flag during construction of the pair.
+        """
+
+        def raise_mismatch_error(
+            attribute_name: str, actual_value: Any, expected_value: Any
+        ) -> NoReturn:
+            self._fail(
+                AssertionError,
+                f"The values for attribute '{attribute_name}' do not match: {actual_value} != {expected_value}.",
+            )
+
+        if actual.shape != expected.shape:
+            raise_mismatch_error("shape", actual.shape, expected.shape)
+
+        if actual.is_quantized != expected.is_quantized:
+            raise_mismatch_error(
+                "is_quantized", actual.is_quantized, expected.is_quantized
+            )
+        elif actual.is_quantized and actual.qscheme() != expected.qscheme():
+            raise_mismatch_error("qscheme()", actual.qscheme(), expected.qscheme())
+
+        if actual.layout != expected.layout:
+            if self.check_layout:
+                raise_mismatch_error("layout", actual.layout, expected.layout)
+        elif (
+            actual.layout == torch.strided
+            and self.check_stride
+            and actual.stride() != expected.stride()
+        ):
+            raise_mismatch_error("stride()", actual.stride(), expected.stride())
+
+        if self.check_device and actual.device != expected.device:
+            raise_mismatch_error("device", actual.device, expected.device)
+
+        if self.check_dtype and actual.dtype != expected.dtype:
+            raise_mismatch_error("dtype", actual.dtype, expected.dtype)
+
+    def _equalize_attributes(
+        self, actual: torch.Tensor, expected: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Equalizes some attributes of two tensors for value comparison.
+
+        If ``actual`` and ``expected`` are ...
+
+        - ... not on the same :attr:`~torch.Tensor.device`, they are moved CPU memory.
+        - ... not of the same ``dtype``, they are promoted  to a common ``dtype`` (according to
+            :func:`torch.promote_types`).
+        - ... not of the same ``layout``, they are converted to strided tensors.
+
+        Args:
+            actual (Tensor): Actual tensor.
+            expected (Tensor): Expected tensor.
+
+        Returns:
+            (Tuple[Tensor, Tensor]): Equalized tensors.
+        """
+        # The comparison logic uses operators currently not supported by the MPS backends.
+        #  See https://github.com/pytorch/pytorch/issues/77144 for details.
+        # TODO: Remove this conversion as soon as all operations are supported natively by the MPS backend
+        if actual.is_mps or expected.is_mps:  # type: ignore[attr-defined]
+            actual = actual.cpu()
+            expected = expected.cpu()
+
+        if actual.device != expected.device:
+            actual = actual.cpu()
+            expected = expected.cpu()
+
+        if actual.dtype != expected.dtype:
+            actual_dtype = actual.dtype
+            expected_dtype = expected.dtype
+            # For uint64, this is not sound in general, which is why promote_types doesn't
+            # allow it, but for easy testing, we're unlikely to get confused
+            # by large uint64 overflowing into negative int64
+            if actual_dtype in [torch.uint64, torch.uint32, torch.uint16]:
+                actual_dtype = torch.int64
+            if expected_dtype in [torch.uint64, torch.uint32, torch.uint16]:
+                expected_dtype = torch.int64
+            dtype = torch.promote_types(actual_dtype, expected_dtype)
+            actual = actual.to(dtype)
+            expected = expected.to(dtype)
+
+        if actual.layout != expected.layout:
+            # These checks are needed, since Tensor.to_dense() fails on tensors that are already strided
+            actual = actual.to_dense() if actual.layout != torch.strided else actual
+            expected = (
+                expected.to_dense() if expected.layout != torch.strided else expected
+            )
+
+        return actual, expected
+
+    def _compare_values(self, actual: torch.Tensor, expected: torch.Tensor) -> None:
+        if actual.is_quantized:
+            compare_fn = self._compare_quantized_values
+        elif actual.is_sparse:
+            compare_fn = self._compare_sparse_coo_values
+        elif actual.layout in {
+            torch.sparse_csr,
+            torch.sparse_csc,
+            torch.sparse_bsr,
+            torch.sparse_bsc,
+        }:
+            compare_fn = self._compare_sparse_compressed_values
+        else:
+            compare_fn = self._compare_regular_values_close
+
+        compare_fn(
+            actual, expected, rtol=self.rtol, atol=self.atol, equal_nan=self.equal_nan
+        )
+
+    def _compare_quantized_values(
+        self,
+        actual: torch.Tensor,
+        expected: torch.Tensor,
+        *,
+        rtol: float,
+        atol: float,
+        equal_nan: bool,
+    ) -> None:
+        """Compares quantized tensors by comparing the :meth:`~torch.Tensor.dequantize`'d variants for closeness.
+
+        .. note::
+
+            A detailed discussion about why only the dequantized variant is checked for closeness rather than checking
+            the individual quantization parameters for closeness and the integer representation for equality can be
+            found in https://github.com/pytorch/pytorch/issues/68548.
+        """
+        return self._compare_regular_values_close(
+            actual.dequantize(),
+            expected.dequantize(),
+            rtol=rtol,
+            atol=atol,
+            equal_nan=equal_nan,
+            identifier=lambda default_identifier: f"Quantized {default_identifier.lower()}",
+        )
+
+    def _compare_sparse_coo_values(
+        self,
+        actual: torch.Tensor,
+        expected: torch.Tensor,
+        *,
+        rtol: float,
+        atol: float,
+        equal_nan: bool,
+    ) -> None:
+        """Compares sparse COO tensors by comparing
+
+        - the number of sparse dimensions,
+        - the number of non-zero elements (nnz) for equality,
+        - the indices for equality, and
+        - the values for closeness.
+        """
+        if actual.sparse_dim() != expected.sparse_dim():
+            self._fail(
+                AssertionError,
+                (
+                    f"The number of sparse dimensions in sparse COO tensors does not match: "
+                    f"{actual.sparse_dim()} != {expected.sparse_dim()}"
+                ),
+            )
+
+        if actual._nnz() != expected._nnz():
+            self._fail(
+                AssertionError,
+                (
+                    f"The number of specified values in sparse COO tensors does not match: "
+                    f"{actual._nnz()} != {expected._nnz()}"
+                ),
+            )
+
+        self._compare_regular_values_equal(
+            actual._indices(),
+            expected._indices(),
+            identifier="Sparse COO indices",
+        )
+        self._compare_regular_values_close(
+            actual._values(),
+            expected._values(),
+            rtol=rtol,
+            atol=atol,
+            equal_nan=equal_nan,
+            identifier="Sparse COO values",
+        )
+
+    def _compare_sparse_compressed_values(
+        self,
+        actual: torch.Tensor,
+        expected: torch.Tensor,
+        *,
+        rtol: float,
+        atol: float,
+        equal_nan: bool,
+    ) -> None:
+        """Compares sparse compressed tensors by comparing
+
+        - the number of non-zero elements (nnz) for equality,
+        - the plain indices for equality,
+        - the compressed indices for equality, and
+        - the values for closeness.
+        """
+        format_name, compressed_indices_method, plain_indices_method = {
+            torch.sparse_csr: (
+                "CSR",
+                torch.Tensor.crow_indices,
+                torch.Tensor.col_indices,
+            ),
+            torch.sparse_csc: (
+                "CSC",
+                torch.Tensor.ccol_indices,
+                torch.Tensor.row_indices,
+            ),
+            torch.sparse_bsr: (
+                "BSR",
+                torch.Tensor.crow_indices,
+                torch.Tensor.col_indices,
+            ),
+            torch.sparse_bsc: (
+                "BSC",
+                torch.Tensor.ccol_indices,
+                torch.Tensor.row_indices,
+            ),
+        }[actual.layout]
+
+        if actual._nnz() != expected._nnz():
+            self._fail(
+                AssertionError,
+                (
+                    f"The number of specified values in sparse {format_name} tensors does not match: "
+                    f"{actual._nnz()} != {expected._nnz()}"
+                ),
+            )
+
+        # Compressed and plain indices in the CSR / CSC / BSR / BSC sparse formates can be `torch.int32` _or_
+        # `torch.int64`. While the same dtype is enforced for the compressed and plain indices of a single tensor, it
+        # can be different between two tensors. Thus, we need to convert them to the same dtype, or the comparison will
+        # fail.
+        actual_compressed_indices = compressed_indices_method(actual)
+        expected_compressed_indices = compressed_indices_method(expected)
+        indices_dtype = torch.promote_types(
+            actual_compressed_indices.dtype, expected_compressed_indices.dtype
+        )
+
+        self._compare_regular_values_equal(
+            actual_compressed_indices.to(indices_dtype),
+            expected_compressed_indices.to(indices_dtype),
+            identifier=f"Sparse {format_name} {compressed_indices_method.__name__}",
+        )
+        self._compare_regular_values_equal(
+            plain_indices_method(actual).to(indices_dtype),
+            plain_indices_method(expected).to(indices_dtype),
+            identifier=f"Sparse {format_name} {plain_indices_method.__name__}",
+        )
+        self._compare_regular_values_close(
+            actual.values(),
+            expected.values(),
+            rtol=rtol,
+            atol=atol,
+            equal_nan=equal_nan,
+            identifier=f"Sparse {format_name} values",
+        )
+
+    def _compare_regular_values_equal(
+        self,
+        actual: torch.Tensor,
+        expected: torch.Tensor,
+        *,
+        equal_nan: bool = False,
+        identifier: Optional[Union[str, Callable[[str], str]]] = None,
+    ) -> None:
+        """Checks if the values of two tensors are equal."""
+        self._compare_regular_values_close(
+            actual, expected, rtol=0, atol=0, equal_nan=equal_nan, identifier=identifier
+        )
+
+    def _compare_regular_values_close(
+        self,
+        actual: torch.Tensor,
+        expected: torch.Tensor,
+        *,
+        rtol: float,
+        atol: float,
+        equal_nan: bool,
+        identifier: Optional[Union[str, Callable[[str], str]]] = None,
+    ) -> None:
+        """Checks if the values of two tensors are close up to a desired tolerance."""
+        matches = torch.isclose(
+            actual, expected, rtol=rtol, atol=atol, equal_nan=equal_nan
+        )
+        if torch.all(matches):
+            return
+
+        if actual.shape == torch.Size([]):
+            msg = make_scalar_mismatch_msg(
+                actual.item(),
+                expected.item(),
+                rtol=rtol,
+                atol=atol,
+                identifier=identifier,
+            )
+        else:
+            msg = make_tensor_mismatch_msg(
+                actual, expected, matches, rtol=rtol, atol=atol, identifier=identifier
+            )
+        self._fail(AssertionError, msg)
+
+    def extra_repr(self) -> Sequence[str]:
+        return (
+            "rtol",
+            "atol",
+            "equal_nan",
+            "check_device",
+            "check_dtype",
+            "check_layout",
+            "check_stride",
+        )
+
+
+def originate_pairs(
+    actual: Any,
+    expected: Any,
+    *,
+    pair_types: Sequence[Type[Pair]],
+    sequence_types: Tuple[Type, ...] = (collections.abc.Sequence,),
+    mapping_types: Tuple[Type, ...] = (collections.abc.Mapping,),
+    id: Tuple[Any, ...] = (),
+    **options: Any,
+) -> List[Pair]:
+    """Originates pairs from the individual inputs.
+
+    ``actual`` and ``expected`` can be possibly nested :class:`~collections.abc.Sequence`'s or
+    :class:`~collections.abc.Mapping`'s. In this case the pairs are originated by recursing through them.
+
+    Args:
+        actual (Any): Actual input.
+        expected (Any): Expected input.
+        pair_types (Sequence[Type[Pair]]): Sequence of pair types that will be tried to construct with the inputs.
+            First successful pair will be used.
+        sequence_types (Tuple[Type, ...]): Optional types treated as sequences that will be checked elementwise.
+        mapping_types (Tuple[Type, ...]): Optional types treated as mappings that will be checked elementwise.
+        id (Tuple[Any, ...]): Optional id of a pair that will be included in an error message.
+        **options (Any): Options passed to each pair during construction.
+
+    Raises:
+        ErrorMeta: With :class`AssertionError`, if the inputs are :class:`~collections.abc.Sequence`'s, but their
+            length does not match.
+        ErrorMeta: With :class`AssertionError`, if the inputs are :class:`~collections.abc.Mapping`'s, but their set of
+            keys do not match.
+        ErrorMeta: With :class`TypeError`, if no pair is able to handle the inputs.
+        ErrorMeta: With any expected exception that happens during the construction of a pair.
+
+    Returns:
+        (List[Pair]): Originated pairs.
+    """
+    # We explicitly exclude str's here since they are self-referential and would cause an infinite recursion loop:
+    # "a" == "a"[0][0]...
+    if (
+        isinstance(actual, sequence_types)
+        and not isinstance(actual, str)
+        and isinstance(expected, sequence_types)
+        and not isinstance(expected, str)
+    ):
+        actual_len = len(actual)
+        expected_len = len(expected)
+        if actual_len != expected_len:
+            raise ErrorMeta(
+                AssertionError,
+                f"The length of the sequences mismatch: {actual_len} != {expected_len}",
+                id=id,
+            )
+
+        pairs = []
+        for idx in range(actual_len):
+            pairs.extend(
+                originate_pairs(
+                    actual[idx],
+                    expected[idx],
+                    pair_types=pair_types,
+                    sequence_types=sequence_types,
+                    mapping_types=mapping_types,
+                    id=(*id, idx),
+                    **options,
+                )
+            )
+        return pairs
+
+    elif isinstance(actual, mapping_types) and isinstance(expected, mapping_types):
+        actual_keys = set(actual.keys())
+        expected_keys = set(expected.keys())
+        if actual_keys != expected_keys:
+            missing_keys = expected_keys - actual_keys
+            additional_keys = actual_keys - expected_keys
+            raise ErrorMeta(
+                AssertionError,
+                (
+                    f"The keys of the mappings do not match:\n"
+                    f"Missing keys in the actual mapping: {sorted(missing_keys)}\n"
+                    f"Additional keys in the actual mapping: {sorted(additional_keys)}"
+                ),
+                id=id,
+            )
+
+        keys: Collection = actual_keys
+        # Since the origination aborts after the first failure, we try to be deterministic
+        with contextlib.suppress(Exception):
+            keys = sorted(keys)
+
+        pairs = []
+        for key in keys:
+            pairs.extend(
+                originate_pairs(
+                    actual[key],
+                    expected[key],
+                    pair_types=pair_types,
+                    sequence_types=sequence_types,
+                    mapping_types=mapping_types,
+                    id=(*id, key),
+                    **options,
+                )
+            )
+        return pairs
+
+    else:
+        for pair_type in pair_types:
+            try:
+                return [pair_type(actual, expected, id=id, **options)]
+            # Raising an `UnsupportedInputs` during origination indicates that the pair type is not able to handle the
+            # inputs. Thus, we try the next pair type.
+            except UnsupportedInputs:
+                continue
+            # Raising an `ErrorMeta` during origination is the orderly way to abort and so we simply re-raise it. This
+            # is only in a separate branch, because the one below would also except it.
+            except ErrorMeta:
+                raise
+            # Raising any other exception during origination is unexpected and will give some extra information about
+            # what happened. If applicable, the exception should be expected in the future.
+            except Exception as error:
+                raise RuntimeError(
+                    f"Originating a {pair_type.__name__}() at item {''.join(str([item]) for item in id)} with\n\n"
+                    f"{type(actual).__name__}(): {actual}\n\n"
+                    f"and\n\n"
+                    f"{type(expected).__name__}(): {expected}\n\n"
+                    f"resulted in the unexpected exception above. "
+                    f"If you are a user and see this message during normal operation "
+                    "please file an issue at https://github.com/pytorch/pytorch/issues. "
+                    "If you are a developer and working on the comparison functions, "
+                    "please except the previous error and raise an expressive `ErrorMeta` instead."
+                ) from error
+        else:
+            raise ErrorMeta(
+                TypeError,
+                f"No comparison pair was able to handle inputs of type {type(actual)} and {type(expected)}.",
+                id=id,
+            )
+
+
+def not_close_error_metas(
+    actual: Any,
+    expected: Any,
+    *,
+    pair_types: Sequence[Type[Pair]] = (ObjectPair,),
+    sequence_types: Tuple[Type, ...] = (collections.abc.Sequence,),
+    mapping_types: Tuple[Type, ...] = (collections.abc.Mapping,),
+    **options: Any,
+) -> List[ErrorMeta]:
+    """Asserts that inputs are equal.
+
+    ``actual`` and ``expected`` can be possibly nested :class:`~collections.abc.Sequence`'s or
+    :class:`~collections.abc.Mapping`'s. In this case the comparison happens elementwise by recursing through them.
+
+    Args:
+        actual (Any): Actual input.
+        expected (Any): Expected input.
+        pair_types (Sequence[Type[Pair]]): Sequence of :class:`Pair` types that will be tried to construct with the
+            inputs. First successful pair will be used. Defaults to only using :class:`ObjectPair`.
+        sequence_types (Tuple[Type, ...]): Optional types treated as sequences that will be checked elementwise.
+        mapping_types (Tuple[Type, ...]): Optional types treated as mappings that will be checked elementwise.
+        **options (Any): Options passed to each pair during construction.
+    """
+    # Hide this function from `pytest`'s traceback
+    __tracebackhide__ = True
+
+    try:
+        pairs = originate_pairs(
+            actual,
+            expected,
+            pair_types=pair_types,
+            sequence_types=sequence_types,
+            mapping_types=mapping_types,
+            **options,
+        )
+    except ErrorMeta as error_meta:
+        # Explicitly raising from None to hide the internal traceback
+        raise error_meta.to_error() from None
+
+    error_metas: List[ErrorMeta] = []
+    for pair in pairs:
+        try:
+            pair.compare()
+        except ErrorMeta as error_meta:
+            error_metas.append(error_meta)
+        # Raising any exception besides `ErrorMeta` while comparing is unexpected and will give some extra information
+        # about what happened. If applicable, the exception should be expected in the future.
+        except Exception as error:
+            raise RuntimeError(
+                f"Comparing\n\n"
+                f"{pair}\n\n"
+                f"resulted in the unexpected exception above. "
+                f"If you are a user and see this message during normal operation "
+                "please file an issue at https://github.com/pytorch/pytorch/issues. "
+                "If you are a developer and working on the comparison functions, "
+                "please except the previous error and raise an expressive `ErrorMeta` instead."
+            ) from error
+
+    # [ErrorMeta Cycles]
+    # ErrorMeta objects in this list capture
+    # tracebacks that refer to the frame of this function.
+    # The local variable `error_metas` refers to the error meta
+    # objects, creating a reference cycle. Frames in the traceback
+    # would not get freed until cycle collection, leaking cuda memory in tests.
+    # We break the cycle by removing the reference to the error_meta objects
+    # from this frame as it returns.
+    error_metas = [error_metas]
+    return error_metas.pop()
+
+
+def assert_close(
+    actual: Any,
+    expected: Any,
+    *,
+    allow_subclasses: bool = True,
+    rtol: Optional[float] = None,
+    atol: Optional[float] = None,
+    equal_nan: bool = False,
+    check_device: bool = True,
+    check_dtype: bool = True,
+    check_layout: bool = True,
+    check_stride: bool = False,
+    msg: Optional[Union[str, Callable[[str], str]]] = None,
+):
+    r"""Asserts that ``actual`` and ``expected`` are close.
+
+    If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if
+
+    .. math::
+
+        \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert
+
+    Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are
+    only considered equal to each other if ``equal_nan`` is ``True``.
+
+    In addition, they are only considered close if they have the same
+
+    - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``),
+    - ``dtype`` (if ``check_dtype`` is ``True``),
+    - ``layout`` (if ``check_layout`` is ``True``), and
+    - stride (if ``check_stride`` is ``True``).
+
+    If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed.
+
+    If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are
+    checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR,
+    or ``ccol_indices``  and ``row_indices`` for CSC and BSC layouts, respectively,
+    are always checked for equality whereas the values are checked for closeness according to the definition above.
+
+    If ``actual`` and ``expected`` are quantized, they are considered close if they have the same
+    :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the
+    definition above.
+
+    ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which
+    :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types
+    have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s
+    or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all
+    their elements are considered close according to the above definition.
+
+    .. note::
+
+        Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e.
+        :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus,
+        Python scalars of different types can be checked, but require ``check_dtype=False``.
+
+    Args:
+        actual (Any): Actual input.
+        expected (Any): Expected input.
+        allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types
+            are allowed. Otherwise type equality is required.
+        rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default
+            values based on the :attr:`~torch.Tensor.dtype` are selected with the below table.
+        atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default
+            values based on the :attr:`~torch.Tensor.dtype` are selected with the below table.
+        equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal.
+        check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same
+            :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different
+            :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared.
+        check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this
+            check is disabled, tensors with different ``dtype``'s are promoted  to a common ``dtype`` (according to
+            :func:`torch.promote_types`) before being compared.
+        check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this
+            check is disabled, tensors with different ``layout``'s are converted to strided tensors before being
+            compared.
+        check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride.
+        msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during
+            the comparison. Can also passed as callable in which case it will be called with the generated message and
+            should return the new message.
+
+    Raises:
+        ValueError: If no :class:`torch.Tensor` can be constructed from an input.
+        ValueError: If only ``rtol`` or ``atol`` is specified.
+        AssertionError: If corresponding inputs are not Python scalars and are not directly related.
+        AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have
+            different types.
+        AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match.
+        AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match.
+        AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`.
+        AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same
+            :attr:`~torch.Tensor.layout`.
+        AssertionError: If only one of corresponding tensors is quantized.
+        AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s.
+        AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same
+            :attr:`~torch.Tensor.device`.
+        AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``.
+        AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride.
+        AssertionError: If the values of corresponding tensors are not close according to the definition above.
+
+    The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching
+    ``dtype``'s, the maximum of both tolerances is used.
+
+    +---------------------------+------------+----------+
+    | ``dtype``                 | ``rtol``   | ``atol`` |
+    +===========================+============+==========+
+    | :attr:`~torch.float16`    | ``1e-3``   | ``1e-5`` |
+    +---------------------------+------------+----------+
+    | :attr:`~torch.bfloat16`   | ``1.6e-2`` | ``1e-5`` |
+    +---------------------------+------------+----------+
+    | :attr:`~torch.float32`    | ``1.3e-6`` | ``1e-5`` |
+    +---------------------------+------------+----------+
+    | :attr:`~torch.float64`    | ``1e-7``   | ``1e-7`` |
+    +---------------------------+------------+----------+
+    | :attr:`~torch.complex32`  | ``1e-3``   | ``1e-5`` |
+    +---------------------------+------------+----------+
+    | :attr:`~torch.complex64`  | ``1.3e-6`` | ``1e-5`` |
+    +---------------------------+------------+----------+
+    | :attr:`~torch.complex128` | ``1e-7``   | ``1e-7`` |
+    +---------------------------+------------+----------+
+    | :attr:`~torch.quint8`     | ``1.3e-6`` | ``1e-5`` |
+    +---------------------------+------------+----------+
+    | :attr:`~torch.quint2x4`   | ``1.3e-6`` | ``1e-5`` |
+    +---------------------------+------------+----------+
+    | :attr:`~torch.quint4x2`   | ``1.3e-6`` | ``1e-5`` |
+    +---------------------------+------------+----------+
+    | :attr:`~torch.qint8`      | ``1.3e-6`` | ``1e-5`` |
+    +---------------------------+------------+----------+
+    | :attr:`~torch.qint32`     | ``1.3e-6`` | ``1e-5`` |
+    +---------------------------+------------+----------+
+    | other                     | ``0.0``    | ``0.0``  |
+    +---------------------------+------------+----------+
+
+    .. note::
+
+        :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged
+        to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might
+        define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default:
+
+        >>> import functools
+        >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0)
+        >>> assert_equal(1e-9, 1e-10)
+        Traceback (most recent call last):
+        ...
+        AssertionError: Scalars are not equal!
+        <BLANKLINE>
+        Expected 1e-10 but got 1e-09.
+        Absolute difference: 9.000000000000001e-10
+        Relative difference: 9.0
+
+    Examples:
+        >>> # tensor to tensor comparison
+        >>> expected = torch.tensor([1e0, 1e-1, 1e-2])
+        >>> actual = torch.acos(torch.cos(expected))
+        >>> torch.testing.assert_close(actual, expected)
+
+        >>> # scalar to scalar comparison
+        >>> import math
+        >>> expected = math.sqrt(2.0)
+        >>> actual = 2.0 / math.sqrt(2.0)
+        >>> torch.testing.assert_close(actual, expected)
+
+        >>> # numpy array to numpy array comparison
+        >>> import numpy as np
+        >>> expected = np.array([1e0, 1e-1, 1e-2])
+        >>> actual = np.arccos(np.cos(expected))
+        >>> torch.testing.assert_close(actual, expected)
+
+        >>> # sequence to sequence comparison
+        >>> import numpy as np
+        >>> # The types of the sequences do not have to match. They only have to have the same
+        >>> # length and their elements have to match.
+        >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)]
+        >>> actual = tuple(expected)
+        >>> torch.testing.assert_close(actual, expected)
+
+        >>> # mapping to mapping comparison
+        >>> from collections import OrderedDict
+        >>> import numpy as np
+        >>> foo = torch.tensor(1.0)
+        >>> bar = 2.0
+        >>> baz = np.array(3.0)
+        >>> # The types and a possible ordering of mappings do not have to match. They only
+        >>> # have to have the same set of keys and their elements have to match.
+        >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)])
+        >>> actual = {"baz": baz, "bar": bar, "foo": foo}
+        >>> torch.testing.assert_close(actual, expected)
+
+        >>> expected = torch.tensor([1.0, 2.0, 3.0])
+        >>> actual = expected.clone()
+        >>> # By default, directly related instances can be compared
+        >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected)
+        >>> # This check can be made more strict with allow_subclasses=False
+        >>> torch.testing.assert_close(
+        ...     torch.nn.Parameter(actual), expected, allow_subclasses=False
+        ... )
+        Traceback (most recent call last):
+        ...
+        TypeError: No comparison pair was able to handle inputs of type
+        <class 'torch.nn.parameter.Parameter'> and <class 'torch.Tensor'>.
+        >>> # If the inputs are not directly related, they are never considered close
+        >>> torch.testing.assert_close(actual.numpy(), expected)
+        Traceback (most recent call last):
+        ...
+        TypeError: No comparison pair was able to handle inputs of type <class 'numpy.ndarray'>
+        and <class 'torch.Tensor'>.
+        >>> # Exceptions to these rules are Python scalars. They can be checked regardless of
+        >>> # their type if check_dtype=False.
+        >>> torch.testing.assert_close(1.0, 1, check_dtype=False)
+
+        >>> # NaN != NaN by default.
+        >>> expected = torch.tensor(float("Nan"))
+        >>> actual = expected.clone()
+        >>> torch.testing.assert_close(actual, expected)
+        Traceback (most recent call last):
+        ...
+        AssertionError: Scalars are not close!
+        <BLANKLINE>
+        Expected nan but got nan.
+        Absolute difference: nan (up to 1e-05 allowed)
+        Relative difference: nan (up to 1.3e-06 allowed)
+        >>> torch.testing.assert_close(actual, expected, equal_nan=True)
+
+        >>> expected = torch.tensor([1.0, 2.0, 3.0])
+        >>> actual = torch.tensor([1.0, 4.0, 5.0])
+        >>> # The default error message can be overwritten.
+        >>> torch.testing.assert_close(actual, expected, msg="Argh, the tensors are not close!")
+        Traceback (most recent call last):
+        ...
+        AssertionError: Argh, the tensors are not close!
+        >>> # If msg is a callable, it can be used to augment the generated message with
+        >>> # extra information
+        >>> torch.testing.assert_close(
+        ...     actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter"
+        ... )
+        Traceback (most recent call last):
+        ...
+        AssertionError: Header
+        <BLANKLINE>
+        Tensor-likes are not close!
+        <BLANKLINE>
+        Mismatched elements: 2 / 3 (66.7%)
+        Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed)
+        Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed)
+        <BLANKLINE>
+        Footer
+    """
+    # Hide this function from `pytest`'s traceback
+    __tracebackhide__ = True
+
+    error_metas = not_close_error_metas(
+        actual,
+        expected,
+        pair_types=(
+            NonePair,
+            BooleanPair,
+            NumberPair,
+            TensorLikePair,
+        ),
+        allow_subclasses=allow_subclasses,
+        rtol=rtol,
+        atol=atol,
+        equal_nan=equal_nan,
+        check_device=check_device,
+        check_dtype=check_dtype,
+        check_layout=check_layout,
+        check_stride=check_stride,
+        msg=msg,
+    )
+
+    if error_metas:
+        # TODO: compose all metas into one AssertionError
+        raise error_metas[0].to_error(msg)
+
+
+def assert_allclose(
+    actual: Any,
+    expected: Any,
+    rtol: Optional[float] = None,
+    atol: Optional[float] = None,
+    equal_nan: bool = True,
+    msg: str = "",
+) -> None:
+    """
+    .. warning::
+
+       :func:`torch.testing.assert_allclose` is deprecated since ``1.12`` and will be removed in a future release.
+       Please use :func:`torch.testing.assert_close` instead. You can find detailed upgrade instructions
+       `here <https://github.com/pytorch/pytorch/issues/61844>`_.
+    """
+    warnings.warn(
+        "`torch.testing.assert_allclose()` is deprecated since 1.12 and will be removed in a future release. "
+        "Please use `torch.testing.assert_close()` instead. "
+        "You can find detailed upgrade instructions in https://github.com/pytorch/pytorch/issues/61844.",
+        FutureWarning,
+        stacklevel=2,
+    )
+
+    if not isinstance(actual, torch.Tensor):
+        actual = torch.tensor(actual)
+    if not isinstance(expected, torch.Tensor):
+        expected = torch.tensor(expected, dtype=actual.dtype)
+
+    if rtol is None and atol is None:
+        rtol, atol = default_tolerances(
+            actual,
+            expected,
+            dtype_precisions={
+                torch.float16: (1e-3, 1e-3),
+                torch.float32: (1e-4, 1e-5),
+                torch.float64: (1e-5, 1e-8),
+            },
+        )
+
+    torch.testing.assert_close(
+        actual,
+        expected,
+        rtol=rtol,
+        atol=atol,
+        equal_nan=equal_nan,
+        check_device=True,
+        check_dtype=False,
+        check_stride=False,
+        msg=msg or None,
+    )

venv/lib/python3.10/site-packages/torch/testing/_creation.py

@@ -0,0 +1,267 @@
+"""
+This module contains tensor creation utilities.
+"""
+
+import collections.abc
+import math
+import warnings
+from typing import cast, List, Optional, Tuple, Union
+
+import torch
+
+_INTEGRAL_TYPES = [
+    torch.uint8,
+    torch.int8,
+    torch.int16,
+    torch.int32,
+    torch.int64,
+    torch.uint16,
+    torch.uint32,
+    torch.uint64,
+]
+_FLOATING_TYPES = [torch.float16, torch.bfloat16, torch.float32, torch.float64]
+_FLOATING_8BIT_TYPES = [
+    torch.float8_e4m3fn,
+    torch.float8_e5m2,
+    torch.float8_e4m3fnuz,
+    torch.float8_e5m2fnuz,
+]
+_COMPLEX_TYPES = [torch.complex32, torch.complex64, torch.complex128]
+_BOOLEAN_OR_INTEGRAL_TYPES = [torch.bool, *_INTEGRAL_TYPES]
+_FLOATING_OR_COMPLEX_TYPES = [*_FLOATING_TYPES, *_COMPLEX_TYPES]
+
+
+def _uniform_random_(t: torch.Tensor, low: float, high: float) -> torch.Tensor:
+    # uniform_ requires to-from <= std::numeric_limits<scalar_t>::max()
+    # Work around this by scaling the range before and after the PRNG
+    if high - low >= torch.finfo(t.dtype).max:
+        return t.uniform_(low / 2, high / 2).mul_(2)
+    else:
+        return t.uniform_(low, high)
+
+
+def make_tensor(
+    *shape: Union[int, torch.Size, List[int], Tuple[int, ...]],
+    dtype: torch.dtype,
+    device: Union[str, torch.device],
+    low: Optional[float] = None,
+    high: Optional[float] = None,
+    requires_grad: bool = False,
+    noncontiguous: bool = False,
+    exclude_zero: bool = False,
+    memory_format: Optional[torch.memory_format] = None,
+) -> torch.Tensor:
+    r"""Creates a tensor with the given :attr:`shape`, :attr:`device`, and :attr:`dtype`, and filled with
+    values uniformly drawn from ``[low, high)``.
+
+    If :attr:`low` or :attr:`high` are specified and are outside the range of the :attr:`dtype`'s representable
+    finite values then they are clamped to the lowest or highest representable finite value, respectively.
+    If ``None``, then the following table describes the default values for :attr:`low` and :attr:`high`,
+    which depend on :attr:`dtype`.
+
+    +---------------------------+------------+----------+
+    | ``dtype``                 | ``low``    | ``high`` |
+    +===========================+============+==========+
+    | boolean type              | ``0``      | ``2``    |
+    +---------------------------+------------+----------+
+    | unsigned integral type    | ``0``      | ``10``   |
+    +---------------------------+------------+----------+
+    | signed integral types     | ``-9``     | ``10``   |
+    +---------------------------+------------+----------+
+    | floating types            | ``-9``     | ``9``    |
+    +---------------------------+------------+----------+
+    | complex types             | ``-9``     | ``9``    |
+    +---------------------------+------------+----------+
+
+    Args:
+        shape (Tuple[int, ...]): Single integer or a sequence of integers defining the shape of the output tensor.
+        dtype (:class:`torch.dtype`): The data type of the returned tensor.
+        device (Union[str, torch.device]): The device of the returned tensor.
+        low (Optional[Number]): Sets the lower limit (inclusive) of the given range. If a number is provided it is
+            clamped to the least representable finite value of the given dtype. When ``None`` (default),
+            this value is determined based on the :attr:`dtype` (see the table above). Default: ``None``.
+        high (Optional[Number]): Sets the upper limit (exclusive) of the given range. If a number is provided it is
+            clamped to the greatest representable finite value of the given dtype. When ``None`` (default) this value
+            is determined based on the :attr:`dtype` (see the table above). Default: ``None``.
+
+            .. deprecated:: 2.1
+
+                Passing ``low==high`` to :func:`~torch.testing.make_tensor` for floating or complex types is deprecated
+                since 2.1 and will be removed in 2.3. Use :func:`torch.full` instead.
+
+        requires_grad (Optional[bool]): If autograd should record operations on the returned tensor. Default: ``False``.
+        noncontiguous (Optional[bool]): If `True`, the returned tensor will be noncontiguous. This argument is
+            ignored if the constructed tensor has fewer than two elements. Mutually exclusive with ``memory_format``.
+        exclude_zero (Optional[bool]): If ``True`` then zeros are replaced with the dtype's small positive value
+            depending on the :attr:`dtype`. For bool and integer types zero is replaced with one. For floating
+            point types it is replaced with the dtype's smallest positive normal number (the "tiny" value of the
+            :attr:`dtype`'s :func:`~torch.finfo` object), and for complex types it is replaced with a complex number
+            whose real and imaginary parts are both the smallest positive normal number representable by the complex
+            type. Default ``False``.
+        memory_format (Optional[torch.memory_format]): The memory format of the returned tensor. Mutually exclusive
+            with ``noncontiguous``.
+
+    Raises:
+        ValueError: If ``requires_grad=True`` is passed for integral `dtype`
+        ValueError: If ``low >= high``.
+        ValueError: If either :attr:`low` or :attr:`high` is ``nan``.
+        ValueError: If both :attr:`noncontiguous` and :attr:`memory_format` are passed.
+        TypeError: If :attr:`dtype` isn't supported by this function.
+
+    Examples:
+        >>> # xdoctest: +SKIP
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
+        >>> from torch.testing import make_tensor
+        >>> # Creates a float tensor with values in [-1, 1)
+        >>> make_tensor((3,), device='cpu', dtype=torch.float32, low=-1, high=1)
+        >>> # xdoctest: +SKIP
+        tensor([ 0.1205, 0.2282, -0.6380])
+        >>> # Creates a bool tensor on CUDA
+        >>> make_tensor((2, 2), device='cuda', dtype=torch.bool)
+        tensor([[False, False],
+                [False, True]], device='cuda:0')
+    """
+
+    def modify_low_high(
+        low: Optional[float],
+        high: Optional[float],
+        *,
+        lowest_inclusive: float,
+        highest_exclusive: float,
+        default_low: float,
+        default_high: float,
+    ) -> Tuple[float, float]:
+        """
+        Modifies (and raises ValueError when appropriate) low and high values given by the user (input_low, input_high)
+        if required.
+        """
+
+        def clamp(a: float, l: float, h: float) -> float:
+            return min(max(a, l), h)
+
+        low = low if low is not None else default_low
+        high = high if high is not None else default_high
+
+        if any(isinstance(value, float) and math.isnan(value) for value in [low, high]):
+            raise ValueError(
+                f"`low` and `high` cannot be NaN, but got {low=} and {high=}"
+            )
+        elif low == high and dtype in _FLOATING_OR_COMPLEX_TYPES:
+            warnings.warn(
+                "Passing `low==high` to `torch.testing.make_tensor` for floating or complex types "
+                "is deprecated since 2.1 and will be removed in 2.3. "
+                "Use torch.full(...) instead.",
+                FutureWarning,
+            )
+        elif low >= high:
+            raise ValueError(f"`low` must be less than `high`, but got {low} >= {high}")
+        elif high < lowest_inclusive or low >= highest_exclusive:
+            raise ValueError(
+                f"The value interval specified by `low` and `high` is [{low}, {high}), "
+                f"but {dtype} only supports [{lowest_inclusive}, {highest_exclusive})"
+            )
+
+        low = clamp(low, lowest_inclusive, highest_exclusive)
+        high = clamp(high, lowest_inclusive, highest_exclusive)
+
+        if dtype in _BOOLEAN_OR_INTEGRAL_TYPES:
+            # 1. `low` is ceiled to avoid creating values smaller than `low` and thus outside the specified interval
+            # 2. Following the same reasoning as for 1., `high` should be floored. However, the higher bound of
+            #    `torch.randint` is exclusive, and thus we need to ceil here as well.
+            return math.ceil(low), math.ceil(high)
+
+        return low, high
+
+    if len(shape) == 1 and isinstance(shape[0], collections.abc.Sequence):
+        shape = shape[0]  # type: ignore[assignment]
+    shape = cast(Tuple[int, ...], tuple(shape))
+
+    if noncontiguous and memory_format is not None:
+        raise ValueError(
+            f"The parameters `noncontiguous` and `memory_format` are mutually exclusive, "
+            f"but got {noncontiguous=} and {memory_format=}"
+        )
+
+    if requires_grad and dtype in _BOOLEAN_OR_INTEGRAL_TYPES:
+        raise ValueError(
+            f"`requires_grad=True` is not supported for boolean and integral dtypes, but got {dtype=}"
+        )
+
+    if dtype is torch.bool:
+        low, high = cast(
+            Tuple[int, int],
+            modify_low_high(
+                low,
+                high,
+                lowest_inclusive=0,
+                highest_exclusive=2,
+                default_low=0,
+                default_high=2,
+            ),
+        )
+        result = torch.randint(low, high, shape, device=device, dtype=dtype)
+    elif dtype in _BOOLEAN_OR_INTEGRAL_TYPES:
+        low, high = cast(
+            Tuple[int, int],
+            modify_low_high(
+                low,
+                high,
+                lowest_inclusive=torch.iinfo(dtype).min,
+                highest_exclusive=torch.iinfo(dtype).max
+                # In theory, `highest_exclusive` should always be the maximum value + 1. However, `torch.randint`
+                # internally converts the bounds to an int64 and would overflow. In other words: `torch.randint` cannot
+                # sample 2**63 - 1, i.e. the maximum value of `torch.int64` and we need to account for that here.
+                + (1 if dtype is not torch.int64 else 0),
+                # This is incorrect for `torch.uint8`, but since we clamp to `lowest`, i.e. 0 for `torch.uint8`,
+                # _after_ we use the default value, we don't need to special case it here
+                default_low=-9,
+                default_high=10,
+            ),
+        )
+        result = torch.randint(low, high, shape, device=device, dtype=dtype)
+    elif dtype in _FLOATING_OR_COMPLEX_TYPES:
+        low, high = modify_low_high(
+            low,
+            high,
+            lowest_inclusive=torch.finfo(dtype).min,
+            highest_exclusive=torch.finfo(dtype).max,
+            default_low=-9,
+            default_high=9,
+        )
+        result = torch.empty(shape, device=device, dtype=dtype)
+        _uniform_random_(
+            torch.view_as_real(result) if dtype in _COMPLEX_TYPES else result, low, high
+        )
+    elif dtype in _FLOATING_8BIT_TYPES:
+        low, high = modify_low_high(
+            low,
+            high,
+            lowest_inclusive=torch.finfo(dtype).min,
+            highest_exclusive=torch.finfo(dtype).max,
+            default_low=-9,
+            default_high=9,
+        )
+        result = torch.empty(shape, device=device, dtype=torch.float32)
+        _uniform_random_(result, low, high)
+        result = result.to(dtype)
+    else:
+        raise TypeError(
+            f"The requested dtype '{dtype}' is not supported by torch.testing.make_tensor()."
+            " To request support, file an issue at: https://github.com/pytorch/pytorch/issues"
+        )
+
+    if noncontiguous and result.numel() > 1:
+        result = torch.repeat_interleave(result, 2, dim=-1)
+        result = result[..., ::2]
+    elif memory_format is not None:
+        result = result.clone(memory_format=memory_format)
+
+    if exclude_zero:
+        result[result == 0] = (
+            1 if dtype in _BOOLEAN_OR_INTEGRAL_TYPES else torch.finfo(dtype).tiny
+        )
+
+    if dtype in _FLOATING_OR_COMPLEX_TYPES:
+        result.requires_grad = requires_grad
+
+    return result

venv/lib/python3.10/site-packages/torch/testing/_internal/common_cuda.py

@@ -0,0 +1,270 @@
+# mypy: ignore-errors
+
+r"""This file is allowed to initialize CUDA context when imported."""
+
+import functools
+import torch
+import torch.cuda
+from torch.testing._internal.common_utils import LazyVal, TEST_NUMBA, TEST_WITH_ROCM, TEST_CUDA, IS_WINDOWS
+import inspect
+import contextlib
+import os
+
+
+CUDA_ALREADY_INITIALIZED_ON_IMPORT = torch.cuda.is_initialized()
+
+
+TEST_MULTIGPU = TEST_CUDA and torch.cuda.device_count() >= 2
+CUDA_DEVICE = torch.device("cuda:0") if TEST_CUDA else None
+# note: if ROCm is targeted, TEST_CUDNN is code for TEST_MIOPEN
+if TEST_WITH_ROCM:
+    TEST_CUDNN = LazyVal(lambda: TEST_CUDA)
+else:
+    TEST_CUDNN = LazyVal(lambda: TEST_CUDA and torch.backends.cudnn.is_acceptable(torch.tensor(1., device=CUDA_DEVICE)))
+
+TEST_CUDNN_VERSION = LazyVal(lambda: torch.backends.cudnn.version() if TEST_CUDNN else 0)
+
+SM53OrLater = LazyVal(lambda: torch.cuda.is_available() and torch.cuda.get_device_capability() >= (5, 3))
+SM60OrLater = LazyVal(lambda: torch.cuda.is_available() and torch.cuda.get_device_capability() >= (6, 0))
+SM70OrLater = LazyVal(lambda: torch.cuda.is_available() and torch.cuda.get_device_capability() >= (7, 0))
+SM75OrLater = LazyVal(lambda: torch.cuda.is_available() and torch.cuda.get_device_capability() >= (7, 5))
+SM80OrLater = LazyVal(lambda: torch.cuda.is_available() and torch.cuda.get_device_capability() >= (8, 0))
+SM90OrLater = LazyVal(lambda: torch.cuda.is_available() and torch.cuda.get_device_capability() >= (9, 0))
+
+def evaluate_gfx_arch_exact(matching_arch):
+    if not torch.cuda.is_available():
+        return False
+    gcn_arch_name = torch.cuda.get_device_properties('cuda').gcnArchName
+    arch = os.environ.get('PYTORCH_DEBUG_FLASH_ATTENTION_GCN_ARCH_OVERRIDE', gcn_arch_name)
+    return arch == matching_arch
+
+GFX90A_Exact = LazyVal(lambda: evaluate_gfx_arch_exact('gfx90a:sramecc+:xnack-'))
+GFX942_Exact = LazyVal(lambda: evaluate_gfx_arch_exact('gfx942:sramecc+:xnack-'))
+
+def evaluate_platform_supports_flash_attention():
+    if TEST_WITH_ROCM:
+        return evaluate_gfx_arch_exact('gfx90a:sramecc+:xnack-') or evaluate_gfx_arch_exact('gfx942:sramecc+:xnack-')
+    if TEST_CUDA:
+        return not IS_WINDOWS and SM80OrLater
+    return False
+
+PLATFORM_SUPPORTS_FLASH_ATTENTION: bool = LazyVal(lambda: evaluate_platform_supports_flash_attention())
+PLATFORM_SUPPORTS_MEM_EFF_ATTENTION: bool = LazyVal(lambda: TEST_CUDA and not TEST_WITH_ROCM)
+# TODO(eqy): gate this against a cuDNN version
+PLATFORM_SUPPORTS_CUDNN_ATTENTION: bool = LazyVal(lambda: TEST_CUDA and not TEST_WITH_ROCM and
+                                                  torch.backends.cuda.cudnn_sdp_enabled())
+# This condition always evaluates to PLATFORM_SUPPORTS_MEM_EFF_ATTENTION but for logical clarity we keep it separate
+PLATFORM_SUPPORTS_FUSED_ATTENTION: bool = LazyVal(lambda: PLATFORM_SUPPORTS_FLASH_ATTENTION or PLATFORM_SUPPORTS_MEM_EFF_ATTENTION)
+
+PLATFORM_SUPPORTS_FUSED_SDPA: bool = TEST_CUDA and not TEST_WITH_ROCM
+
+if TEST_NUMBA:
+    try:
+        import numba.cuda
+        TEST_NUMBA_CUDA = numba.cuda.is_available()
+    except Exception as e:
+        TEST_NUMBA_CUDA = False
+        TEST_NUMBA = False
+else:
+    TEST_NUMBA_CUDA = False
+
+# Used below in `initialize_cuda_context_rng` to ensure that CUDA context and
+# RNG have been initialized.
+__cuda_ctx_rng_initialized = False
+
+
+# after this call, CUDA context and RNG must have been initialized on each GPU
+def initialize_cuda_context_rng():
+    global __cuda_ctx_rng_initialized
+    assert TEST_CUDA, 'CUDA must be available when calling initialize_cuda_context_rng'
+    if not __cuda_ctx_rng_initialized:
+        # initialize cuda context and rng for memory tests
+        for i in range(torch.cuda.device_count()):
+            torch.randn(1, device=f"cuda:{i}")
+        __cuda_ctx_rng_initialized = True
+
+
+# Test whether hardware TF32 math mode enabled. It is enabled only on:
+# - CUDA >= 11
+# - arch >= Ampere
+def tf32_is_not_fp32():
+    if not torch.cuda.is_available() or torch.version.cuda is None:
+        return False
+    if torch.cuda.get_device_properties(torch.cuda.current_device()).major < 8:
+        return False
+    if int(torch.version.cuda.split('.')[0]) < 11:
+        return False
+    return True
+
+
+@contextlib.contextmanager
+def tf32_off():
+    old_allow_tf32_matmul = torch.backends.cuda.matmul.allow_tf32
+    try:
+        torch.backends.cuda.matmul.allow_tf32 = False
+        with torch.backends.cudnn.flags(enabled=None, benchmark=None, deterministic=None, allow_tf32=False):
+            yield
+    finally:
+        torch.backends.cuda.matmul.allow_tf32 = old_allow_tf32_matmul
+
+
+@contextlib.contextmanager
+def tf32_on(self, tf32_precision=1e-5):
+    old_allow_tf32_matmul = torch.backends.cuda.matmul.allow_tf32
+    old_precision = self.precision
+    try:
+        torch.backends.cuda.matmul.allow_tf32 = True
+        self.precision = tf32_precision
+        with torch.backends.cudnn.flags(enabled=None, benchmark=None, deterministic=None, allow_tf32=True):
+            yield
+    finally:
+        torch.backends.cuda.matmul.allow_tf32 = old_allow_tf32_matmul
+        self.precision = old_precision
+
+
+# This is a wrapper that wraps a test to run this test twice, one with
+# allow_tf32=True, another with allow_tf32=False. When running with
+# allow_tf32=True, it will use reduced precision as specified by the
+# argument. For example:
+#    @dtypes(torch.float32, torch.float64, torch.complex64, torch.complex128)
+#    @tf32_on_and_off(0.005)
+#    def test_matmul(self, device, dtype):
+#        a = ...; b = ...;
+#        c = torch.matmul(a, b)
+#        self.assertEqual(c, expected)
+# In the above example, when testing torch.float32 and torch.complex64 on CUDA
+# on a CUDA >= 11 build on an >=Ampere architecture, the matmul will be running at
+# TF32 mode and TF32 mode off, and on TF32 mode, the assertEqual will use reduced
+# precision to check values.
+#
+# This decorator can be used for function with or without device/dtype, such as
+# @tf32_on_and_off(0.005)
+# def test_my_op(self)
+# @tf32_on_and_off(0.005)
+# def test_my_op(self, device)
+# @tf32_on_and_off(0.005)
+# def test_my_op(self, device, dtype)
+# @tf32_on_and_off(0.005)
+# def test_my_op(self, dtype)
+# if neither device nor dtype is specified, it will check if the system has ampere device
+# if device is specified, it will check if device is cuda
+# if dtype is specified, it will check if dtype is float32 or complex64
+# tf32 and fp32 are different only when all the three checks pass
+def tf32_on_and_off(tf32_precision=1e-5):
+    def with_tf32_disabled(self, function_call):
+        with tf32_off():
+            function_call()
+
+    def with_tf32_enabled(self, function_call):
+        with tf32_on(self, tf32_precision):
+            function_call()
+
+    def wrapper(f):
+        params = inspect.signature(f).parameters
+        arg_names = tuple(params.keys())
+
+        @functools.wraps(f)
+        def wrapped(*args, **kwargs):
+            for k, v in zip(arg_names, args):
+                kwargs[k] = v
+            cond = tf32_is_not_fp32()
+            if 'device' in kwargs:
+                cond = cond and (torch.device(kwargs['device']).type == 'cuda')
+            if 'dtype' in kwargs:
+                cond = cond and (kwargs['dtype'] in {torch.float32, torch.complex64})
+            if cond:
+                with_tf32_disabled(kwargs['self'], lambda: f(**kwargs))
+                with_tf32_enabled(kwargs['self'], lambda: f(**kwargs))
+            else:
+                f(**kwargs)
+
+        return wrapped
+    return wrapper
+
+
+# This is a wrapper that wraps a test to run it with TF32 turned off.
+# This wrapper is designed to be used when a test uses matmul or convolutions
+# but the purpose of that test is not testing matmul or convolutions.
+# Disabling TF32 will enforce torch.float tensors to be always computed
+# at full precision.
+def with_tf32_off(f):
+    @functools.wraps(f)
+    def wrapped(*args, **kwargs):
+        with tf32_off():
+            return f(*args, **kwargs)
+
+    return wrapped
+
+def _get_magma_version():
+    if 'Magma' not in torch.__config__.show():
+        return (0, 0)
+    position = torch.__config__.show().find('Magma ')
+    version_str = torch.__config__.show()[position + len('Magma '):].split('\n')[0]
+    return tuple(int(x) for x in version_str.split("."))
+
+def _get_torch_cuda_version():
+    if torch.version.cuda is None:
+        return (0, 0)
+    cuda_version = str(torch.version.cuda)
+    return tuple(int(x) for x in cuda_version.split("."))
+
+def _get_torch_rocm_version():
+    if not TEST_WITH_ROCM:
+        return (0, 0)
+    rocm_version = str(torch.version.hip)
+    rocm_version = rocm_version.split("-")[0]    # ignore git sha
+    return tuple(int(x) for x in rocm_version.split("."))
+
+def _check_cusparse_generic_available():
+    return not TEST_WITH_ROCM
+
+def _check_hipsparse_generic_available():
+    if not TEST_WITH_ROCM:
+        return False
+
+    rocm_version = str(torch.version.hip)
+    rocm_version = rocm_version.split("-")[0]    # ignore git sha
+    rocm_version_tuple = tuple(int(x) for x in rocm_version.split("."))
+    return not (rocm_version_tuple is None or rocm_version_tuple < (5, 1))
+
+
+TEST_CUSPARSE_GENERIC = _check_cusparse_generic_available()
+TEST_HIPSPARSE_GENERIC = _check_hipsparse_generic_available()
+
+# Shared by test_torch.py and test_multigpu.py
+def _create_scaling_models_optimizers(device="cuda", optimizer_ctor=torch.optim.SGD, optimizer_kwargs=None):
+    # Create a module+optimizer that will use scaling, and a control module+optimizer
+    # that will not use scaling, against which the scaling-enabled module+optimizer can be compared.
+    mod_control = torch.nn.Sequential(torch.nn.Linear(8, 8), torch.nn.Linear(8, 8)).to(device=device)
+    mod_scaling = torch.nn.Sequential(torch.nn.Linear(8, 8), torch.nn.Linear(8, 8)).to(device=device)
+    with torch.no_grad():
+        for c, s in zip(mod_control.parameters(), mod_scaling.parameters()):
+            s.copy_(c)
+
+    kwargs = {"lr": 1.0}
+    if optimizer_kwargs is not None:
+        kwargs.update(optimizer_kwargs)
+    opt_control = optimizer_ctor(mod_control.parameters(), **kwargs)
+    opt_scaling = optimizer_ctor(mod_scaling.parameters(), **kwargs)
+
+    return mod_control, mod_scaling, opt_control, opt_scaling
+
+# Shared by test_torch.py, test_cuda.py and test_multigpu.py
+def _create_scaling_case(device="cuda", dtype=torch.float, optimizer_ctor=torch.optim.SGD, optimizer_kwargs=None):
+    data = [(torch.randn((8, 8), dtype=dtype, device=device), torch.randn((8, 8), dtype=dtype, device=device)),
+            (torch.randn((8, 8), dtype=dtype, device=device), torch.randn((8, 8), dtype=dtype, device=device)),
+            (torch.randn((8, 8), dtype=dtype, device=device), torch.randn((8, 8), dtype=dtype, device=device)),
+            (torch.randn((8, 8), dtype=dtype, device=device), torch.randn((8, 8), dtype=dtype, device=device))]
+
+    loss_fn = torch.nn.MSELoss().to(device)
+
+    skip_iter = 2
+
+    return _create_scaling_models_optimizers(
+        device=device, optimizer_ctor=optimizer_ctor, optimizer_kwargs=optimizer_kwargs,
+    ) + (data, loss_fn, skip_iter)
+
+
+# Importing this module should NOT eagerly initialize CUDA
+if not CUDA_ALREADY_INITIALIZED_ON_IMPORT:
+    assert not torch.cuda.is_initialized()

venv/lib/python3.10/site-packages/torch/testing/_internal/common_device_type.py

@@ -0,0 +1,1525 @@
+# mypy: ignore-errors
+
+import copy
+import gc
+import inspect
+import runpy
+import sys
+import threading
+from collections import namedtuple
+from enum import Enum
+from functools import wraps, partial
+from typing import List, Any, ClassVar, Optional, Sequence, Tuple, Union, Dict, Set
+import unittest
+import os
+import torch
+from torch.testing._internal.common_utils import TestCase, TEST_WITH_ROCM, TEST_MKL, \
+    skipCUDANonDefaultStreamIf, TEST_WITH_ASAN, TEST_WITH_UBSAN, TEST_WITH_TSAN, \
+    IS_SANDCASTLE, IS_FBCODE, IS_REMOTE_GPU, IS_WINDOWS, TEST_MPS, \
+    _TestParametrizer, compose_parametrize_fns, dtype_name, \
+    TEST_WITH_MIOPEN_SUGGEST_NHWC, NATIVE_DEVICES, skipIfTorchDynamo, \
+    get_tracked_input, clear_tracked_input, PRINT_REPRO_ON_FAILURE, \
+    TEST_WITH_TORCHINDUCTOR
+from torch.testing._internal.common_cuda import _get_torch_cuda_version, \
+    TEST_CUSPARSE_GENERIC, TEST_HIPSPARSE_GENERIC, _get_torch_rocm_version
+from torch.testing._internal.common_dtype import get_all_dtypes
+
+try:
+    import psutil  # type: ignore[import]
+    HAS_PSUTIL = True
+except ImportError:
+    HAS_PSUTIL = False
+
+# Note [Writing Test Templates]
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+# This note was written shortly after the PyTorch 1.9 release.
+# If you notice it's out-of-date or think it could be improved then please
+# file an issue.
+#
+# PyTorch has its own framework for instantiating test templates. That is, for
+#   taking test classes that look similar to unittest or pytest
+#   compatible test classes and optionally doing the following:
+#
+#     - instantiating a version of the test class for each available device type
+#         (often the CPU, CUDA, and META device types)
+#     - further instantiating a version of each test that's always specialized
+#         on the test class's device type, and optionally specialized further
+#         on datatypes or operators
+#
+# This functionality is similar to pytest's parametrize functionality
+#   (see https://docs.pytest.org/en/6.2.x/parametrize.html), but with considerable
+#   additional logic that specializes the instantiated test classes for their
+#   device types (see CPUTestBase and CUDATestBase below), supports a variety
+#   of composable decorators that allow for test filtering and setting
+#   tolerances, and allows tests parametrized by operators to instantiate
+#   only the subset of device type x dtype that operator supports.
+#
+# This framework was built to make it easier to write tests that run on
+#   multiple device types, multiple datatypes (dtypes), and for multiple
+#   operators. It's also useful for controlling which tests are run. For example,
+#   only tests that use a CUDA device can be run on platforms with CUDA.
+#   Let's dive in with an example to get an idea for how it works:
+#
+# --------------------------------------------------------
+# A template class (looks like a regular unittest TestCase)
+# class TestClassFoo(TestCase):
+#
+#   # A template test that can be specialized with a device
+#   # NOTE: this test case is not runnable by unittest or pytest because it
+#   #   accepts an extra positional argument, "device", that they do not understand
+#   def test_bar(self, device):
+#     pass
+#
+# # Function that instantiates a template class and its tests
+# instantiate_device_type_tests(TestCommon, globals())
+# --------------------------------------------------------
+#
+# In the above code example we see a template class and a single test template
+#   that can be instantiated with a device. The function
+#   instantiate_device_type_tests(), called at file scope, instantiates
+#   new test classes, one per available device type, and new tests in those
+#   classes from these templates. It actually does this by removing
+#   the class TestClassFoo and replacing it with classes like TestClassFooCPU
+#   and TestClassFooCUDA, instantiated test classes that inherit from CPUTestBase
+#   and CUDATestBase respectively. Additional device types, like XLA,
+#   (see https://github.com/pytorch/xla) can further extend the set of
+#   instantiated test classes to create classes like TestClassFooXLA.
+#
+# The test template, test_bar(), is also instantiated. In this case the template
+#   is only specialized on a device, so (depending on the available device
+#   types) it might become test_bar_cpu() in TestClassFooCPU and test_bar_cuda()
+#   in TestClassFooCUDA. We can think of the instantiated test classes as
+#   looking like this:
+#
+# --------------------------------------------------------
+# # An instantiated test class for the CPU device type
+# class TestClassFooCPU(CPUTestBase):
+#
+#   # An instantiated test that calls the template with the string representation
+#   #   of a device from the test class's device type
+#   def test_bar_cpu(self):
+#     test_bar(self, 'cpu')
+#
+# # An instantiated test class for the CUDA device type
+# class TestClassFooCUDA(CUDATestBase):
+#
+#   # An instantiated test that calls the template with the string representation
+#   #   of a device from the test class's device type
+#   def test_bar_cuda(self):
+#     test_bar(self, 'cuda:0')
+# --------------------------------------------------------
+#
+# These instantiated test classes ARE discoverable and runnable by both
+#   unittest and pytest. One thing that may be confusing, however, is that
+#   attempting to run "test_bar" will not work, despite it appearing in the
+#   original template code. This is because "test_bar" is no longer discoverable
+#   after instantiate_device_type_tests() runs, as the above snippet shows.
+#   Instead "test_bar_cpu" and "test_bar_cuda" may be run directly, or both
+#   can be run with the option "-k test_bar".
+#
+# Removing the template class and adding the instantiated classes requires
+#   passing "globals()" to instantiate_device_type_tests(), because it
+#   edits the file's Python objects.
+#
+# As mentioned, tests can be additionally parametrized on dtypes or
+#   operators. Datatype parametrization uses the @dtypes decorator and
+#   require a test template like this:
+#
+# --------------------------------------------------------
+# # A template test that can be specialized with a device and a datatype (dtype)
+# @dtypes(torch.float32, torch.int64)
+# def test_car(self, device, dtype)
+#   pass
+# --------------------------------------------------------
+#
+# If the CPU and CUDA device types are available this test would be
+#   instantiated as 4 tests that cover the cross-product of the two dtypes
+#   and two device types:
+#
+#     - test_car_cpu_float32
+#     - test_car_cpu_int64
+#     - test_car_cuda_float32
+#     - test_car_cuda_int64
+#
+# The dtype is passed as a torch.dtype object.
+#
+# Tests parametrized on operators (actually on OpInfos, more on that in a
+#   moment...) use the @ops decorator and require a test template like this:
+# --------------------------------------------------------
+# # A template test that can be specialized with a device, dtype, and OpInfo
+# @ops(op_db)
+# def test_car(self, device, dtype, op)
+#   pass
+# --------------------------------------------------------
+#
+# See the documentation for the @ops decorator below for additional details
+#   on how to use it and see the note [OpInfos] in
+#   common_methods_invocations.py for more details on OpInfos.
+#
+# A test parametrized over the entire "op_db", which contains hundreds of
+#   OpInfos, will likely have hundreds or thousands of instantiations. The
+#   test will be instantiated on the cross-product of device types, operators,
+#   and the dtypes the operator supports on that device type. The instantiated
+#   tests will have names like:
+#
+#     - test_car_add_cpu_float32
+#     - test_car_sub_cuda_int64
+#
+# The first instantiated test calls the original test_car() with the OpInfo
+#   for torch.add as its "op" argument, the string 'cpu' for its "device" argument,
+#   and the dtype torch.float32 for is "dtype" argument. The second instantiated
+#   test calls the test_car() with the OpInfo for torch.sub, a CUDA device string
+#   like 'cuda:0' or 'cuda:1' for its "device" argument, and the dtype
+#   torch.int64 for its "dtype argument."
+#
+# In addition to parametrizing over device, dtype, and ops via OpInfos, the
+#   @parametrize decorator is supported for arbitrary parametrizations:
+# --------------------------------------------------------
+# # A template test that can be specialized with a device, dtype, and value for x
+# @parametrize("x", range(5))
+# def test_car(self, device, dtype, x)
+#   pass
+# --------------------------------------------------------
+#
+# See the documentation for @parametrize in common_utils.py for additional details
+#   on this. Note that the instantiate_device_type_tests() function will handle
+#   such parametrizations; there is no need to additionally call
+#   instantiate_parametrized_tests().
+#
+# Clever test filtering can be very useful when working with parametrized
+#   tests. "-k test_car" would run every instantiated variant of the test_car()
+#   test template, and "-k test_car_add" runs every variant instantiated with
+#   torch.add.
+#
+# It is important to use the passed device and dtype as appropriate. Use
+#   helper functions like make_tensor() that require explicitly specifying
+#   the device and dtype so they're not forgotten.
+#
+# Test templates can use a variety of composable decorators to specify
+#   additional options and requirements, some are listed here:
+#
+#     - @deviceCountAtLeast(<minimum number of devices to run test with>)
+#         Passes a list of strings representing all available devices of
+#         the test class's device type as the test template's "device" argument.
+#         If there are fewer devices than the value passed to the decorator
+#         the test is skipped.
+#     - @dtypes(<list of tuples of dtypes>)
+#         In addition to accepting multiple dtypes, the @dtypes decorator
+#         can accept a sequence of tuple pairs of dtypes. The test template
+#         will be called with each tuple for its "dtype" argument.
+#     - @onlyNativeDeviceTypes
+#         Skips the test if the device is not a native device type (currently CPU, CUDA, Meta)
+#     - @onlyCPU
+#         Skips the test if the device is not a CPU device
+#     - @onlyCUDA
+#         Skips the test if the device is not a CUDA device
+#     - @onlyMPS
+#         Skips the test if the device is not a MPS device
+#     - @skipCPUIfNoLapack
+#         Skips the test if the device is a CPU device and LAPACK is not installed
+#     - @skipCPUIfNoMkl
+#         Skips the test if the device is a CPU device and MKL is not installed
+#     - @skipCUDAIfNoMagma
+#         Skips the test if the device is a CUDA device and MAGMA is not installed
+#     - @skipCUDAIfRocm
+#         Skips the test if the device is a CUDA device and ROCm is being used
+
+
+# Note [Adding a Device Type]
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+# To add a device type:
+#
+#   (1) Create a new "TestBase" extending DeviceTypeTestBase.
+#       See CPUTestBase and CUDATestBase below.
+#   (2) Define the "device_type" attribute of the base to be the
+#       appropriate string.
+#   (3) Add logic to this file that appends your base class to
+#       device_type_test_bases when your device type is available.
+#   (4) (Optional) Write setUpClass/tearDownClass class methods that
+#       instantiate dependencies (see MAGMA in CUDATestBase).
+#   (5) (Optional) Override the "instantiate_test" method for total
+#       control over how your class creates tests.
+#
+# setUpClass is called AFTER tests have been created and BEFORE and ONLY IF
+# they are run. This makes it useful for initializing devices and dependencies.
+
+
+# Note [Overriding methods in generic tests]
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+# Device generic tests look a lot like normal test classes, but they differ
+# from ordinary classes in some important ways.  In particular, overriding
+# methods in generic tests doesn't work quite the way you expect.
+#
+#     class TestFooDeviceType(TestCase):
+#         # Intention is to override
+#         def assertEqual(self, x, y):
+#             # This DOESN'T WORK!
+#             super().assertEqual(x, y)
+#
+# If you try to run this code, you'll get an error saying that TestFooDeviceType
+# is not in scope.  This is because after instantiating our classes, we delete
+# it from the parent scope.  Instead, you need to hardcode a direct invocation
+# of the desired subclass call, e.g.,
+#
+#     class TestFooDeviceType(TestCase):
+#         # Intention is to override
+#         def assertEqual(self, x, y):
+#             TestCase.assertEqual(x, y)
+#
+# However, a less error-prone way of customizing the behavior of TestCase
+# is to either (1) add your functionality to TestCase and make it toggled
+# by a class attribute, or (2) create your own subclass of TestCase, and
+# then inherit from it for your generic test.
+
+
+def _dtype_test_suffix(dtypes):
+    """ Returns the test suffix for a dtype, sequence of dtypes, or None. """
+    if isinstance(dtypes, (list, tuple)):
+        if len(dtypes) == 0:
+            return ''
+        return '_' + '_'.join(dtype_name(d) for d in dtypes)
+    elif dtypes:
+        return f'_{dtype_name(dtypes)}'
+    else:
+        return ''
+
+
+def _update_param_kwargs(param_kwargs, name, value):
+    """ Adds a kwarg with the specified name and value to the param_kwargs dict. """
+    # Make name plural (e.g. devices / dtypes) if the value is composite.
+    plural_name = f'{name}s'
+
+    # Clear out old entries of the arg if any.
+    if name in param_kwargs:
+        del param_kwargs[name]
+    if plural_name in param_kwargs:
+        del param_kwargs[plural_name]
+
+    if isinstance(value, (list, tuple)):
+        param_kwargs[plural_name] = value
+    elif value is not None:
+        param_kwargs[name] = value
+
+    # Leave param_kwargs as-is when value is None.
+
+
+class DeviceTypeTestBase(TestCase):
+    device_type: str = 'generic_device_type'
+
+    # Flag to disable test suite early due to unrecoverable error such as CUDA error.
+    _stop_test_suite = False
+
+    # Precision is a thread-local setting since it may be overridden per test
+    _tls = threading.local()
+    _tls.precision = TestCase._precision
+    _tls.rel_tol = TestCase._rel_tol
+
+    @property
+    def precision(self):
+        return self._tls.precision
+
+    @precision.setter
+    def precision(self, prec):
+        self._tls.precision = prec
+
+    @property
+    def rel_tol(self):
+        return self._tls.rel_tol
+
+    @rel_tol.setter
+    def rel_tol(self, prec):
+        self._tls.rel_tol = prec
+
+    # Returns a string representing the device that single device tests should use.
+    # Note: single device tests use this device exclusively.
+    @classmethod
+    def get_primary_device(cls):
+        return cls.device_type
+
+    @classmethod
+    def _init_and_get_primary_device(cls):
+        try:
+            return cls.get_primary_device()
+        except Exception:
+            # For CUDATestBase, XLATestBase, and possibly others, the primary device won't be available
+            # until setUpClass() sets it. Call that manually here if needed.
+            if hasattr(cls, 'setUpClass'):
+                cls.setUpClass()
+            return cls.get_primary_device()
+
+    # Returns a list of strings representing all available devices of this
+    # device type. The primary device must be the first string in the list
+    # and the list must contain no duplicates.
+    # Note: UNSTABLE API. Will be replaced once PyTorch has a device generic
+    #   mechanism of acquiring all available devices.
+    @classmethod
+    def get_all_devices(cls):
+        return [cls.get_primary_device()]
+
+    # Returns the dtypes the test has requested.
+    # Prefers device-specific dtype specifications over generic ones.
+    @classmethod
+    def _get_dtypes(cls, test):
+        if not hasattr(test, 'dtypes'):
+            return None
+
+        default_dtypes = test.dtypes.get('all')
+        msg = f"@dtypes is mandatory when using @dtypesIf however '{test.__name__}' didn't specify it"
+        assert default_dtypes is not None, msg
+
+        return test.dtypes.get(cls.device_type, default_dtypes)
+
+    def _get_precision_override(self, test, dtype):
+        if not hasattr(test, 'precision_overrides'):
+            return self.precision
+        return test.precision_overrides.get(dtype, self.precision)
+
+    def _get_tolerance_override(self, test, dtype):
+        if not hasattr(test, 'tolerance_overrides'):
+            return self.precision, self.rel_tol
+        return test.tolerance_overrides.get(dtype, tol(self.precision, self.rel_tol))
+
+    def _apply_precision_override_for_test(self, test, param_kwargs):
+        dtype = param_kwargs['dtype'] if 'dtype' in param_kwargs else None
+        dtype = param_kwargs['dtypes'] if 'dtypes' in param_kwargs else dtype
+        if dtype:
+            self.precision = self._get_precision_override(test, dtype)
+            self.precision, self.rel_tol = self._get_tolerance_override(test, dtype)
+
+    # Creates device-specific tests.
+    @classmethod
+    def instantiate_test(cls, name, test, *, generic_cls=None):
+
+        def instantiate_test_helper(cls, name, *, test, param_kwargs=None, decorator_fn=lambda _: []):
+            # Add the device param kwarg if the test needs device or devices.
+            param_kwargs = {} if param_kwargs is None else param_kwargs
+            test_sig_params = inspect.signature(test).parameters
+            if 'device' in test_sig_params or 'devices' in test_sig_params:
+                device_arg: str = cls._init_and_get_primary_device()
+                if hasattr(test, 'num_required_devices'):
+                    device_arg = cls.get_all_devices()
+                _update_param_kwargs(param_kwargs, 'device', device_arg)
+
+            # Apply decorators based on param kwargs.
+            for decorator in decorator_fn(param_kwargs):
+                test = decorator(test)
+
+            # Constructs the test
+            @wraps(test)
+            def instantiated_test(self, param_kwargs=param_kwargs):
+                # Sets precision and runs test
+                # Note: precision is reset after the test is run
+                guard_precision = self.precision
+                guard_rel_tol = self.rel_tol
+                try:
+                    self._apply_precision_override_for_test(test, param_kwargs)
+                    result = test(self, **param_kwargs)
+                except RuntimeError as rte:
+                    # check if rte should stop entire test suite.
+                    self._stop_test_suite = self._should_stop_test_suite()
+                    # Check if test has been decorated with `@expectedFailure`
+                    # Using `__unittest_expecting_failure__` attribute, see
+                    # https://github.com/python/cpython/blob/ffa505b580464/Lib/unittest/case.py#L164
+                    # In that case, make it fail with "unexpected success" by suppressing exception
+                    if getattr(test, "__unittest_expecting_failure__", False) and self._stop_test_suite:
+                        import sys
+                        print("Suppressing fatal exception to trigger unexpected success", file=sys.stderr)
+                        return
+                    # raise the runtime error as is for the test suite to record.
+                    raise rte
+                finally:
+                    self.precision = guard_precision
+                    self.rel_tol = guard_rel_tol
+
+                return result
+
+            assert not hasattr(cls, name), f"Redefinition of test {name}"
+            setattr(cls, name, instantiated_test)
+
+        def default_parametrize_fn(test, generic_cls, device_cls):
+            # By default, no parametrization is needed.
+            yield (test, '', {}, lambda _: [])
+
+        # Parametrization decorators set the parametrize_fn attribute on the test.
+        parametrize_fn = getattr(test, "parametrize_fn", default_parametrize_fn)
+
+        # If one of the @dtypes* decorators is present, also parametrize over the dtypes set by it.
+        dtypes = cls._get_dtypes(test)
+        if dtypes is not None:
+
+            def dtype_parametrize_fn(test, generic_cls, device_cls, dtypes=dtypes):
+                for dtype in dtypes:
+                    param_kwargs: Dict[str, Any] = {}
+                    _update_param_kwargs(param_kwargs, "dtype", dtype)
+
+                    # Note that an empty test suffix is set here so that the dtype can be appended
+                    # later after the device.
+                    yield (test, '', param_kwargs, lambda _: [])
+
+            parametrize_fn = compose_parametrize_fns(dtype_parametrize_fn, parametrize_fn)
+
+        # Instantiate the parametrized tests.
+        for (test, test_suffix, param_kwargs, decorator_fn) in parametrize_fn(test, generic_cls, cls):  # noqa: B020
+            test_suffix = '' if test_suffix == '' else '_' + test_suffix
+            device_suffix = '_' + cls.device_type
+
+            # Note: device and dtype suffix placement
+            # Special handling here to place dtype(s) after device according to test name convention.
+            dtype_kwarg = None
+            if 'dtype' in param_kwargs or 'dtypes' in param_kwargs:
+                dtype_kwarg = param_kwargs['dtypes'] if 'dtypes' in param_kwargs else param_kwargs['dtype']
+            test_name = f'{name}{test_suffix}{device_suffix}{_dtype_test_suffix(dtype_kwarg)}'
+
+            instantiate_test_helper(cls=cls, name=test_name, test=test, param_kwargs=param_kwargs,
+                                    decorator_fn=decorator_fn)
+
+    def run(self, result=None):
+        super().run(result=result)
+        # Early terminate test if _stop_test_suite is set.
+        if self._stop_test_suite:
+            result.stop()
+
+
+class CPUTestBase(DeviceTypeTestBase):
+    device_type = 'cpu'
+
+    # No critical error should stop CPU test suite
+    def _should_stop_test_suite(self):
+        return False
+
+class CUDATestBase(DeviceTypeTestBase):
+    device_type = 'cuda'
+    _do_cuda_memory_leak_check = True
+    _do_cuda_non_default_stream = True
+    primary_device: ClassVar[str]
+    cudnn_version: ClassVar[Any]
+    no_magma: ClassVar[bool]
+    no_cudnn: ClassVar[bool]
+
+    def has_cudnn(self):
+        return not self.no_cudnn
+
+    @classmethod
+    def get_primary_device(cls):
+        return cls.primary_device
+
+    @classmethod
+    def get_all_devices(cls):
+        primary_device_idx = int(cls.get_primary_device().split(':')[1])
+        num_devices = torch.cuda.device_count()
+
+        prim_device = cls.get_primary_device()
+        cuda_str = 'cuda:{0}'
+        non_primary_devices = [cuda_str.format(idx) for idx in range(num_devices) if idx != primary_device_idx]
+        return [prim_device] + non_primary_devices
+
+    @classmethod
+    def setUpClass(cls):
+        # has_magma shows up after cuda is initialized
+        t = torch.ones(1).cuda()
+        cls.no_magma = not torch.cuda.has_magma
+
+        # Determines if cuDNN is available and its version
+        cls.no_cudnn = not torch.backends.cudnn.is_acceptable(t)
+        cls.cudnn_version = None if cls.no_cudnn else torch.backends.cudnn.version()
+
+        # Acquires the current device as the primary (test) device
+        cls.primary_device = f'cuda:{torch.cuda.current_device()}'
+
+# See Note [Lazy Tensor tests in device agnostic testing]
+lazy_ts_backend_init = False
+class LazyTestBase(DeviceTypeTestBase):
+    device_type = 'lazy'
+
+    def _should_stop_test_suite(self):
+        return False
+
+    @classmethod
+    def setUpClass(cls):
+        import torch._lazy
+        import torch._lazy.metrics
+        import torch._lazy.ts_backend
+        global lazy_ts_backend_init
+        if not lazy_ts_backend_init:
+            # Need to connect the TS backend to lazy key before running tests
+            torch._lazy.ts_backend.init()
+            lazy_ts_backend_init = True
+
+class MPSTestBase(DeviceTypeTestBase):
+    device_type = 'mps'
+    primary_device: ClassVar[str]
+
+    @classmethod
+    def get_primary_device(cls):
+        return cls.primary_device
+
+    @classmethod
+    def get_all_devices(cls):
+        # currently only one device is supported on MPS backend
+        prim_device = cls.get_primary_device()
+        return [prim_device]
+
+    @classmethod
+    def setUpClass(cls):
+        cls.primary_device = 'mps:0'
+
+    def _should_stop_test_suite(self):
+        return False
+
+class PrivateUse1TestBase(DeviceTypeTestBase):
+    primary_device: ClassVar[str]
+    device_mod = None
+    device_type = 'privateuse1'
+
+    @classmethod
+    def get_primary_device(cls):
+        return cls.primary_device
+
+    @classmethod
+    def get_all_devices(cls):
+        primary_device_idx = int(cls.get_primary_device().split(':')[1])
+        num_devices = cls.device_mod.device_count()
+        prim_device = cls.get_primary_device()
+        device_str = f'{cls.device_type}:{{0}}'
+        non_primary_devices = [device_str.format(idx) for idx in range(num_devices) if idx != primary_device_idx]
+        return [prim_device] + non_primary_devices
+
+    @classmethod
+    def setUpClass(cls):
+        cls.device_type = torch._C._get_privateuse1_backend_name()
+        cls.device_mod = getattr(torch, cls.device_type, None)
+        assert cls.device_mod is not None, f'''torch has no module of `{cls.device_type}`, you should register
+                                            a module by `torch._register_device_module`.'''
+        cls.primary_device = f'{cls.device_type}:{cls.device_mod.current_device()}'
+
+# Adds available device-type-specific test base classes
+def get_device_type_test_bases():
+    # set type to List[Any] due to mypy list-of-union issue:
+    # https://github.com/python/mypy/issues/3351
+    test_bases: List[Any] = list()
+
+    if IS_SANDCASTLE or IS_FBCODE:
+        if IS_REMOTE_GPU:
+            # Skip if sanitizer is enabled
+            if not TEST_WITH_ASAN and not TEST_WITH_TSAN and not TEST_WITH_UBSAN:
+                test_bases.append(CUDATestBase)
+        else:
+            test_bases.append(CPUTestBase)
+    else:
+        test_bases.append(CPUTestBase)
+        if torch.cuda.is_available():
+            test_bases.append(CUDATestBase)
+        device_type = torch._C._get_privateuse1_backend_name()
+        device_mod = getattr(torch, device_type, None)
+        if hasattr(device_mod, "is_available") and device_mod.is_available():
+            test_bases.append(PrivateUse1TestBase)
+        # Disable MPS testing in generic device testing temporarily while we're
+        # ramping up support.
+        # elif torch.backends.mps.is_available():
+        #   test_bases.append(MPSTestBase)
+
+    return test_bases
+
+device_type_test_bases = get_device_type_test_bases()
+
+
+def filter_desired_device_types(device_type_test_bases, except_for=None, only_for=None):
+    # device type cannot appear in both except_for and only_for
+    intersect = set(except_for if except_for else []) & set(only_for if only_for else [])
+    assert not intersect, f"device ({intersect}) appeared in both except_for and only_for"
+
+    if except_for:
+        device_type_test_bases = filter(
+            lambda x: x.device_type not in except_for, device_type_test_bases)
+    if only_for:
+        device_type_test_bases = filter(
+            lambda x: x.device_type in only_for, device_type_test_bases)
+
+    return list(device_type_test_bases)
+
+
+# Note [How to extend DeviceTypeTestBase to add new test device]
+# The following logic optionally allows downstream projects like pytorch/xla to
+# add more test devices.
+# Instructions:
+#  - Add a python file (e.g. pytorch/xla/test/pytorch_test_base.py) in downstream project.
+#    - Inside the file, one should inherit from `DeviceTypeTestBase` class and define
+#      a new DeviceTypeTest class (e.g. `XLATestBase`) with proper implementation of
+#      `instantiate_test` method.
+#    - DO NOT import common_device_type inside the file.
+#      `runpy.run_path` with `globals()` already properly setup the context so that
+#      `DeviceTypeTestBase` is already available.
+#    - Set a top-level variable `TEST_CLASS` equal to your new class.
+#      E.g. TEST_CLASS = XLATensorBase
+#  - To run tests with new device type, set `TORCH_TEST_DEVICE` env variable to path
+#    to this file. Multiple paths can be separated by `:`.
+# See pytorch/xla/test/pytorch_test_base.py for a more detailed example.
+_TORCH_TEST_DEVICES = os.environ.get('TORCH_TEST_DEVICES', None)
+if _TORCH_TEST_DEVICES:
+    for path in _TORCH_TEST_DEVICES.split(':'):
+        # runpy (a stdlib module) lacks annotations
+        mod = runpy.run_path(path, init_globals=globals())  # type: ignore[func-returns-value]
+        device_type_test_bases.append(mod['TEST_CLASS'])
+
+
+PYTORCH_CUDA_MEMCHECK = os.getenv('PYTORCH_CUDA_MEMCHECK', '0') == '1'
+
+PYTORCH_TESTING_DEVICE_ONLY_FOR_KEY = 'PYTORCH_TESTING_DEVICE_ONLY_FOR'
+PYTORCH_TESTING_DEVICE_EXCEPT_FOR_KEY = 'PYTORCH_TESTING_DEVICE_EXCEPT_FOR'
+
+
+def get_desired_device_type_test_bases(except_for=None, only_for=None, include_lazy=False, allow_mps=False):
+    # allow callers to specifically opt tests into being tested on MPS, similar to `include_lazy`
+    test_bases = device_type_test_bases.copy()
+    if allow_mps and TEST_MPS and MPSTestBase not in test_bases:
+        test_bases.append(MPSTestBase)
+    # Filter out the device types based on user inputs
+    desired_device_type_test_bases = filter_desired_device_types(test_bases, except_for, only_for)
+    if include_lazy:
+        # Note [Lazy Tensor tests in device agnostic testing]
+        # Right now, test_view_ops.py runs with LazyTensor.
+        # We don't want to opt every device-agnostic test into using the lazy device,
+        # because many of them will fail.
+        # So instead, the only way to opt a specific device-agnostic test file into
+        # lazy tensor testing is with include_lazy=True
+        if IS_FBCODE:
+            print("TorchScript backend not yet supported in FBCODE/OVRSOURCE builds", file=sys.stderr)
+        else:
+            desired_device_type_test_bases.append(LazyTestBase)
+
+    def split_if_not_empty(x: str):
+        return x.split(",") if x else []
+
+    # Filter out the device types based on environment variables if available
+    # Usage:
+    # export PYTORCH_TESTING_DEVICE_ONLY_FOR=cuda,cpu
+    # export PYTORCH_TESTING_DEVICE_EXCEPT_FOR=xla
+    env_only_for = split_if_not_empty(os.getenv(PYTORCH_TESTING_DEVICE_ONLY_FOR_KEY, ''))
+    env_except_for = split_if_not_empty(os.getenv(PYTORCH_TESTING_DEVICE_EXCEPT_FOR_KEY, ''))
+
+    return filter_desired_device_types(desired_device_type_test_bases, env_except_for, env_only_for)
+
+
+
+# Adds 'instantiated' device-specific test cases to the given scope.
+# The tests in these test cases are derived from the generic tests in
+# generic_test_class. This function should be used instead of
+# instantiate_parametrized_tests() if the test class contains
+# device-specific tests (NB: this supports additional @parametrize usage).
+#
+# See note "Writing Test Templates"
+def instantiate_device_type_tests(generic_test_class, scope, except_for=None, only_for=None, include_lazy=False, allow_mps=False):
+    # Removes the generic test class from its enclosing scope so its tests
+    # are not discoverable.
+    del scope[generic_test_class.__name__]
+
+    # Creates an 'empty' version of the generic_test_class
+    # Note: we don't inherit from the generic_test_class directly because
+    #   that would add its tests to our test classes and they would be
+    #   discovered (despite not being runnable). Inherited methods also
+    #   can't be removed later, and we can't rely on load_tests because
+    #   pytest doesn't support it (as of this writing).
+    empty_name = generic_test_class.__name__ + "_base"
+    empty_class = type(empty_name, generic_test_class.__bases__, {})
+
+    # Acquires members names
+    # See Note [Overriding methods in generic tests]
+    generic_members = set(generic_test_class.__dict__.keys()) - set(empty_class.__dict__.keys())
+    generic_tests = [x for x in generic_members if x.startswith('test')]
+
+    # Creates device-specific test cases
+    for base in get_desired_device_type_test_bases(except_for, only_for, include_lazy, allow_mps):
+        class_name = generic_test_class.__name__ + base.device_type.upper()
+
+        # type set to Any and suppressed due to unsupport runtime class:
+        # https://github.com/python/mypy/wiki/Unsupported-Python-Features
+        device_type_test_class: Any = type(class_name, (base, empty_class), {})
+
+        for name in generic_members:
+            if name in generic_tests:  # Instantiates test member
+                test = getattr(generic_test_class, name)
+                # XLA-compat shim (XLA's instantiate_test takes doesn't take generic_cls)
+                sig = inspect.signature(device_type_test_class.instantiate_test)
+                if len(sig.parameters) == 3:
+                    # Instantiates the device-specific tests
+                    device_type_test_class.instantiate_test(name, copy.deepcopy(test), generic_cls=generic_test_class)
+                else:
+                    device_type_test_class.instantiate_test(name, copy.deepcopy(test))
+            else:  # Ports non-test member
+                assert name not in device_type_test_class.__dict__, f"Redefinition of directly defined member {name}"
+                nontest = getattr(generic_test_class, name)
+                setattr(device_type_test_class, name, nontest)
+
+        # Mimics defining the instantiated class in the caller's file
+        # by setting its module to the given class's and adding
+        # the module to the given scope.
+        # This lets the instantiated class be discovered by unittest.
+        device_type_test_class.__module__ = generic_test_class.__module__
+        scope[class_name] = device_type_test_class
+
+
+# Category of dtypes to run an OpInfo-based test for
+# Example use: @ops(dtype=OpDTypes.supported)
+#
+# There are 5 categories:
+# - supported: Every dtype supported by the operator. Use for exhaustive
+#              testing of all dtypes.
+# - unsupported: Run tests on dtypes not supported by the operator. e.g. for
+#                testing the operator raises an error and doesn't crash.
+# - supported_backward: Every dtype supported by the operator's backward pass.
+# - unsupported_backward: Run tests on dtypes not supported by the operator's backward pass.
+# - any_one: Runs a test for one dtype the operator supports. Prioritizes dtypes the
+#     operator supports in both forward and backward.
+# - none: Useful for tests that are not dtype-specific. No dtype will be passed to the test
+#         when this is selected.
+class OpDTypes(Enum):
+    supported = 0  # Test all supported dtypes (default)
+    unsupported = 1  # Test only unsupported dtypes
+    supported_backward = 2  # Test all supported backward dtypes
+    unsupported_backward = 3  # Test only unsupported backward dtypes
+    any_one = 4  # Test precisely one supported dtype
+    none = 5  # Instantiate no dtype variants (no dtype kwarg needed)
+    any_common_cpu_cuda_one = 6  # Test precisely one supported dtype that is common to both cuda and cpu
+
+
+# Arbitrary order
+ANY_DTYPE_ORDER = (
+    torch.float32,
+    torch.float64,
+    torch.complex64,
+    torch.complex128,
+    torch.float16,
+    torch.bfloat16,
+    torch.long,
+    torch.int32,
+    torch.int16,
+    torch.int8,
+    torch.uint8,
+    torch.bool
+)
+
+def _serialize_sample(sample_input):
+    # NB: For OpInfos, SampleInput.summary() prints in a cleaner way.
+    if getattr(sample_input, "summary", None) is not None:
+        return sample_input.summary()
+    return str(sample_input)
+
+# Decorator that defines the OpInfos a test template should be instantiated for.
+#
+# Example usage:
+#
+# @ops(unary_ufuncs)
+# def test_numerics(self, device, dtype, op):
+#   <test_code>
+#
+# This will instantiate variants of test_numerics for each given OpInfo,
+# on each device the OpInfo's operator supports, and for every dtype supported by
+# that operator. There are a few caveats to the dtype rule, explained below.
+#
+# The @ops decorator can accept two
+# additional arguments, "dtypes" and "allowed_dtypes". If "dtypes" is specified
+# then the test variants are instantiated for those dtypes, regardless of
+# what the operator supports. If given "allowed_dtypes" then test variants
+# are instantiated only for the intersection of allowed_dtypes and the dtypes
+# they would otherwise be instantiated with. That is, allowed_dtypes composes
+# with the options listed above and below.
+#
+# The "dtypes" argument can also accept additional values (see OpDTypes above):
+#   OpDTypes.supported - the test is instantiated for all dtypes the operator
+#     supports
+#   OpDTypes.unsupported - the test is instantiated for all dtypes the operator
+#     doesn't support
+#   OpDTypes.supported_backward - the test is instantiated for all dtypes the
+#     operator's gradient formula supports
+#   OpDTypes.unsupported_backward - the test is instantiated for all dtypes the
+#     operator's gradient formula doesn't support
+#   OpDTypes.any_one - the test is instantiated for one dtype the
+#     operator supports. The dtype supports forward and backward if possible.
+#   OpDTypes.none - the test is instantiated without any dtype. The test signature
+#     should not include a dtype kwarg in this case.
+#
+# These options allow tests to have considerable control over the dtypes
+#   they're instantiated for.
+
+class ops(_TestParametrizer):
+    def __init__(self, op_list, *, dtypes: Union[OpDTypes, Sequence[torch.dtype]] = OpDTypes.supported,
+                 allowed_dtypes: Optional[Sequence[torch.dtype]] = None, skip_if_dynamo=True):
+        self.op_list = list(op_list)
+        self.opinfo_dtypes = dtypes
+        self.allowed_dtypes = set(allowed_dtypes) if allowed_dtypes is not None else None
+        self.skip_if_dynamo = skip_if_dynamo
+
+    def _parametrize_test(self, test, generic_cls, device_cls):
+        """ Parameterizes the given test function across each op and its associated dtypes. """
+        if device_cls is None:
+            raise RuntimeError('The @ops decorator is only intended to be used in a device-specific '
+                               'context; use it with instantiate_device_type_tests() instead of '
+                               'instantiate_parametrized_tests()')
+
+        op = check_exhausted_iterator = object()
+        for op in self.op_list:
+            # Determine the set of dtypes to use.
+            dtypes: Union[Set[torch.dtype], Set[None]]
+            if isinstance(self.opinfo_dtypes, Sequence):
+                dtypes = set(self.opinfo_dtypes)
+            elif self.opinfo_dtypes == OpDTypes.unsupported_backward:
+                dtypes = set(get_all_dtypes()).difference(op.supported_backward_dtypes(device_cls.device_type))
+            elif self.opinfo_dtypes == OpDTypes.supported_backward:
+                dtypes = op.supported_backward_dtypes(device_cls.device_type)
+            elif self.opinfo_dtypes == OpDTypes.unsupported:
+                dtypes = set(get_all_dtypes()).difference(op.supported_dtypes(device_cls.device_type))
+            elif self.opinfo_dtypes == OpDTypes.supported:
+                dtypes = op.supported_dtypes(device_cls.device_type)
+            elif self.opinfo_dtypes == OpDTypes.any_one:
+                # Tries to pick a dtype that supports both forward or backward
+                supported = op.supported_dtypes(device_cls.device_type)
+                supported_backward = op.supported_backward_dtypes(device_cls.device_type)
+                supported_both = supported.intersection(supported_backward)
+                dtype_set = supported_both if len(supported_both) > 0 else supported
+                for dtype in ANY_DTYPE_ORDER:
+                    if dtype in dtype_set:
+                        dtypes = {dtype}
+                        break
+                else:
+                    dtypes = {}
+            elif self.opinfo_dtypes == OpDTypes.any_common_cpu_cuda_one:
+                # Tries to pick a dtype that supports both CPU and CUDA
+                supported = op.dtypes.intersection(op.dtypesIfCUDA)
+                if supported:
+                    dtypes = {next(dtype for dtype in ANY_DTYPE_ORDER if dtype in supported)}
+                else:
+                    dtypes = {}
+
+            elif self.opinfo_dtypes == OpDTypes.none:
+                dtypes = {None}
+            else:
+                raise RuntimeError(f"Unknown OpDType: {self.opinfo_dtypes}")
+
+            if self.allowed_dtypes is not None:
+                dtypes = dtypes.intersection(self.allowed_dtypes)
+
+            # Construct the test name; device / dtype parts are handled outside.
+            # See [Note: device and dtype suffix placement]
+            test_name = op.formatted_name
+
+            for dtype in dtypes:
+                # Construct parameter kwargs to pass to the test.
+                param_kwargs = {'op': op}
+                _update_param_kwargs(param_kwargs, 'dtype', dtype)
+
+                # NOTE: test_wrapper exists because we don't want to apply
+                #   op-specific decorators to the original test.
+                #   Test-specific decorators are applied to the original test,
+                #   however.
+                try:
+                    @wraps(test)
+                    def test_wrapper(*args, **kwargs):
+                        try:
+                            return test(*args, **kwargs)
+                        except unittest.SkipTest as e:
+                            raise e
+                        except Exception as e:
+                            tracked_input = get_tracked_input()
+                            if PRINT_REPRO_ON_FAILURE and tracked_input is not None:
+                                raise Exception(
+                                    f"Caused by {tracked_input.type_desc} "
+                                    f"at index {tracked_input.index}: "
+                                    f"{_serialize_sample(tracked_input.val)}") from e
+                            raise e
+                        finally:
+                            clear_tracked_input()
+
+                    if self.skip_if_dynamo and not TEST_WITH_TORCHINDUCTOR:
+                        test_wrapper = skipIfTorchDynamo("Policy: we don't run OpInfo tests w/ Dynamo")(test_wrapper)
+
+                    # Initialize info for the last input seen. This is useful for tracking
+                    # down which inputs caused a test failure. Note that TrackedInputIter is
+                    # responsible for managing this.
+                    test.tracked_input = None
+
+                    decorator_fn = partial(op.get_decorators, generic_cls.__name__,
+                                           test.__name__, device_cls.device_type, dtype)
+
+                    yield (test_wrapper, test_name, param_kwargs, decorator_fn)
+                except Exception as ex:
+                    # Provides an error message for debugging before rethrowing the exception
+                    print(f"Failed to instantiate {test_name} for op {op.name}!")
+                    raise ex
+        if op is check_exhausted_iterator:
+            raise ValueError('An empty op_list was passed to @ops. '
+                             'Note that this may result from reuse of a generator.')
+
+# Decorator that skips a test if the given condition is true.
+# Notes:
+#   (1) Skip conditions stack.
+#   (2) Skip conditions can be bools or strings. If a string the
+#       test base must have defined the corresponding attribute to be False
+#       for the test to run. If you want to use a string argument you should
+#       probably define a new decorator instead (see below).
+#   (3) Prefer the existing decorators to defining the 'device_type' kwarg.
+class skipIf:
+
+    def __init__(self, dep, reason, device_type=None):
+        self.dep = dep
+        self.reason = reason
+        self.device_type = device_type
+
+    def __call__(self, fn):
+
+        @wraps(fn)
+        def dep_fn(slf, *args, **kwargs):
+            if self.device_type is None or self.device_type == slf.device_type:
+                if (isinstance(self.dep, str) and getattr(slf, self.dep, True)) or (isinstance(self.dep, bool) and self.dep):
+                    raise unittest.SkipTest(self.reason)
+
+            return fn(slf, *args, **kwargs)
+        return dep_fn
+
+
+# Skips a test on CPU if the condition is true.
+class skipCPUIf(skipIf):
+
+    def __init__(self, dep, reason):
+        super().__init__(dep, reason, device_type='cpu')
+
+
+# Skips a test on CUDA if the condition is true.
+class skipCUDAIf(skipIf):
+
+    def __init__(self, dep, reason):
+        super().__init__(dep, reason, device_type='cuda')
+
+# Skips a test on Lazy if the condition is true.
+class skipLazyIf(skipIf):
+
+    def __init__(self, dep, reason):
+        super().__init__(dep, reason, device_type='lazy')
+
+# Skips a test on Meta if the condition is true.
+class skipMetaIf(skipIf):
+
+    def __init__(self, dep, reason):
+        super().__init__(dep, reason, device_type='meta')
+
+# Skips a test on MPS if the condition is true.
+class skipMPSIf(skipIf):
+
+    def __init__(self, dep, reason):
+        super().__init__(dep, reason, device_type='mps')
+
+# Skips a test on XLA if the condition is true.
+class skipXLAIf(skipIf):
+
+    def __init__(self, dep, reason):
+        super().__init__(dep, reason, device_type='xla')
+
+class skipPRIVATEUSE1If(skipIf):
+
+    def __init__(self, dep, reason):
+        device_type = torch._C._get_privateuse1_backend_name()
+        super().__init__(dep, reason, device_type=device_type)
+
+def _has_sufficient_memory(device, size):
+    if torch.device(device).type == 'cuda':
+        if not torch.cuda.is_available():
+            return False
+        gc.collect()
+        torch.cuda.empty_cache()
+        # torch.cuda.mem_get_info, aka cudaMemGetInfo, returns a tuple of (free memory, total memory) of a GPU
+        if device == 'cuda':
+            device = 'cuda:0'
+        return torch.cuda.memory.mem_get_info(device)[0] >= size
+
+    if device == 'xla':
+        raise unittest.SkipTest('TODO: Memory availability checks for XLA?')
+
+    if device != 'cpu':
+        raise unittest.SkipTest('Unknown device type')
+
+    # CPU
+    if not HAS_PSUTIL:
+        raise unittest.SkipTest('Need psutil to determine if memory is sufficient')
+
+    # The sanitizers have significant memory overheads
+    if TEST_WITH_ASAN or TEST_WITH_TSAN or TEST_WITH_UBSAN:
+        effective_size = size * 10
+    else:
+        effective_size = size
+
+    if psutil.virtual_memory().available < effective_size:
+        gc.collect()
+    return psutil.virtual_memory().available >= effective_size
+
+
+def largeTensorTest(size, device=None):
+    """Skip test if the device has insufficient memory to run the test
+
+    size may be a number of bytes, a string of the form "N GB", or a callable
+
+    If the test is a device generic test, available memory on the primary device will be checked.
+    It can also be overriden by the optional `device=` argument.
+    In other tests, the `device=` argument needs to be specified.
+    """
+    if isinstance(size, str):
+        assert size.endswith(('GB', 'gb')), "only bytes or GB supported"
+        size = 1024 ** 3 * int(size[:-2])
+
+    def inner(fn):
+        @wraps(fn)
+        def dep_fn(self, *args, **kwargs):
+            size_bytes = size(self, *args, **kwargs) if callable(size) else size
+            _device = device if device is not None else self.get_primary_device()
+            if not _has_sufficient_memory(_device, size_bytes):
+                raise unittest.SkipTest(f'Insufficient {_device} memory')
+
+            return fn(self, *args, **kwargs)
+        return dep_fn
+    return inner
+
+
+class expectedFailure:
+
+    def __init__(self, device_type):
+        self.device_type = device_type
+
+    def __call__(self, fn):
+
+        @wraps(fn)
+        def efail_fn(slf, *args, **kwargs):
+            if self.device_type is None or self.device_type == slf.device_type:
+                try:
+                    fn(slf, *args, **kwargs)
+                except Exception:
+                    return
+                else:
+                    slf.fail('expected test to fail, but it passed')
+
+            return fn(slf, *args, **kwargs)
+        return efail_fn
+
+
+class onlyOn:
+
+    def __init__(self, device_type):
+        self.device_type = device_type
+
+    def __call__(self, fn):
+
+        @wraps(fn)
+        def only_fn(slf, *args, **kwargs):
+            if self.device_type != slf.device_type:
+                reason = f"Only runs on {self.device_type}"
+                raise unittest.SkipTest(reason)
+
+            return fn(slf, *args, **kwargs)
+
+        return only_fn
+
+
+# Decorator that provides all available devices of the device type to the test
+# as a list of strings instead of providing a single device string.
+# Skips the test if the number of available devices of the variant's device
+# type is less than the 'num_required_devices' arg.
+class deviceCountAtLeast:
+
+    def __init__(self, num_required_devices):
+        self.num_required_devices = num_required_devices
+
+    def __call__(self, fn):
+        assert not hasattr(fn, 'num_required_devices'), f"deviceCountAtLeast redefinition for {fn.__name__}"
+        fn.num_required_devices = self.num_required_devices
+
+        @wraps(fn)
+        def multi_fn(slf, devices, *args, **kwargs):
+            if len(devices) < self.num_required_devices:
+                reason = f"fewer than {self.num_required_devices} devices detected"
+                raise unittest.SkipTest(reason)
+
+            return fn(slf, devices, *args, **kwargs)
+
+        return multi_fn
+
+# Only runs the test on the native device type (currently CPU, CUDA, Meta and PRIVATEUSE1)
+def onlyNativeDeviceTypes(fn):
+    @wraps(fn)
+    def only_fn(self, *args, **kwargs):
+        if self.device_type not in NATIVE_DEVICES:
+            reason = f"onlyNativeDeviceTypes: doesn't run on {self.device_type}"
+            raise unittest.SkipTest(reason)
+
+        return fn(self, *args, **kwargs)
+
+    return only_fn
+
+# Specifies per-dtype precision overrides.
+# Ex.
+#
+# @precisionOverride({torch.half : 1e-2, torch.float : 1e-4})
+# @dtypes(torch.half, torch.float, torch.double)
+# def test_X(self, device, dtype):
+#   ...
+#
+# When the test is instantiated its class's precision will be set to the
+# corresponding override, if it exists.
+# self.precision can be accessed directly, and it also controls the behavior of
+# functions like self.assertEqual().
+#
+# Note that self.precision is a scalar value, so if you require multiple
+# precisions (or are working with multiple dtypes) they should be specified
+# explicitly and computed using self.precision (e.g.
+# self.precision *2, max(1, self.precision)).
+class precisionOverride:
+
+    def __init__(self, d):
+        assert isinstance(d, dict), "precisionOverride not given a dtype : precision dict!"
+        for dtype in d.keys():
+            assert isinstance(dtype, torch.dtype), f"precisionOverride given unknown dtype {dtype}"
+
+        self.d = d
+
+    def __call__(self, fn):
+        fn.precision_overrides = self.d
+        return fn
+
+# Specifies per-dtype tolerance overrides tol(atol, rtol). It has priority over
+# precisionOverride.
+# Ex.
+#
+# @toleranceOverride({torch.float : tol(atol=1e-2, rtol=1e-3},
+#                     torch.double : tol{atol=1e-4, rtol = 0})
+# @dtypes(torch.half, torch.float, torch.double)
+# def test_X(self, device, dtype):
+#   ...
+#
+# When the test is instantiated its class's tolerance will be set to the
+# corresponding override, if it exists.
+# self.rtol and self.precision can be accessed directly, and they also control
+# the behavior of functions like self.assertEqual().
+#
+# The above example sets atol = 1e-2 and rtol = 1e-3 for torch.float and
+# atol = 1e-4 and rtol = 0 for torch.double.
+tol = namedtuple('tol', ['atol', 'rtol'])
+
+class toleranceOverride:
+    def __init__(self, d):
+        assert isinstance(d, dict), "toleranceOverride not given a dtype : tol dict!"
+        for dtype, prec in d.items():
+            assert isinstance(dtype, torch.dtype), f"toleranceOverride given unknown dtype {dtype}"
+            assert isinstance(prec, tol), "toleranceOverride not given a dtype : tol dict!"
+
+        self.d = d
+
+    def __call__(self, fn):
+        fn.tolerance_overrides = self.d
+        return fn
+
+# Decorator that instantiates a variant of the test for each given dtype.
+# Notes:
+#   (1) Tests that accept the dtype argument MUST use this decorator.
+#   (2) Can be overridden for CPU or CUDA, respectively, using dtypesIfCPU
+#       or dtypesIfCUDA.
+#   (3) Can accept an iterable of dtypes or an iterable of tuples
+#       of dtypes.
+# Examples:
+# @dtypes(torch.float32, torch.float64)
+# @dtypes((torch.long, torch.float32), (torch.int, torch.float64))
+class dtypes:
+
+    def __init__(self, *args, device_type="all"):
+        if len(args) > 0 and isinstance(args[0], (list, tuple)):
+            for arg in args:
+                assert isinstance(arg, (list, tuple)), \
+                    "When one dtype variant is a tuple or list, " \
+                    "all dtype variants must be. " \
+                    f"Received non-list non-tuple dtype {str(arg)}"
+                assert all(isinstance(dtype, torch.dtype) for dtype in arg), f"Unknown dtype in {str(arg)}"
+        else:
+            assert all(isinstance(arg, torch.dtype) for arg in args), f"Unknown dtype in {str(args)}"
+
+        self.args = args
+        self.device_type = device_type
+
+    def __call__(self, fn):
+        d = getattr(fn, 'dtypes', {})
+        assert self.device_type not in d, f"dtypes redefinition for {self.device_type}"
+        d[self.device_type] = self.args
+        fn.dtypes = d
+        return fn
+
+
+# Overrides specified dtypes on the CPU.
+class dtypesIfCPU(dtypes):
+
+    def __init__(self, *args):
+        super().__init__(*args, device_type='cpu')
+
+
+# Overrides specified dtypes on CUDA.
+class dtypesIfCUDA(dtypes):
+
+    def __init__(self, *args):
+        super().__init__(*args, device_type='cuda')
+
+class dtypesIfMPS(dtypes):
+
+    def __init__(self, *args):
+        super().__init__(*args, device_type='mps')
+
+class dtypesIfPRIVATEUSE1(dtypes):
+
+    def __init__(self, *args):
+        super().__init__(*args, device_type=torch._C._get_privateuse1_backend_name())
+
+def onlyCPU(fn):
+    return onlyOn('cpu')(fn)
+
+
+def onlyCUDA(fn):
+    return onlyOn('cuda')(fn)
+
+
+def onlyMPS(fn):
+    return onlyOn('mps')(fn)
+
+def onlyPRIVATEUSE1(fn):
+    device_type = torch._C._get_privateuse1_backend_name()
+    device_mod = getattr(torch, device_type, None)
+    if device_mod is None:
+        reason = f"Skip as torch has no module of {device_type}"
+        return unittest.skip(reason)(fn)
+    return onlyOn(device_type)(fn)
+
+def onlyCUDAAndPRIVATEUSE1(fn):
+    @wraps(fn)
+    def only_fn(self, *args, **kwargs):
+        if self.device_type not in ('cuda', torch._C._get_privateuse1_backend_name()):
+            reason = f"onlyCUDAAndPRIVATEUSE1: doesn't run on {self.device_type}"
+            raise unittest.SkipTest(reason)
+
+        return fn(self, *args, **kwargs)
+
+    return only_fn
+
+def disablecuDNN(fn):
+
+    @wraps(fn)
+    def disable_cudnn(self, *args, **kwargs):
+        if self.device_type == 'cuda' and self.has_cudnn():
+            with torch.backends.cudnn.flags(enabled=False):
+                return fn(self, *args, **kwargs)
+        return fn(self, *args, **kwargs)
+
+    return disable_cudnn
+
+def disableMkldnn(fn):
+
+    @wraps(fn)
+    def disable_mkldnn(self, *args, **kwargs):
+        if torch.backends.mkldnn.is_available():
+            with torch.backends.mkldnn.flags(enabled=False):
+                return fn(self, *args, **kwargs)
+        return fn(self, *args, **kwargs)
+
+    return disable_mkldnn
+
+
+def expectedFailureCPU(fn):
+    return expectedFailure('cpu')(fn)
+
+
+def expectedFailureCUDA(fn):
+    return expectedFailure('cuda')(fn)
+
+def expectedFailureMeta(fn):
+    return skipIfTorchDynamo()(expectedFailure('meta')(fn))
+
+def expectedFailureXLA(fn):
+    return expectedFailure('xla')(fn)
+
+# Skips a test on CPU if LAPACK is not available.
+def skipCPUIfNoLapack(fn):
+    return skipCPUIf(not torch._C.has_lapack, "PyTorch compiled without Lapack")(fn)
+
+
+# Skips a test on CPU if FFT is not available.
+def skipCPUIfNoFFT(fn):
+    return skipCPUIf(not torch._C.has_spectral, "PyTorch is built without FFT support")(fn)
+
+
+# Skips a test on CPU if MKL is not available.
+def skipCPUIfNoMkl(fn):
+    return skipCPUIf(not TEST_MKL, "PyTorch is built without MKL support")(fn)
+
+
+# Skips a test on CPU if MKL Sparse is not available (it's not linked on Windows).
+def skipCPUIfNoMklSparse(fn):
+    return skipCPUIf(IS_WINDOWS or not TEST_MKL, "PyTorch is built without MKL support")(fn)
+
+
+# Skips a test on CPU if mkldnn is not available.
+def skipCPUIfNoMkldnn(fn):
+    return skipCPUIf(not torch.backends.mkldnn.is_available(), "PyTorch is built without mkldnn support")(fn)
+
+
+# Skips a test on CUDA if MAGMA is not available.
+def skipCUDAIfNoMagma(fn):
+    return skipCUDAIf('no_magma', "no MAGMA library detected")(skipCUDANonDefaultStreamIf(True)(fn))
+
+def has_cusolver():
+    return not TEST_WITH_ROCM
+
+def has_hipsolver():
+    rocm_version = _get_torch_rocm_version()
+    # hipSOLVER is disabled on ROCM < 5.3
+    return rocm_version >= (5, 3)
+
+# Skips a test on CUDA/ROCM if cuSOLVER/hipSOLVER is not available
+def skipCUDAIfNoCusolver(fn):
+    return skipCUDAIf(not has_cusolver() and not has_hipsolver(), "cuSOLVER not available")(fn)
+
+
+# Skips a test if both cuSOLVER and MAGMA are not available
+def skipCUDAIfNoMagmaAndNoCusolver(fn):
+    if has_cusolver():
+        return fn
+    else:
+        # cuSolver is disabled on cuda < 10.1.243, tests depend on MAGMA
+        return skipCUDAIfNoMagma(fn)
+
+# Skips a test if both cuSOLVER/hipSOLVER and MAGMA are not available
+def skipCUDAIfNoMagmaAndNoLinalgsolver(fn):
+    if has_cusolver() or has_hipsolver():
+        return fn
+    else:
+        # cuSolver is disabled on cuda < 10.1.243, tests depend on MAGMA
+        return skipCUDAIfNoMagma(fn)
+
+# Skips a test on CUDA when using ROCm.
+def skipCUDAIfRocm(func=None, *, msg="test doesn't currently work on the ROCm stack"):
+    def dec_fn(fn):
+        reason = f"skipCUDAIfRocm: {msg}"
+        return skipCUDAIf(TEST_WITH_ROCM, reason=reason)(fn)
+    if func:
+        return dec_fn(func)
+    return dec_fn
+
+# Skips a test on CUDA when not using ROCm.
+def skipCUDAIfNotRocm(fn):
+    return skipCUDAIf(not TEST_WITH_ROCM, "test doesn't currently work on the CUDA stack")(fn)
+
+# Skips a test on CUDA if ROCm is unavailable or its version is lower than requested.
+def skipCUDAIfRocmVersionLessThan(version=None):
+
+    def dec_fn(fn):
+        @wraps(fn)
+        def wrap_fn(self, *args, **kwargs):
+            if self.device_type == 'cuda':
+                if not TEST_WITH_ROCM:
+                    reason = "ROCm not available"
+                    raise unittest.SkipTest(reason)
+                rocm_version_tuple = _get_torch_rocm_version()
+                if rocm_version_tuple is None or version is None or rocm_version_tuple < tuple(version):
+                    reason = f"ROCm {rocm_version_tuple} is available but {version} required"
+                    raise unittest.SkipTest(reason)
+
+            return fn(self, *args, **kwargs)
+
+        return wrap_fn
+    return dec_fn
+
+# Skips a test on CUDA when using ROCm.
+def skipCUDAIfNotMiopenSuggestNHWC(fn):
+    return skipCUDAIf(not TEST_WITH_MIOPEN_SUGGEST_NHWC, "test doesn't currently work without MIOpen NHWC activation")(fn)
+
+# Skips a test for specified CUDA versions, given in the form of a list of [major, minor]s.
+def skipCUDAVersionIn(versions : List[Tuple[int, int]] = None):
+    def dec_fn(fn):
+        @wraps(fn)
+        def wrap_fn(self, *args, **kwargs):
+            version = _get_torch_cuda_version()
+            if version == (0, 0):  # cpu or rocm
+                return fn(self, *args, **kwargs)
+            if version in (versions or []):
+                reason = f"test skipped for CUDA version {version}"
+                raise unittest.SkipTest(reason)
+            return fn(self, *args, **kwargs)
+
+        return wrap_fn
+    return dec_fn
+
+# Skips a test for CUDA versions less than specified, given in the form of [major, minor].
+def skipCUDAIfVersionLessThan(versions : Tuple[int, int] = None):
+    def dec_fn(fn):
+        @wraps(fn)
+        def wrap_fn(self, *args, **kwargs):
+            version = _get_torch_cuda_version()
+            if version == (0, 0):  # cpu or rocm
+                return fn(self, *args, **kwargs)
+            if version < versions:
+                reason = f"test skipped for CUDA versions < {version}"
+                raise unittest.SkipTest(reason)
+            return fn(self, *args, **kwargs)
+
+        return wrap_fn
+    return dec_fn
+
+# Skips a test on CUDA if cuDNN is unavailable or its version is lower than requested.
+def skipCUDAIfCudnnVersionLessThan(version=0):
+
+    def dec_fn(fn):
+        @wraps(fn)
+        def wrap_fn(self, *args, **kwargs):
+            if self.device_type == 'cuda':
+                if self.no_cudnn:
+                    reason = "cuDNN not available"
+                    raise unittest.SkipTest(reason)
+                if self.cudnn_version is None or self.cudnn_version < version:
+                    reason = f"cuDNN version {self.cudnn_version} is available but {version} required"
+                    raise unittest.SkipTest(reason)
+
+            return fn(self, *args, **kwargs)
+
+        return wrap_fn
+    return dec_fn
+
+# Skips a test on CUDA if cuSparse generic API is not available
+def skipCUDAIfNoCusparseGeneric(fn):
+    return skipCUDAIf(not TEST_CUSPARSE_GENERIC, "cuSparse Generic API not available")(fn)
+
+def skipCUDAIfNoHipsparseGeneric(fn):
+    return skipCUDAIf(not TEST_HIPSPARSE_GENERIC, "hipSparse Generic API not available")(fn)
+
+def skipCUDAIfNoSparseGeneric(fn):
+    return skipCUDAIf(not (TEST_CUSPARSE_GENERIC or TEST_HIPSPARSE_GENERIC), "Sparse Generic API not available")(fn)
+
+def skipCUDAIfNoCudnn(fn):
+    return skipCUDAIfCudnnVersionLessThan(0)(fn)
+
+def skipCUDAIfMiopen(fn):
+    return skipCUDAIf(torch.version.hip is not None, "Marked as skipped for MIOpen")(fn)
+
+def skipCUDAIfNoMiopen(fn):
+    return skipCUDAIf(torch.version.hip is None, "MIOpen is not available")(skipCUDAIfNoCudnn(fn))
+
+def skipLazy(fn):
+    return skipLazyIf(True, "test doesn't work with lazy tensors")(fn)
+
+def skipMeta(fn):
+    return skipMetaIf(True, "test doesn't work with meta tensors")(fn)
+
+def skipXLA(fn):
+    return skipXLAIf(True, "Marked as skipped for XLA")(fn)
+
+def skipMPS(fn):
+    return skipMPSIf(True, "test doesn't work on MPS backend")(fn)
+
+def skipPRIVATEUSE1(fn):
+    return skipPRIVATEUSE1If(True, "test doesn't work on privateuse1 backend")(fn)
+
+# TODO: the "all" in the name isn't true anymore for quite some time as we have also have for example XLA and MPS now.
+#  This should probably enumerate all available device type test base classes.
+def get_all_device_types() -> List[str]:
+    return ['cpu'] if not torch.cuda.is_available() else ['cpu', 'cuda']

venv/lib/python3.10/site-packages/torch/testing/_internal/common_dist_composable.py

@@ -0,0 +1,111 @@
+# mypy: ignore-errors
+
+# Owner(s): ["oncall: distributed"]
+
+from typing import Tuple
+
+import torch
+import torch.nn as nn
+
+
+class UnitModule(nn.Module):
+    def __init__(self, device: torch.device):
+        super().__init__()
+        self.l1 = nn.Linear(100, 100, device=device)
+        self.seq = nn.Sequential(
+            nn.ReLU(),
+            nn.Linear(100, 100, device=device),
+            nn.ReLU(),
+        )
+        self.l2 = nn.Linear(100, 100, device=device)
+
+    def forward(self, x):
+        return self.l2(self.seq(self.l1(x)))
+
+
+class CompositeModel(nn.Module):
+    def __init__(self, device: torch.device):
+        super().__init__()
+        self.l1 = nn.Linear(100, 100, device=device)
+        self.u1 = UnitModule(device)
+        self.u2 = UnitModule(device)
+        self.l2 = nn.Linear(100, 100, device=device)
+
+    def forward(self, x):
+        return self.l2(self.u2(self.u1(self.l1(x))))
+
+
+class UnitParamModule(nn.Module):
+    def __init__(self, device: torch.device):
+        super().__init__()
+        self.l = nn.Linear(100, 100, device=device)
+        self.seq = nn.Sequential(
+            nn.ReLU(),
+            nn.Linear(100, 100, device=device),
+            nn.ReLU(),
+        )
+        self.p = nn.Parameter(torch.randn((100, 100), device=device))
+
+    def forward(self, x):
+        return torch.mm(self.seq(self.l(x)), self.p)
+
+
+class CompositeParamModel(nn.Module):
+    def __init__(self, device: torch.device):
+        super().__init__()
+        self.l = nn.Linear(100, 100, device=device)
+        self.u1 = UnitModule(device)
+        self.u2 = UnitModule(device)
+        self.p = nn.Parameter(torch.randn((100, 100), device=device))
+        self.register_buffer(
+            "buffer", torch.randn((100, 100), device=device), persistent=True
+        )
+
+    def forward(self, x):
+        a = self.u2(self.u1(self.l(x)))
+        b = self.p
+        return torch.mm(a, b)
+
+
+class FakeSequential(nn.Module):
+    # Define this class to achieve a desired nested wrapping using the module
+    # wrap policy with `nn.Sequential`
+    def __init__(self, *modules: Tuple[nn.Module, ...]) -> None:
+        super().__init__()
+        self._module_sequence = list(modules)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        for module in self._module_sequence:
+            x = module(x)
+        return x
+
+
+class NestedSequentialModel(nn.Module):
+    def __init__(self, device: torch.device) -> None:
+        super().__init__()
+        # This nested structure exercises traversal order to catch differences
+        # between valid traversals (e.g. BFS and DFS variations).
+        self.seq1 = nn.Sequential(
+            nn.Linear(1, 1, device=device),
+            FakeSequential(
+                nn.Linear(1, 1, device=device),
+                nn.ReLU(),
+                FakeSequential(
+                    nn.Linear(1, 1, device=device),
+                ),
+                nn.ReLU(),
+            ),
+            nn.Linear(1, 2, device=device),
+        )
+        self.lin = nn.Linear(2, 2, device=device)
+        self.seq2 = nn.Sequential(
+            nn.ReLU(),
+            nn.Linear(2, 3, device=device),
+            FakeSequential(
+                nn.Linear(3, 2, bias=False, device=device),
+                nn.Linear(2, 4, bias=False, device=device),
+            ),
+        )
+
+        def forward(self, x: torch.Tensor) -> torch.Tensor:
+            return self.seq2(self.lin(self.seq1(x)))

venv/lib/python3.10/site-packages/torch/testing/_internal/common_dtype.py

@@ -0,0 +1,134 @@
+# mypy: ignore-errors
+
+from typing import List
+
+import torch
+
+
+# Functions and classes for describing the dtypes a function supports
+# NOTE: these helpers should correspond to PyTorch's C++ dispatch macros
+
+# Verifies each given dtype is a torch.dtype
+def _validate_dtypes(*dtypes):
+    for dtype in dtypes:
+        assert isinstance(dtype, torch.dtype)
+    return dtypes
+
+# class for tuples corresponding to a PyTorch dispatch macro
+class _dispatch_dtypes(tuple):
+    def __add__(self, other):
+        assert isinstance(other, tuple)
+        return _dispatch_dtypes(tuple.__add__(self, other))
+
+_empty_types = _dispatch_dtypes(())
+def empty_types():
+    return _empty_types
+
+_floating_types = _dispatch_dtypes((torch.float32, torch.float64))
+def floating_types():
+    return _floating_types
+
+_floating_types_and_half = _floating_types + (torch.half,)
+def floating_types_and_half():
+    return _floating_types_and_half
+
+def floating_types_and(*dtypes):
+    return _floating_types + _validate_dtypes(*dtypes)
+
+_floating_and_complex_types = _floating_types + (torch.cfloat, torch.cdouble)
+def floating_and_complex_types():
+    return _floating_and_complex_types
+
+def floating_and_complex_types_and(*dtypes):
+    return _floating_and_complex_types + _validate_dtypes(*dtypes)
+
+_double_types = _dispatch_dtypes((torch.float64, torch.complex128))
+def double_types():
+    return _double_types
+
+# NB: Does not contain uint16/uint32/uint64 for BC reasons
+_integral_types = _dispatch_dtypes((torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64))
+def integral_types():
+    return _integral_types
+
+def integral_types_and(*dtypes):
+    return _integral_types + _validate_dtypes(*dtypes)
+
+_all_types = _floating_types + _integral_types
+def all_types():
+    return _all_types
+
+def all_types_and(*dtypes):
+    return _all_types + _validate_dtypes(*dtypes)
+
+_complex_types = _dispatch_dtypes((torch.cfloat, torch.cdouble))
+def complex_types():
+    return _complex_types
+
+def complex_types_and(*dtypes):
+    return _complex_types + _validate_dtypes(*dtypes)
+
+_all_types_and_complex = _all_types + _complex_types
+def all_types_and_complex():
+    return _all_types_and_complex
+
+def all_types_and_complex_and(*dtypes):
+    return _all_types_and_complex + _validate_dtypes(*dtypes)
+
+_all_types_and_half = _all_types + (torch.half,)
+def all_types_and_half():
+    return _all_types_and_half
+
+def custom_types(*dtypes):
+    """Create a list of arbitrary dtypes"""
+    return _empty_types + _validate_dtypes(*dtypes)
+
+# The functions below are used for convenience in our test suite and thus have no corresponding C++ dispatch macro
+
+# See AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS.
+def get_all_dtypes(include_half=True,
+                   include_bfloat16=True,
+                   include_bool=True,
+                   include_complex=True,
+                   include_complex32=False,
+                   include_qint=False,
+                   ) -> List[torch.dtype]:
+    dtypes = get_all_int_dtypes() + get_all_fp_dtypes(include_half=include_half, include_bfloat16=include_bfloat16)
+    if include_bool:
+        dtypes.append(torch.bool)
+    if include_complex:
+        dtypes += get_all_complex_dtypes(include_complex32)
+    if include_qint:
+        dtypes += get_all_qint_dtypes()
+    return dtypes
+
+def get_all_math_dtypes(device) -> List[torch.dtype]:
+    return get_all_int_dtypes() + get_all_fp_dtypes(include_half=device.startswith('cuda'),
+                                                    include_bfloat16=False) + get_all_complex_dtypes()
+
+def get_all_complex_dtypes(include_complex32=False) -> List[torch.dtype]:
+    return [torch.complex32, torch.complex64, torch.complex128] if include_complex32 else [torch.complex64, torch.complex128]
+
+
+def get_all_int_dtypes() -> List[torch.dtype]:
+    return [torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64]
+
+
+def get_all_fp_dtypes(include_half=True, include_bfloat16=True) -> List[torch.dtype]:
+    dtypes = [torch.float32, torch.float64]
+    if include_half:
+        dtypes.append(torch.float16)
+    if include_bfloat16:
+        dtypes.append(torch.bfloat16)
+    return dtypes
+
+
+def get_all_qint_dtypes() -> List[torch.dtype]:
+    return [torch.qint8, torch.quint8, torch.qint32, torch.quint4x2, torch.quint2x4]
+
+
+float_to_corresponding_complex_type_map = {
+    torch.float16: torch.complex32,
+    torch.float32: torch.complex64,
+    torch.float64: torch.complex128,
+}

venv/lib/python3.10/site-packages/torch/testing/_internal/common_optimizers.py

@@ -0,0 +1,2033 @@
+# mypy: ignore-errors
+
+import functools
+import itertools
+import unittest
+from copy import deepcopy
+from enum import Enum
+from typing import Any, Dict, List, Tuple, Union
+
+import torch
+from torch import Tensor
+from torch.nn import Parameter
+from torch.optim import (
+    Adadelta,
+    Adagrad,
+    Adam,
+    Adamax,
+    AdamW,
+    ASGD,
+    LBFGS,
+    NAdam,
+    Optimizer,
+    RAdam,
+    RMSprop,
+    Rprop,
+    SGD,
+    SparseAdam,
+)
+from torch.testing._internal.common_device_type import tol, toleranceOverride
+from torch.testing._internal.common_methods_invocations import DecorateInfo
+from torch.testing._internal.common_utils import (
+    _TestParametrizer,
+    set_single_threaded_if_parallel_tbb,
+    skipIfMps,
+    skipIfTorchDynamo,
+    TEST_WITH_TORCHDYNAMO,
+)
+from torch.utils._foreach_utils import (
+    _get_foreach_kernels_supported_devices,
+    _get_fused_kernels_supported_devices,
+)
+
+
+class OptimizerInput:
+    """Contains args / kwargs to be passed to an optimizer constructor."""
+
+    __slots__ = ["params", "kwargs", "desc"]
+
+    def __init__(
+        self,
+        params: Union[List[Parameter], List[Tensor], Dict[Any, Any]],
+        kwargs: Dict[str, Any],
+        desc: str = "",
+    ):
+        # params can be a list of Tensors OR param_groups OR None
+        self.params = params
+        self.kwargs = kwargs
+        self.desc = desc
+
+    def __repr__(self):
+        return f"params={self.params}, kwargs={self.kwargs}, desc={self.desc}"
+
+
+class OptimizerErrorEnum(Enum):
+    """Enumerates when an error is raised when testing optimizers."""
+
+    CONSTRUCTION_ERROR = 0
+    STEP_ERROR = 1
+
+
+class ErrorOptimizerInput:
+    """
+    An OptimizerInput that will cause the optimizer to throw an error when constructed.
+    Includes the type and string of the resulting error.
+    """
+
+    __slots__ = ["optimizer_error_input", "error_on", "error_type", "error_regex"]
+
+    def __init__(
+        self,
+        optimizer_error_input,
+        *,
+        error_on=OptimizerErrorEnum.CONSTRUCTION_ERROR,
+        error_type=RuntimeError,
+        error_regex="",
+    ):
+        self.optimizer_error_input = optimizer_error_input
+        self.error_on = error_on
+        self.error_type = error_type
+        self.error_regex = error_regex
+
+
+class OptimizerInfo:
+    """Optimizer information to be used in testing."""
+
+    def __init__(
+        self,
+        optim_cls: Optimizer,  # Class object for the Optimizer under test
+        *,
+        # Function to generate optimizer inputs EXCLUDING params. We delegate params responsibility
+        # to the test using the OptimizerInfo. OptimizerInput.params is likely None.
+        # Can optionally take in device to filter out certain unsupported configs
+        optim_inputs_func,
+        # A subset of the global-cliquey flags (fused, foreach, differentiable) the optimizer
+        # supports. See NOTE: [optimizer kwarg categories] for what global-cliquey means.
+        supported_impls: Tuple[str] = ("foreach", "differentiable"),
+        # the devices on which the optim supports sparse tensors for params and grads, see SGD
+        supports_sparse_on: Tuple[str] = (),
+        # the optim only supports one config: sparse grads w/ dense params, see SparseAdam
+        only_supports_sparse_grads: bool = False,
+        # the optim supports complex parameters
+        supports_complex: bool = True,
+        # whether the optimizer.step() function requires a closure to be passed
+        step_requires_closure: bool = False,
+        # whether the optimizer supports per-param options with parameter groups
+        supports_param_groups: bool = True,
+        # whether the optimizer supports parameters on multiple devices
+        supports_multiple_devices: bool = True,
+        skips=(),  # Indicates which tests to skip
+        decorators=None,  # Additional decorators to apply to generated tests
+        optim_error_inputs_func=None,  # Function to generate optim inputs that error
+    ):
+        self.optim_cls = optim_cls
+        self.optim_inputs_func = optim_inputs_func
+        self.supported_impls = supported_impls
+        self.supports_sparse_on = supports_sparse_on
+        self.only_supports_sparse_grads = only_supports_sparse_grads
+        self.supports_complex = supports_complex
+        self.step_requires_closure = step_requires_closure
+        self.supports_param_groups = supports_param_groups
+        self.supports_multiple_devices = supports_multiple_devices
+        self.decorators = (
+            *(decorators if decorators else []),
+            *(skips if skips else []),
+        )
+        self.optim_error_inputs_func = optim_error_inputs_func
+
+    def get_decorators(self, test_class, test_name, device, dtype, param_kwargs):
+        result = [set_single_threaded_if_parallel_tbb]
+        for decorator in self.decorators:
+            if isinstance(decorator, DecorateInfo):
+                if decorator.is_active(
+                    test_class, test_name, device, dtype, param_kwargs
+                ):
+                    result.extend(decorator.decorators)
+            else:
+                result.append(decorator)
+        return result
+
+    @property
+    def name(self):
+        return self.optim_cls.__name__
+
+
+class optims(_TestParametrizer):
+    """Decorator for specifying a list of optimizers over which to run a test."""
+
+    def __init__(self, optim_info_iterable, dtypes=None):
+        self.optim_info_list = list(optim_info_iterable)
+
+        # optimizers aren't limited to be one dtype as parameters can have different dtypes
+        # We default to torch.float32, but dtypes should be specified through passed in
+        # parameters.
+        self.dtypes = dtypes if dtypes is not None else [torch.float32]
+
+    def _parametrize_test(self, test, generic_cls, device_cls):
+        if device_cls is None:
+            raise RuntimeError(
+                "The @optims decorator is only intended to be used in a device-specific "
+                "context; use it with instantiate_device_type_tests() instead of "
+                "instantiate_parametrized_tests()"
+            )
+
+        for optim_info, dtype in itertools.product(self.optim_info_list, self.dtypes):
+            # Construct the test name; device / dtype parts are handled outside.
+            # See [Note: device and dtype suffix placement]
+            test_name = optim_info.name
+
+            # Construct parameter kwargs to pass to the test.
+            param_kwargs = {"optim_info": optim_info, "dtype": dtype}
+
+            try:
+
+                @functools.wraps(test)
+                def test_wrapper(*args, **kwargs):
+                    return test(*args, **kwargs)
+
+                decorator_fn = functools.partial(
+                    optim_info.get_decorators,
+                    generic_cls.__name__,
+                    test.__name__,
+                    device_cls.device_type,
+                    dtype,
+                )
+
+                yield (test_wrapper, test_name, param_kwargs, decorator_fn)
+            except Exception as ex:
+                # Provides an error message for debugging before rethrowing the exception
+                print(
+                    f"Failed to instantiate {test_name} for module {optim_info.name}!"
+                )
+                raise ex
+
+
+# Helper function for generating error inputs for all optimizers, used below.
+def get_error_inputs_for_all_optims(device, dtype):
+    if str(device) == "cpu":
+        sample_param = Parameter(torch.randn(1, device=device, dtype=dtype))
+        return [
+            ErrorOptimizerInput(
+                OptimizerInput(
+                    params=sample_param,
+                    kwargs={},
+                    desc="invalid param type",
+                ),
+                error_type=TypeError,
+                error_regex="params argument given to the optimizer should be an iterable of Tensors or dicts",
+            ),
+            ErrorOptimizerInput(
+                OptimizerInput(
+                    params=[sample_param, sample_param],
+                    kwargs={},
+                    desc="a param group cannot have duplicate parameters",
+                ),
+                error_type=UserWarning,
+                error_regex=".*a parameter group with duplicate parameters.*",
+            ),
+            ErrorOptimizerInput(
+                OptimizerInput(
+                    params=[{"params": sample_param}, {"params": sample_param}],
+                    kwargs={},
+                    desc="duplicate parameters should not occur across param groups either",
+                ),
+                error_type=ValueError,
+                error_regex="some parameters appear in more than one parameter group",
+            ),
+        ]
+    else:
+        return []
+
+
+# ------------------------------------------------------------------------------------------
+# NOTE: [optimizer kwarg categories]
+# We categorize optimizer kwargs as 3 types:
+#  1. optimizer-specific flags are like amsgrad or rho or beta, flags that are specific to
+#     algorithms and thus only show up for certain optimizers. There are many of these, so I
+#     do not bother gathering them all and listing them here. The converse to these would be
+#     global flags that every optimizer ideally _should_ support. We break global flags into
+#     2 further categories and list them all below.
+#  2. global-friendly = ["lr", "weight_decay", "maximize", "capturable"]
+#     global-friendly flags are global flags who play nicely with all other global flags,
+#     i.e., are mutually exclusive in function. This means that any pair of the following
+#     flags can be toggled at once (e.g., maximize and weight_decay). Furthermore, any of the
+#     following flags theoretically can be enabled with ANY other global flag, including the
+#     cliquey ones (e.g, capturable and foreach).
+#  3. global-cliquey = ["foreach", "fused", "differentiable"]
+#     global-cliquey flags are global flags that do NOT coexist with other cliquey flags,
+#     usually because they contradict each other in function. For example, one should not flip
+#     both foreach AND fused to True, because they are two differing performance optimizations
+#     in which you can only opt into one.
+#
+# The following optim_inputs_func_* sampling functions only return constructor combinations of
+# optimizer-specific and global-friendly flags. This is because we are confident they would mesh
+# well with additional kwargs. On the flip side of the same coin, we reserve setting the
+# global-cliquey flags to individual tests and fully expect tests to edit OptimizerInput.kwargs.
+
+
+def optim_inputs_func_adadelta(device):
+    return [
+        OptimizerInput(params=None, kwargs={}, desc="default"),
+        OptimizerInput(params=None, kwargs={"lr": 0.01}, desc="non-default lr"),
+        OptimizerInput(
+            params=None, kwargs={"weight_decay": 0.1}, desc="nonzero weight_decay"
+        ),
+        OptimizerInput(
+            params=None,
+            kwargs={"weight_decay": 0.1, "maximize": True},
+            desc="maximize",
+        ),
+        OptimizerInput(
+            params=None, kwargs={"rho": 0.95, "weight_decay": 0.9}, desc="rho"
+        ),
+    ]
+
+
+def optim_error_inputs_func_adadelta(device, dtype):
+    error_inputs = get_error_inputs_for_all_optims(device, dtype)
+    if str(device) == "cpu":
+        error_inputs += [
+            ErrorOptimizerInput(
+                OptimizerInput(
+                    params=None,
+                    kwargs=dict(lr=1e-2, rho=1.1),
+                    desc="rho should be between 0 and 1",
+                ),
+                error_type=ValueError,
+                error_regex="Invalid rho value: 1.1",
+            ),
+        ]
+    return error_inputs
+
+
+def optim_inputs_func_adagrad(device):
+    return [
+        OptimizerInput(params=None, kwargs={}, desc="default"),
+        OptimizerInput(
+            params=None, kwargs={"weight_decay": 0.1}, desc="nonzero weight_decay"
+        ),
+        OptimizerInput(
+            params=None,
+            kwargs={"weight_decay": 0.1, "maximize": True},
+            desc="maximize",
+        ),
+        OptimizerInput(params=None, kwargs={"lr": 0.1}, desc="non-default lr"),
+        OptimizerInput(
+            params=None,
+            kwargs={"initial_accumulator_value": 0.1, "weight_decay": 0.1},
+            desc="initial_accumulator_value",
+        ),
+        OptimizerInput(
+            params=None,
+            kwargs={"lr": 0.1, "lr_decay": 0.5, "weight_decay": 0.1},
+            desc="lr_decay",
+        ),  # TODO: Move out to testing in param_group?
+    ]
+
+
+def optim_error_inputs_func_adagrad(device, dtype):
+    error_inputs = get_error_inputs_for_all_optims(device, dtype)
+    if str(device) == "cpu":
+        error_inputs += [
+            ErrorOptimizerInput(
+                OptimizerInput(
+                    params=None,
+                    kwargs=dict(lr=1e-2, lr_decay=-0.5),
+                    desc="lr_decay must be bigger than 0",
+                ),
+                error_type=ValueError,
+                error_regex="Invalid lr_decay value: -0.5",
+            ),
+        ]
+    return error_inputs
+
+
+# TODO: consider tensor LR! See multi_tensor_optimizer_configs in test_optim.py --> tensor LR should work
+# with all implementation code paths...
+def optim_inputs_func_adam(device):
+    cuda_supported_configs = [
+        OptimizerInput(params=None, kwargs={"capturable": True}, desc="capturable"),
+        OptimizerInput(
+            params=None,
+            kwargs={"weight_decay": 0.1, "amsgrad": True, "capturable": True},
+            desc="capturable, amsgrad",
+        ),
+        OptimizerInput(
+            params=None,
+            kwargs={"lr": torch.tensor(0.001), "amsgrad": True, "capturable": True},
+            desc="Tensor lr with capturable and amsgrad",
+        ),
+    ]
+
+    return [
+        OptimizerInput(params=None, kwargs={}, desc="default"),
+        OptimizerInput(params=None, kwargs={"lr": 0.01}, desc="non-default lr"),
+        OptimizerInput(
+            params=None, kwargs={"weight_decay": 0.1}, desc="nonzero weight_decay"
+        ),
+        OptimizerInput(
+            params=None,
+            kwargs={"weight_decay": 0.1, "maximize": True},
+            desc="maximize",
+        ),
+        OptimizerInput(
+            params=None, kwargs={"weight_decay": 0.1, "amsgrad": True}, desc="amsgrad"
+        ),
+    ] + (cuda_supported_configs if "cuda" in str(device) else [])
+
+
+def optim_error_inputs_func_adam(device, dtype):
+    error_inputs = get_error_inputs_for_all_optims(device, dtype)
+    if str(device) == "cpu":
+        error_inputs += [
+            ErrorOptimizerInput(
+                OptimizerInput(
+                    params=None,
+                    kwargs=dict(lr=1e-2, betas=(1.0, 0.0)),
+                    desc="beta1 should be between 0 and 1",
+                ),
+                error_type=ValueError,
+                error_regex="Invalid beta parameter at index 0: 1.0",
+            ),
+            ErrorOptimizerInput(
+                OptimizerInput(
+                    params=None,
+                    kwargs=dict(lr=1e-2, weight_decay=-1),
+                    desc="weight_decay should > 0",
+                ),
+                error_type=ValueError,
+                error_regex="Invalid weight_decay value: -1",
+            ),
+            ErrorOptimizerInput(
+                OptimizerInput(
+                    params=None,
+                    kwargs=dict(lr=torch.tensor(0.001), foreach=True),
+                    desc="lr as Tensor doesn't work with foreach & not capturable",
+                ),
+                error_type=ValueError,
+                error_regex="lr as a Tensor is not supported for capturable=False and foreach=True",
+            ),
+        ]
+    if "cuda" in str(device):
+        sample_tensor = torch.empty((), device=device, dtype=dtype)
+        error_inputs += [
+            ErrorOptimizerInput(
+                OptimizerInput(
+                    params=[sample_tensor],
+                    kwargs={"foreach": True, "fused": True},
+                    desc="`fused` and `foreach` cannot be `True` together",
+                ),
+                error_type=RuntimeError,
+                error_regex="`fused` and `foreach` cannot be `True` together",
+            ),
+            ErrorOptimizerInput(
+                OptimizerInput(
+                    params=[sample_tensor],
+                    kwargs={"fused": True, "differentiable": True},
+                    desc="`fused` does not support `differentiable`",
+                ),
+                error_type=RuntimeError,
+                error_regex="`fused` does not support `differentiable`",
+            ),
+        ]
+    return error_inputs
+
+
+def optim_inputs_func_adamax(device):
+    cuda_supported_configs = [
+        OptimizerInput(params=None, kwargs={"capturable": True}, desc="capturable"),
+        OptimizerInput(
+            params=None,
+            kwargs={"weight_decay": 0.9, "maximize": True, "capturable": True},
+            desc="capturable, maximize, weight_decay",
+        ),
+        OptimizerInput(
+            params=None,
+            kwargs={"weight_decay": 0, "maximize": True, "capturable": True},
+            desc="capturable, maximize",
+        ),
+        OptimizerInput(
+            params=None,
+            kwargs={"weight_decay": 0.9, "maximize": False, "capturable": True},
+            desc="capturable, weight_decay",
+        ),
+    ]
+
+    return [
+        OptimizerInput(params=None, kwargs={}, desc="default"),
+        OptimizerInput(params=None, kwargs={"lr": 0.1}, desc="non-default lr"),
+        OptimizerInput(
+            params=None, kwargs={"weight_decay": 0.1}, desc="nonzero weight_decay"
+        ),
+        OptimizerInput(
+            params=None,
+            kwargs={"weight_decay": 0.1, "maximize": True},
+            desc="maximize",
+        ),
+    ] + (cuda_supported_configs if "cuda" in str(device) else [])
+
+
+def optim_error_inputs_func_adamax(device, dtype):
+    error_inputs = get_error_inputs_for_all_optims(device, dtype)
+    if str(device) == "cpu":
+        error_inputs += [
+            ErrorOptimizerInput(
+                OptimizerInput(
+                    params=None,
+                    kwargs=dict(lr=1e-2, betas=(0.0, 1.0)),
+                    desc="beta2 should be between 0 and 1",
+                ),
+                error_type=ValueError,
+                error_regex="Invalid beta parameter at index 1: 1.0",
+            ),
+        ]
+    return error_inputs
+
+
+def optim_inputs_func_adamw(device):
+    return optim_inputs_func_adam(device)
+
+
+def optim_error_inputs_func_adamw(device, dtype):
+    return optim_error_inputs_func_adam(device, dtype)
+
+
+def optim_inputs_func_asgd(device):
+    cuda_supported_configs = [
+        OptimizerInput(params=None, kwargs={"capturable": True}, desc="capturable"),
+        OptimizerInput(
+            params=None,
+            kwargs={"maximize": True, "capturable": True},
+            desc="maximize, capturable",
+        ),
+        OptimizerInput(
+            params=None,
+            kwargs={"weight_decay": 0.1, "capturable": True},
+            desc="weight_decay, capturable",
+        ),
+        OptimizerInput(
+            params=None,
+            kwargs={"weight_decay": 0.1, "maximize": True, "capturable": True},
+            desc="maximize, weight_decay, capturable",
+        ),
+    ]
+    return [
+        OptimizerInput(params=None, kwargs={}, desc="default"),
+        OptimizerInput(params=None, kwargs={"lr": 0.02}, desc="non-default lr"),
+        OptimizerInput(params=None, kwargs={"t0": 100}, desc="t0"),
+        OptimizerInput(params=None, kwargs={"maximize": True}, desc="maximize"),
+        OptimizerInput(
+            params=None, kwargs={"weight_decay": 0.1}, desc="nonzero weight_decay"
+        ),
+        OptimizerInput(
+            params=None,
+            kwargs={"weight_decay": 0.1, "maximize": True},
+            desc="maximize, nonzero weight_decay",
+        ),
+    ] + (cuda_supported_configs if "cuda" in str(device) else [])
+
+
+def optim_error_inputs_func_asgd(device, dtype):
+    error_inputs = get_error_inputs_for_all_optims(device, dtype)
+    if str(device) == "cpu":
+        error_inputs += [
+            ErrorOptimizerInput(
+                OptimizerInput(
+                    params=None,
+                    kwargs=dict(lr=1e-2, weight_decay=-0.5),
+                    desc="weight_decay should > 0",
+                ),
+                error_type=ValueError,
+                error_regex="Invalid weight_decay value: -0.5",
+            ),
+        ]
+    return error_inputs
+
+
+def optim_inputs_func_lbfgs(device):
+    return [
+        OptimizerInput(params=None, kwargs={}, desc="default"),
+        OptimizerInput(params=None, kwargs={"lr": 0.01}, desc="non-default lr"),
+        OptimizerInput(
+            params=None, kwargs={"tolerance_grad": 1e-6}, desc="tolerance_grad"
+        ),
+        OptimizerInput(
+            params=None,
+            kwargs={"line_search_fn": "strong_wolfe"},
+            desc="strong_wolfe",
+        ),
+    ]
+
+
+def optim_error_inputs_func_lbfgs(device, dtype):
+    error_inputs = get_error_inputs_for_all_optims(device, dtype)
+    return error_inputs
+
+
+# Weird story bro, NAdam and RAdam do not have maximize.
+def optim_inputs_func_nadam(device):
+    cuda_supported_configs = [
+        OptimizerInput(params=None, kwargs={"capturable": True}, desc="capturable"),
+        OptimizerInput(
+            params=None,
+            kwargs={"weight_decay": 0.9, "momentum_decay": 6e-3, "capturable": True},
+            desc="weight_decay, capturable",
+        ),
+        OptimizerInput(
+            params=None,
+            kwargs={
+                "weight_decay": 0.9,
+                "momentum_decay": 6e-3,
+                "decoupled_weight_decay": True,
+                "capturable": True,
+            },
+            desc="decoupled_weight_decay, capturable",
+        ),
+    ]
+    return [
+        OptimizerInput(params=None, kwargs={}, desc="default"),
+        OptimizerInput(params=None, kwargs={"lr": 1e-3}, desc="non-default lr"),
+        OptimizerInput(
+            params=None,
+            kwargs={"momentum_decay": 6e-3},
+            desc="non-zero momentum_decay",
+        ),
+        OptimizerInput(
+            params=None,
+            kwargs={"weight_decay": 0.1, "momentum_decay": 6e-3},
+            desc="weight_decay",
+        ),
+        OptimizerInput(
+            params=None,
+            kwargs={
+                "weight_decay": 0.1,
+                "momentum_decay": 6e-3,
+                "decoupled_weight_decay": True,
+            },
+            desc="decoupled_weight_decay",
+        ),
+    ] + (cuda_supported_configs if "cuda" in str(device) else [])
+
+
+def optim_error_inputs_func_nadam(device, dtype):
+    error_inputs = get_error_inputs_for_all_optims(device, dtype)
+    if str(device) == "cpu":
+        error_inputs += [
+            ErrorOptimizerInput(
+                OptimizerInput(
+                    params=None,
+                    kwargs=dict(lr=1e-2, betas=(1.0, 0.0)),
+                    desc="beta1 should be between 0 and 1",
+                ),
+                error_type=ValueError,
+                error_regex="Invalid beta parameter at index 0: 1.0",
+            ),
+            ErrorOptimizerInput(
+                OptimizerInput(
+                    params=None,
+                    kwargs=dict(lr=1e-2, momentum_decay=-0.2),
+                    desc="momentum_decay should > 0",
+                ),
+                error_type=ValueError,
+                error_regex="Invalid momentum_decay value: -0.2",
+            ),
+        ]
+    return error_inputs
+
+
+# Weird story bro, NAdam and RAdam do not have maximize.
+def optim_inputs_func_radam(device=None):
+    cuda_supported_configs = [
+        OptimizerInput(params=None, kwargs={"capturable": True}, desc="capturable"),
+        OptimizerInput(
+            params=None,
+            kwargs={
+                "capturable": True,
+                "weight_decay": 0.1,
+            },
+            desc="capturable, weight_decay",
+        ),
+        OptimizerInput(
+            params=None,
+            kwargs={
+                "capturable": True,
+                "weight_decay": 0.1,
+                "decoupled_weight_decay": True,
+            },
+            desc="capturable, weight_decay, decoupled_weight_decay",
+        ),
+    ]
+    return [
+        OptimizerInput(params=None, kwargs={}, desc="default"),
+        OptimizerInput(params=None, kwargs={"lr": 2e-3}, desc="non-default lr"),
+        OptimizerInput(params=None, kwargs={"eps": 1e-6}, desc="non-default eps"),
+        OptimizerInput(
+            params=None, kwargs={"weight_decay": 0.1}, desc="nonzero weight_decay"
+        ),
+        OptimizerInput(
+            params=None,
+            kwargs={"weight_decay": 0.1, "decoupled_weight_decay": True},
+            desc="decoupled_weight_decay",
+        ),
+    ] + (cuda_supported_configs if "cuda" in str(device) else [])
+
+
+def optim_error_inputs_func_radam(device, dtype):
+    error_inputs = get_error_inputs_for_all_optims(device, dtype)
+    if str(device) == "cpu":
+        error_inputs += [
+            ErrorOptimizerInput(
+                OptimizerInput(
+                    params=None,
+                    kwargs=dict(lr=1e-2, betas=(1.0, 0.0)),
+                    desc="beta1 should be between 0 and 1",
+                ),
+                error_type=ValueError,
+                error_regex="Invalid beta parameter at index 0: 1.0",
+            ),
+            ErrorOptimizerInput(
+                OptimizerInput(
+                    params=None,
+                    kwargs=dict(lr=1e-2, weight_decay=-1),
+                    desc="weight_decay should > 0",
+                ),
+                error_type=ValueError,
+                error_regex="Invalid weight_decay value: -1",
+            ),
+        ]
+    return error_inputs
+
+
+def optim_inputs_func_rmsprop(device):
+    return [
+        OptimizerInput(params=None, kwargs={}, desc="default"),
+        OptimizerInput(params=None, kwargs={"lr": 1e-3}, desc="non-default lr"),
+        OptimizerInput(
+            params=None, kwargs={"weight_decay": 0.1}, desc="nonzero weight_decay"
+        ),
+        OptimizerInput(
+            params=None,
+            kwargs={"weight_decay": 0.1, "centered": True},
+            desc="centered",
+        ),
+        OptimizerInput(
+            params=None,
+            kwargs={"weight_decay": 0.1, "centered": True, "momentum": 0.1},
+            desc="momentum",
+        ),
+        OptimizerInput(
+            params=None,
+            kwargs={
+                "weight_decay": 0.1,
+                "centered": True,
+                "momentum": 0.1,
+                "maximize": True,
+            },
+            desc="maximize",
+        ),
+    ]
+
+
+def optim_error_inputs_func_rmsprop(device, dtype):
+    error_inputs = get_error_inputs_for_all_optims(device, dtype)
+    if str(device) == "cpu":
+        error_inputs += [
+            ErrorOptimizerInput(
+                OptimizerInput(
+                    params=None,
+                    kwargs=dict(lr=1e-2, momentum=-1.0),
+                    desc="momentum should be between 0 and 1",
+                ),
+                error_type=ValueError,
+                error_regex="Invalid momentum value: -1.0",
+            ),
+        ]
+    return error_inputs
+
+
+def optim_inputs_func_rprop(device):
+    return [
+        OptimizerInput(params=None, kwargs={}, desc="default"),
+        OptimizerInput(params=None, kwargs={"lr": 2e-4}, desc="non-default lr"),
+        OptimizerInput(
+            params=None, kwargs={"etas": (0.5, 1.5)}, desc="non-default etas"
+        ),
+        OptimizerInput(
+            params=None,
+            kwargs={"step_sizes": (2e-6, 100)},
+            desc="non-default step_sizes",
+        ),
+        OptimizerInput(params=None, kwargs={"maximize": True}, desc="maximize"),
+    ]
+
+
+def optim_error_inputs_func_rprop(device, dtype):
+    error_inputs = get_error_inputs_for_all_optims(device, dtype)
+    if str(device) == "cpu":
+        error_inputs += [
+            ErrorOptimizerInput(
+                OptimizerInput(
+                    params=None,
+                    kwargs=dict(lr=1e-2, etas=(1.0, 0.5)),
+                    desc="0 < eta1 < 1 < eta2",
+                ),
+                error_type=ValueError,
+                error_regex="Invalid eta values: 1.0, 0.5",
+            ),
+        ]
+    return error_inputs
+
+
+def optim_inputs_func_sgd(device):
+    return [
+        OptimizerInput(params=None, kwargs={}, desc="default"),
+        OptimizerInput(params=None, kwargs={"lr": 1e-2}, desc="non-default lr"),
+        OptimizerInput(params=None, kwargs={"momentum": 0.9}, desc="momentum"),
+        OptimizerInput(
+            params=None,
+            kwargs={"momentum": 0.9, "dampening": 0.5},
+            desc="dampening",
+        ),
+        OptimizerInput(
+            params=None,
+            kwargs={"momentum": 0.9, "weight_decay": 0.1},
+            desc="non-zero weight_decay",
+        ),
+        OptimizerInput(
+            params=None,
+            kwargs={"momentum": 0.9, "nesterov": True, "weight_decay": 0.1},
+            desc="nesterov",
+        ),
+        OptimizerInput(
+            params=None,
+            kwargs={"weight_decay": 0.1, "maximize": True},
+            desc="maximize",
+        ),
+    ]
+
+
+def optim_error_inputs_func_sgd(device, dtype):
+    error_inputs = get_error_inputs_for_all_optims(device, dtype)
+    if str(device) == "cpu":
+        error_inputs += [
+            ErrorOptimizerInput(
+                OptimizerInput(
+                    params=None,
+                    kwargs=dict(lr=1e-2, momentum=-0.5),
+                    desc="momentum should be between 0 and 1",
+                ),
+                error_type=ValueError,
+                error_regex="Invalid momentum value: -0.5",
+            ),
+        ]
+    return error_inputs
+
+
+def optim_inputs_func_sparseadam(device):
+    return [
+        OptimizerInput(params=None, kwargs={}, desc="default"),
+        OptimizerInput(
+            params=None, kwargs={"lr": 0.01}, desc="non-default lr"
+        ),  # TODO: Move out to testing in param_group?
+        OptimizerInput(params=None, kwargs={"maximize": True}, desc="maximize"),
+    ]
+
+
+def optim_error_inputs_func_sparseadam(device, dtype):
+    error_inputs = get_error_inputs_for_all_optims(device, dtype)
+
+    if str(device) == "cpu":
+        # SparseAdam raises a warning and not an error for the first entry. We
+        # update it here:
+        error_inputs[0].error_type = FutureWarning
+        error_inputs[
+            0
+        ].error_regex = "Passing in a raw Tensor as ``params`` to SparseAdam"
+
+        error_inputs += [
+            ErrorOptimizerInput(
+                OptimizerInput(
+                    params=None,
+                    kwargs=dict(lr=1e-2, betas=(1.0, 0.0)),
+                    desc="beta1 should be between 0 and 1",
+                ),
+                error_type=ValueError,
+                error_regex="Invalid beta parameter at index 0: 1.0",
+            ),
+            ErrorOptimizerInput(
+                OptimizerInput(
+                    params=[
+                        torch.zeros(
+                            3, layout=torch.sparse_coo, device=device, dtype=dtype
+                        )
+                    ],
+                    kwargs={},
+                    desc="dense params required",
+                ),
+                error_type=ValueError,
+                error_regex="SparseAdam requires dense parameter tensors",
+            ),
+            ErrorOptimizerInput(
+                OptimizerInput(
+                    params=[
+                        {
+                            "params": [
+                                torch.zeros(
+                                    3,
+                                    layout=torch.sparse_coo,
+                                    device=device,
+                                    dtype=dtype,
+                                )
+                            ]
+                        }
+                    ],
+                    kwargs={},
+                    desc="dense params required in param_groups",
+                ),
+                error_type=ValueError,
+                error_regex="SparseAdam requires dense parameter tensors",
+            ),
+            ErrorOptimizerInput(
+                OptimizerInput(
+                    params=[torch.rand(2, 3, device=device, dtype=torch.complex64)],
+                    kwargs=dict(),
+                    desc="complex not supported",
+                ),
+                error_type=ValueError,
+                error_regex="SparseAdam does not support complex parameters",
+            ),
+        ]
+    return error_inputs
+
+
+def _get_device_type(device: Union[str, torch.device]) -> str:
+    # Returns the device type as a string, e.g., "cpu" or "cuda"
+    if isinstance(device, torch.device):
+        device = str(device.type)
+    assert isinstance(device, str)
+    return device.split(":")[0]
+
+
+def _get_optim_inputs_including_global_cliquey_kwargs(
+    device, dtype, optim_info, skip=()
+) -> List[OptimizerInput]:
+    """
+    Return a list of all configs for a given optimizer as a list of OptimizerInputs,
+    including configs that have supported global cliquey kwargs (foreach, fused,
+    differentiable) based on optim_info.supported_impls.
+
+    The configs (optim_inputs) returned by optim_info.optim_inputs_func(...)
+    intentionally do NOT include global cliquey kwargs to give flexibility to tests.
+    For example, testing correctness between toggling foreach on and off is now
+    trivial. That said, we sometimes want to test for all possible configs on an
+    optimizer including all supported flags, so this helper returns all optim inputs.
+    """
+    assert all(
+        x in ["foreach", "fused", "differentiable"] for x in skip
+    ), "skip must be a subset of ['foreach', 'fused', 'differentiable']"
+
+    optim_inputs = optim_info.optim_inputs_func(device)
+
+    supported_impls = tuple(
+        x
+        for x in optim_info.supported_impls
+        if x not in skip
+        and (
+            _get_device_type(device) in _get_fused_kernels_supported_devices()
+            or x != "fused"
+        )
+        and (
+            _get_device_type(device) in _get_foreach_kernels_supported_devices()
+            or x != "foreach"
+        )
+    )
+
+    all_optim_inputs = []
+    for optim_input in optim_inputs:
+        # Add the base config where all the flags are False
+        base_kwargs = deepcopy(optim_input.kwargs)
+        if len(supported_impls) != 0:
+            for flag in supported_impls:
+                base_kwargs[flag] = False
+            all_optim_inputs.append(
+                OptimizerInput(params=None, kwargs=base_kwargs, desc=optim_input.desc)
+            )
+        else:
+            all_optim_inputs.append(optim_input)
+        # Add a config for when each of the global cliquey kwargs is True
+        # Note that in [optimizer kwarg categories], these kwargs are mutually
+        # exclusive, so we do not need to product them together.
+        for flag in supported_impls:
+            new_kwargs = deepcopy(base_kwargs)
+            new_kwargs[flag] = True
+            all_optim_inputs.append(
+                OptimizerInput(
+                    params=None, kwargs=new_kwargs, desc=f"{optim_input.desc} & {flag}"
+                )
+            )
+    return all_optim_inputs
+
+
+# Database of OptimizerInfo entries in alphabetical order.
+optim_db: List[OptimizerInfo] = [
+    OptimizerInfo(
+        Adadelta,
+        optim_inputs_func=optim_inputs_func_adadelta,
+        optim_error_inputs_func=optim_error_inputs_func_adadelta,
+        supported_impls=("foreach", "differentiable"),
+        skips=(
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "No closure handling, https://github.com/pytorch/pytorch/issues/116494"
+                ),
+                "TestOptimRenewed",
+                "test_forloop_goes_right_direction",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "No closure handling, https://github.com/pytorch/pytorch/issues/116494"
+                ),
+                "TestOptimRenewed",
+                "test_forloop_goes_right_direction_multigpu",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "See https://github.com/pytorch/pytorch/issues/115679"
+                ),
+                "TestOptimRenewed",
+                "test_foreach_matches_forloop",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "Dynamo memory usage is flaky, see https://github.com/pytorch/pytorch/issues/116046"
+                ),
+                "TestOptimRenewed",
+                "test_peak_memory_foreach",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "See https://github.com/pytorch/pytorch/issues/115679 and #116028"
+                ),
+                "TestOptimRenewed",
+                "test_set_default_dtype_works_with_foreach",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "Accessing grad.real errors, see https://github.com/pytorch/pytorch/issues/117184"
+                ),
+                "TestOptimRenewed",
+                "test_complex_2d",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "No closure handling, https://github.com/pytorch/pytorch/issues/116494"
+                ),
+                "TestOptimRenewed",
+                "test_state_dict_deterministic",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "See https://github.com/pytorch/pytorch/issues/115679"
+                ),
+                "TestOptimRenewed",
+                "test_state_dict_with_cuda_params",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "fails, https://github.com/pytorch/pytorch/issues/117165"
+                ),
+                "TestOptimRenewed",
+                "test_deepcopy_copies_all_public_attrs",
+            ),
+            # Note on tolerances:
+            # test_correctness_Adadelta_cuda_float32
+            # Mismatched elements: 10 / 100 (10.0%)
+            # Greatest absolute difference: 4.838220775127411e-05 at index (7, 4) (up to 1e-05 allowed)
+            # Greatest relative difference: 0.007270356640219688 at index (7, 2) (up to 1e-05 allowed)
+            # This is due to floating point ordering error + usage of sqrt
+            DecorateInfo(
+                toleranceOverride(
+                    {
+                        torch.float32: tol(
+                            rtol=5.5e-4,
+                            atol=5e-5,
+                        )
+                    }
+                ),
+                "CompiledOptimizerParityTests",
+                "test_correctness",
+            ),
+        ),
+    ),
+    OptimizerInfo(
+        Adagrad,
+        optim_inputs_func=optim_inputs_func_adagrad,
+        optim_error_inputs_func=optim_error_inputs_func_adagrad,
+        supported_impls=("foreach", "differentiable"),
+        supports_sparse_on=("cpu"),
+        skips=(
+            DecorateInfo(
+                skipIfMps,  # addcdiv doesn't work for non-contiguous, see #118115
+                "TestOptimRenewed",
+                "test_forloop_goes_right_direction",
+                active_if=lambda kwargs: not kwargs["contiguous"],
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "No closure handling, https://github.com/pytorch/pytorch/issues/116494"
+                ),
+                "TestOptimRenewed",
+                "test_forloop_goes_right_direction",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "No closure handling, https://github.com/pytorch/pytorch/issues/116494"
+                ),
+                "TestOptimRenewed",
+                "test_forloop_goes_right_direction_multigpu",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "See https://github.com/pytorch/pytorch/issues/115607"
+                ),
+                "TestOptimRenewed",
+                "test_foreach_matches_forloop",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "Dynamo memory usage is flaky, see https://github.com/pytorch/pytorch/issues/116046"
+                ),
+                "TestOptimRenewed",
+                "test_peak_memory_foreach",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "See https://github.com/pytorch/pytorch/issues/115607 and #116028"
+                ),
+                "TestOptimRenewed",
+                "test_set_default_dtype_works_with_foreach",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "Accessing grad.real errors, see https://github.com/pytorch/pytorch/issues/117184"
+                ),
+                "TestOptimRenewed",
+                "test_complex_2d",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "No closure handling, https://github.com/pytorch/pytorch/issues/116494"
+                ),
+                "TestOptimRenewed",
+                "test_state_dict_deterministic",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "fails, https://github.com/pytorch/pytorch/issues/117165"
+                ),
+                "TestOptimRenewed",
+                "test_deepcopy_copies_all_public_attrs",
+            ),
+        ),
+    ),
+    OptimizerInfo(
+        Adam,
+        optim_inputs_func=optim_inputs_func_adam,
+        optim_error_inputs_func=optim_error_inputs_func_adam,
+        supported_impls=("foreach", "differentiable", "fused"),
+        skips=(
+            DecorateInfo(
+                skipIfMps,  # addcdiv doesn't work for non-contiguous, see #118115
+                "TestOptimRenewed",
+                "test_forloop_goes_right_direction",
+                active_if=lambda kwargs: not kwargs["contiguous"],
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "No closure handling, https://github.com/pytorch/pytorch/issues/116494"
+                ),
+                "TestOptimRenewed",
+                "test_forloop_goes_right_direction",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "No closure handling, https://github.com/pytorch/pytorch/issues/116494"
+                ),
+                "TestOptimRenewed",
+                "test_forloop_goes_right_direction_multigpu",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "Errors w/ Global state changed, see https://github.com/pytorch/pytorch/issues/116028"
+                ),
+                "TestOptimRenewed",
+                "test_set_default_dtype_works_with_foreach",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "Fixing #115607 should fix this test. fused is correct, but forloop is not."
+                ),
+                "TestOptimRenewed",
+                "test_fused_matches_forloop",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "See https://github.com/pytorch/pytorch/issues/116046"
+                ),
+                "TestOptimRenewed",
+                "test_peak_memory_foreach",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "Accessing grad.real errors, see https://github.com/pytorch/pytorch/issues/117184"
+                ),
+                "TestOptimRenewed",
+                "test_complex_2d",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "No closure handling, https://github.com/pytorch/pytorch/issues/116494"
+                ),
+                "TestOptimRenewed",
+                "test_state_dict_deterministic",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "fails, https://github.com/pytorch/pytorch/issues/117165"
+                ),
+                "TestOptimRenewed",
+                "test_deepcopy_copies_all_public_attrs",
+            ),
+        ),
+    ),
+    OptimizerInfo(
+        Adamax,
+        optim_inputs_func=optim_inputs_func_adamax,
+        optim_error_inputs_func=optim_error_inputs_func_adamax,
+        supported_impls=("foreach", "differentiable"),
+        skips=(
+            DecorateInfo(
+                skipIfMps,  # addcdiv doesn't work for non-contiguous, see #118115
+                "TestOptimRenewed",
+                "test_forloop_goes_right_direction",
+                active_if=lambda kwargs: not kwargs["contiguous"],
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "No closure handling, https://github.com/pytorch/pytorch/issues/116494"
+                ),
+                "TestOptimRenewed",
+                "test_forloop_goes_right_direction",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "No closure handling, https://github.com/pytorch/pytorch/issues/116494"
+                ),
+                "TestOptimRenewed",
+                "test_forloop_goes_right_direction_multigpu",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo("Mismatched _foreach_addcdiv_ types, see #118159"),
+                "TestOptimRenewed",
+                "test_complex",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "See https://github.com/pytorch/pytorch/issues/115607"
+                ),
+                "TestOptimRenewed",
+                "test_foreach_matches_forloop",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "See https://github.com/pytorch/pytorch/issues/115607 and #116028"
+                ),
+                "TestOptimRenewed",
+                "test_set_default_dtype_works_with_foreach",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "See https://github.com/pytorch/pytorch/issues/116046"
+                ),
+                "TestOptimRenewed",
+                "test_peak_memory_foreach",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "Accessing grad.real errors, see https://github.com/pytorch/pytorch/issues/117184"
+                ),
+                "TestOptimRenewed",
+                "test_complex_2d",
+            ),
+            DecorateInfo(
+                unittest.skip("Uses too much memory, even for H100, surprisingly."),
+                "TestOptimRenewed",
+                "test_foreach_large_tensor",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "No closure handling, https://github.com/pytorch/pytorch/issues/116494"
+                ),
+                "TestOptimRenewed",
+                "test_state_dict_deterministic",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "fails, https://github.com/pytorch/pytorch/issues/117165"
+                ),
+                "TestOptimRenewed",
+                "test_deepcopy_copies_all_public_attrs",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo("cpu fails due to #115607"),
+                "TestOptimRenewed",
+                "test_can_load_older_state_dict",
+                device_type="cpu",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "capturable path no longer called after hitting cache limit, see #121178"
+                ),
+                "TestOptimRenewed",
+                "test_save_load_equality_with_weights_only",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "capturable path no longer called after hitting cache limit, see #121178"
+                ),
+                "TestOptimRenewed",
+                "test_load_nontensor_step",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "capturable path no longer called after hitting cache limit, see #121178"
+                ),
+                "TestOptimRenewed",
+                "test_param_groups_lr",
+            ),
+        ),
+    ),
+    OptimizerInfo(
+        AdamW,
+        optim_inputs_func=optim_inputs_func_adamw,
+        optim_error_inputs_func=optim_error_inputs_func_adamw,
+        supported_impls=("foreach", "differentiable", "fused"),
+        skips=(
+            DecorateInfo(
+                skipIfMps,  # addcdiv doesn't work for non-contiguous, see #118115
+                "TestOptimRenewed",
+                "test_forloop_goes_right_direction",
+                active_if=lambda kwargs: not kwargs["contiguous"],
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "No closure handling, https://github.com/pytorch/pytorch/issues/116494"
+                ),
+                "TestOptimRenewed",
+                "test_forloop_goes_right_direction",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "No closure handling, https://github.com/pytorch/pytorch/issues/116494"
+                ),
+                "TestOptimRenewed",
+                "test_forloop_goes_right_direction_multigpu",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "Errors w/ Global state changed, see https://github.com/pytorch/pytorch/issues/116028"
+                ),
+                "TestOptimRenewed",
+                "test_set_default_dtype_works_with_foreach",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "Fixing #115607 should fix this test. fused is correct, but forloop is not."
+                ),
+                "TestOptimRenewed",
+                "test_fused_matches_forloop",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "See https://github.com/pytorch/pytorch/issues/116046"
+                ),
+                "TestOptimRenewed",
+                "test_peak_memory_foreach",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "Accessing grad.real errors, see https://github.com/pytorch/pytorch/issues/117184"
+                ),
+                "TestOptimRenewed",
+                "test_complex_2d",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "No closure handling, https://github.com/pytorch/pytorch/issues/116494"
+                ),
+                "TestOptimRenewed",
+                "test_state_dict_deterministic",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "fails, https://github.com/pytorch/pytorch/issues/117165"
+                ),
+                "TestOptimRenewed",
+                "test_deepcopy_copies_all_public_attrs",
+            ),
+        ),
+    ),
+    OptimizerInfo(
+        ASGD,
+        optim_inputs_func=optim_inputs_func_asgd,
+        optim_error_inputs_func=optim_error_inputs_func_asgd,
+        supported_impls=("foreach", "differentiable"),
+        skips=(
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "No closure handling, https://github.com/pytorch/pytorch/issues/116494"
+                ),
+                "TestOptimRenewed",
+                "test_forloop_goes_right_direction",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "No closure handling, https://github.com/pytorch/pytorch/issues/116494"
+                ),
+                "TestOptimRenewed",
+                "test_forloop_goes_right_direction_multigpu",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "See discrepancy in https://github.com/pytorch/pytorch/issues/115607"
+                ),
+                "TestOptimRenewed",
+                "test_foreach_matches_forloop",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "Dynamo memory usage is flaky, see https://github.com/pytorch/pytorch/issues/116046"
+                ),
+                "TestOptimRenewed",
+                "test_peak_memory_foreach",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "Errors w/ Global state changed, see https://github.com/pytorch/pytorch/issues/116028"
+                ),
+                "TestOptimRenewed",
+                "test_set_default_dtype_works_with_foreach",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "Accessing grad.real errors, see https://github.com/pytorch/pytorch/issues/117184"
+                ),
+                "TestOptimRenewed",
+                "test_complex_2d",
+            ),
+            DecorateInfo(
+                toleranceOverride(
+                    {
+                        torch.float32: tol(atol=1.5e-5, rtol=1e-5),
+                    }
+                ),
+                "TestOptimRenewed",
+                "test_step_is_noop_for_zero_grads",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "No closure handling, https://github.com/pytorch/pytorch/issues/116494"
+                ),
+                "TestOptimRenewed",
+                "test_state_dict_deterministic",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "fails, https://github.com/pytorch/pytorch/issues/117165"
+                ),
+                "TestOptimRenewed",
+                "test_deepcopy_copies_all_public_attrs",
+            ),
+        ),
+    ),
+    OptimizerInfo(
+        LBFGS,
+        optim_inputs_func=optim_inputs_func_lbfgs,
+        optim_error_inputs_func=optim_error_inputs_func_lbfgs,
+        supported_impls=(),
+        step_requires_closure=True,
+        supports_param_groups=False,
+        supports_multiple_devices=False,
+        skips=(
+            # Fails on MacOS 13.2.1 in CI https://github.com/pytorch/pytorch/issues/117094
+            DecorateInfo(
+                skipIfMps, "TestOptimRenewed", "test_can_load_older_state_dict"
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "fails, https://github.com/pytorch/pytorch/issues/117165"
+                ),
+                "TestOptimRenewed",
+                "test_deepcopy_copies_all_public_attrs",
+            ),
+            DecorateInfo(
+                unittest.skip("Does not support param groups"),
+                "TestOptimRenewed",
+                "test_param_groups_lr",
+            ),
+            DecorateInfo(
+                unittest.skip("Does not support param groups"),
+                "TestOptimRenewed",
+                "test_param_groups_weight_decay",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "No closure handling, https://github.com/pytorch/pytorch/issues/116494"
+                ),
+                "TestOptimRenewed",
+                "test_forloop_goes_right_direction",
+            ),
+            DecorateInfo(
+                unittest.skip("LBFGS doesn't support multidevice"),
+                "TestOptimRenewed",
+                "test_forloop_goes_right_direction_multigpu",
+            ),
+        ),
+    ),
+    OptimizerInfo(
+        NAdam,
+        optim_inputs_func=optim_inputs_func_nadam,
+        optim_error_inputs_func=optim_error_inputs_func_nadam,
+        supported_impls=("foreach", "differentiable"),
+        skips=(
+            DecorateInfo(
+                skipIfMps,  # addcdiv doesn't work for non-contiguous, see #118115
+                "TestOptimRenewed",
+                "test_forloop_goes_right_direction",
+                active_if=lambda kwargs: not kwargs["contiguous"],
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "No closure handling, https://github.com/pytorch/pytorch/issues/116494"
+                ),
+                "TestOptimRenewed",
+                "test_forloop_goes_right_direction",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "No closure handling, https://github.com/pytorch/pytorch/issues/116494"
+                ),
+                "TestOptimRenewed",
+                "test_forloop_goes_right_direction_multigpu",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "Errors w/ Global state changed, see https://github.com/pytorch/pytorch/issues/116028"
+                ),
+                "TestOptimRenewed",
+                "test_set_default_dtype_works_with_foreach",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "See https://github.com/pytorch/pytorch/issues/116046"
+                ),
+                "TestOptimRenewed",
+                "test_peak_memory_foreach",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "Accessing grad.real errors, see https://github.com/pytorch/pytorch/issues/117184"
+                ),
+                "TestOptimRenewed",
+                "test_complex_2d",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "No closure handling, https://github.com/pytorch/pytorch/issues/116494"
+                ),
+                "TestOptimRenewed",
+                "test_state_dict_deterministic",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "See https://github.com/pytorch/pytorch/issues/116499"
+                ),
+                "TestOptimRenewed",
+                "test_can_load_older_state_dict",
+                device_type="cuda",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "Errors, https://github.com/pytorch/pytorch/issues/117150"
+                ),
+                "TestOptimRenewed",
+                "test_load_nontensor_step",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "Errors, see https://github.com/pytorch/pytorch/issues/117150"
+                ),
+                "TestOptimRenewed",
+                "test_state_dict_with_cuda_params",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "fails, https://github.com/pytorch/pytorch/issues/117165"
+                ),
+                "TestOptimRenewed",
+                "test_deepcopy_copies_all_public_attrs",
+            ),
+        ),
+    ),
+    OptimizerInfo(
+        RAdam,
+        optim_inputs_func=optim_inputs_func_radam,
+        optim_error_inputs_func=optim_error_inputs_func_radam,
+        supported_impls=("foreach", "differentiable"),
+        skips=(
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "No closure handling, https://github.com/pytorch/pytorch/issues/116494"
+                ),
+                "TestOptimRenewed",
+                "test_forloop_goes_right_direction",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "No closure handling, https://github.com/pytorch/pytorch/issues/116494"
+                ),
+                "TestOptimRenewed",
+                "test_forloop_goes_right_direction_multigpu",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "Dynamo memory usage is flaky, see https://github.com/pytorch/pytorch/issues/116046"
+                ),
+                "TestOptimRenewed",
+                "test_peak_memory_foreach",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "Errors w/ Global state changed, see https://github.com/pytorch/pytorch/issues/116028"
+                ),
+                "TestOptimRenewed",
+                "test_set_default_dtype_works_with_foreach",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "Accessing grad.real errors, see https://github.com/pytorch/pytorch/issues/117184"
+                ),
+                "TestOptimRenewed",
+                "test_complex_2d",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "fails, https://github.com/pytorch/pytorch/issues/117165"
+                ),
+                "TestOptimRenewed",
+                "test_deepcopy_copies_all_public_attrs",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "See https://github.com/pytorch/pytorch/issues/115607"
+                ),
+                "TestOptimRenewed",
+                "test_foreach_matches_forloop",
+            ),
+            DecorateInfo(
+                toleranceOverride(
+                    {
+                        # previously atol=1e-7, rtol=1e-7
+                        torch.float64: tol(atol=1.5e-7, rtol=1.1e-7)
+                    }
+                ),
+                "TestOptimRenewed",
+                "test_foreach_matches_forloop",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "See https://github.com/pytorch/pytorch/issues/116494"
+                ),
+                "TestOptimRenewed",
+                "test_state_dict_deterministic",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "Should be fixed by https://github.com/pytorch/pytorch/issues/115607"
+                ),
+                "TestOptimRenewed",
+                "test_can_load_older_state_dict",
+                device_type="cpu",
+            ),
+        ),
+    ),
+    OptimizerInfo(
+        RMSprop,
+        optim_inputs_func=optim_inputs_func_rmsprop,
+        optim_error_inputs_func=optim_error_inputs_func_rmsprop,
+        supported_impls=("foreach", "differentiable"),
+        skips=(
+            DecorateInfo(
+                skipIfMps,  # addcdiv doesn't work for non-contiguous, see #118115
+                "TestOptimRenewed",
+                "test_forloop_goes_right_direction",
+                active_if=lambda kwargs: not kwargs["contiguous"],
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "No closure handling, https://github.com/pytorch/pytorch/issues/116494"
+                ),
+                "TestOptimRenewed",
+                "test_forloop_goes_right_direction",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "No closure handling, https://github.com/pytorch/pytorch/issues/116494"
+                ),
+                "TestOptimRenewed",
+                "test_forloop_goes_right_direction_multigpu",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "See https://github.com/pytorch/pytorch/issues/115679"
+                ),
+                "TestOptimRenewed",
+                "test_foreach_matches_forloop",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "Dynamo memory usage is flaky, see https://github.com/pytorch/pytorch/issues/116046"
+                ),
+                "TestOptimRenewed",
+                "test_peak_memory_foreach",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "See https://github.com/pytorch/pytorch/issues/115679 and #116028"
+                ),
+                "TestOptimRenewed",
+                "test_set_default_dtype_works_with_foreach",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "Accessing grad.real errors, see https://github.com/pytorch/pytorch/issues/117184"
+                ),
+                "TestOptimRenewed",
+                "test_complex_2d",
+            ),
+            DecorateInfo(
+                toleranceOverride(
+                    {  # previously atol=5-05, rtol=0.001, https://github.com/pytorch/pytorch/issues/116202
+                        torch.float32: tol(atol=5e-04, rtol=0.01),
+                    }
+                ),
+                "TestOptimRenewed",
+                "test_mixed_device_dtype",
+                active_if=TEST_WITH_TORCHDYNAMO,
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "No closure handling, https://github.com/pytorch/pytorch/issues/116494"
+                ),
+                "TestOptimRenewed",
+                "test_state_dict_deterministic",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "See https://github.com/pytorch/pytorch/issues/115679"
+                ),
+                "TestOptimRenewed",
+                "test_state_dict_with_cuda_params",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "fails, https://github.com/pytorch/pytorch/issues/117165"
+                ),
+                "TestOptimRenewed",
+                "test_deepcopy_copies_all_public_attrs",
+            ),
+        ),
+    ),
+    OptimizerInfo(
+        Rprop,
+        optim_inputs_func=optim_inputs_func_rprop,
+        optim_error_inputs_func=optim_error_inputs_func_rprop,
+        supported_impls=("foreach", "differentiable"),
+        skips=(
+            DecorateInfo(
+                skipIfMps,  # Rprop doesn't update for non-contiguous, see #118117
+                "TestOptimRenewed",
+                "test_forloop_goes_right_direction",
+                active_if=lambda kwargs: not kwargs["contiguous"],
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "No closure handling, https://github.com/pytorch/pytorch/issues/116494"
+                ),
+                "TestOptimRenewed",
+                "test_forloop_goes_right_direction",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "No closure handling, https://github.com/pytorch/pytorch/issues/116494"
+                ),
+                "TestOptimRenewed",
+                "test_forloop_goes_right_direction_multigpu",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "See https://github.com/pytorch/pytorch/issues/115679"
+                ),
+                "TestOptimRenewed",
+                "test_foreach_matches_forloop",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "Dynamo memory usage is flaky, see https://github.com/pytorch/pytorch/issues/116046"
+                ),
+                "TestOptimRenewed",
+                "test_peak_memory_foreach",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "See https://github.com/pytorch/pytorch/issues/115679 and #116028"
+                ),
+                "TestOptimRenewed",
+                "test_set_default_dtype_works_with_foreach",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "Accessing grad.real errors, see https://github.com/pytorch/pytorch/issues/117184"
+                ),
+                "TestOptimRenewed",
+                "test_complex_2d",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "No closure handling, https://github.com/pytorch/pytorch/issues/116494"
+                ),
+                "TestOptimRenewed",
+                "test_state_dict_deterministic",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "See https://github.com/pytorch/pytorch/issues/115679"
+                ),
+                "TestOptimRenewed",
+                "test_state_dict_with_cuda_params",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "fails, https://github.com/pytorch/pytorch/issues/117165"
+                ),
+                "TestOptimRenewed",
+                "test_deepcopy_copies_all_public_attrs",
+            ),
+        ),
+    ),
+    OptimizerInfo(
+        SGD,
+        optim_inputs_func=optim_inputs_func_sgd,
+        optim_error_inputs_func=optim_error_inputs_func_sgd,
+        supported_impls=("foreach", "differentiable", "fused"),
+        supports_sparse_on=("cpu", "cuda"),
+        skips=(
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "No closure handling, https://github.com/pytorch/pytorch/issues/116494"
+                ),
+                "TestOptimRenewed",
+                "test_forloop_goes_right_direction",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "No closure handling, https://github.com/pytorch/pytorch/issues/116494"
+                ),
+                "TestOptimRenewed",
+                "test_forloop_goes_right_direction_multigpu",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "Dynamo memory usage is flaky, see https://github.com/pytorch/pytorch/issues/116046"
+                ),
+                "TestOptimRenewed",
+                "test_peak_memory_foreach",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "Errors w/ Global state changed, see https://github.com/pytorch/pytorch/issues/116028"
+                ),
+                "TestOptimRenewed",
+                "test_set_default_dtype_works_with_foreach",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "Accessing grad.real errors, see https://github.com/pytorch/pytorch/issues/117184"
+                ),
+                "TestOptimRenewed",
+                "test_complex_2d",
+            ),
+            DecorateInfo(
+                toleranceOverride(
+                    {  # previously atol=5-05, rtol=0.001, https://github.com/pytorch/pytorch/issues/116202
+                        torch.float32: tol(atol=5e-04, rtol=0.007),
+                    }
+                ),
+                "TestOptimRenewed",
+                "test_mixed_device_dtype",
+                active_if=TEST_WITH_TORCHDYNAMO,
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "Errors with list out of range, see https://github.com/pytorch/pytorch/issues/116061"
+                ),
+                "TestOptimRenewed",
+                "test_step_is_noop_for_zero_grads",
+                device_type="cpu",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "No closure handling, https://github.com/pytorch/pytorch/issues/116494"
+                ),
+                "TestOptimRenewed",
+                "test_state_dict_deterministic",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "Errors with list out of range, see https://github.com/pytorch/pytorch/issues/116061"
+                ),
+                "TestOptimRenewed",
+                "test_param_groups_weight_decay",
+                device_type="cpu",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "Errors with list out of range, see https://github.com/pytorch/pytorch/issues/116061"
+                ),
+                "TestOptimRenewed",
+                "test_param_groups_lr",
+                device_type="cpu",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "Errors with list out of range, see https://github.com/pytorch/pytorch/issues/116061"
+                ),
+                "TestOptimRenewed",
+                "test_load_nontensor_step",
+                device_type="cpu",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "momentum_buffer inconsistency, https://github.com/pytorch/pytorch/issues/117147"
+                ),
+                "TestOptimRenewed",
+                "test_state_dict_with_cuda_params",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo(
+                    "fails, https://github.com/pytorch/pytorch/issues/117165"
+                ),
+                "TestOptimRenewed",
+                "test_deepcopy_copies_all_public_attrs",
+            ),
+        ),
+    ),
+    OptimizerInfo(
+        SparseAdam,
+        optim_inputs_func=optim_inputs_func_sparseadam,
+        optim_error_inputs_func=optim_error_inputs_func_sparseadam,
+        supported_impls=(),
+        only_supports_sparse_grads=True,
+        supports_complex=False,  # Missing complex support, see #118153
+        skips=(
+            DecorateInfo(
+                skipIfMps,  # SparseAdam does not support MPS
+                "TestOptimRenewed",
+            ),
+            DecorateInfo(
+                unittest.skip(
+                    "SparseAdam does not support dense gradients, see #116507"
+                ),
+                "TestOptimRenewed",
+                "test_state_dict_deterministic",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo("cannot call to_sparse on p.grad, see #117184"),
+                "TestOptimRenewed",
+                "test_param_groups_lr",
+            ),
+            DecorateInfo(
+                unittest.skip(
+                    "SparseAdam does not support dense gradients, see #116507"
+                ),
+                "TestOptimRenewed",
+                "test_can_load_older_state_dict",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo("cannot call to_sparse on p.grad, see #117184"),
+                "TestOptimRenewed",
+                "test_load_nontensor_step",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo("cannot call to_sparse on p.grad, see #117184"),
+                "TestOptimRenewed",
+                "test_forloop_goes_right_direction",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo("cannot call to_sparse on p.grad, see #117184"),
+                "TestOptimRenewed",
+                "test_forloop_goes_right_direction_multigpu",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo("cannot call to_sparse on p.grad, see #117184"),
+                "TestOptimRenewed",
+                "test_state_dict_with_cuda_params",
+            ),
+            DecorateInfo(
+                skipIfTorchDynamo("cannot call to_sparse on p.grad, see #117184"),
+                "TestOptimRenewed",
+                "test_deepcopy_copies_all_public_attrs",
+            ),
+        ),
+    ),
+]
+
+
+class TensorTracker:
+    """
+    A utility to track tensor clones in a list, with the expectation of popping them later (in
+    order) to make fair comparisons between two multi-step computation. The intended use case is
+    usually when comparing two supposed equal computations, such as an optimizer step that each
+    individually consists of multiple steps, where numerical deviation could multiply.
+
+    The goal is to be able to compare and align numbers at every milestone so as to minimize
+    numerical discrepancies, and so when the test fails, it is likely a real problem.
+    """
+
+    def __init__(self):
+        self.tensors = []
+
+    def add(self, tensor):
+        """
+        Add a clone().detach()'d version of the tensor
+        """
+        self.tensors.append(tensor.clone().detach())
+
+    # pops from beginning, like a queue and not a stack!
+    def pop_check_set(self, tensor_to_set, testcase):
+        """
+        Pop the first element in the tensor tracker, assert equality between the popped tensor and
+        the input tensor, and then set the input tensor to have the same values as the popped tensor
+        (with copy_).
+        """
+        testcase.assertGreater(len(self.tensors), 0, "no tensors to pop")
+        ref = self.tensors.pop(0)
+
+        testcase.assertTrue(isinstance(ref, Tensor), f"{type(ref)=}")
+        testcase.assertEqual(tensor_to_set, ref)
+
+        with torch.no_grad():
+            tensor_to_set.copy_(ref)
+
+    def all_popped(self):
+        return len(self.tensors) == 0

venv/lib/python3.10/site-packages/torch/testing/_internal/composite_compliance.py

@@ -0,0 +1,581 @@
+# mypy: ignore-errors
+
+import torch
+from torch import Tensor
+import itertools
+
+from torch.utils._python_dispatch import TorchDispatchMode
+from torch.utils._pytree import tree_map, tree_flatten, tree_unflatten
+from torch.utils import _pytree as pytree
+from functools import partial
+from torch.utils._mode_utils import no_dispatch, all_same_mode
+import torch.autograd.forward_ad as fwAD
+from typing import Callable
+import re
+
+
+def check_attr_consistency(wrapper_tensor, metadata_name, metadata_accessor):
+    elem = wrapper_tensor.elem
+    metadata_wrapper_tensor = metadata_accessor(wrapper_tensor)
+    metadata_elem = metadata_accessor(elem)
+    if metadata_wrapper_tensor == metadata_elem:
+        return
+    raise RuntimeError(
+        f"This operator is not Composite Compliant: the "
+        f"{metadata_name} of the tensor was modified directly without "
+        f"going through the PyTorch dispatcher.")
+
+def check_metadata_consistency(wrapper_tensor, CCT):
+    # CCT: CompositeCompliantTensor class which is generated using generate_cct
+    if not isinstance(wrapper_tensor, CCT):
+        return
+    things_to_check = {
+        'shape': Tensor.size,
+        'dtype': lambda x: x.dtype,
+        'device': lambda x: x.device,
+        'numel': Tensor.numel,
+        'stride': Tensor.stride,
+        'storage_offset': Tensor.storage_offset,
+    }
+    for metadata_name, metadata_accessor in things_to_check.items():
+        check_attr_consistency(wrapper_tensor, metadata_name, metadata_accessor)
+
+def is_view_fn(func):
+    return func.overloadpacket.__name__ in {
+        'as_strided',
+        'detach',
+        'diagonal',
+        'expand',
+        'expand_as',
+        'movedim',
+        'narrow',
+        'permute',
+        'select',
+        'squeeze',
+        'transpose',
+        't',
+        'real',
+        'imag',
+        'view_as_real',
+        'view_as_complex',
+        'unflatten',
+        'unfold',
+        'unsqueeze',
+        'view',
+        'view_as',
+        'unbind',
+        'split',
+        'split_with_sizes',
+        'vsplit',
+        'hsplit',
+        'tensor_split',
+        'chunk',
+        'swapaxes',
+        'slice',
+        '_reshape_alias',
+        '_unsafe_view',
+        '_conj',
+        'alias',
+    }
+
+# manually populated from native_functions that have inplace_view: True.
+# In the future we will probably be able to grab that list directly
+def is_inplace_view_fn(func):
+    return func.overloadpacket.__name__ in {
+        'as_strided_',
+        'detach_',
+        'squeeze_',
+        'swapaxes_',
+        'swapdims_',
+        't_',
+        'transpose_',
+        'unsqueeze_',
+    }
+
+
+# Introspection please save us
+def is_inplace(func):
+    name = func.overloadpacket.__name__
+    if re.match('__i.+__', name):
+        return True
+    if re.match('__.+__', name):
+        return False
+    return name[-1] == '_'
+
+
+def generate_cct_and_mode(autograd_view_consistency=True):
+    # This function returns a new class CompositeCompliantTensor
+    # The two arguments control the behaviour described below.
+
+    # autograd_view_consistency:
+    #   If True, alias result using `set_` if func returns a view
+    #   (See Note [Alias Result]).
+    #   Since Forward AD doesn't work with `set_`
+    #   we disable it by setting alias to False.
+
+    class CompositeCompliantTensor(torch.Tensor):
+        elem: torch.Tensor
+
+        __slots__ = ['elem']
+
+        @staticmethod
+        def __new__(cls, elem, mode, *args, **kwargs):
+            assert type(elem) is not cls, \
+                "Wrapping a CompositeCompliantTensor in a CompositeCompliantTensor is not supported"
+
+            # The storage of CompositeCompliantTensor should never be used directly
+            # by a Composite operation; if the Composite
+            # operator attempts to read from the storage without dispatching then it'll
+            # raise a RuntimeError due to it being a meta storage.
+            r = torch.Tensor._make_wrapper_subclass(  # type: ignore[attr-defined]
+                cls, elem.size(),
+                dtype=elem.dtype, layout=elem.layout,
+                device=elem.device, requires_grad=elem.requires_grad,
+                strides=elem.stride(), storage_offset=elem.storage_offset())
+
+            if elem.requires_grad:
+                # CompositeCompliantTensor steals the "requires_grad"-ness.
+                # Why a new copy of `elem`? Because sometimes OpInfo shares inputs between tests...
+                tmp = torch.empty_strided(elem.shape, elem.stride(), dtype=elem.dtype,
+                                          device=elem.device, layout=elem.layout,
+                                          requires_grad=False)
+                tmp.copy_(elem.detach())
+                r.elem = tmp
+            else:
+                r.elem = elem
+
+            assert r.stride() == r.elem.stride()
+
+            # Propagate conjugate bits to the wrapper tensor
+            # Ref: https://github.com/albanD/subclass_zoo/issues/24
+            # Ref: https://github.com/albanD/subclass_zoo/issues/21
+            torch._C._set_conj(r, r.elem.is_conj())
+            torch._C._set_neg(r, r.elem.is_neg())
+
+            r.mode = mode
+            return r
+
+        def __repr__(self):
+            return f"CompositeCompliantTensor({self.elem})"
+
+        @classmethod
+        def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
+            all_args = pytree.arg_tree_leaves(*args, **(kwargs or {}))
+            modes = tuple(e.mode for e in all_args if isinstance(e, CompositeCompliantTensor))
+            if not all_same_mode(modes):
+                raise RuntimeError("Multiple CompositeCompliantTensorModes NYI")
+            with modes[0]:
+                return func(*args, **kwargs)
+
+    class CompositeCompliantTensorMode(TorchDispatchMode):
+        def __torch_dispatch__(self, func, types, args=(), kwargs=None):
+            def unwrap(e):
+                return e.elem if isinstance(e, CompositeCompliantTensor) else e
+
+            def wrap(e):
+                return CompositeCompliantTensor(e, self) if isinstance(e, torch.Tensor) else e
+
+            if func == torch.ops.aten._local_scalar_dense.default:
+                raise RuntimeError(
+                    ".item() is not allowed to be called inside of composite "
+                    "functions in the PyTorch library because not all backends "
+                    "and/or Tensor subclasses (e.g. vmap, ProxyTensor) support them.")
+
+            if func.overloadpacket.__name__ in ('set_', 'resize_'):
+                raise RuntimeError(
+                    f"{func.__name__} is not allowed to be called inside of "
+                    f"Composite operators.")
+
+            if is_inplace(func):
+                # NB: We are making an assumption that if the function is in-place,
+                # then the first argument is being written to. Introspection please save us!
+                mutated_argument = args[0]
+                if not isinstance(mutated_argument, CompositeCompliantTensor) and \
+                        any(isinstance(a, CompositeCompliantTensor) for a in args[1:]):
+                    raise RuntimeError(
+                        'Not composite compliant: performing in-place operation '
+                        f'{func.__name__} where the Tensor being written to is '
+                        'regular Tensor but the other tensors are Tensor Subclasses. '
+                        'Please try to avoid this in-place operation.')
+
+            unwrapped_args = tree_map(unwrap, args)
+            unwrapped_kwargs = tree_map(unwrap, kwargs)
+            unwrapped_rs = func(*unwrapped_args, **unwrapped_kwargs)
+            rs = tree_map(wrap, unwrapped_rs)
+
+            if is_view_fn(func) and autograd_view_consistency:
+                # Note [Alias Result]
+                # Autograd asserts that for B = A.view_fn(...), B and A's storages
+                # are the same. Here we try to make B alias A to avoid those asserts.
+                # See https://github.com/pytorch/pytorch/issues/65339 for more information
+                # about the issue.
+                with no_dispatch():
+                    # Idea: this is a weird way of getting a storage that aliases the input.
+                    # This is a workaround for #65339.
+                    # 1. under no_dispatch, all of the wrapper tensors look like regular
+                    #    tensors with special storage (the storage is nullptr and
+                    #    advertises CPU/CUDA device.
+                    # 2. we run func, which ends up running the view operation
+                    # 3. All view operations reuse the input's storage and return
+                    #    result Tensor(s) with new sizes/strides/offset that alias
+                    #    the input.
+                    # 4. we set the storage (and sizes/strides/offset) of the wrapper
+                    #    tensor results to be that of the tensors that alias the input
+                    result = func(*args, **kwargs)
+                    if isinstance(result, (tuple, list)):
+                        for a, b in zip(rs, result):
+                            a.set_(b)
+                    else:
+                        rs.set_(result)
+
+            # Some operations are allowed to in-place modify the metadata of the
+            # inputs. The only ones are the "inplace view functions"; when we
+            # run into these, we manually modify the metadata of the input.
+            with no_dispatch():
+                if is_inplace_view_fn(func):
+                    func(*args, **kwargs)
+
+            # For each CompositeCompliantTensor t, we check that t and t.elem
+            # have consistent metadata. If they don't have consistent metadata,
+            # that means the operator did something fishy.
+            check = partial(check_metadata_consistency, CCT=CompositeCompliantTensor)
+            pytree.tree_map_(check, args)
+            pytree.tree_map_(check, kwargs)
+            pytree.tree_map_(check, rs)
+            return rs
+
+    return CompositeCompliantTensor, CompositeCompliantTensorMode()
+
+def is_tensorlist(lst):
+    if not isinstance(lst, list) and not isinstance(lst, tuple):
+        return False
+    if len(lst) == 0:
+        return False
+    all_tensors = all(isinstance(elt, torch.Tensor) for elt in lst)
+    if all_tensors:
+        return True
+    exists_one_tensor = all(isinstance(elt, torch.Tensor) for elt in lst)
+    if exists_one_tensor:
+        raise RuntimeError('This test assumes that PyTorch APIs cannot take '
+                           'mixed lists of Tensor and other things')
+    return False
+
+
+def maybe_map(fn, should_map, arg):
+    return fn(arg) if should_map else arg
+
+
+def wrap(arg, CCT, cct_mode):
+    # CCT: CompositeCompliantTensor class which is generated using generate_cct_and_mode
+    if isinstance(arg, torch.Tensor):
+        return CCT(arg, cct_mode)
+    if is_tensorlist(arg):
+        return [CCT(a, cct_mode) for a in arg]
+    raise RuntimeError("wrap assumes that the input can be wrapped")
+
+
+# Given a list of flat arguments, some of which may be Tensors, return all
+# possible ways some of the arguments could be CompositeCompliantTensors (CCT).
+# For example, given Tensors A, B, C and flat_args = [A, 1, B],
+# We would return the following 4 options:
+# [CCT(A), 1, CCT(B)]
+# [CCT(A), 1, B]
+# [A, 1, CCT(B)]
+# [A, 1, B]
+# NB: Yes, this is exponential. No, we don't care too much because PyTorch ops
+# don't accept that many input Tensors.
+def generate_subclass_choices(flat_args, CCT, cct_mode):
+    # CCT: CompositeCompliantTensor class which is generated using generate_cct_and_mode
+    is_tensor_likes = [isinstance(arg, torch.Tensor) or is_tensorlist(arg) for arg in flat_args]
+    subclass_options = [[False, True] if is_tensor_like else [False] for is_tensor_like in is_tensor_likes]
+
+    for which_args_are_wrapped in itertools.product(*subclass_options):
+
+        result = [maybe_map(partial(wrap, CCT=CCT, cct_mode=cct_mode), should_wrap_arg, arg)
+                  for should_wrap_arg, arg in zip(which_args_are_wrapped, flat_args)]
+        yield result, which_args_are_wrapped
+
+
+# For an operation f(*args, **kwargs), each Tensor argument may either be
+# a regular Tensor or a Tensor Subclass. This iterator iterates through
+# all of those options.
+def generate_subclass_choices_args_kwargs(args, kwargs, CCT, cct_mode):
+    # CCT: CompositeCompliantTensor class which is generated using generate_cct_and_mode
+    flat_kwargs, spec = tree_flatten(kwargs)
+    flat_args_kwargs = list(args) + list(flat_kwargs)
+    for choice, debug_metadata in generate_subclass_choices(flat_args_kwargs, CCT, cct_mode):
+        new_args = choice[:len(args)]
+        new_kwargs = tree_unflatten(choice[len(args):], spec)
+        which_args_are_wrapped = debug_metadata[:len(args)]
+        which_kwargs_are_wrapped = tree_unflatten(debug_metadata[len(args):], spec)
+        yield new_args, new_kwargs, which_args_are_wrapped, which_kwargs_are_wrapped
+
+
+def raise_composite_compliance_error(err, additional_info=''):
+    raise RuntimeError(
+        "Composite compliance check failed with "
+        "the above error.\n"
+        f"{additional_info}"
+        "If you are adding an OpInfo of an "
+        "existing operator, please feel free to skip this test "
+        "because the problem was pre-existing and file an issue. "
+        "Otherwise, if you added a new operator, please read "
+        "through the Composite Compliance section in "
+        "aten/src/ATen/native/README.md for how to resolve this. "
+    ) from err
+
+
+# This test checks ALL possible permutations of calling `op` with arguments
+# that are individually either a regular Tensor or a Tensor subclass.
+#
+# The general strategy is to wrap some Tensor args and kwargs in
+# CompositeCompliantTensor wrappers and call the operation.
+
+# If some composite operation does any non-compliant behavior,
+# CompositeCompliantTensor will raise an error.
+def check_all_permutations(op, args, kwargs, assert_equal_fn):
+    CCT, cct_mode = generate_cct_and_mode()
+    expected = op(*args, **kwargs)
+    for choice in generate_subclass_choices_args_kwargs(args, kwargs, CCT, cct_mode):
+        new_args, new_kwargs, which_args_are_wrapped, which_kwargs_are_wrapped = choice
+
+        try:
+            actual = op(*new_args, **new_kwargs)
+        # NOTE: [What errors are Composite Compliance trying to catch?]
+        #
+        # There's two things we want to catch:
+        # - errors that would raise within the torch_dispatch impl
+        # - data_ptr accesses
+        # The first is easy to filter for (we could make the error a different
+        # error class), the second is always going to be a RuntimeError due to
+        # how it is implemented (if you try to access the data_ptr of thex
+        # wrapper Tensor, it raises you some internal RuntimeError).
+        #
+        # So the most general thing to catch here was RuntimeError. If you
+        # are here and debugging why your test failed, it's plausible that
+        # the operator itself is broken and that there are other tests failing.
+        except RuntimeError as err:
+            raise_composite_compliance_error(
+                err,
+                f"- wrapped_args: {which_args_are_wrapped}\n"
+                f"- wrapped_kwargs: {which_kwargs_are_wrapped}\n"
+            )
+
+        def unwrap(e):
+            return e.elem if isinstance(e, CCT) else e
+
+        assert_equal_fn(tree_map(unwrap, actual), expected)
+
+# Checks via the usage of torch dispatch mode certain anti-patterns that
+# are not composite compliant.
+#
+# In particular, the anti-pattern we are trying to prevent is a user
+# creating an empty tensor and then resize_-ing it. Torch Dispatch Mode helps
+# here because all factory functions will create tensors that are
+# CompositeCompliantTensor.
+#
+# The general strategy is to wrap all Tensor args and kwargs in
+# CompositeCompliantTensor wrappers. If an operator that is
+# Composite does any non-compliant behavior,
+# CompositeCompliantTensor will raise an error.
+def check_with_mode(op, args, kwargs, assert_equal_fn):
+    CCT, cct_mode = generate_cct_and_mode()
+
+    def wrap(e):
+        return CCT(e, cct_mode) if isinstance(e, torch.Tensor) else e
+
+    expected = op(*args, **kwargs)
+
+    args = tree_map(wrap, args)
+    kwargs = tree_map(wrap, kwargs)
+    try:
+        with cct_mode:
+            actual = op(*args, **kwargs)
+    # see NOTE: [What errors are Composite Compliance trying to catch?]
+    except RuntimeError as err:
+        raise_composite_compliance_error(err)
+
+    def unwrap(e):
+        return e.elem if isinstance(e, CCT) else e
+
+    assert_equal_fn(tree_map(unwrap, actual), expected)
+
+def gather_leaf_tensors(args, kwargs):
+    leaf_tensors = []
+    args, args_spec = tree_flatten(args)
+    kwargs, kwargs_spec = tree_flatten(kwargs)
+    args = args + kwargs
+    for arg in args:
+        if not isinstance(arg, torch.Tensor):
+            continue
+        if arg.requires_grad:
+            leaf_tensors.append(arg)
+    return leaf_tensors
+
+
+def compute_expected_grads(op, args, kwargs, output_process_fn_grad=None, gradcheck_wrapper=None):
+    if gradcheck_wrapper is None:
+        results = op(*args, **kwargs)
+    else:
+        results = gradcheck_wrapper(op, *args, **kwargs)
+
+    if output_process_fn_grad is not None:
+        results = output_process_fn_grad(results)
+
+    flat_results = pytree.tree_leaves(results)
+    flat_results = [r for r in flat_results if isinstance(r, torch.Tensor)]
+    flat_diff_results = [r for r in flat_results if r.requires_grad]
+    assert len(flat_diff_results) > 0
+
+    grads = [torch.ones(r.shape, device=r.device, dtype=r.dtype) for r in flat_diff_results]
+    leaf_tensors = gather_leaf_tensors(args, kwargs)
+    assert len(leaf_tensors) > 0
+    return torch.autograd.grad(flat_diff_results, leaf_tensors,
+                               grads, allow_unused=True, retain_graph=True)
+
+
+# Checks if the backward formula is composite compliant by testing
+# all possible permutations of {inputs, grad_outputs} being
+# CompositeCompliantTensor or regular Tensors.
+#
+# NB: it is important that op is accepted as a Callable and not an OpInfo,
+# this means we can apply check_backward_formula to things that aren't OpInfos
+# while debugging.
+def check_backward_formula(op: Callable, args, kwargs,
+                           output_process_fn_grad=None,
+                           gradcheck_wrapper=None, assert_equal_fn=None):
+    CCT, cct_mode = generate_cct_and_mode()
+
+    expected = compute_expected_grads(op, args, kwargs, output_process_fn_grad, gradcheck_wrapper)
+
+    for choice in generate_subclass_choices_args_kwargs(args, kwargs, CCT, cct_mode):
+        new_args, new_kwargs, which_args_are_wrapped, which_kwargs_are_wrapped = choice
+        leaf_tensors = gather_leaf_tensors(new_args, new_kwargs)
+        assert len(leaf_tensors) > 0
+
+        try:
+            if gradcheck_wrapper is None:
+                results = op(*new_args, **new_kwargs)
+            else:
+                results = gradcheck_wrapper(op, *new_args, **new_kwargs)
+            if output_process_fn_grad is not None:
+                results = output_process_fn_grad(results)
+        # see NOTE: [What errors are Composite Compliance trying to catch?]
+        except RuntimeError as err:
+            raise_composite_compliance_error(
+                err,
+                f"- wrapped_args: {which_args_are_wrapped}\n"
+                f"- wrapped_kwargs: {which_kwargs_are_wrapped}\n"
+            )
+
+        flat_results = pytree.tree_leaves(results)
+        flat_results = [r for r in flat_results if isinstance(r, torch.Tensor)]
+        flat_diff_results = [r for r in flat_results if r.requires_grad]
+        assert len(flat_diff_results) > 0
+
+        # NB: ones, not ones_like, so we get a regular Tensor here
+        grads = [torch.ones(r.shape, device=r.device, dtype=r.dtype)
+                 for r in flat_diff_results]
+        for flat_new_grads, which_grad_is_batched in generate_subclass_choices(grads, CCT, cct_mode):
+            try:
+                actual = torch.autograd.grad(flat_diff_results, leaf_tensors, flat_new_grads,
+                                             allow_unused=True, retain_graph=True)
+            # see NOTE: [What errors are Composite Compliance trying to catch?]
+            except RuntimeError as err:
+                raise_composite_compliance_error(
+                    err,
+                    f"- wrapped_args: {which_args_are_wrapped}\n"
+                    f"- wrapped_kwargs: {which_kwargs_are_wrapped}\n"
+                    f"- wrapped_grads: {which_grad_is_batched}\n"
+                )
+
+            def unwrap(e):
+                return e.elem if isinstance(e, CCT) else e
+
+            assert_equal_fn(tuple(map(unwrap, actual)), expected, equal_nan=True)
+
+# Checks if the forward AD formula is composite compliant by testing
+# all possible permutations of {primals, tangents} being
+# CompositeCompliantTensor or regular Tensors.
+#
+# NB: it is important that op is accepted as a Callable and not an OpInfo,
+# this means we can apply check_forward_ad_formula to things that aren't OpInfos
+# while debugging.
+def check_forward_ad_formula(op: Callable, args, kwargs, gradcheck_wrapper=None, assert_equal_fn=None):
+    CCT, cct_mode = generate_cct_and_mode(autograd_view_consistency=False)
+
+    def maybe_tangent(t):
+        assert type(t) is not CCT
+        # Generate `tangent` tensor
+        # if given object is a Tensor and requires grad is set.
+        if isinstance(t, torch.Tensor) and t.requires_grad:
+            return torch.randn_like(t)
+        elif is_tensorlist(t):
+            return [torch.randn_like(e) if e.requires_grad else None for e in t]
+        return None
+
+    tangent_args = tuple(maybe_tangent(arg) for arg in args)
+    flat_kwargs, spec = tree_flatten(kwargs)
+    flat_tangent_kwargs = tuple(maybe_tangent(arg) for arg in flat_kwargs)
+    tangent_kwargs = tree_unflatten(flat_tangent_kwargs, spec)
+
+    with fwAD.dual_level():
+        def maybe_make_dual(dual):
+            # Returns dual tensor if primal is a tensor/tensor subclass
+            # with requires_grad set.
+            primal, tangent = dual
+            if isinstance(primal, torch.Tensor) and primal.requires_grad:
+                return fwAD.make_dual(primal.detach(), tangent)
+            elif is_tensorlist(primal):
+                return tuple(fwAD.make_dual(pri.detach(), tang) if tang is not None else pri
+                             for pri, tang in zip(primal, tangent))
+            return primal
+
+        def compute_expected_grad(args, tangent_args, kwargs, tangent_kwargs):
+            op_args = tuple(map(maybe_make_dual, zip(args, tangent_args)))
+            op_kwargs = {k: maybe_make_dual((v, tangent_kwargs[k])) for k, v in kwargs.items()}
+
+            if gradcheck_wrapper is None:
+                return op(*op_args, **op_kwargs)
+            return gradcheck_wrapper(op, *op_args, **op_kwargs)
+
+        expected = compute_expected_grad(args, tangent_args, kwargs, tangent_kwargs)
+        expected = tree_map(fwAD.unpack_dual, expected)
+        expected_primals = tree_map(lambda x: x.primal, expected)
+        expected_tangents = tree_map(lambda x: x.tangent, expected)
+
+        # Permutations of arg and kwargs in CCT.
+        for choice in generate_subclass_choices_args_kwargs(args, kwargs, CCT, cct_mode):
+            new_args, new_kwargs, which_args_are_wrapped, which_kwargs_are_wrapped = choice
+
+            # Permutations tangent arg and tangent kwargs in CCT.
+            for tang_choice in generate_subclass_choices_args_kwargs(tangent_args, tangent_kwargs, CCT, cct_mode):
+                new_tang_args, new_tang_kwargs, \
+                    which_tang_args_are_wrapped, which_tang_kwargs_are_wrapped = tang_choice
+
+                op_args = tuple(map(maybe_make_dual, zip(new_args, new_tang_args)))
+                op_kwargs = {k: maybe_make_dual((v, new_tang_kwargs[k])) for k, v in new_kwargs.items()}
+
+                try:
+                    if gradcheck_wrapper is None:
+                        actual = op(*op_args, **op_kwargs)
+                    else:
+                        actual = gradcheck_wrapper(op, *op_args, **op_kwargs)
+                # see NOTE: [What errors are Composite Compliance trying to catch?]
+                except RuntimeError as err:
+                    raise_composite_compliance_error(
+                        err,
+                        f"- wrapped_args: {which_args_are_wrapped}\n"
+                        f"- wrapped_kwargs: {which_kwargs_are_wrapped}\n"
+                        f"- wrapped_tangent_args: {which_tang_args_are_wrapped}\n"
+                        f"- wrapped_tangent_kwargs: {which_tang_kwargs_are_wrapped}\n"
+                    )
+
+                def unwrap(e):
+                    return e.elem if isinstance(e, CCT) else e
+
+                actual = tree_map(fwAD.unpack_dual, actual)
+                actual_primals = tree_map(lambda x: unwrap(x.primal), actual)
+                actual_tangents = tree_map(lambda x: unwrap(x.tangent), actual)
+                assert_equal_fn(actual_primals, expected_primals, equal_nan=True)
+                assert_equal_fn(actual_tangents, expected_tangents, equal_nan=True)

venv/lib/python3.10/site-packages/torch/testing/_internal/control_flow_opinfo_db.py

@@ -0,0 +1,77 @@
+# mypy: ignore-errors
+
+import torch
+import functools
+from torch.testing import make_tensor
+from functorch.experimental.control_flow import map
+from torch.testing._internal.opinfo.core import (
+    OpInfo,
+    SampleInput,
+)
+from torch.testing._internal.common_dtype import all_types_and
+
+def sample_inputs_map(opinfo, device, dtype, requires_grad, **kwargs):
+    make_arg = functools.partial(
+        make_tensor, device=device, dtype=dtype, requires_grad=requires_grad)
+    yield SampleInput([make_arg(2, 2, 2, low=0.1, high=2), make_arg(2, 2, 2, low=0.1, high=2)],
+                      args=(make_arg(1, low=0.1, high=2), make_arg(1, low=0.1, high=2)))
+
+def inner_f(x, y0, y1):
+    return [x[0].cos().add_(1.) * y0, (x[1] + y1.sin()).cos_().view(x[1].size())]
+
+def simple_map(xs, y0, y1):
+    def f(x, y0, y1):
+        return inner_f(x, y0, y1)
+    return map(f, xs, y0, y1)
+
+def nested_map(xs, y0, y1):
+    def f1(xx, y0, y1):
+        def f2(x, y0, y1):
+            return inner_f(x, y0, y1)
+        return map(f2, xx, y0, y1)
+    return map(f1, xs, y0, y1)
+
+def triple_nested_map(xs, y0, y1):
+    def f0(xs, y0, y1):
+        def f1(xx, y0, y1):
+            def f2(x, y0, y1):
+                return inner_f(x, y0, y1)
+            return map(f2, xx, y0, y1)
+        return map(f1, xs, y0, y1)
+    return map(f0, xs, y0, y1)
+
+control_flow_opinfo_db = [
+    OpInfo(
+        "MapControlflowOp",
+        op=simple_map,
+        sample_inputs_func=sample_inputs_map,
+        dtypes=all_types_and(torch.bool, torch.half),
+        supports_out=False,
+        check_batched_grad=False,
+        check_batched_gradgrad=False,
+        check_batched_forward_grad=False,
+        check_inplace_batched_forward_grad=False,
+    ),
+    OpInfo(
+        "NestedMapControlflowOp",
+        op=nested_map,
+        sample_inputs_func=sample_inputs_map,
+        dtypes=all_types_and(torch.bool, torch.half),
+        supports_out=False,
+        check_batched_grad=False,
+        check_batched_gradgrad=False,
+        check_batched_forward_grad=False,
+        check_inplace_batched_forward_grad=False,
+    ),
+    OpInfo(
+        "TripleNestedMapControlflowOp",
+        op=triple_nested_map,
+        sample_inputs_func=sample_inputs_map,
+        dtypes=all_types_and(torch.bool, torch.half),
+        supports_out=False,
+        check_batched_grad=False,
+        check_batched_gradgrad=False,
+        check_batched_forward_grad=False,
+        check_inplace_batched_forward_grad=False,
+    )
+]

venv/lib/python3.10/site-packages/torch/testing/_internal/custom_op_db.py

@@ -0,0 +1,456 @@
+# mypy: ignore-errors
+
+import torch
+import functools
+from torch.testing import make_tensor
+from torch.testing._internal.opinfo.core import (
+    OpInfo,
+    SampleInput,
+)
+from torch.testing._internal.common_dtype import all_types_and
+import numpy as np
+from torch.testing._internal.autograd_function_db import (
+    sample_inputs_numpy_cube,
+    sample_inputs_numpy_mul,
+    sample_inputs_numpy_sort,
+    sample_inputs_numpy_take,
+)
+from torch import Tensor
+from torch.types import Number
+from typing import *  # noqa: F403
+import torch._custom_ops as custom_ops
+
+# Note: [custom op db]
+#
+# This is a collection of custom operator test cases written as OpInfos
+# so they can easily be consumed by OpInfo-based tests to check if subsystems
+# support them correctly.
+
+def to_numpy(tensor):
+    return tensor.cpu().numpy()
+
+@custom_ops.custom_op('_torch_testing::numpy_cube')
+def numpy_cube(x: Tensor) -> Tuple[Tensor, Tensor]:
+    raise NotImplementedError()
+
+@custom_ops.impl('_torch_testing::numpy_cube')
+def numpy_cube_impl(x):
+    x_np = to_numpy(x)
+    dx = torch.tensor(3 * x_np ** 2, device=x.device)
+    return torch.tensor(x_np ** 3, device=x.device), dx
+
+@custom_ops.impl_abstract('_torch_testing::numpy_cube')
+def numpy_cube_abstract(x):
+    return x.clone(), x.clone()
+
+@custom_ops.impl_save_for_backward('_torch_testing::numpy_cube')
+def numpy_cube_save_for_backward(inputs, output):
+    return (inputs.x, output[1])
+
+@custom_ops.impl_backward('_torch_testing::numpy_cube')
+def numpy_cube_backward(ctx, saved, grad_out, grad_dx):
+    x, dx = saved
+    grad_x = torch.ops._torch_testing.numpy_mul(grad_out, dx) + 6 * torch.ops._torch_testing.numpy_mul(grad_dx, x)
+    return {'x': grad_x}
+
+@custom_ops.custom_op('_torch_testing::numpy_mul')
+def numpy_mul(x: Tensor, y: Tensor) -> Tensor:
+    raise NotImplementedError()
+
+@custom_ops.impl('_torch_testing::numpy_mul')
+def numpy_mul_impl(x, y):
+    return torch.tensor(to_numpy(x) * to_numpy(y), device=x.device)
+
+@custom_ops.impl_abstract('_torch_testing::numpy_mul')
+def numpy_mul_abstract(x, y):
+    assert x.device == y.device
+    return (x * y).contiguous()
+
+@custom_ops.impl_save_for_backward('_torch_testing::numpy_mul')
+def numpy_mul_save_for_backward(inputs, output):
+    saved = {}
+    saved['x_requires_grad'] = inputs.x.requires_grad
+    saved['y_requires_grad'] = inputs.y.requires_grad
+    # Optimization: only save what is necessary
+    saved['y'] = inputs.y if inputs.x.requires_grad else None
+    saved['x'] = inputs.x if inputs.y.requires_grad else None
+    return saved
+
+@custom_ops.impl_backward('_torch_testing::numpy_mul')
+def numpy_mul_backward(ctx, saved, grad_out):
+    grad_x = grad_out * saved['y'] if saved['x_requires_grad'] else None
+    grad_y = grad_out * saved['x'] if saved['x_requires_grad'] else None
+    return {'y': grad_y, 'x': grad_x}
+
+@custom_ops.custom_op('_torch_testing::numpy_sort')
+def numpy_sort(x: Tensor, dim: int) -> Tuple[Tensor, Tensor, Tensor]:
+    raise NotImplementedError()
+
+@custom_ops.impl("_torch_testing::numpy_sort")
+def numpy_sort_impl(x, dim):
+    device = x.device
+    x = to_numpy(x)
+    ind = np.argsort(x, axis=dim)
+    ind_inv = np.argsort(ind, axis=dim)
+    result = np.take_along_axis(x, ind, axis=dim)
+    return (
+        torch.tensor(result, device=device),
+        torch.tensor(ind, device=device),
+        torch.tensor(ind_inv, device=device),
+    )
+
+@custom_ops.impl_abstract('_torch_testing::numpy_sort')
+def numpy_sort_abstract(x, dim):
+    return torch.empty_like(x), torch.empty_like(x, dtype=torch.long), torch.empty_like(x, dtype=torch.long)
+
+@custom_ops.impl_save_for_backward('_torch_testing::numpy_sort')
+def numpy_sort_save_for_backward(inputs, output):
+    out, ind, ind_inv = output
+    return [inputs.dim, ind, ind_inv]
+
+@custom_ops.impl_backward('_torch_testing::numpy_sort', output_differentiability=[True, False, False])
+def numpy_sort_backward(ctx, saved, grad_out, grad_ind, grad_ind_inv):
+    dim, ind, ind_inv = saved
+    return {'x': torch.ops._torch_testing.numpy_take(grad_out, ind_inv, ind, dim)}
+
+@custom_ops.custom_op('_torch_testing::numpy_take')
+def numpy_take(x: Tensor, ind: Tensor, ind_inv: Tensor, dim: int) -> Tensor:
+    raise NotImplementedError()
+
+@custom_ops.impl("_torch_testing::numpy_take")
+def numpy_take_impl(x, ind, ind_inv, dim):
+    device = x.device
+    x = to_numpy(x)
+    ind = to_numpy(ind)
+    return torch.tensor(np.take_along_axis(x, ind, dim), device=device)
+
+@custom_ops.impl_abstract('_torch_testing::numpy_take')
+def numpy_take_abstract(x, ind, ind_inv, dim):
+    assert x.device == ind.device
+    assert x.device == ind_inv.device
+    assert ind.dtype == torch.long
+    assert ind_inv.dtype == torch.long
+    return torch.empty_like(x)
+
+@custom_ops.impl_save_for_backward('_torch_testing::numpy_take')
+def numpy_take_save_for_backward(inputs, output):
+    return {
+        'dim': inputs.dim,
+        'ind': inputs.ind,
+        'ind_inv': inputs.ind_inv,
+    }
+
+@custom_ops.impl_backward('_torch_testing::numpy_take')
+def numpy_take_backward(ctx, saved, grad_out):
+    return {
+        'x': torch.ops._torch_testing.numpy_take(grad_out, saved['ind_inv'], saved['ind'], saved['dim']),
+        'ind': None,
+        'ind_inv': None,
+    }
+
+@custom_ops.custom_op('_torch_testing::numpy_nonzero')
+def numpy_nonzero(x: Tensor) -> Tensor:
+    raise NotImplementedError()
+
+@custom_ops.impl('_torch_testing::numpy_nonzero')
+def numpy_nonzero_impl(x):
+    x_np = to_numpy(x)
+    res = np.stack(np.nonzero(x_np), axis=1)
+    if res.shape[0] <= 1:
+        raise RuntimeError("not supported")
+    return torch.tensor(res, device=x.device)
+
+@custom_ops.impl_abstract('_torch_testing::numpy_nonzero')
+def numpy_nonzero_abstract(x):
+    ctx = torch._custom_op.impl.get_ctx()
+    i0 = ctx.create_unbacked_symint()
+    shape = [i0, x.dim()]
+    result = x.new_empty(shape, dtype=torch.long)
+    return result
+
+def sample_inputs_numpy_nonzero(opinfo, device, dtype, requires_grad, **kwargs):
+    make_arg = functools.partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad)
+    shape = 10
+    result = make_arg(shape, low=0.9, high=2)
+    mask = make_tensor(shape, low=0, high=2, device=device, dtype=torch.long)
+    with torch.no_grad():
+        result *= mask
+
+    yield SampleInput(result, args=())
+
+@custom_ops.custom_op('_torch_testing::numpy_view_copy')
+def numpy_view_copy(x: Tensor, shape: Sequence[int]) -> Tensor:
+    raise NotImplementedError()
+
+@custom_ops.impl('_torch_testing::numpy_view_copy')
+def numpy_view_copy_impl(x, shape) -> Tensor:
+    return torch.tensor(np.copy(to_numpy(x).reshape(shape)), device=x.device)
+
+@custom_ops.impl_abstract('_torch_testing::numpy_view_copy')
+def numpy_view_copy_abstract(x, shape) -> Tensor:
+    return x.clone().view(shape).clone()
+
+@custom_ops.impl_save_for_backward('_torch_testing::numpy_view_copy')
+def numpy_view_copy_save_for_backward(inputs, output) -> Tensor:
+    return inputs.x.shape
+
+@custom_ops.impl_backward('_torch_testing::numpy_view_copy')
+def numpy_view_copy_backward(ctx, x_shape, grad_out) -> Tensor:
+    return {'x': torch.ops._torch_testing.numpy_view_copy(grad_out, x_shape)}
+
+def sample_inputs_numpy_view_copy(opinfo, device, dtype, requires_grad, **kwargs):
+    make_arg = functools.partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad)
+    result = make_arg(2, 3, 4, low=0.9, high=2)
+    yield SampleInput(result, args=([2, 12],))
+
+@custom_ops.custom_op('_torch_testing::numpy_cat')
+def numpy_cat(xs: Sequence[Tensor], dim: int) -> Tensor:
+    raise NotImplementedError()
+
+@custom_ops.impl('_torch_testing::numpy_cat')
+def numpy_cat_impl(xs, dim):
+    assert len(xs) > 0
+    assert all(x.device == xs[0].device for x in xs)
+    assert all(x.dtype == xs[0].dtype for x in xs)
+    np_xs = [to_numpy(x) for x in xs]
+    np_out = np.concatenate(np_xs, axis=dim)
+    return torch.tensor(np_out, device=xs[0].device)
+
+@custom_ops.impl_abstract('_torch_testing::numpy_cat')
+def numpy_cat_abstract(xs, dim):
+    assert len(xs) > 0
+    assert all(x.device == xs[0].device for x in xs)
+    assert all(x.dtype == xs[0].dtype for x in xs)
+    return torch.cat(xs, dim=dim)
+
+@custom_ops.impl_save_for_backward('_torch_testing::numpy_cat')
+def numpy_cat_save_for_backward(inputs, output):
+    dim_sizes = [x.shape[inputs.dim] for x in inputs.xs]
+    return dim_sizes, inputs.dim
+
+@custom_ops.impl_backward('_torch_testing::numpy_cat')
+def numpy_cat_backward(ctx, saved, grad_out):
+    dim_sizes, dim = saved
+    splits = list(np.cumsum(dim_sizes)[:-1])
+    grad_xs = torch.ops._torch_testing.numpy_split_copy(grad_out, splits, dim)
+    return {'xs': grad_xs}
+
+def sample_inputs_numpy_cat(opinfo, device, dtype, requires_grad, **kwargs):
+    make_arg = functools.partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad)
+    r0 = make_arg(2, 3, 4, low=0.9, high=2)
+    r1 = make_arg(4, 3, 4, low=0.9, high=2)
+    r2 = make_arg(5, 3, 4, low=0.9, high=2)
+    yield SampleInput([r0, r1, r2], args=(0,))
+
+@custom_ops.custom_op('_torch_testing::numpy_split_copy')
+def numpy_split_copy(x: Tensor, sections: Sequence[int], dim: int) -> List[Tensor]:
+    raise NotImplementedError()
+
+@custom_ops.impl('_torch_testing::numpy_split_copy')
+def numpy_split_copy_impl(x, splits, dim):
+    x_np = to_numpy(x)
+    arrs = np.split(x_np, splits, axis=dim)
+    return [torch.tensor(arr, device=x.device, dtype=x.dtype) for arr in arrs]
+
+@custom_ops.impl_abstract('_torch_testing::numpy_split_copy')
+def numpy_split_copy_abstract(x, splits, dim):
+    return [xi.clone() for xi in torch.tensor_split(x, splits, dim)]
+
+@custom_ops.impl_save_for_backward('_torch_testing::numpy_split_copy')
+def numpy_split_copy_save_for_backward(inputs, output):
+    return inputs.dim
+
+@custom_ops.impl_backward('_torch_testing::numpy_split_copy')
+def numpy_split_copy_backward(ctx, saved, grad_out):
+    dim = saved
+    return {'x': torch.ops._torch_testing.numpy_cat(grad_out, dim=dim)}
+
+def sample_inputs_numpy_split_copy(opinfo, device, dtype, requires_grad, **kwargs):
+    make_arg = functools.partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad)
+    x = make_arg(2, 9, low=0.9, high=2)
+    yield SampleInput(x, args=([1, 3, 6], 1))
+
+@custom_ops.custom_op('_torch_testing::numpy_split_copy_with_int')
+def numpy_split_copy_with_int(x: Tensor, sections: Sequence[int], dim: int) -> Tuple[List[Tensor], int]:
+    raise NotImplementedError()
+
+@custom_ops.impl('_torch_testing::numpy_split_copy_with_int')
+def numpy_split_copy_with_int_impl(x, splits, dim):
+    x_np = to_numpy(x)
+    arrs = np.split(x_np, splits, axis=dim)
+    return [torch.tensor(arr, device=x.device, dtype=x.dtype) for arr in arrs], len(splits)
+
+@custom_ops.impl_abstract('_torch_testing::numpy_split_copy_with_int')
+def numpy_split_copy_with_int_abstract(x, splits, dim):
+    return [xi.clone() for xi in torch.tensor_split(x, splits, dim)], len(splits)
+
+@custom_ops.impl_save_for_backward(
+    '_torch_testing::numpy_split_copy_with_int')
+def numpy_split_copy_with_int_save_for_backward(inputs, output):
+    return inputs.dim
+
+@custom_ops.impl_backward(
+    '_torch_testing::numpy_split_copy_with_int',
+    output_differentiability=[True, False])
+def numpy_split_copy_with_int_backward(ctx, saved, grad_out, _):
+    dim = saved
+    return {'x': torch.ops._torch_testing.numpy_cat(grad_out, dim=dim)}
+
+@custom_ops.custom_op('_torch_testing::numpy_nms')
+def numpy_nms(boxes: Tensor, scores: Tensor, iou_threshold: Number) -> Tensor:
+    raise NotImplementedError()
+
+@custom_ops.impl('_torch_testing::numpy_nms')
+def numpy_nms_impl(boxes, scores, iou_threshold):
+    # Adapted from Ross Girshick's fast-rcnn implementation at
+    # https://github.com/rbgirshick/fast-rcnn/blob/master/lib/utils/nms.py
+    assert boxes.device == scores.device
+    device = boxes.device
+
+    boxes = to_numpy(boxes)
+    scores = to_numpy(scores)
+
+    N = boxes.shape[0]
+    assert boxes.shape == (N, 4)
+    assert scores.shape == (N,)
+
+    x1 = boxes[:, 0]
+    y1 = boxes[:, 1]
+    x2 = boxes[:, 2]
+    y2 = boxes[:, 3]
+
+    areas = (x2 - x1 + 1) * (y2 - y1 + 1)
+    order = scores.argsort()[::-1]
+
+    keep = []
+    while order.size > 0:
+        i = order[0]
+        keep.append(i)
+        xx1 = np.maximum(x1[i], x1[order[1:]])
+        yy1 = np.maximum(y1[i], y1[order[1:]])
+        xx2 = np.minimum(x2[i], x2[order[1:]])
+        yy2 = np.minimum(y2[i], y2[order[1:]])
+
+        w = np.maximum(0.0, xx2 - xx1 + 1)
+        h = np.maximum(0.0, yy2 - yy1 + 1)
+        inter = w * h
+        ovr = inter / (areas[i] + areas[order[1:]] - inter)
+
+        inds = np.where(ovr <= iou_threshold)[0]
+        order = order[inds + 1]
+
+    result = torch.tensor(np.stack(keep), device=device)
+    # Needed for data-dependent condition :(
+    assert result.size(0) >= 2
+    return result
+
+@custom_ops.impl_abstract('_torch_testing::numpy_nms')
+def numpy_nms_abstract(boxes, scores, iou_threshold):
+    assert boxes.device == scores.device
+    N = boxes.shape[0]
+    assert boxes.shape == (N, 4)
+    assert scores.shape == (N,)
+
+    ctx = torch._custom_op.impl.get_ctx()
+    i0 = ctx.create_unbacked_symint()
+    result = boxes.new_empty([i0], dtype=torch.int64)
+    return result
+
+def sample_inputs_numpy_nms(opinfo, device, dtype, requires_grad, **kwargs):
+    make_arg = functools.partial(make_tensor, device=device, dtype=dtype)
+    N = 64
+    xs = make_arg([N], low=0, high=28)
+    dx = make_arg([N], low=0, high=4)
+    ys = make_arg([N], low=0, high=28)
+    dy = make_arg([N], low=0, high=4)
+    boxes = torch.stack([xs, ys, xs + dx, ys + dy], dim=1).requires_grad_(requires_grad)
+    scores = make_arg([N], low=0, high=1, requires_grad=requires_grad)
+    iou_threshold = make_arg([], low=0, high=1).item()
+
+    yield SampleInput(boxes, args=(scores, iou_threshold))
+
+custom_op_db = [
+    OpInfo(
+        'NumpyCubeCustomOp',
+        op=torch.ops._torch_testing.numpy_cube,
+        sample_inputs_func=sample_inputs_numpy_cube,
+        dtypes=all_types_and(torch.bool, torch.half),
+        supports_out=False,
+    ),
+    OpInfo(
+        'NumpyMulCustomOp',
+        op=torch.ops._torch_testing.numpy_mul,
+        sample_inputs_func=sample_inputs_numpy_mul,
+        dtypes=all_types_and(torch.bool, torch.half),
+        supports_out=False,
+    ),
+    OpInfo(
+        'NumpySortCustomOp',
+        op=torch.ops._torch_testing.numpy_sort,
+        sample_inputs_func=sample_inputs_numpy_sort,
+        dtypes=all_types_and(torch.bool, torch.half),
+        supports_out=False,
+    ),
+    OpInfo(
+        'NumpyTakeCustomOp',
+        op=torch.ops._torch_testing.numpy_take,
+        sample_inputs_func=sample_inputs_numpy_take,
+        dtypes=all_types_and(torch.bool, torch.half),
+        supports_out=False,
+    ),
+    OpInfo(
+        'NumpyNonzeroCustomOp',
+        op=torch.ops._torch_testing.numpy_nonzero,
+        sample_inputs_func=sample_inputs_numpy_nonzero,
+        dtypes=all_types_and(torch.bool, torch.half),
+        supports_autograd=False,
+        supports_out=False,
+    ),
+    OpInfo(
+        'NumpyNMSCustomOp',
+        op=torch.ops._torch_testing.numpy_nms,
+        sample_inputs_func=sample_inputs_numpy_nms,
+        dtypes=all_types_and(torch.bool, torch.half),
+        supports_autograd=False,
+        supports_out=False,
+    ),
+    OpInfo(
+        'NumpyViewCopyCustomOp',
+        op=torch.ops._torch_testing.numpy_view_copy,
+        sample_inputs_func=sample_inputs_numpy_view_copy,
+        dtypes=all_types_and(torch.bool, torch.half),
+        supports_autograd=True,
+        supports_out=False,
+    ),
+    OpInfo(
+        'NumpyCatCustomOp',
+        op=torch.ops._torch_testing.numpy_cat,
+        sample_inputs_func=sample_inputs_numpy_cat,
+        dtypes=all_types_and(torch.bool, torch.half),
+        supports_autograd=True,
+        check_batched_grad=False,
+        check_batched_gradgrad=False,
+        supports_out=False,
+    ),
+    OpInfo(
+        'NumpySplitCopyCustomOp',
+        op=torch.ops._torch_testing.numpy_split_copy,
+        sample_inputs_func=sample_inputs_numpy_split_copy,
+        dtypes=all_types_and(torch.bool, torch.half),
+        supports_autograd=True,
+        check_batched_grad=False,
+        check_batched_gradgrad=False,
+        supports_out=False,
+    ),
+    OpInfo(
+        'NumpySplitCopyWithIntCustomOp',
+        op=torch.ops._torch_testing.numpy_split_copy_with_int,
+        sample_inputs_func=sample_inputs_numpy_split_copy,
+        dtypes=all_types_and(torch.bool, torch.half),
+        gradcheck_wrapper=lambda op, *args, **kwargs: op(*args, **kwargs)[0],
+        supports_autograd=True,
+        check_batched_grad=False,
+        check_batched_gradgrad=False,
+        supports_out=False,
+    ),
+]

venv/lib/python3.10/site-packages/torch/testing/_internal/dist_utils.py

@@ -0,0 +1,206 @@
+# mypy: ignore-errors
+
+import re
+import sys
+import time
+from functools import partial, wraps
+from typing import Tuple
+
+import torch.distributed as dist
+import torch.distributed.rpc as rpc
+from torch.distributed.rpc import _rref_context_get_debug_info
+from torch.testing._internal.common_utils import FILE_SCHEMA, TEST_WITH_TSAN
+
+
+if not dist.is_available():
+    print("c10d not available, skipping tests", file=sys.stderr)
+    sys.exit(0)
+
+
+INIT_METHOD_TEMPLATE = FILE_SCHEMA + "{file_name}"
+
+def dist_init(
+    old_test_method=None,
+    setup_rpc: bool = True,
+    clean_shutdown: bool = True,
+    faulty_messages=None,
+    messages_to_delay=None,
+):
+    """
+    We use this decorator for setting up and tearing down state since
+    MultiProcessTestCase runs each `test*` method in a separate process and
+    each process just runs the `test*` method without actually calling
+    'setUp' and 'tearDown' methods of unittest.
+
+    Note: pass the string representation of MessageTypes that should be used
+    with the faulty agent's send function. By default, all retriable messages
+    ("RREF_FORK_REQUEST", "RREF_CHILD_ACCEPT", "RREF_USER_DELETE",
+    "CLEANUP_AUTOGRAD_CONTEXT_REQ") will use the faulty send (this default is
+    set from faulty_rpc_agent_test_fixture.py).
+    """
+    # If we use dist_init without arguments (ex: @dist_init), old_test_method is
+    # appropriately set and we return the wrapper appropriately. On the other
+    # hand if dist_init has arguments (ex: @dist_init(clean_shutdown=False)),
+    # old_test_method is None and we return a functools.partial which is the real
+    # decorator that is used and as a result we recursively call dist_init with
+    # old_test_method and the rest of the arguments appropriately set.
+    if old_test_method is None:
+        return partial(
+            dist_init,
+            setup_rpc=setup_rpc,
+            clean_shutdown=clean_shutdown,
+            faulty_messages=faulty_messages,
+            messages_to_delay=messages_to_delay,
+        )
+
+    @wraps(old_test_method)
+    def new_test_method(self, *arg, **kwargs):
+        # Setting _ignore_rref_leak to make sure OwnerRRefs are properly deleted
+        # in tests.
+        import torch.distributed.rpc.api as api
+
+        api._ignore_rref_leak = False
+        self.worker_id = self.rank
+        self.setup_fault_injection(faulty_messages, messages_to_delay)
+
+        rpc_backend_options = self.rpc_backend_options
+        if setup_rpc:
+            if TEST_WITH_TSAN:
+                # TSAN runs much slower.
+                rpc_backend_options.rpc_timeout = rpc.constants.DEFAULT_RPC_TIMEOUT_SEC * 5
+                rpc.constants.DEFAULT_SHUTDOWN_TIMEOUT = 60
+
+            rpc.init_rpc(
+                name="worker%d" % self.rank,
+                backend=self.rpc_backend,
+                rank=self.rank,
+                world_size=self.world_size,
+                rpc_backend_options=rpc_backend_options,
+            )
+
+        return_value = old_test_method(self, *arg, **kwargs)
+
+        if setup_rpc:
+            rpc.shutdown(graceful=clean_shutdown)
+
+        return return_value
+
+    return new_test_method
+
+
+def noop() -> None:
+    pass
+
+
+def wait_until_node_failure(rank: int, expected_error_regex: str = ".*") -> str:
+    """
+    Loops until an RPC to the given rank fails. This is used to
+    indicate that the node has failed in unit tests.
+    Args:
+    rank (int): Rank of the node expected to fail
+    expected_error_regex (optional, str): Regex of exception message expected. Useful to ensure a specific failure
+    occurs, not just any.
+    """
+    while True:
+        try:
+            rpc.rpc_sync(f"worker{rank}", noop, args=())
+            time.sleep(0.1)
+        except Exception as e:
+            if re.search(pattern=expected_error_regex, string=str(e)):
+                return str(e)
+
+
+def wait_until_pending_futures_and_users_flushed(timeout: int = 20) -> None:
+    """
+    The RRef protocol holds forkIds of rrefs in a map until those forks are
+    confirmed by the owner. The message confirming the fork may arrive after
+    our tests check whether this map is empty, which leads to failures and
+    flaky tests. to_here also does not guarantee that we have finished
+    processind the owner's confirmation message for the RRef. This function
+    loops until the map is empty, which means the messages have been received
+    as processed. Call this function before asserting the map returned by
+    _get_debug_info is empty.
+    """
+    start = time.time()
+    while True:
+        debug_info = _rref_context_get_debug_info()
+        num_pending_futures = int(debug_info["num_pending_futures"])
+        num_pending_users = int(debug_info["num_pending_users"])
+        if num_pending_futures == 0 and num_pending_users == 0:
+            break
+        time.sleep(0.1)
+        if time.time() - start > timeout:
+            raise ValueError(
+                "Timed out waiting to flush pending futures and users, had {} pending futures and {} pending users".format(
+                    num_pending_futures, num_pending_users
+                )
+            )
+
+
+def get_num_owners_and_forks() -> Tuple[str, str]:
+    """
+    Retrieves number of OwnerRRefs and forks on this node from
+    _rref_context_get_debug_info.
+    """
+    rref_dbg_info = _rref_context_get_debug_info()
+    num_owners = rref_dbg_info["num_owner_rrefs"]
+    num_forks = rref_dbg_info["num_forks"]
+    return num_owners, num_forks
+
+
+def wait_until_owners_and_forks_on_rank(
+    num_owners: int, num_forks: int, rank: int, timeout: int = 20
+) -> None:
+    """
+    Waits until timeout for num_forks and num_owners to exist on the rank. Used
+    to ensure proper deletion of RRefs in tests.
+    """
+    start = time.time()
+    while True:
+        num_owners_on_rank, num_forks_on_rank = rpc.rpc_sync(
+            worker_name(rank), get_num_owners_and_forks, args=(), timeout=5
+        )
+        num_owners_on_rank = int(num_owners_on_rank)
+        num_forks_on_rank = int(num_forks_on_rank)
+        if num_owners_on_rank == num_owners and num_forks_on_rank == num_forks:
+            return
+        time.sleep(1)
+        if time.time() - start > timeout:
+            raise ValueError(
+                "Timed out waiting {} sec for {} owners and {} forks on rank, had {} owners and {} forks".format(
+                    timeout,
+                    num_owners,
+                    num_forks,
+                    num_owners_on_rank,
+                    num_forks_on_rank,
+                )
+            )
+
+
+def initialize_pg(init_method, rank: int, world_size: int) -> None:
+    # This is for tests using `dist.barrier`.
+    if not dist.is_initialized():
+        dist.init_process_group(
+            backend="gloo",
+            init_method=init_method,
+            rank=rank,
+            world_size=world_size,
+        )
+
+
+def worker_name(rank: int) -> str:
+    return f"worker{rank}"
+
+
+def get_function_event(function_events, partial_event_name):
+    """
+    Returns the first event that matches partial_event_name in the provided
+    function_events. These function_events should be the output of
+    torch.autograd.profiler.function_events().
+
+    Args:
+    function_events: function_events returned by the profiler.
+    event_name (str): partial key that the event was profiled with.
+    """
+    event = [event for event in function_events if partial_event_name in event.name][0]  # noqa: RUF015
+    return event

venv/lib/python3.10/site-packages/torch/testing/_internal/distributed/_shard/__init__.py

@@ -0,0 +1 @@
+# mypy: ignore-errors

venv/lib/python3.10/site-packages/torch/testing/_internal/distributed/_shard/sharded_tensor/_test_st_common.py

@@ -0,0 +1,66 @@
+# mypy: ignore-errors
+
+import copy
+import random
+import torch
+from torch.distributed._shard import sharded_tensor
+
+from torch.distributed._shard.sharding_spec import (
+    ChunkShardingSpec,
+)
+
+PLACEMENTS = [
+    "rank:0/cuda:0",
+    "rank:1/cuda:1",
+    "rank:2/cuda:2",
+    "rank:3/cuda:3",
+]
+
+DEFAULT_GPU_NUM = 4
+
+
+def _chunk_sharding_specs_list_for_test(sharding_dims, seed=0):
+    spec_list = []
+    for i in range(len(sharding_dims)):
+        random.Random(seed + i).shuffle(PLACEMENTS)
+        spec_list.append(
+            ChunkShardingSpec(
+                dim=sharding_dims[i],
+                placements=copy.deepcopy(PLACEMENTS),
+            )
+        )
+    return spec_list
+
+class MyShardedModel2(torch.nn.Module):
+    def __init__(
+        self,
+        spec=None,
+        group=None,
+        init_rrefs=True
+    ) -> None:
+        super().__init__()
+        if spec is not None:
+            self.sharded_tensor2 = sharded_tensor.rand(
+                spec, 10, 20, process_group=group, init_rrefs=init_rrefs
+            )
+        else:
+            self.sharded_tensor2 = None
+        self.random_tensor2 = torch.nn.Parameter(torch.rand(2, 2))
+
+
+class MyShardedModel1(torch.nn.Module):
+    def __init__(
+        self,
+        spec=None,
+        group=None,
+        init_rrefs=True
+    ) -> None:
+        super().__init__()
+        if spec is not None:
+            self.sharded_tensor1 = sharded_tensor.rand(
+                spec, 10, 20, process_group=group, init_rrefs=init_rrefs
+            )
+        else:
+            self.sharded_tensor1 = None
+        self.random_tensor1 = torch.nn.Parameter(torch.rand(2, 2))
+        self.submodule = MyShardedModel2(spec, group, init_rrefs)

venv/lib/python3.10/site-packages/torch/testing/_internal/distributed/_shard/test_common.py

@@ -0,0 +1,42 @@
+# mypy: ignore-errors
+
+import torch
+import torch.nn as nn
+
+from torch.distributed._shard.sharded_tensor import ShardedTensor
+
+
+class SimpleMegatronLM(nn.Module):
+    def __init__(self, linear_size, rank=None, dtype=torch.float32):
+        super().__init__()
+        self.fc1 = nn.Linear(*linear_size[0], dtype=dtype)
+        self.gelu = nn.GELU()
+        self.fc2 = nn.Linear(*linear_size[1], dtype=dtype)
+        if rank is not None:
+            self.fc1.cuda(rank)
+            self.fc2.cuda(rank)
+
+    def forward(self, inp):
+        return self.fc2(self.gelu(self.fc1(inp)))
+
+    def get_weights(self):
+        if isinstance(self.fc1.weight, ShardedTensor):
+            weight1 = self.fc1.weight.local_tensor()
+        else:
+            weight1 = self.fc1.weight
+
+        if isinstance(self.fc2.weight, ShardedTensor):
+            weight2 = self.fc2.weight.local_tensor()
+        else:
+            weight2 = self.fc2.weight
+
+        return (weight1, weight2)
+
+    def get_biases(self):
+        return (self.fc1.bias, self.fc2.bias)
+
+    def get_weight_grads(self):
+        return (self.fc1.weight.grad, self.fc2.weight.grad)
+
+    def get_bias_grads(self):
+        return (self.fc1.bias.grad, self.fc2.bias.grad)

venv/lib/python3.10/site-packages/torch/testing/_internal/distributed/checkpoint_utils.py

@@ -0,0 +1,51 @@
+# mypy: ignore-errors
+
+# Copyright (c) Meta Platforms, Inc. and affiliates
+
+import os
+import shutil
+import tempfile
+from functools import wraps
+from typing import Any, Callable, Dict, Optional, Tuple
+
+import torch.distributed as dist
+
+
+def with_temp_dir(
+    func: Optional[Callable] = None,
+) -> Optional[Callable]:
+    """
+    Wrapper to initialize temp directory for distributed checkpoint.
+    """
+    assert func is not None
+
+    @wraps(func)
+    def wrapper(self, *args: Tuple[object], **kwargs: Dict[str, Any]) -> None:
+        if dist.is_initialized():
+            # Only create temp_dir when rank is 0
+            if dist.get_rank() == 0:
+                temp_dir = tempfile.mkdtemp()
+                print(f"Using temp directory: {temp_dir}")
+            else:
+                temp_dir = ""
+            object_list = [temp_dir]
+
+            # Broadcast temp_dir to all the other ranks
+            os.sync()
+            dist.broadcast_object_list(object_list)
+            self.temp_dir = object_list[0]
+            os.sync()
+        else:
+            temp_dir = tempfile.mkdtemp()
+            print(f"No process group initialized, using temp directory: {temp_dir}")
+            self.temp_dir = temp_dir
+
+        try:
+            func(self, *args, **kwargs)
+        finally:
+            if dist.is_initialized() and dist.get_rank() == 0:
+                shutil.rmtree(self.temp_dir, ignore_errors=True)
+            else:
+                shutil.rmtree(self.temp_dir, ignore_errors=True)
+
+    return wrapper

venv/lib/python3.10/site-packages/torch/testing/_internal/distributed/common_state_dict.py

@@ -0,0 +1,113 @@
+# mypy: ignore-errors
+
+# Owner(s): ["oncall: distributed"]
+
+import copy
+from itertools import chain
+from typing import Any, Dict
+
+import torch
+import torch.nn as nn
+
+from torch.distributed._sharded_tensor import ShardedTensor
+from torch.distributed._state_dict_utils import _gather_state_dict
+from torch.distributed._tensor import DTensor
+from torch.distributed.checkpoint.state_dict import (
+    PG,
+    set_state_dict,
+    STATE,
+    StateDictOptions,
+)
+
+
+class VerifyStateDictMixin:
+    def _compare_tensor(self, orig_tensor, dist_tensor):
+        if isinstance(dist_tensor, (DTensor, ShardedTensor)):
+            dist_tensor = _gather_state_dict({"mykey": dist_tensor}).pop("mykey")
+        self.assertTrue(isinstance(dist_tensor, torch.Tensor))
+        self.assertTrue(torch.allclose(orig_tensor, dist_tensor))
+
+    def _verify_msd(
+        self,
+        msd: Dict[str, Any],
+        dist_msd: Dict[str, Any],
+        options: StateDictOptions = StateDictOptions(),
+    ) -> None:
+        if not options.ignore_frozen_params:
+            self.assertEqual(len(msd), len(dist_msd))
+        for fqn, param in msd.items():
+            dist_param = dist_msd.get(fqn, None)
+            if not options.ignore_frozen_params:
+                self.assertIsNotNone(dist_param, f"{fqn=}")
+                self._compare_tensor(param, dist_param)
+            elif dist_param is None:
+                self.assertFalse(param.requires_grad, f"{fqn=}")
+
+    def _verify_osd(
+        self,
+        model: nn.Module,
+        optim: torch.optim.Optimizer,
+        osd: Dict[str, Any],
+        dist_osd: Dict[str, Any],
+    ) -> None:
+        params = list(chain.from_iterable(g["params"] for g in optim.param_groups))
+        param_pid_mapping = dict(zip(params, range(len(params))))
+        fqn_pid_mapping = {}
+        for fqn, param in model.named_parameters():
+            pid = param_pid_mapping[param]
+            fqn_pid_mapping[fqn] = pid
+            fqn_pid_mapping[pid] = fqn
+        # Check optimizer_state_dict state
+
+        self.assertEqual(len(osd[STATE]), len(dist_osd[STATE]))
+        for pid, states in osd[STATE].items():
+            fqn = fqn_pid_mapping[pid]
+            dist_states = dist_osd[STATE].get(fqn, None)
+            self.assertIsNotNone(dist_states, fqn)
+            self.assertEqual(len(states), len(dist_states))
+            for key, state in states.items():
+                dist_state = states.get(key, None)
+                self.assertIsNotNone(dist_state)
+                self._compare_tensor(state, dist_state)
+
+        # Check optimizer_state_dict param_group
+        old_dist_osd_pg = dist_osd[PG]
+        if len(osd[PG]) != len(dist_osd[PG]):
+            self.assertTrue(len(dist_osd[PG]) > len(osd[PG]))
+            new_pg = copy.deepcopy(dist_osd[PG][0])
+            new_pg["params"] = []
+            for dist_group in dist_osd[PG]:
+                new_pg["params"].extend(dist_group["params"])
+            dist_osd[PG] = [new_pg]
+
+        self.assertEqual(len(osd[PG]), len(dist_osd[PG]))
+        for group, dist_group in zip(osd[PG], dist_osd[PG]):
+            self.assertEqual(len(group), len(dist_group))
+            for key, value in group.items():
+                # Below doesn't work because param_groups can have None
+                # values.
+                # dist_value = dist_group.get(key, None)
+                # self.assertIsNotNone(dist_value, (dist_group, group))
+                dist_value = dist_group[key]
+                if key == "params":
+                    fqns = [fqn_pid_mapping[pid] for pid in value]
+                    self.assertEqual(sorted(fqns), sorted(dist_value))
+                else:
+                    self.assertEqual(value, dist_value)
+        dist_osd[PG] = old_dist_osd_pg
+
+    def _verify_osd_by_load(
+        self,
+        model: nn.Module,
+        optim: torch.optim.Optimizer,
+        new_optim: torch.optim.Optimizer,
+        dist_osd: Dict[str, Any],
+    ) -> None:
+        new_dist_osd = _gather_state_dict(dist_osd)
+        set_state_dict(
+            model,
+            optimizers=new_optim,
+            model_state_dict={},
+            optim_state_dict=new_dist_osd,
+        )
+        self.assertEqual(optim.state_dict(), new_optim.state_dict())

venv/lib/python3.10/site-packages/torch/testing/_internal/distributed/ddp_under_dist_autograd_test.py

@@ -0,0 +1,733 @@
+# mypy: ignore-errors
+
+import contextlib
+import enum
+import logging
+import os
+import threading
+from typing import NamedTuple
+
+import torch
+import torch.distributed as dist
+import torch.distributed.autograd as dist_autograd
+import torch.nn as nn
+from torch.distributed import rpc
+from torch.distributed.nn import RemoteModule
+from torch.nn.parallel import DistributedDataParallel
+from torch.testing._internal.common_distributed import (
+    requires_gloo,
+    requires_nccl,
+    skip_if_lt_x_gpu,
+    skip_if_rocm,
+)
+from torch.testing._internal.dist_utils import INIT_METHOD_TEMPLATE, dist_init
+from torch.testing._internal.distributed.rpc.rpc_agent_test_fixture import (
+    RpcAgentTestFixture,
+)
+
+
+NUM_EM_ROW = 2
+D_SPARSE = 3
+D_DENSE = 2
+D_HID = 3
+D_OUT = 1
+NUM_TRAINERS = 4
+# Trainers + the master + the remote worker
+WORLD_SIZE = NUM_TRAINERS + 2
+TRAINER_RANKS = list(range(NUM_TRAINERS))
+REMOTE_WORKER_RANK = TRAINER_RANKS[-1] + 1
+MASTER_RANK = REMOTE_WORKER_RANK + 1
+
+
+class DdpMode(enum.Enum):
+    # Don't apply DDP
+    NONE = enum.auto()
+    # Apply DDP to the top level nn.Module
+    OUTSIDE = enum.auto()
+    # Embed DDP inside the top level nn.Module
+    INSIDE = enum.auto()
+
+
+def init_logger():
+    logger = logging.getLogger(__name__)
+    level = logging.DEBUG if "debug" in os.environ else logging.INFO
+    logger.setLevel(level)
+    console = logging.StreamHandler()
+    formatter = logging.Formatter(
+        "%(asctime)s %(filename)s:%(lineno)s %(levelname)s p:%(processName)s t:%(threadName)s: %(message)s"
+    )
+    console.setFormatter(formatter)
+    console.setLevel(level)
+    # add the handlers to the logger
+    logger.addHandler(console)
+    logger.propagate = False
+    return logger
+
+
+gLogger = init_logger()
+
+
+class FeatureSet(NamedTuple):
+    """ A feature set has 2 types of features"""
+
+    dense_features: torch.Tensor
+    sparse_features: torch.LongTensor
+    values: torch.Tensor
+
+
+def _call_method(method, rref, *args, **kwargs):
+    return method(rref.local_value(), *args, **kwargs)
+
+
+def _remote_method(method, rref, *args, **kwargs):
+    args_tup = tuple([method, rref] + list(args))
+    return rpc.rpc_sync(rref.owner(), _call_method, args=args_tup, kwargs=kwargs)
+
+
+def _remote_method_async(method, rref, *args, **kwargs):
+    args_tup = tuple([method, rref] + list(args))
+    return rpc.rpc_async(rref.owner(), _call_method, args=args_tup, kwargs=kwargs)
+
+
+class RemoteEM(nn.Module):
+    def __init__(self, num_embeddings: int, embedding_dim: int):
+        gLogger.info("Initing RemoteEM with %s %s", num_embeddings, embedding_dim)
+        super().__init__()
+        init_em = [0.5] * embedding_dim
+        self.em = nn.EmbeddingBag(
+            num_embeddings,
+            embedding_dim,
+            _weight=torch.tensor([init_em] * num_embeddings),
+        )
+
+    def forward(self, input: torch.Tensor):
+        gLogger.debug("Running RemoteEM.forward() on: %s", input)
+        return self.em(input, offsets=torch.LongTensor(range(input.shape[0])))
+
+
+# Return a linear module with predefined parameters.
+def getLinear(d_in, d_out):
+    l = nn.Linear(d_in, d_out, bias=False)
+    w = torch.ones((d_out, d_in))
+    w[0][0] = -1
+    w.requires_grad_()
+    l.weight.data = w
+    return l
+
+
+class RemoteNet(nn.Module):
+    def __init__(self, d_in: int, d_out: int):
+        gLogger.info("Initing RemoteNet with %s %s", d_in, d_out)
+        super().__init__()
+        self.fc = getLinear(d_in, d_out)
+        self.relu = nn.ReLU()
+
+    def forward(self, input: torch.Tensor):
+        gLogger.debug("Running RemoteNet.forward() on: %s", input)
+        return self.relu(self.fc(input))
+
+
+class HybridModel(nn.Module):
+    def __init__(
+        self,
+        remote_em_rref: rpc.RRef,
+        remote_net_rref: rpc.RRef,
+        process_group_for_ddp: dist.ProcessGroup = None,
+    ):
+        super().__init__()
+        self.remote_em_rref = remote_em_rref
+        self.remote_net_rref = remote_net_rref
+        self.fc1 = getLinear(D_DENSE, D_DENSE)
+        self.fc2 = getLinear(D_HID, D_OUT)
+
+        self.non_ddp_params = tuple(self.fc1.parameters()) + tuple(
+            self.fc2.parameters()
+        )
+        self.ddp_params = ()
+
+        if process_group_for_ddp is not None:
+            self.non_ddp_params, self.ddp_params = (
+                tuple(self.fc1.parameters()),
+                tuple(self.fc2.parameters()),
+            )
+            gLogger.info("Use DDP for the second local net.")
+            self.fc2 = DistributedDataParallel(
+                self.fc2, check_reduction=True, process_group=process_group_for_ddp
+            )
+
+        gLogger.info(
+            "HybridModel has %s groups of parameters.", len(list(self.parameters()))
+        )
+
+    def forward(self, input: FeatureSet):
+        gLogger.debug("Running HybridModel.forward on %s", input)
+        sparse = _remote_method(
+            RemoteEM.forward, self.remote_em_rref, input.sparse_features
+        )
+        # The same size of mini batch.
+        assert sparse.shape[0] == input.dense_features.shape[0]
+        dense = self.fc1(input.dense_features)
+        x = torch.cat((dense, sparse), 1)
+        gLogger.debug("Concatenated feature: %s", x)
+        x = _remote_method(RemoteNet.forward, self.remote_net_rref, x)
+        return self.fc2(x)
+
+
+class Trainer:
+    def __init__(
+        self,
+        remote_em_rref: rpc.RRef,
+        remote_net_rref: rpc.RRef,
+        ddp_mode: DdpMode,
+        rank: int,
+    ):
+        self.rank = rank
+        self.trainer_group = (
+            dist.new_group(TRAINER_RANKS)
+            if ddp_mode in (DdpMode.INSIDE, DdpMode.OUTSIDE)
+            else None
+        )
+        self.remote_em_rref = remote_em_rref
+        self.remote_net_rref = remote_net_rref
+        self.hybrid_module = HybridModel(
+            self.remote_em_rref,
+            self.remote_net_rref,
+            self.trainer_group if ddp_mode in (DdpMode.INSIDE,) else None,
+        )
+        self.ddp_params, self.non_ddp_params = (
+            self.hybrid_module.ddp_params,
+            self.hybrid_module.non_ddp_params,
+        )
+        if ddp_mode == DdpMode.OUTSIDE:
+            gLogger.info("Wrapping the whole hybrid module into DDP.")
+            self.ddp_params += self.non_ddp_params
+            self.non_ddp_params = ()
+            self.hybrid_module = DistributedDataParallel(
+                self.hybrid_module,
+                check_reduction=True,
+                process_group=self.trainer_group,
+            )
+        gLogger.info(
+            "Succeeded in creating a HybridModel instance with "
+            "%s ddp params and %s other local params.",
+            len(self.ddp_params), len(self.non_ddp_params)
+        )
+
+    def destroy_pg(self):
+        if self.trainer_group:
+            dist.destroy_process_group(self.trainer_group)
+
+    def train_batch(
+        self,
+        mini_batch: FeatureSet,
+        trainer_has_less_inputs: bool,
+        simulate_uneven_inputs: bool,
+    ):
+        grads_dict = None
+
+        if not simulate_uneven_inputs:
+            input_batches = [mini_batch]
+        else:
+            # Split into microbatches, and trim to simulate uneven inputs.
+            dense_features = mini_batch.dense_features
+            sparse_features = mini_batch.sparse_features
+            values = mini_batch.values
+
+            dense_microbatch = torch.split(dense_features, 2)
+            sparse_microbatch = torch.split(sparse_features, 2)
+            values_microbatch = torch.split(values, 2)
+            batches = []
+            for d, s, v in zip(dense_microbatch, sparse_microbatch, values_microbatch):
+                feature_set = FeatureSet(dense_features=d, sparse_features=s, values=v)
+                batches.append(feature_set)
+
+            if trainer_has_less_inputs:
+                input_batches = batches[: len(batches) // 2]
+                gLogger.info(
+                    "Trainer reduced input patches from %s "
+                    "to %s to simulate uneven inputs.",
+                    len(batches), len(input_batches)
+                )
+            else:
+                input_batches = batches
+
+        with self.hybrid_module.join() if simulate_uneven_inputs else contextlib.nullcontext():
+            for b in input_batches:
+                with dist_autograd.context() as context_id:
+                    output = self.hybrid_module.forward(b)
+                    loss = (output * mini_batch.values).sum()
+                    dist_autograd.backward(context_id, [loss])
+                    grads_dict = dist_autograd.get_gradients(context_id)
+                    gLogger.info(
+                        "Loss is %s for mini batch: %s. "
+                        "Grads dict has %s entries: %s", loss, mini_batch, len(grads_dict), grads_dict
+                    )
+        return (
+            tuple(grads_dict[param] for param in self.ddp_params),
+            tuple(grads_dict[param] for param in self.non_ddp_params),
+        )
+
+
+def get_training_examples():
+    n = 16
+    training_examples = FeatureSet(
+        dense_features=torch.zeros((n, D_DENSE)),
+        sparse_features=torch.zeros(n, dtype=torch.long),
+        values=torch.zeros(n),
+    )
+    idx = 0
+    # Every example has another one that has exactly the same features but an
+    # opposite value. Therefore, their grads cancel each other in all-reduce.
+    for value in (-1, 1):
+        for x in (-1.0 * value, 1.0 * value):
+            for y in (1.0 * value, -1.0 * value):
+                for z in (0, 1):
+                    training_examples.dense_features[idx, :] = torch.tensor((x, y))
+                    training_examples.sparse_features[idx] = z
+                    training_examples.values[idx] = value
+                    idx += 1
+
+    # Split the examples among NUM_TRAINERS trainers
+    assert 0 == (n % NUM_TRAINERS)
+    examples_per_trainer = int(n / NUM_TRAINERS)
+    return [
+        FeatureSet(
+            dense_features=training_examples.dense_features[
+                start : start + examples_per_trainer, :
+            ],
+            sparse_features=training_examples.sparse_features[
+                start : start + examples_per_trainer
+            ],
+            values=training_examples.values[start : start + examples_per_trainer],
+        )
+        for start in range(0, n, examples_per_trainer)
+    ]
+
+
+shutdown_signal = threading.Condition()
+
+
+def set_shutdown_signal():
+    global shutdown_signal
+    with shutdown_signal:
+        shutdown_signal.notify()
+
+
+class DdpUnderDistAutogradTest(RpcAgentTestFixture):
+    @property
+    def world_size(self) -> int:
+        return WORLD_SIZE
+
+    def remote_worker_name(self) -> str:
+        # The name has to be consistent with that in 'dist_init' decorator.
+        return f"worker{REMOTE_WORKER_RANK}"
+
+    def trainer_name(self, rank):
+        # The name has to be consistent with that in 'dist_init' decorator.
+        return f"worker{rank}"
+
+    def _remote_worker_process(self, ddp_mode):
+        gLogger.info("The remote worker is running.")
+        dist.init_process_group(
+            backend="gloo",
+            init_method=INIT_METHOD_TEMPLATE.format(file_name=self.file_name),
+            world_size=self.world_size,
+            rank=self.rank,
+        )
+
+        if ddp_mode in (DdpMode.INSIDE, DdpMode.OUTSIDE):
+            # new_group needs to be called on ranks.
+            dist.new_group(TRAINER_RANKS)
+
+        global shutdown_signal
+        with shutdown_signal:
+            shutdown_signal.wait()
+        gLogger.info("Exiting remote worker.")
+        dist.destroy_process_group()
+
+    def _trainer_process(self, rank: int):
+        gLogger.info("Running the trainer #%s...", rank)
+        gLogger.info(
+            "Initing trainer process group by trainer #%s with ranks %s", rank, TRAINER_RANKS
+        )
+        dist.init_process_group(
+            backend="gloo",
+            init_method=INIT_METHOD_TEMPLATE.format(file_name=self.file_name),
+            world_size=self.world_size,
+            rank=self.rank,
+        )
+
+        gLogger.info("Waiting for shutdown signal on trainer #%s...", rank)
+
+        global shutdown_signal
+        with shutdown_signal:
+            shutdown_signal.wait()
+        gLogger.info("Exiting the trainer #%s...", rank)
+        dist.destroy_process_group()
+
+    def _master_process(self, ddp_mode: DdpMode, simulate_uneven_inputs: bool):
+        gLogger.info("Running the master process...")
+        dist.init_process_group(
+            backend="gloo",
+            init_method=INIT_METHOD_TEMPLATE.format(file_name=self.file_name),
+            world_size=self.world_size,
+            rank=self.rank,
+        )
+
+        remote_em_rref = rpc.remote(
+            self.remote_worker_name(), RemoteEM, args=(NUM_EM_ROW, D_SPARSE)
+        )
+        remote_net_rref = rpc.remote(
+            self.remote_worker_name(), RemoteNet, args=(D_DENSE + D_SPARSE, D_HID)
+        )
+        gLogger.info("Created remote rrefs on master")
+        self.do_test_on_master(
+            ddp_mode, simulate_uneven_inputs, remote_em_rref, remote_net_rref
+        )
+
+    def do_test_on_master(
+        self,
+        ddp_mode: DdpMode,
+        simulate_uneven_inputs: bool,
+        remote_em_rref: rpc.RRef,
+        remote_net_rref: rpc.RRef,
+    ):
+        if simulate_uneven_inputs:
+            gLogger.info(
+                "Running DDP + RPC test with simulating uneven inputs across trainers."
+            )
+
+        trainer_rrefs = []
+        for rank in TRAINER_RANKS:
+            trainer = self.trainer_name(rank)
+            trainer_rrefs.append(
+                rpc.remote(
+                    trainer,
+                    Trainer,
+                    args=(remote_em_rref, remote_net_rref, ddp_mode, rank),
+                )
+            )
+
+        if ddp_mode in (DdpMode.INSIDE, DdpMode.OUTSIDE):
+            # new_group needs to be called on ranks.
+            dist.new_group(TRAINER_RANKS)
+
+        training_examples = get_training_examples()
+        for _ in range(3):
+            futures = []
+            num_trainers = len(trainer_rrefs)
+            for idx, trainer_rref in enumerate(trainer_rrefs):
+                # Half the trainers will deplete inputs earlier than the rest.
+                trainer_has_less_inputs = (
+                    simulate_uneven_inputs and idx < num_trainers // 2
+                )
+                futures.append(
+                    _remote_method_async(
+                        Trainer.train_batch,
+                        trainer_rref,
+                        training_examples[idx],
+                        trainer_has_less_inputs,
+                        simulate_uneven_inputs,
+                    )
+                )
+
+            for future in futures:
+                ddp_grads, non_ddp_grads = future.wait()
+                # When there are uneven inputs, it is not necessary that grads
+                # cancel each other out, since some trainers contribute 0 grad.
+                if not simulate_uneven_inputs:
+                    for grad in ddp_grads:
+                        self.assertEqual(
+                            grad,
+                            torch.zeros_like(grad),
+                            msg=f"The grad for any ddp parameter should be zeros, because "
+                            "the training examples' grads cancel each other. Received "
+                            f"gradient {grad}",
+                        )
+                for grad in non_ddp_grads:
+                    self.assertNotEqual(
+                        grad,
+                        torch.zeros_like(grad),
+                        msg="The grad for any non-ddp parameter shouldn't be zeros",
+                    )
+
+        # Destroy process groups
+        for idx, trainer_rref in enumerate(trainer_rrefs):
+            _remote_method_async(Trainer.destroy_pg, trainer_rref).wait()
+
+        # Send shutdown signals.
+        for rank in TRAINER_RANKS:
+            trainer = self.trainer_name(rank)
+            rpc.rpc_sync(trainer, set_shutdown_signal, args=())
+
+        rpc.rpc_sync(self.remote_worker_name(), set_shutdown_signal, args=())
+
+    def _do_test(self, ddp_mode, simulate_uneven_inputs=False):
+        if self.rank == MASTER_RANK:
+            self._master_process(ddp_mode, simulate_uneven_inputs)
+        elif self.rank == REMOTE_WORKER_RANK:
+            self._remote_worker_process(ddp_mode)
+        elif self.rank in TRAINER_RANKS:
+            self._trainer_process(self.rank)
+        else:
+            raise RuntimeError(f"Unknown process rank: {self.rank}")
+
+    @requires_gloo()
+    @dist_init
+    def test_backward_no_ddp(self):
+        self._do_test(DdpMode.NONE)
+
+    @requires_gloo()
+    @dist_init
+    def test_backward_ddp_outside(self):
+        self._do_test(DdpMode.OUTSIDE)
+
+    @requires_gloo()
+    @dist_init
+    def test_backward_ddp_outside_uneven_inputs(self):
+        self._do_test(DdpMode.OUTSIDE, simulate_uneven_inputs=True)
+
+    @requires_gloo()
+    @dist_init
+    def test_backward_ddp_inside(self):
+        self._do_test(DdpMode.INSIDE)
+
+
+# Common utils for both CPU and CUDA test suites
+class CommonDdpComparisonTest(RpcAgentTestFixture):
+    @property
+    def world_size(self) -> int:
+        return NUM_TRAINERS
+
+    def trainer_name(self, rank):
+        # The name has to be consistent with that in 'dist_init' decorator.
+        return f"worker{rank}"
+
+    @staticmethod
+    def get_remote_grads(rref, context_id):
+        return dist_autograd.get_gradients(context_id)[rref.local_value().weight]
+
+
+class DdpComparisonTest(CommonDdpComparisonTest):
+    def _run_test_ddp_comparision(self, simulate_uneven_inputs=False):
+        gLogger.info("Running trainer rank: %s", self.rank)
+        # Each trainer uses a different random seed. Otherwise, they are going
+        # to have exactly the same initial model parameters, input, and
+        # therefore grads. That means the grads will be the same before and
+        # after DDP's all-reduce.
+        torch.manual_seed(self.rank)
+        dist.init_process_group(
+            backend="gloo",
+            # Postfix file_name with "pg" since file_name is also used by RPC agent
+            init_method=INIT_METHOD_TEMPLATE.format(file_name=f"{self.file_name}_pg"),
+            world_size=self.world_size,
+            rank=self.rank,
+        )
+        net = nn.Linear(2, 3)
+        ddp_net = DistributedDataParallel(net)
+
+        # Odd ranks join early if simulate_uneven_inputs.
+        num_inputs = 1
+        if simulate_uneven_inputs:
+            if self.rank % 2 == 0:
+                num_inputs += 2
+        inputs_list = [torch.rand((3, 2)) for _ in range(num_inputs)]
+
+        if simulate_uneven_inputs:
+            gLogger.info("Rank %s training with %s inputs.", self.rank, len(inputs_list))
+
+        # Use distributed autograd. The gradients will be in RPC context map.
+        grads_dict = {}
+        with ddp_net.join(simulate_uneven_inputs):
+            for i, inputs in enumerate(inputs_list):
+                with dist_autograd.context() as context_id:
+                    loss = ddp_net(inputs).norm()
+                    dist_autograd.backward(context_id, [loss])
+                    grads_dict = dist_autograd.get_gradients(context_id)
+                gLogger.info("Trainer #%s got grad dict: %s", self.rank, grads_dict)
+
+                # Use local autograd. The gradients will be in each variable's '.grad'.
+                ddp_net.zero_grad()
+                loss = ddp_net(inputs).norm()
+                loss.backward()
+
+                # The gradients should be the same
+                for param in net.parameters():
+                    self.assertTrue(
+                        param in grads_dict,
+                        msg=f"Param {param} is not in dist_auto grad dict {grads_dict} for iteration {i}",
+                    )
+                    self.assertEqual(
+                        grads_dict[param],
+                        param.grad,
+                        msg=f"The grads for param {param} are different under local "
+                        f"and dist autograd: {param.grad} \n---\n {grads_dict[param]} for iteration {i}",
+                    )
+        dist.destroy_process_group()
+
+    @requires_gloo()
+    @dist_init
+    def test_ddp_comparison(self):
+        self._run_test_ddp_comparision()
+
+    @requires_gloo()
+    @dist_init
+    def test_ddp_comparison_uneven_inputs(self):
+        # test with simulating uneven inputs in DDP
+        self._run_test_ddp_comparision(simulate_uneven_inputs=True)
+
+    @requires_gloo()
+    @dist_init
+    def test_ddp_dist_autograd_sparse_grads(self):
+        # Each trainer uses a different random seed. Otherwise, they are going
+        # to have exactly the same initial model parameters, input, and
+        # therefore grads. That means the grads will be the same before and
+        # after DDP's all-reduce.
+        torch.manual_seed(self.rank)
+        dist.init_process_group(
+            backend="gloo",
+            init_method=INIT_METHOD_TEMPLATE.format(file_name=self.file_name),
+            world_size=self.world_size,
+            rank=self.rank,
+        )
+
+        model = nn.EmbeddingBag(10, 3, sparse=True)
+        ddp_model = DistributedDataParallel(model)
+
+        # Different inputs for each
+        input = torch.LongTensor(10).random_(0, 10)
+        offsets = torch.LongTensor([0, 4])
+
+        # Run local.
+        loss = ddp_model(input, offsets).sum()
+        loss.backward()
+
+        with dist_autograd.context() as context_id:
+            loss = ddp_model(input, offsets).sum()
+            dist_autograd.backward(context_id, [loss])
+            grads_dict = dist_autograd.get_gradients(context_id)
+            self.assertEqual(1, len(grads_dict))
+            self.assertEqual(model.weight.grad, grads_dict[model.weight])
+
+    @requires_gloo()
+    @dist_init
+    def test_ddp_dist_autograd_local_vs_remote(self):
+        # Each trainer uses a different random seed. Otherwise, they are going
+        # to have exactly the same initial model parameters, input, and
+        # therefore grads. That means the grads will be the same before and
+        # after DDP's all-reduce.
+        torch.manual_seed(self.rank)
+        dist.init_process_group(
+            backend="gloo",
+            init_method=INIT_METHOD_TEMPLATE.format(file_name=self.file_name),
+            world_size=self.world_size,
+            rank=self.rank,
+        )
+
+        # Use two different remote device input string, w/ and w/o the default
+        # device string "cpu", respectively.
+        for remote_device in ["worker0/cpu", "worker0"]:
+            remote_layer1 = RemoteModule(
+                remote_device=remote_device, module_cls=nn.Linear, args=(10, 5, False)
+            )
+            layer1 = nn.Linear(10, 5, False)
+            # Start with the same parameters for remote and local
+            layer1.weight = remote_layer1.module_rref.to_here().weight
+
+            # Run local case.
+            layer2 = nn.Linear(5, 1)
+            inputs = torch.rand((10, 10))
+            ddp_model = DistributedDataParallel(layer2)
+            loss = ddp_model(layer1(inputs)).sum()
+            loss.backward()
+
+            # Run remote case.
+            with dist_autograd.context() as context_id:
+                loss = ddp_model(remote_layer1(inputs)).sum()
+                dist_autograd.backward(context_id, [loss])
+                grads_dict = dist_autograd.get_gradients(context_id)
+                dist.barrier()
+                self.assertEqual(layer2.weight.grad, grads_dict[layer2.weight])
+                self.assertEqual(
+                    layer1.weight.grad,
+                    rpc.rpc_sync(
+                        "worker0",
+                        CommonDdpComparisonTest.get_remote_grads,
+                        args=(remote_layer1.module_rref, context_id),
+                    ),
+                )
+
+
+class CudaDdpComparisonTest(CommonDdpComparisonTest):
+    @skip_if_lt_x_gpu(NUM_TRAINERS)
+    @requires_nccl()
+    @dist_init
+    @skip_if_rocm
+    def test_ddp_dist_autograd_local_vs_remote_gpu(self):
+        # Each trainer uses a different random seed. Otherwise, they are going
+        # to have exactly the same initial model parameters, input, and
+        # therefore grads. That means the grads will be the same before and
+        # after DDP's all-reduce.
+        torch.manual_seed(self.rank)
+        dist.init_process_group(
+            backend="gloo",
+            init_method=INIT_METHOD_TEMPLATE.format(file_name=self.file_name),
+            world_size=self.world_size,
+            rank=self.rank,
+        )
+
+        remote_layer1 = RemoteModule(
+            remote_device="worker0/cpu", module_cls=nn.Linear, args=(10, 7, False)
+        )
+        layer1 = nn.Linear(10, 7, False)
+        # Start with the same parameters for remote and local
+        layer1.weight = remote_layer1.module_rref.to_here().weight
+
+        layer2 = nn.Linear(7, 5).cuda(self.rank)
+        ddp_layer2 = DistributedDataParallel(layer2, device_ids=[self.rank])
+
+        remote_layer3 = RemoteModule(
+            remote_device="worker0/cpu", module_cls=nn.Linear, args=(5, 3, False)
+        )
+        layer3 = nn.Linear(5, 3, False)
+        # Start with the same parameters for remote and local
+        layer3.weight = remote_layer3.module_rref.to_here().weight
+
+        layer4 = nn.Linear(3, 1).cuda(self.rank)
+        ddp_layer4 = DistributedDataParallel(layer4, device_ids=[self.rank])
+
+        # Run local case.
+        inputs = torch.rand((10, 10))
+        loss = ddp_layer4(
+            layer3(ddp_layer2(layer1(inputs).cuda(self.rank)).cpu()).cuda(self.rank)
+        ).sum()
+        loss.backward()
+
+        # Run remote case.
+        with dist_autograd.context() as context_id:
+            loss = ddp_layer4(
+                remote_layer3(
+                    ddp_layer2(remote_layer1(inputs).cuda(self.rank)).cpu()
+                ).cuda(self.rank)
+            ).sum()
+            dist_autograd.backward(context_id, [loss])
+            grads_dict = dist_autograd.get_gradients(context_id)
+            dist.barrier()
+            self.assertEqual(
+                layer1.weight.grad,
+                rpc.rpc_sync(
+                    "worker0",
+                    CommonDdpComparisonTest.get_remote_grads,
+                    args=(remote_layer1.module_rref, context_id),
+                ),
+            )
+            self.assertEqual(layer2.weight.grad, grads_dict[layer2.weight])
+            self.assertEqual(
+                layer3.weight.grad,
+                rpc.rpc_sync(
+                    "worker0",
+                    CommonDdpComparisonTest.get_remote_grads,
+                    args=(remote_layer3.module_rref, context_id),
+                ),
+            )
+            self.assertEqual(layer4.weight.grad, grads_dict[layer4.weight])

venv/lib/python3.10/site-packages/torch/testing/_internal/distributed/distributed_utils.py

@@ -0,0 +1,66 @@
+# mypy: ignore-errors
+
+from contextlib import contextmanager
+from datetime import timedelta
+from functools import (
+    partial,
+    wraps,
+)
+
+import torch.distributed as dist
+import torch.distributed.distributed_c10d as c10d
+
+class MockProcessGroup(dist.ProcessGroup):
+
+    def __init__(self, rank, world):
+        super().__init__(rank, world)
+
+    def getBackendName(self):
+        return "mock_process_group"
+
+def create_mock_pg(prefix_store, rank, world_size, timeout):
+    return MockProcessGroup(rank, world_size)
+
+dist.Backend.register_backend('mock_process_group', create_mock_pg)
+
+def mock_init_dist(rank, world_size):
+    # !!! WARNING !!!
+    # Kids don't try this at home, this is a cute pile of hacks that
+    # depends on a small mountain of c10d internals
+    assert not dist.is_initialized()
+    store = dist.HashStore()
+    # Trick _store_based_barrier into believing everyone else already checked-in
+    # Zero is the group index
+    store.add(f"{c10d.STORE_BASED_BARRIER_PREFIX}:0", world_size - 1)
+    dist.init_process_group(
+        backend="mock_process_group",
+        rank=rank,
+        world_size=world_size,
+        store=store,
+        group_name="fake",
+        timeout=timedelta(seconds=1))
+
+@contextmanager
+def with_dist(rank=0, world_size=2):
+    """
+    Context manager that initializer c10d with a fake process group.
+    """
+    mock_init_dist(rank=rank, world_size=world_size)
+    try:
+        yield
+    finally:
+        dist.destroy_process_group()
+
+def with_fake_comms(func=None, rank=0, world_size=2):
+    """
+    Function wrapper that inits a fake process group designed for testing.
+    Right now only querying for world size is available
+    """
+    if func is None:
+        return partial(with_fake_comms, rank=rank, world_size=world_size)
+
+    @wraps(func)
+    def wrapper(self, *args, **kwargs):
+        with with_dist(rank, world_size):
+            func(self, *args, **kwargs)
+    return wrapper

venv/lib/python3.10/site-packages/torch/testing/_internal/distributed/fake_pg.py

@@ -0,0 +1,32 @@
+# mypy: ignore-errors
+
+import torch.distributed as dist
+
+from torch._C._distributed_c10d import (
+    FakeProcessGroup,
+)
+
+
+class FakeStore(dist.Store):
+    """
+    A fake store is a fake Key-Value store simply for initialization usage
+    the of fake process group, one can either use FakeStore or HashStore.
+    """
+    pass
+
+
+def _create_fake_pg(prefix_store, rank, world_size, timeout):
+    """
+    A fake process group (not related to FakeTensor) is a process group which
+    doesn't actually do any communication, it just hallucinates some
+    communication.  You can run a single rank with a fake process group
+    without needing multiple processes (simulates per-rank behavior)
+
+    NOTE: This is not a real process group, and it would produce wrong results
+    for every collective. It should be used as a convinient tool when playing
+    with distributed but don't care about the actual data.
+    """
+    return FakeProcessGroup(rank, world_size)
+
+
+dist.Backend.register_backend("fake", _create_fake_pg, devices=['cpu', 'cuda'])

venv/lib/python3.10/site-packages/torch/testing/_internal/distributed/multi_threaded_pg.py

@@ -0,0 +1,494 @@
+# mypy: ignore-errors
+
+import sys
+import threading
+from dataclasses import dataclass
+from typing import Dict, List, Optional, Tuple, Union
+from functools import partial, reduce
+
+import torch
+import torch.distributed as dist
+import weakref
+from torch._C._distributed_c10d import (
+    _create_work_from_future,
+    AllgatherOptions,
+    AllreduceOptions,
+    AllToAllOptions,
+    BarrierOptions,
+    BroadcastOptions,
+    ReduceScatterOptions,
+    ScatterOptions,
+    Store,
+    ReduceOp,
+)
+from torch.distributed.distributed_c10d import _CollOp, _store_based_barrier, P2POp
+from torch.futures import Future
+from torch.utils import _pytree as pytree
+
+"""
+TODO:
+Lots of missing collectives.
+Collectives validation.
+Make timeout robust by making collectives respect the test deadline.
+Make tests robust by making collectives interruptible.
+We need some synchronization around cleanup to ensure that timedout ranks don't cause spurious failures.
+
+"""
+
+
+def flatten_list(lst):
+    return pytree.tree_leaves(lst)
+
+
+def ret_work(ret):
+    fut = Future()
+    fut.set_result(ret)
+    return _create_work_from_future(fut)
+
+def binop_reduce(tensors, op):
+    res = op(torch.stack(tensors), dim=0)
+    if isinstance(res, torch.Tensor):
+        return res
+    # min/max return a namedtuple
+    return res.values
+
+def bitwise_reduce(tensors, op):
+    return reduce(op, tensors)
+
+_reduce_ops = {
+    ReduceOp.SUM: partial(binop_reduce, op=torch.sum),
+    ReduceOp.AVG: partial(binop_reduce, op=torch.mean),
+    ReduceOp.PRODUCT: partial(binop_reduce, op=torch.prod),
+    ReduceOp.MIN: partial(binop_reduce, op=torch.min),
+    ReduceOp.MAX: partial(binop_reduce, op=torch.max),
+    ReduceOp.BAND: partial(bitwise_reduce, op=torch.bitwise_and),
+    ReduceOp.BOR: partial(bitwise_reduce, op=torch.bitwise_or),
+    ReduceOp.BXOR: partial(bitwise_reduce, op=torch.bitwise_xor),
+}
+
+class AllToAll:
+    @torch.no_grad()
+    def work(self, data):
+        world_size = len(data)
+        for dest_rank in range(world_size):
+            output_tensor_list, _ = data[dest_rank]
+            for src_rank in range(world_size):
+                _, input_tensor_list = data[src_rank]
+                output_tensor_list[src_rank].copy_(input_tensor_list[dest_rank])
+
+class AllReduce:
+    def __init__(self, op):
+        if op.op not in _reduce_ops:
+            raise NotImplementedError(
+                f"AllReduce op {op.op} not supported on multithreaded pg for now."
+            )
+        self.op = op.op
+
+    @torch.no_grad()
+    def work(self, data):
+        for i in range(len(data[0])):
+            tensors = []
+            # use rank0 as the device for sum
+            rank_0_device = data[0][i].device
+            # collect all data to the list and make them
+            # all on rank 0 device
+            for src_rank in range(0, len(data)):
+                tensors.append(data[src_rank][i].to(rank_0_device))
+
+            # now mimic reduce across all ranks
+            res = _reduce_ops[self.op](tensors)
+
+            # copy all the reduced value to each rank
+            for src_rank in range(len(data)):
+                data[src_rank][i].copy_(res.to(data[src_rank][i].device))
+
+
+class AllGather:
+    @torch.no_grad()
+    def work(self, data):
+        for src_rank in range(len(data)):
+            in_tensor_list = data[src_rank][1]
+            # Can't handle all_gather with multiple tensors
+            assert len(in_tensor_list) == 1
+            src_tensor = in_tensor_list[0]
+
+            for dest in data:
+                dest_tensor = dest[0][0][src_rank]
+                dest_tensor.copy_(src_tensor)
+
+
+class Scatter:
+    def __init__(self, src):
+        self.src = src
+
+    @torch.no_grad()
+    def work(self, data):
+        src_in_tensor_list = data[self.src][1]
+        # Can't handle scatter with multiple input tensor list
+        assert len(src_in_tensor_list) == 1
+        src_in_tensors = src_in_tensor_list[0]
+
+        for rank, each_rank_data in enumerate(data):
+            out_tensor_list = each_rank_data[0]
+            # Can't handle scatter with multiple output tensor
+            assert len(out_tensor_list) == 1
+            dest_tensor = out_tensor_list[0]
+            dest_tensor.copy_(src_in_tensors[rank])
+
+
+class Gather:
+    def __init__(self, dst):
+        self.dst = dst
+
+    @torch.no_grad()
+    def work(self, data):
+        # Can't handle gather with multiple tensor lists
+        assert len(data[self.dst][0]) == 1
+        out_tensor_list = data[self.dst][0][0]
+        for rank, each_rank_data in enumerate(data):
+            src_in_tensor_list = each_rank_data[1]
+            # Can't handle gather with multiple tensor lists
+            assert len(src_in_tensor_list) == 1
+            dest_tensor = out_tensor_list[rank]
+            dest_tensor.copy_(src_in_tensor_list[0])
+
+class ReduceScatter:
+    def __init__(self, op):
+        if op != dist.ReduceOp.SUM and op != dist.ReduceOp.AVG:
+            raise NotImplementedError(f"ReduceScatter does not support {op}")
+        self.op = op
+
+    @torch.no_grad()
+    def work(self, data):
+        start_reduction = [False for _ in range(len(data))]
+        for each_rank_data in data:
+            # Can't handle reduce_scatter with multiple scatter list
+            assert len(each_rank_data[1]) == 1
+            to_scatter = each_rank_data[1][0]
+            for i in range(len(to_scatter)):
+                dest_tensor_on_rank_i = data[i][0]
+                # Can't handle reduce_scatter with multiple output tensor
+                assert len(dest_tensor_on_rank_i) == 1
+                dst_tensor_device = dest_tensor_on_rank_i[0].device
+                if not start_reduction[i]:
+                    dest_tensor_on_rank_i[0].copy_(to_scatter[i].to(dst_tensor_device))
+                    start_reduction[i] = True
+                else:
+                    dest_tensor_on_rank_i[0].add_(to_scatter[i].to(dst_tensor_device))
+        if self.op == dist.ReduceOp.AVG:
+            num_ranks = len(data)
+            for each_rank_data in data:
+                each_rank_data[0][0] /= num_ranks
+
+
+class Broadcast:
+    def __init__(self, src):
+        self.src = src
+
+    @torch.no_grad()
+    def work(self, data):
+        in_tensor_list = flatten_list(data[self.src])
+        for i in range(len(data)):
+            out_tensor_list = flatten_list(data[i])
+            for j in range(len(in_tensor_list)):
+                out_tensor_list[j].copy_(in_tensor_list[j])
+
+
+class Collective:
+    def __init__(self, world_size, collective, pg):
+        self._world_size = world_size
+        self._collective = collective
+
+        self._start_cond = threading.Condition()
+        self._done_cond = threading.Condition()
+
+        self._data = [None] * world_size
+        self._count = 0
+        self._done = False
+
+        self._pg = pg
+
+    def join(self, rank, data):
+        with self._start_cond:
+            self._data[rank] = data
+            self._count += 1
+
+            # notify rank 0
+            if self._count == self._world_size:
+                if rank > 0:
+                    self._start_cond.notify()
+
+            if rank == 0:
+                self._start_cond.wait_for(
+                    lambda: self._count == self._world_size or self._pg._terminate.is_set()
+                )
+                # SystemExit is not a subclass of Exception but BaseException
+                # and can be distinguished from normal exception raised from program errors
+                # so that we can hide it from the exception queue
+                if self._pg._terminate.is_set():
+                    sys.exit("Test termination event occurs.")
+
+        with self._done_cond:
+            # wait for rank 0 to finish
+            if rank > 0:
+                self._done_cond.wait_for(lambda: self._done or self._pg._terminate.is_set())
+                if self._pg._terminate.is_set():
+                    sys.exit("Test termination event occurs.")
+            else:
+                # copy data around
+                self._collective.work(self._data)
+                self._done = True
+                self._done_cond.notify_all()
+        return ret_work(data)
+
+
+class ProcessLocalGroup(dist.ProcessGroup):
+    _coll_lock = threading.Lock()
+    _cur_coll_on_pgs = {}
+
+    _terminate = threading.Event()
+
+    @classmethod
+    def _start_coll(cls, collective, pg):
+        with cls._coll_lock:
+            # pg_name is unique, we use that to record the mapping between pg and collective
+            if pg.pg_name not in cls._cur_coll_on_pgs:
+                cls._cur_coll_on_pgs[pg.pg_name] = Collective(pg.size(), collective, cls)
+            return cls._cur_coll_on_pgs[pg.pg_name]
+
+    @classmethod
+    def _end_coll(cls, collective, pg):
+        # This is racily called by all ranks, so only one will work
+        with cls._coll_lock:
+            if pg.pg_name in cls._cur_coll_on_pgs and cls._cur_coll_on_pgs[pg.pg_name] == collective:
+                cls._cur_coll_on_pgs.pop(pg.pg_name)
+
+    @classmethod
+    def exception_handle(cls, exc):
+        cls._terminate.set()
+        for coll in cls._cur_coll_on_pgs.values():
+            with coll._start_cond:
+                coll._start_cond.notify()
+            with coll._done_cond:
+                coll._done_cond.notify_all()
+
+    @classmethod
+    def reset(cls):
+        with cls._coll_lock:
+            cls._cur_coll_on_pgs = {}
+            cls._terminate.clear()
+
+    def alltoall(self, output_tensor_list, input_tensor_list, opts=AllToAllOptions()):
+        coll = ProcessLocalGroup._start_coll(AllToAll(), self)
+        res = coll.join(self._rank, (output_tensor_list, input_tensor_list))
+        ProcessLocalGroup._end_coll(coll, self)
+        return res
+
+    def allreduce(self, tensor_list, opts=AllreduceOptions()):
+        coll = ProcessLocalGroup._start_coll(AllReduce(opts.reduceOp), self)
+        res = coll.join(self._rank, tensor_list)
+        ProcessLocalGroup._end_coll(coll, self)
+        return res
+
+    def allreduce_coalesced(self, tensor_list, opts=AllreduceOptions()):
+        coll = ProcessLocalGroup._start_coll(AllReduce(opts.reduceOp), self)
+        res = coll.join(self._rank, tensor_list)
+        ProcessLocalGroup._end_coll(coll, self)
+        return res
+
+    def barrier(self, opts=BarrierOptions()):
+        return self.allreduce(tensor_list=[torch.ones(1)])
+
+    def allgather(self, output_tensors, input_tensor, opts=AllgatherOptions()):
+        coll = ProcessLocalGroup._start_coll(AllGather(), self)
+        res = coll.join(self._rank, (output_tensors, input_tensor))
+        ProcessLocalGroup._end_coll(coll, self)
+        return res
+
+    def _allgather_base(self, output_tensor, input_tensor, opts=AllgatherOptions()):
+        tensor_list = list(torch.chunk(output_tensor, self._world_size))
+        return self.allgather([tensor_list], [input_tensor], opts)
+
+    def broadcast(self, tensor_list, opts=BroadcastOptions()):
+        coll = ProcessLocalGroup._start_coll(Broadcast(opts.rootRank), self)
+        res = coll.join(self._rank, tensor_list)
+        ProcessLocalGroup._end_coll(coll, self)
+        return res
+
+    def scatter(self, output_tensors, input_tensors, opts=ScatterOptions()):
+        coll = ProcessLocalGroup._start_coll(Scatter(opts.rootRank), self)
+        res = coll.join(self._rank, (output_tensors, input_tensors))
+        ProcessLocalGroup._end_coll(coll, self)
+        return res
+
+    def gather(self, output_tensors, input_tensors, opts=ScatterOptions()):
+        coll = ProcessLocalGroup._start_coll(Gather(opts.rootRank), self)
+        res = coll.join(self._rank, (output_tensors, input_tensors))
+        ProcessLocalGroup._end_coll(coll, self)
+        return res
+
+    def reduce_scatter(self, output_tensor, scatter_list, opts=ReduceScatterOptions()):
+        coll = ProcessLocalGroup._start_coll(ReduceScatter(opts.reduceOp), self)
+        res = coll.join(self._rank, (output_tensor, scatter_list))
+        ProcessLocalGroup._end_coll(coll, self)
+        return res
+
+    def _reduce_scatter_base(self, output_tensor, input_tensor, opts=ReduceScatterOptions()):
+        tensor_list = list(torch.chunk(input_tensor, self._world_size))
+        return self.reduce_scatter([output_tensor], [tensor_list], opts)
+
+    def reduce_scatter_tensor_coalesced(self, output_tensors, input_tensors, opts=ReduceScatterOptions()):
+        works = [
+            self._reduce_scatter_base(output_tensor, input_tensor, opts)
+            for output_tensor, input_tensor
+            in zip(output_tensors, input_tensors)
+        ]
+        for work in works[:-1]:
+            work.wait()
+        return works[-1]
+
+    def allgather_into_tensor_coalesced(self, output_tensor_list, input_tensor_list, opts=AllgatherOptions()):
+        res = None
+        for o_t, i_t in zip(output_tensor_list, input_tensor_list):
+            res = self._allgather_base(o_t, i_t)
+        return res
+
+    def __init__(self, rank, world_size):
+        super().__init__(rank, world_size)
+        self._rank = rank
+        self._world_size = world_size
+        world = dist.distributed_c10d._world
+        if isinstance(world, ThreadLocalWorld):
+            world = world._get_world()
+        self._world = weakref.ref(world)
+        self._ctx = torch.autograd.set_multithreading_enabled(False)
+
+    def size(self):
+        return self._world_size
+
+    @property
+    def pg_name(self):
+        """
+        return the global registered name of the current pg in the world
+        """
+        return self._world().pg_names[self]
+
+    @property
+    def group_name(self):
+        return self.pg_name
+
+    def getBackendName(self):
+        return "threaded"
+
+    def __repr__(self):
+        return f"ThreadedPG world_size:{self._world_size} rank:{self._rank}"
+
+
+def _create_threaded_pg(prefix_store, rank, world_size, timeout):
+    pg = ProcessLocalGroup(rank, world_size)
+    # https://github.com/pytorch/pytorch/pull/103033 changed store based barrier to optional
+    # When device mesh involves sub groups while store based barrier is not enabled in c10d,
+    # even though threaded pg actual collectives are assumed to be single threaded,
+    # different threads may be initializing different groups,
+    # leading to race conditions.
+    # For example, if we have a mesh of [[0, 1], [2, 3]], the sub groups
+    # (dim 0 and 1) would be initialized in different threads independently.
+    # In this case we can no longer rely on class or global variables
+    # but have to rely on store based barrier to make sure each group
+    # is ready separately before we can invoke collectives in any of the groups.
+
+    # the prefix store is already per group so we pass an empty name here
+    _store_based_barrier(rank, prefix_store, "", world_size, timeout)
+    return pg
+
+
+dist.Backend.register_backend("threaded", _create_threaded_pg, devices=["cpu", "cuda"])
+
+
+@dataclass
+class WorldData:
+    default_pg: dist.ProcessGroup
+    pg_map: Dict[dist.ProcessGroup, Tuple[str, Optional[Store]]]
+    pg_names: Dict[dist.ProcessGroup, str]
+    pg_group_ranks: Dict[dist.ProcessGroup, Dict[int, int]]
+    pg_backend_config: Dict[dist.ProcessGroup, str]
+    group_count: int
+    tags_to_pg: Dict[str, List[dist.ProcessGroup]]
+    pg_to_tag: Dict[dist.ProcessGroup, str]
+    pg_coalesce_state: Dict[dist.ProcessGroup, List[Union[_CollOp, P2POp]]]
+    pg_default_device: Dict[dist.ProcessGroup, torch.device]
+
+
+class ThreadLocalWorld:
+    _world = threading.local()
+
+    def _get_world(self) -> WorldData:
+        if not hasattr(ThreadLocalWorld._world, "world"):
+            ThreadLocalWorld._world.world = WorldData(None, {}, {}, {}, {}, 0, {}, {}, {}, {})
+        return ThreadLocalWorld._world.world
+
+    @property
+    def default_pg(self):
+        return self._get_world().default_pg
+
+    @default_pg.setter
+    def default_pg(self, value):
+        self._get_world().default_pg = value
+
+    @property
+    def pg_map(self):
+        return self._get_world().pg_map
+
+    @property
+    def pg_names(self):
+        return self._get_world().pg_names
+
+    @property
+    def pg_group_ranks(self):
+        return self._get_world().pg_group_ranks
+
+    @property
+    def pg_backend_config(self):
+        return self._get_world().pg_backend_config
+
+    @property
+    def group_count(self) -> int:
+        return self._get_world().group_count
+
+    @group_count.setter
+    def group_count(self, value):
+        self._get_world().group_count = value
+
+    @property
+    def tags_to_pg(self):
+        return self._get_world().tags_to_pg
+
+    @property
+    def pg_to_tag(self):
+        return self._get_world().pg_to_tag
+
+    @property
+    def pg_coalesce_state(self) -> Dict[dist.ProcessGroup, List[Union[_CollOp, P2POp]]]:
+        return self._get_world().pg_coalesce_state
+
+    @property
+    def pg_default_device(self) -> Dict[dist.ProcessGroup, torch.device]:
+        return self._get_world().pg_default_device
+
+
+_old_pg_world = None
+_ctx_manager = None
+
+
+def _install_threaded_pg():
+    global _old_pg_world
+    global _ctx_manager
+    _old_pg_world = dist.distributed_c10d._world
+    dist.distributed_c10d._world = ThreadLocalWorld()
+    _ctx_manager = torch.autograd.set_multithreading_enabled(False)
+
+    return dist.distributed_c10d._world
+
+
+def _uninstall_threaded_pg():
+    dist.distributed_c10d._world = _old_pg_world

venv/lib/python3.10/site-packages/torch/testing/_internal/distributed/pipe_with_ddp_test.py

@@ -0,0 +1,149 @@
+# mypy: ignore-errors
+
+import torch
+import torch.distributed as dist
+
+from torch import nn
+from torch.nn.parallel import DistributedDataParallel
+from torch.testing._internal.dist_utils import INIT_METHOD_TEMPLATE, dist_init
+from torch.testing._internal.distributed.rpc.rpc_agent_test_fixture import (
+    RpcAgentTestFixture,
+)
+from torch.testing._internal.common_distributed import (
+    requires_gloo,
+    requires_nccl,
+    skip_if_lt_x_gpu,
+    skip_if_rocm,
+)
+from torch.distributed.pipeline.sync import Pipe
+
+class PipeWithDDPTest(RpcAgentTestFixture):
+    @property
+    def world_size(self) -> int:
+        return 2
+
+    @skip_if_lt_x_gpu(4)
+    @requires_nccl()
+    @dist_init
+    @skip_if_rocm
+    def test_basic_nccl_ckpt_never(self):
+        self._run_basic_test("nccl", "never")
+
+    @skip_if_lt_x_gpu(4)
+    @requires_nccl()
+    @dist_init
+    @skip_if_rocm
+    def test_basic_nccl_ckpt_never_find_unused(self):
+        self._run_basic_test("nccl", "never", find_unused_parameters=True)
+
+    @skip_if_lt_x_gpu(4)
+    @requires_nccl()
+    @dist_init
+    @skip_if_rocm
+    def test_basic_nccl_ckpt_always(self):
+        self._run_basic_test("nccl", "always", static_graph=True)
+
+    @skip_if_lt_x_gpu(4)
+    @requires_nccl()
+    @dist_init
+    @skip_if_rocm
+    def test_basic_nccl_ckpt_except_last(self):
+        self._run_basic_test("nccl", "except_last", static_graph=True)
+
+    @skip_if_lt_x_gpu(4)
+    @requires_gloo()
+    @dist_init
+    @skip_if_rocm
+    def test_basic_gloo_ckpt_never(self):
+        self._run_basic_test("gloo", "never")
+
+    @skip_if_lt_x_gpu(4)
+    @requires_gloo()
+    @dist_init
+    @skip_if_rocm
+    def test_basic_gloo_ckpt_never_find_unused(self):
+        self._run_basic_test("gloo", "never", find_unused_parameters=True)
+
+    @skip_if_lt_x_gpu(4)
+    @requires_gloo()
+    @dist_init
+    @skip_if_rocm
+    def test_basic_gloo_ckpt_always(self):
+        self._run_basic_test("gloo", "always", static_graph=True)
+
+    @skip_if_lt_x_gpu(4)
+    @requires_gloo()
+    @dist_init
+    @skip_if_rocm
+    def test_basic_gloo_ckpt_except_last(self):
+        self._run_basic_test("gloo", "except_last", static_graph=True)
+
+    def _run_basic_test(self, backend, checkpoint, find_unused_parameters=False, static_graph=False):
+        dist.init_process_group(
+            backend=backend,
+            init_method=INIT_METHOD_TEMPLATE.format(file_name=self.file_name),
+            world_size=self.world_size,
+            rank=self.rank,
+        )
+
+        # Use 4 GPUs, two replicas of a pipe across GPU 0 and 1 and another
+        # pipe between GPU 2 and 3. Both replicas are replicated via DDP.
+        fc1 = nn.Linear(16, 8, bias=False).cuda(2 * self.rank)
+
+        class MyModule(nn.Module):
+            def __init__(self, device):
+                super().__init__()
+                self.fc2 = nn.Linear(8, 4, bias=False).cuda(device)
+                self.fc3 = nn.Linear(4, 2, bias=False).cuda(device)
+
+            def forward(self, inp):
+                if find_unused_parameters:
+                    return self.fc2(inp)
+                else:
+                    return self.fc3(self.fc2(inp))
+
+        layer2 = MyModule(2 * self.rank + 1)
+        model = nn.Sequential(
+            fc1,
+            layer2
+        )
+        model = Pipe(model, chunks=2, checkpoint=checkpoint)
+        model = DistributedDataParallel(
+            model,
+            find_unused_parameters=find_unused_parameters,
+            static_graph=static_graph,
+        )
+
+        # Ensure inputs are different across ranks to verify that gradient
+        # sync indeed occurs.
+        model_input = torch.rand(16, 16).cuda(2 * self.rank) * (self.rank + 1)
+        out = model(model_input).local_value()
+        out.sum().backward()
+
+        # Run forward again for find_unused_parameters to trigger any potential errors.
+        if find_unused_parameters:
+            # Ensure inputs are different across ranks to verify that gradient
+            # sync indeed occurs.
+            unused_param_input = torch.rand(16, 16).cuda(2 * self.rank) * (self.rank + 1)
+            model(unused_param_input).local_value().sum().backward()
+
+        # Run a few more iterations of fwd + bwd to ensure gradient synchronization
+        # occurs properly across iterations via delay_all_reduce/bucketized allreduce.
+        for _ in range(3):
+            model_input = torch.rand(16, 16).cuda(2 * self.rank) * (self.rank + 1)
+            out = model(model_input).local_value()
+            out.sum().backward()
+
+        # Check grads
+        output = [torch.empty_like(fc1.weight.grad), torch.empty_like(fc1.weight.grad)]
+        dist.all_gather(output, fc1.weight.grad)
+        self.assertEqual(output[0], output[1])
+
+        output = [torch.empty_like(layer2.fc2.weight.grad), torch.empty_like(layer2.fc2.weight.grad)]
+        dist.all_gather(output, layer2.fc2.weight.grad)
+        self.assertEqual(output[0], output[1])
+
+        if not find_unused_parameters:
+            output = [torch.empty_like(layer2.fc3.weight.grad), torch.empty_like(layer2.fc3.weight.grad)]
+            dist.all_gather(output, layer2.fc3.weight.grad)
+            self.assertEqual(output[0], output[1])

venv/lib/python3.10/site-packages/torch/testing/_internal/distributed/rpc_utils.py

@@ -0,0 +1,185 @@
+# mypy: ignore-errors
+
+import os
+import sys
+import unittest
+from typing import Dict, List, Type
+
+from torch.testing._internal.common_distributed import MultiProcessTestCase
+from torch.testing._internal.common_utils import (
+    TEST_WITH_DEV_DBG_ASAN,
+    find_free_port,
+    IS_SANDCASTLE,
+)
+from torch.testing._internal.distributed.ddp_under_dist_autograd_test import (
+    CudaDdpComparisonTest,
+    DdpComparisonTest,
+    DdpUnderDistAutogradTest,
+)
+from torch.testing._internal.distributed.pipe_with_ddp_test import (
+    PipeWithDDPTest,
+)
+from torch.testing._internal.distributed.nn.api.remote_module_test import (
+    CudaRemoteModuleTest,
+    RemoteModuleTest,
+    ThreeWorkersRemoteModuleTest,
+)
+from torch.testing._internal.distributed.rpc.dist_autograd_test import (
+    DistAutogradTest,
+    CudaDistAutogradTest,
+    FaultyAgentDistAutogradTest,
+    TensorPipeAgentDistAutogradTest,
+    TensorPipeCudaDistAutogradTest
+)
+from torch.testing._internal.distributed.rpc.dist_optimizer_test import (
+    DistOptimizerTest,
+)
+from torch.testing._internal.distributed.rpc.jit.dist_autograd_test import (
+    JitDistAutogradTest,
+)
+from torch.testing._internal.distributed.rpc.jit.rpc_test import JitRpcTest
+from torch.testing._internal.distributed.rpc.jit.rpc_test_faulty import (
+    JitFaultyAgentRpcTest,
+)
+from torch.testing._internal.distributed.rpc.rpc_agent_test_fixture import (
+    RpcAgentTestFixture,
+)
+from torch.testing._internal.distributed.rpc.faulty_agent_rpc_test import (
+    FaultyAgentRpcTest,
+)
+from torch.testing._internal.distributed.rpc.rpc_test import (
+    CudaRpcTest,
+    RpcTest,
+    TensorPipeAgentRpcTest,
+    TensorPipeAgentCudaRpcTest,
+)
+from torch.testing._internal.distributed.rpc.examples.parameter_server_test import ParameterServerTest
+from torch.testing._internal.distributed.rpc.examples.reinforcement_learning_rpc_test import (
+    ReinforcementLearningRpcTest,
+)
+
+
+def _check_and_set_tcp_init():
+    # if we are running with TCP init, set main address and port
+    # before spawning subprocesses, since different processes could find
+    # different ports.
+    use_tcp_init = os.environ.get("RPC_INIT_WITH_TCP", None)
+    if use_tcp_init == "1":
+        os.environ["MASTER_ADDR"] = '127.0.0.1'
+        os.environ["MASTER_PORT"] = str(find_free_port())
+
+def _check_and_unset_tcp_init():
+    use_tcp_init = os.environ.get("RPC_INIT_WITH_TCP", None)
+    if use_tcp_init == "1":
+        del os.environ["MASTER_ADDR"]
+        del os.environ["MASTER_PORT"]
+
+# The tests for the RPC module need to cover multiple possible combinations:
+# - different aspects of the API, each one having its own suite of tests;
+# - different agents (ProcessGroup, TensorPipe, ...);
+# To avoid a combinatorial explosion in code size, and to prevent forgetting to
+# add a combination, these are generated automatically by the code in this file.
+# Here, we collect all the test suites that we need to cover.
+# We then have one separate file for each agent, from which
+# we call the generate_tests function of this file, passing to it a fixture for
+# the agent, which then gets mixed-in with each test suite.
+
+@unittest.skipIf(
+    TEST_WITH_DEV_DBG_ASAN, "Skip ASAN as torch + multiprocessing spawn have known issues"
+)
+class SpawnHelper(MultiProcessTestCase):
+    def setUp(self):
+        super().setUp()
+        _check_and_set_tcp_init()
+        self._spawn_processes()
+
+    def tearDown(self):
+        _check_and_unset_tcp_init()
+        super().tearDown()
+
+
+# This list contains test suites that are agent-agnostic and that only verify
+# compliance with the generic RPC interface specification. These tests should
+# *not* make use of implementation details of a specific agent (options,
+# attributes, ...). These test suites will be instantiated multiple times, once
+# for each agent (except the faulty agent, which is special).
+GENERIC_TESTS = [
+    RpcTest,
+    ParameterServerTest,
+    DistAutogradTest,
+    DistOptimizerTest,
+    JitRpcTest,
+    JitDistAutogradTest,
+    RemoteModuleTest,
+    ThreeWorkersRemoteModuleTest,
+    DdpUnderDistAutogradTest,
+    DdpComparisonTest,
+    ReinforcementLearningRpcTest,
+]
+GENERIC_CUDA_TESTS = [
+    CudaRpcTest,
+    CudaDistAutogradTest,
+    CudaRemoteModuleTest,
+    CudaDdpComparisonTest,
+    PipeWithDDPTest,
+]
+
+
+# This list contains test suites that will only be run on the TensorPipeAgent.
+# These suites should be standalone, and separate from the ones in the generic
+# list (not subclasses of those!).
+TENSORPIPE_TESTS = [
+    TensorPipeAgentRpcTest,
+    TensorPipeAgentDistAutogradTest,
+]
+TENSORPIPE_CUDA_TESTS = [
+    TensorPipeAgentCudaRpcTest,
+    TensorPipeCudaDistAutogradTest,
+]
+
+
+# This list contains test suites that will only be run on the faulty RPC agent.
+# That agent is special as it's only used to perform fault injection in order to
+# verify the error handling behavior. Thus the faulty agent will only run the
+# suites in this list, which were designed to test such behaviors, and not the
+# ones in the generic list.
+FAULTY_AGENT_TESTS = [
+    FaultyAgentRpcTest,
+    FaultyAgentDistAutogradTest,
+    JitFaultyAgentRpcTest,
+]
+
+
+def generate_tests(
+    prefix: str,
+    mixin: Type[RpcAgentTestFixture],
+    tests: List[Type[RpcAgentTestFixture]],
+    module_name: str,
+) -> Dict[str, Type[RpcAgentTestFixture]]:
+    """Mix in the classes needed to autogenerate the tests based on the params.
+
+    Takes a series of test suites, each written against a "generic" agent (i.e.,
+    derived from the abstract RpcAgentTestFixture class), as the `tests` args.
+    Takes a concrete subclass of RpcAgentTestFixture, which specializes it for a
+    certain agent, as the `mixin` arg. Produces all combinations of them.
+    Returns a dictionary of class names to class type
+    objects which can be inserted into the global namespace of the calling
+    module. The name of each test will be a concatenation of the `prefix` arg
+    and the original name of the test suite.
+    The `module_name` should be the name of the calling module so
+    that the classes can be fixed to make it look like they belong to it, which
+    is necessary for pickling to work on them.
+    """
+    ret: Dict[str, Type[RpcAgentTestFixture]] = {}
+    for test_class in tests:
+        if IS_SANDCASTLE and TEST_WITH_DEV_DBG_ASAN:
+            print(
+                f'Skipping test {test_class} on sandcastle for the following reason: '
+                'Skip dev-asan as torch + multiprocessing spawn have known issues', file=sys.stderr)
+            continue
+
+        name = f"{prefix}{test_class.__name__}"
+        class_ = type(name, (test_class, mixin, SpawnHelper), {})
+        class_.__module__ = module_name
+        ret[name] = class_
+    return ret

venv/lib/python3.10/site-packages/torch/testing/_internal/inductor_utils.py

@@ -0,0 +1,87 @@
+# mypy: ignore-errors
+
+import torch
+import re
+import unittest
+import functools
+from subprocess import CalledProcessError
+
+from torch._inductor.codecache import CppCodeCache
+from torch.utils._triton import has_triton
+from torch.testing._internal.common_utils import (
+    LazyVal,
+    IS_FBCODE,
+)
+from torch._dynamo.backends.registry import register_backend
+from torch._inductor.compile_fx import compile_fx, count_bytes_inner
+from torch.testing._internal.common_utils import TestCase
+
+def test_cpu():
+    try:
+        CppCodeCache.load("")
+        return not IS_FBCODE
+    except (
+        CalledProcessError,
+        OSError,
+        torch._inductor.exc.InvalidCxxCompiler,
+        torch._inductor.exc.CppCompileError,
+    ):
+        return False
+
+HAS_CPU = LazyVal(test_cpu)
+
+HAS_CUDA = torch.cuda.is_available() and has_triton()
+
+HAS_GPU = HAS_CUDA
+
+GPUS = ["cuda"]
+
+HAS_MULTIGPU = any(
+    getattr(torch, gpu).is_available() and getattr(torch, gpu).device_count() >= 2
+    for gpu in GPUS
+)
+
+tmp_gpus = [x for x in GPUS if getattr(torch, x).is_available()]
+assert len(tmp_gpus) <= 1
+GPU_TYPE = "cuda" if len(tmp_gpus) == 0 else tmp_gpus.pop()
+del tmp_gpus
+
+@register_backend
+def count_bytes_inductor(gm, example_inputs):
+    return compile_fx(gm, example_inputs, inner_compile=count_bytes_inner)
+
+def _check_has_dynamic_shape(
+    self: TestCase,
+    code,
+):
+    for_loop_found = False
+    has_dynamic = False
+    lines = code.split("\n")
+    for line in lines:
+        if "for(" in line:
+            for_loop_found = True
+            if re.search(r";.*ks.*;", line) is not None:
+                has_dynamic = True
+                break
+    self.assertTrue(
+        has_dynamic, msg=f"Failed to find dynamic for loop variable\n{code}"
+    )
+    self.assertTrue(for_loop_found, f"Failed to find for loop\n{code}")
+
+
+def skipDeviceIf(cond, msg, *, device):
+    if cond:
+        def decorate_fn(fn):
+            def inner(self, *args, **kwargs):
+                if self.device == device:
+                    raise unittest.SkipTest(msg)
+                return fn(self, *args, **kwargs)
+            return inner
+    else:
+        def decorate_fn(fn):
+            return fn
+
+    return decorate_fn
+
+skipCUDAIf = functools.partial(skipDeviceIf, device="cuda")
+skipCPUIf = functools.partial(skipDeviceIf, device="cpu")

venv/lib/python3.10/site-packages/torch/testing/_internal/jit_metaprogramming_utils.py

@@ -0,0 +1,722 @@
+# mypy: ignore-errors
+
+# Torch
+from torch.jit.annotations import BroadcastingList2, BroadcastingList3  # noqa: F401
+import torch.nn.functional as F
+import torch
+import torch.cuda
+import torch.jit
+import torch.jit._logging
+import torch.jit.frontend
+from torch.testing._internal.common_nn import module_tests, new_module_tests
+from torch.testing._internal.common_utils import is_iterable_of_tensors, noncontiguous_like
+
+import collections
+from copy import deepcopy
+from typing import Any, Dict, List, Union
+import math  # noqa: F401
+
+# Testing utils
+from torch import inf
+
+assert torch.get_default_dtype() == torch.float32
+
+L = 20
+M = 10
+S = 5
+
+
+def unpack_variables(args):
+    if isinstance(args, tuple):
+        return tuple(unpack_variables(elem) for elem in args)
+    else:
+        return args
+
+class dont_convert(tuple):
+    pass
+
+non_differentiable = collections.namedtuple('non_differentiable', ['tensor'])
+
+def create_input(call_args, requires_grad=True, non_contiguous=False, call_kwargs=None, dtype=torch.float, device=None):
+    if not isinstance(call_args, tuple):
+        call_args = (call_args,)
+
+    def map_arg(arg):
+        def maybe_non_contig(tensor):
+            if not non_contiguous or tensor.numel() < 2:
+                return tensor.clone()
+
+            return noncontiguous_like(tensor)
+
+        def conjugate(tensor):
+            return tensor.conj()
+
+        if isinstance(arg, (torch.Size, dont_convert)):
+            return arg
+        elif isinstance(arg, tuple) and len(arg) == 0:
+            var = conjugate(torch.randn((), dtype=dtype, device=device))
+            var.requires_grad = requires_grad
+            return var
+        elif isinstance(arg, tuple) and not isinstance(arg[0], torch.Tensor):
+            return conjugate(maybe_non_contig(torch.randn(*arg, dtype=dtype, device=device))).requires_grad_(requires_grad)
+        # double check casting
+        elif isinstance(arg, non_differentiable):
+            if isinstance(arg.tensor, torch.Tensor):
+                return conjugate(maybe_non_contig(arg.tensor.to(device=device)))
+            return conjugate(maybe_non_contig(arg.tensor.to(device=device)))
+        elif isinstance(arg, torch.Tensor):
+            if arg.is_complex() != dtype.is_complex:
+                raise RuntimeError("User provided tensor is real for a test that runs with complex dtype, ",
+                                   "which is not supported for now")
+            # NOTE: We do clone() after detach() here because we need to be able to change size/storage of v afterwards
+            v = conjugate(maybe_non_contig(arg)).detach().to(device=device).clone()
+            v.requires_grad = requires_grad and (v.is_floating_point() or v.is_complex())
+            return v
+        elif callable(arg):
+            return map_arg(arg(dtype=dtype, device=device))
+        else:
+            return arg
+    args_out = tuple(map_arg(arg) for arg in call_args)
+    kwargs_out = {k: map_arg(v) for k, v in call_kwargs.items()} if call_kwargs else {}
+    return args_out, kwargs_out
+
+# NB: JIT script tests for all nn functional interfaces, script mode does
+# not support in_place operations yet, so no inplace operation tests added.
+# removed all the deprecated functions
+#
+# (
+#   method name,
+#   input size/constructing fn,
+#   args (tuple represents shape of a tensor arg),
+#   test variant name(will be used at test name suffix,
+#       'inplace' skips grad tests),                         // optional
+#   (True, nonfusible_nodes, fusible_nodes) for autodiff     // optional
+#   fn to determine if test should be skipped,               // optional
+#   fn mapping output to part that should be gradcheck'ed,   // optional
+#   kwargs for function,                                     // optional
+# )
+nn_functional_tests = [
+    ('conv1d', (S, S, S), ((S, S, S),)),
+    ('conv2d', (S, S, S, S), ((S, S, S, S),)),
+    ('conv3d', (S, S, S, S, S), ((S, S, S, S, S),)),
+    ('conv_transpose1d', (S, S, S), ((S, S, S),)),
+    ('conv_transpose2d', (S, S, S, S), ((S, S, S, S),)),
+    ('conv_transpose3d', (S, S, S, S, S), ((S, S, S, S, S),)),
+    ('conv_tbc', (S, S, S), ((S, S, S), (S,), 2)),
+    ('avg_pool1d', (S, S, S), (3,)),
+    ('avg_pool2d', (S, S, S, S), (3,), '', (True,)),
+    ('avg_pool3d', (S, S, S, S, S), (3,)),
+    ('fractional_max_pool2d', (S, S, S, S), (3, [2, 3],)),
+    ('max_pool1d', (S, S, S), (2, 1)),
+    ('max_pool1d', (S, S, S), (2, 1, 1, 1, False, True), 'with_indices'),
+    ('max_pool2d', (S, S, S, S), (2, 1), '', (True, 'aten::max_pool2d_with_indices')),
+    ('max_pool2d', (S, S, S, S), (2, 1, 1, 1, False, True), 'with_indices', (True, 'aten::max_pool2d_with_indices')),
+    ('max_pool3d', (S, S, S, S, S), (2, 1)),
+    ('max_unpool1d', torch.tensor([[[2., 4]]]), (torch.tensor([[[1, 3]]]), 2, 2, 0)),
+    ('max_unpool2d', torch.tensor([[[[2., 4]]]]), (torch.tensor([[[[1, 3]]]]), 2, 2, 0)),
+    ('max_unpool3d', torch.tensor([[[[[2., 4]]]]]), (torch.tensor([[[[[1, 3]]]]]), 2, 2, 0)),
+    ('lp_pool1d', (S, S, S), (2., 3, 2,)),
+    ('lp_pool2d', (S, S, S, S), (2., 3, 2,)),
+    ('lp_pool3d', (S, S, S, S, S), (2., 3, 2,)),
+    ('adaptive_max_pool1d', (S, S, S), (5,)),
+    ('adaptive_max_pool2d', (S, S, S, S), ([5, 7],)),
+    ('adaptive_max_pool3d', (S, S, S, S, S), ([3, 2, 2],)),
+    ('adaptive_avg_pool1d', (S, S, S), (5,), '', (True,)),
+    ('adaptive_avg_pool2d', (S, S, S, S), ([5, 7],), '', (True,)),
+    ('adaptive_avg_pool3d', (S, S, S, S, S), ([3, 2, 2],), '', (True,)),
+    ('dropout', (S, S, S), (0.5,), '', (True, 'aten::native_dropout')),
+    ('alpha_dropout', (S, S, S), (0.5,)),
+    ('dropout2d', (S, S, S), (0.5,)),
+    ('dropout2d', (S, S, S, S), (0.5,), 'batched'),
+    ('dropout3d', (S, S, S, S), (0.5,)),
+    ('dropout3d', (S, S, S, S, S), (0.5,), 'batched'),
+    ('feature_alpha_dropout', (S, S, S), (0.5,)),
+    ('threshold', (S, S, S), (0.1, 2.), '', (True,)),
+    ('threshold', (S, S, S), (0.1, 2., True), 'inplace'),
+    ('relu', (S, S, S), (), '', (True,)),
+    ('relu', (S, S, S), (), 'inplace'),
+    ('glu', (S - 1, S - 1, S - 1), (),),
+    ('hardtanh', (S, S, S), (-0.5, 0.5), '', (True,)),
+    ('hardtanh', (S, S, S), (-0.5, 0.5, True), 'inplace'),
+    ('relu6', (S, S, S), (), '', (True,)),
+    ('relu6', (S, S, S), (True), 'inplace'),
+    ('elu', (S, S, S), (0.9,),),
+    ('elu', (S, S, S), (0.9, True), 'inplace'),
+    ('selu', (S, S, S), (),),
+    ('selu', (S, S, S), (True), 'inplace'),
+    ('celu', (S, S, S), (0.9,),),
+    ('celu', (S, S, S), (0.9, True), 'inplace'),
+    ('leaky_relu', (S, S, S), (0.02,), '', (True,)),
+    ('leaky_relu', (S, S, S), (0.02,), 'inplace'),
+    ('rrelu', (S, S), (0.1, 0.3, False),),
+    ('rrelu', (S, S), (0.1, 0.3, False, True), 'inplace'),
+    ('hardshrink', (S, S, S), (0.4,), '', (True,)),
+    ('tanhshrink', (S, S, S), (),),
+    ('softsign', (S, S, S), (),),
+    ('softplus', (S, S, S), (), '', (True,)),
+    ('softmin', (S, S, S), (0,),),
+    ('softmax', (S, S, S), (0,), '', (True,)),
+    ('softmax', (S, S, S), (0, 3, torch.double), 'with_all_args', (True,)),
+    ('tanh', (S, S, S), (), '', (True,)),
+    ('sigmoid', (S, S, S), (), '', (True,)),
+    ('silu', (S, S, S), (), '', (True,)),
+    ('log_softmax', (S, S, S), (0,), '', (True,)),
+    ('linear', (S, S), ((M, S),), '', (True, ['aten::linear'])),
+    ('linear', (S, S), ((M, S), (M,)), 'addmm', (True, ['aten::linear'])),
+    ('bilinear', (S, S, S), ((S, S, M), torch.zeros(M, S, M),),),
+    ('embedding', torch.tensor([[1, 2, 4, 5], [4, 3, 2, 5]]), (torch.rand(6, 3), ), '', (True,)),
+    ('embedding_bag', torch.tensor([1, 2, 4, 2]), (torch.rand(5, 3), torch.tensor([0, 4]),),),
+    ('batch_norm', (S, S),
+        (non_differentiable(torch.randn(S)), non_differentiable(torch.ones(S)), None, None, True, ),
+        'training', (True, 'aten::_batch_norm_impl_index')),
+    ('batch_norm', (0, S, S, S),
+        (non_differentiable(torch.randn(S)), non_differentiable(torch.ones(S)),
+         non_differentiable(torch.randn(S)), non_differentiable(torch.ones(S)), True, ),
+        'size_zero', (True, 'aten::_batch_norm_impl_index')),
+    ('batch_norm', (0, S, S, S),
+        (non_differentiable(torch.randn(S)), non_differentiable(torch.ones(S)),
+         non_differentiable(torch.randn(S)), non_differentiable(torch.ones(S)), True, ),
+        'size_zero_inference', (True, 'aten::_batch_norm_impl_index')),
+    ('batch_norm', (S, S),
+        (non_differentiable(torch.randn(S)), non_differentiable(torch.ones(S)),
+         non_differentiable(torch.randn(S)), non_differentiable(torch.ones(S)), True, ),
+        'with_weight_and_bias_training', (True, 'aten::_batch_norm_impl_index')),
+    ('batch_norm', (S, S), (non_differentiable(torch.randn(S)), non_differentiable(torch.ones(S)),
+                            None, non_differentiable(torch.ones(S)), True, ),
+        'with_only_bias_training', (True, 'aten::_batch_norm_impl_index')),
+    ('batch_norm', (S, S), (non_differentiable(torch.randn(S)), non_differentiable(torch.ones(S)),
+                            non_differentiable(torch.randn(S)), None, True, ),
+        'with_only_weight_training', (True, 'aten::_batch_norm_impl_index')),
+    ('batch_norm', (S, S), (non_differentiable(torch.randn(S)), non_differentiable(torch.ones(S)),
+                            None, None, False, ),
+        'inference', (True, 'aten::_batch_norm_impl_index')),
+    ('batch_norm', (S, S), (non_differentiable(torch.randn(S)), non_differentiable(torch.ones(S)),
+                            non_differentiable(torch.randn(S)), non_differentiable(torch.ones(S)), False, ),
+        'with_weight_and_bias_inference', (True, 'aten::_batch_norm_impl_index')),
+    ('batch_norm', (S, S), (non_differentiable(torch.randn(S)), non_differentiable(torch.ones(S)),
+                            None, non_differentiable(torch.ones(S)), False, ),
+        'with_only_bias_inference', (True, 'aten::_batch_norm_impl_index')),
+    ('batch_norm', (S, S), (non_differentiable(torch.randn(S)), non_differentiable(torch.ones(S)),
+                            non_differentiable(torch.randn(S)), None, False, ),
+        'with_only_weight_inference', (True, 'aten::_batch_norm_impl_index')),
+    ('instance_norm', (S, S, S), (non_differentiable(torch.zeros(S)), non_differentiable(torch.ones(S))),),
+    ('layer_norm', (S, S, S, S), ([5],), '',
+     (False, ['aten::contiguous', 'aten::_batch_norm_impl_index'])),
+    ('layer_norm', (S, S, S, S), ([5], non_differentiable(torch.rand(S)),), 'with_only_weight',
+     (False, ['aten::contiguous', 'aten::_batch_norm_impl_index'])),
+    ('layer_norm', (S, S, S, S), ([5], None, non_differentiable(torch.rand(S)),), 'with_only_bias',
+     (False, ['aten::contiguous', 'aten::_batch_norm_impl_index'])),
+    ('layer_norm', (S, S, S, S), ([5], non_differentiable(torch.rand(S)),
+                                  non_differentiable(torch.rand(S))), 'with_weight_and_bias',
+     (False, ['aten::contiguous', 'aten::_batch_norm_impl_index', 'aten::addcmul'])),
+    ('group_norm', (S, S, S), (1, torch.rand(5),),),
+    ('local_response_norm', (S, S, S), (2, ),),
+    ('nll_loss', F.log_softmax(torch.randn(3, 5), dim=0), (torch.tensor([1, 0, 4]),), '',),
+    ('poisson_nll_loss', torch.rand(S, 2), (torch.rand(S, 2),),),
+    ('poisson_nll_loss', torch.rand(S, 2), (torch.rand(S, 2), True, True), 'full'),
+    ('kl_div', F.log_softmax(torch.randn(S, 10), 1), (F.softmax(torch.randn(S, 10), 1),),),
+    ('cross_entropy', (3, S), (torch.randint(S, (3,), dtype=torch.int64),),),
+    ('binary_cross_entropy_with_logits', (3,), (torch.empty(3).random_(2), ),),
+    ('smooth_l1_loss', (3, S), (non_differentiable(torch.rand(3, S)),),),
+    ('huber_loss', (3, S), (non_differentiable(torch.rand(3, S)),),),
+    ('l1_loss', (3, S), (non_differentiable(torch.rand(3, S)),),),
+    ('mse_loss', (3, S), (non_differentiable(torch.rand(3, S)),),),
+    ('smooth_l1_loss', (3, S), ((torch.rand(3, S)),), 'with_grad'),
+    ('huber_loss', (3, S), ((torch.rand(3, S)),), 'with_grad'),
+    ('l1_loss', (3, S), ((torch.rand(3, S)),), 'with_grad'),
+    ('mse_loss', (3, S), ((torch.rand(3, S)),), 'with_grad'),
+    ('margin_ranking_loss', (S,), ((S,), (S,)),),
+    ('hinge_embedding_loss', (3, S), (non_differentiable(torch.rand(3, S)),),),
+    ('soft_margin_loss', (3, S), (non_differentiable(torch.rand(3, S)),),),
+    ('multilabel_soft_margin_loss', (3, S), (non_differentiable(torch.rand(3, S)),),),
+    ('cosine_embedding_loss', (S, S), ((S, S), non_differentiable(torch.rand(S,))),),
+    ('pixel_shuffle', (1, 9, 4, 4), (3,),),
+    ('pixel_unshuffle', (1, 1, 12, 12), (3,),),
+    ('affine_grid', (S, 2, 3), (torch.Size([S, 1, 7, 7]),),),
+    ('pad', (3, 3, 4, 2), ([1, 1],),),
+    ('pairwise_distance', (S, S), ((S, S),),),
+    ('pdist', (S, S), (),),
+    ('cosine_similarity', (S, S), ((S, S),),),
+    ('triplet_margin_loss', (S, S), ((S, S), (S, S)),),
+    ('normalize', (S, S, S), (),),
+    ('unfold', (S, S, S, S), ([2, 3]),),
+    ('fold', (1, 3 * 2 * 2, 12), ([4, 5], [2, 2]),),
+    ('grid_sample', (S, S, S, S), (non_differentiable(torch.rand(S, S, S, 2)),),),
+    ('gumbel_softmax', (S, S), (2.,), '', (True, ['aten::softmax', 'aten::add', 'aten::div'], ['aten::neg'])),
+    ('gumbel_softmax', (S, S), (2., True,), 'hard', (True, ['aten::softmax', 'aten::add', 'aten::div'], ['aten::neg'])),
+    ('multilabel_margin_loss', torch.tensor([[0.2, -0.2, 0.07]]), (torch.tensor([[0, 0, 1]]),),),
+    ('multi_margin_loss', (S, S), (non_differentiable(torch.randint(S, (S, ), dtype=torch.int64)),
+                                   1, 1., non_differentiable(torch.randn(S))),),
+    ('binary_cross_entropy', torch.randn(3, 2).sigmoid(), (non_differentiable(torch.rand(3, 2)),
+                                                           non_differentiable(torch.randn(3, 2))),),
+    ('binary_cross_entropy', torch.randn(3, 2).sigmoid(),
+        (non_differentiable(torch.rand(3, 2)),
+         non_differentiable(torch.randn(3, 2)), None, None, 'mean'), 'size_average'),
+    ('ctc_loss', torch.rand(S, S, S).log_softmax(2).detach().requires_grad_(),
+     (torch.randint(1, S, (S, S), dtype=torch.long), torch.full((S,), S, dtype=torch.long),
+      torch.randint(1, S, (S,), dtype=torch.long))),
+    ('upsample', torch.randn(S, S, M, M), (None, 2.), 'with_scale'),
+    ('upsample', torch.randn(S, S, M, M), (4,), 'with_size'),
+    ('interpolate', torch.zeros(3, 3).view(1, 1, 3, 3), (2,), 'nearest_4d'),
+    ('interpolate', torch.randn(S, S, M, M), (None, 2.), 'nearest_4d_with_scale'),
+    ('interpolate', torch.randn(S, S, M, M), (4,), 'nearest_4d_with_size'),
+    ('interpolate', torch.zeros(3, 3).view(1, 1, 3, 3), (2,), 'area_4d'),
+    ('interpolate', torch.randn(S, S, M, M), (None, 2.), 'area_4d_with_scale'),
+    ('interpolate', torch.randn(S, S, M, M), (4,), 'area_4d_with_size'),
+    ('interpolate', torch.zeros(3, 3).view(1, 1, 3, 3), (2,), 'bilinear_4d'),
+    ('interpolate', torch.randn(S, S, M, M), (None, 2.), 'bilinear_4d_with_scale'),
+    ('interpolate', torch.randn(S, S, M, M), (4,), 'bilinear_4d_with_size'),
+    ('interpolate', torch.zeros(3, 3).view(1, 1, 3, 3), (2,), 'bicubic_4d'),
+    ('interpolate', torch.randn(S, S, M, M), (None, 2.), 'bicubic_4d_with_scale'),
+    ('interpolate', torch.randn(S, S, M, M), (4,), 'bicubic_4d_with_size'),
+    ('interpolate', torch.zeros(3, 3).view(1, 3, 3), (2,), 'nearest_3d'),
+    ('interpolate', torch.randn(S, M, M), (None, 2.), 'nearest_3d_with_scale'),
+    ('interpolate', torch.randn(S, M, M), (4,), 'nearest_3d_with_size'),
+    ('interpolate', torch.zeros(3, 3).view(1, 3, 3), (2,), 'area_3d'),
+    ('interpolate', torch.randn(S, M, M), (None, 2.), 'area_3d_with_scale'),
+    ('interpolate', torch.randn(S, M, M), (4,), 'area_3d_with_size'),
+    ('interpolate', torch.zeros(3, 3).view(1, 3, 3), (2,), 'linear_3d'),
+    ('interpolate', torch.randn(S, M, M), (None, 2.), 'linear_3d_with_scale'),
+    ('interpolate', torch.randn(S, M, M), (4,), 'linear_3d_with_size'),
+    ('interpolate', torch.randn(S, M, M, M, M), (None, 2.), 'nearest_5d_with_scale'),
+    ('interpolate', torch.randn(S, M, M, M, M), (4,), 'nearest_5d_with_size'),
+    ('interpolate', torch.zeros(3, 3, 3).view(1, 1, 3, 3, 3), (2,), 'area_5d'),
+    ('interpolate', torch.randn(S, M, M, M, M), (None, 2.), 'area_5d_with_scale'),
+    ('interpolate', torch.randn(S, M, M, M, M), (4,), 'area_5d_with_size'),
+    ('interpolate', torch.zeros(3, 3, 3).view(1, 1, 3, 3, 3), (2,), 'trilinear_5d'),
+    ('interpolate', torch.randn(S, M, M, M, M), (None, 2.), 'trilinear_5d_with_scale'),
+    ('interpolate', torch.randn(S, M, M, M, M), (4,), 'trilinear_5d_with_size'),
+    ('interpolate', torch.zeros(3, 3).view(1, 1, 3, 3), (2, None, 'nearest', None, False),
+     'nearest_4d_not_recompute_scale_factor'),
+    ('interpolate', torch.randn(S, S, M, M), (4, None, 'nearest', None, False),
+     'nearest_4d_with_size_not_recompute_scale_factor'),
+    ('interpolate', torch.randn(S, S, M, M), (None, 2., 'bilinear', None, False),
+     'bilinear_4d_with_scale_not_recompute_scale_factor'),
+    ('interpolate', torch.randn(S, S, M, M), (4, None, 'bilinear', None, False),
+     'bilinear_4d_with_size_not_recompute_scale_factor'),
+    ('interpolate', torch.randn(S, S, M, M), (None, 2., 'bicubic', None, False),
+     'bicubic_4d_with_scale_not_recompute_scale_factor'),
+    ('interpolate', torch.randn(S, S, M, M), (4, None, 'bicubic', None, False),
+     'bicubic_4d_with_size_not_recompute_scale_factor'),
+    ('interpolate', torch.randn(S, M, M), (None, 2., 'nearest', None, False),
+     'nearest_3d_with_scale_not_recompute_scale_factor'),
+    ('interpolate', torch.randn(S, M, M), (4, None, 'nearest', None, False),
+     'nearest_3d_with_size_not_recompute_scale_factor'),
+    ('interpolate', torch.randn(S, M, M), (None, 2., 'linear', None, False),
+     'linear_3d_with_scale_not_recompute_scale_factor'),
+    ('interpolate', torch.randn(S, M, M), (4, None, 'linear', None, False),
+     'linear_3d_with_size_not_recompute_scale_factor'),
+    ('interpolate', torch.randn(S, M, M, M, M), (None, 2., 'nearest', None, False),
+     'nearest_5d_with_scale_not_recompute_scale_factor'),
+    ('interpolate', torch.randn(S, M, M, M, M), (4, None, 'nearest', None, False),
+     'nearest_5d_with_size_not_recompute_scale_factor'),
+    ('interpolate', torch.randn(S, M, M, M, M), (None, 2., 'trilinear', None, False),
+     'trilinear_5d_with_scale_not_recompute_scale_factor'),
+    ('interpolate', torch.randn(S, M, M, M, M), (4, None, 'trilinear', None, False),
+     'trilinear_5d_with_size_not_recompute_scale_factor'),
+]
+
+script_template = '''
+def the_method({}):
+    return {}
+'''
+
+def value_to_literal(value):
+    if isinstance(value, str):
+        # Quotes string and escapes special characters
+        return ascii(value)
+    if isinstance(value, torch.Tensor):
+        return 'torch.' + str(value)
+    else:
+        return str(value)
+
+def get_call(method_name, func_type, args, kwargs):
+    kwargs_str = ', '.join([k + '=' + value_to_literal(v) for k, v in kwargs.items()])
+    self_arg = args[0]
+    if func_type == 'method':
+        args = args[1:]
+
+    argument_str = ', '.join(args)
+    argument_str += ', ' if len(args) and len(kwargs) else ''
+    argument_str += kwargs_str
+
+    if func_type == 'functional' or func_type == 'function':
+        call = f'torch.{method_name}({argument_str})'
+    elif func_type == 'method':
+        call = f'{self_arg}.{method_name}({argument_str})'
+    elif func_type == 'nn_functional':
+        call = f'torch.nn.functional.{method_name}({argument_str})'
+    else:
+        raise TypeError('Unsupported function type')
+
+    return call
+
+def get_constant(x):
+    if x == inf:
+        return 'math.inf'
+    if x == -inf:
+        return '-math.inf'
+    return x
+
+def get_script_args(args):
+    formals: List[str] = []
+    tensors: List[Union[torch.Tensor, List[torch.Tensor]]] = []
+    actuals: List[str] = []
+    for arg in args:
+        if isinstance(arg, torch.Tensor):
+            name = f'i{len(formals)}'
+            formals.append(name)
+            actuals.append(name)
+            tensors.append(arg)
+        elif is_iterable_of_tensors(arg):
+            name = f'i{len(formals)}'
+            formals.append(name + ': List[torch.Tensor]')
+            actuals.append(name)
+            tensors.append(list(arg))
+        elif isinstance(arg, str):
+            actuals.append(f"'{arg}'")
+        else:
+            actuals.append(str(get_constant(arg)))
+    return (formals, tensors, actuals)
+
+# create a script function from (name, func_type, output_process_fn),
+# and returns the compiled function and example inputs
+def gen_script_fn_and_args(method_name, func_type, *args, **kwargs):
+    formals, tensors, actuals = get_script_args(args)
+    call = get_call(method_name, func_type, actuals, kwargs)
+    script = script_template.format(', '.join(formals), call)
+    CU = torch.jit.CompilationUnit(script)
+    return CU.the_method, tensors
+
+# create a script function from (name, func_type),
+# returns a function takes in (args, kwargs) and runs the compiled function
+def create_script_fn(self, method_name, func_type):
+    # function returns tuple containing original output and
+    # filtered output to be used in checking gradients
+    def script_fn(*args, **kwargs):
+        fn, tensors = gen_script_fn_and_args(method_name, func_type, *args, **kwargs)
+        self.assertExportImport(fn.graph, tensors)
+        output = fn(*tensors)
+        # skip type annotate function attributes for now, see: https://github.com/python/mypy/issues/2087
+        script_fn.last_graph = fn.graph_for(*tensors)  # type: ignore[attr-defined]
+        return output
+    return script_fn
+
+class SplitInputs:
+    all_tensors: List[Any]
+    tensor_args: List[Any]
+    nontensor_args: List[Any]
+    arg_types: List[str]
+    tensor_kwargs: Dict[str, Any]
+    kwarg_order: List[str]
+    nontensor_kwargs: Dict[str, Any]
+    kwarg_types: Dict[str, Any]
+
+    @staticmethod
+    def _is_tensor_input(arg):
+        return isinstance(arg, torch.Tensor) or is_iterable_of_tensors(arg)
+
+    def __init__(self, args, kwargs):
+        self.arg_types = ['t' if self._is_tensor_input(arg) else 's' for arg in args]
+        self.kwarg_types = {k: 't' if self._is_tensor_input(v) else 's' for k, v in kwargs.items()}
+        self.tensor_args = [arg for arg in args if self._is_tensor_input(arg)]
+        self.nontensor_args = [arg for arg in args if not self._is_tensor_input(arg)]
+        self.tensor_kwargs = {k: v for k, v in kwargs.items() if self._is_tensor_input(v)}
+        self.nontensor_kwargs = {k: v for k, v in kwargs.items() if not self._is_tensor_input(v)}
+        self.all_tensors = [*self.tensor_args, *[v for k, v in self.tensor_kwargs.items()]]
+        self.kwarg_order = [k for k, v in kwargs.items()]
+
+    def nontensors_match(self, other: 'SplitInputs'):
+        if self.arg_types != other.arg_types:
+            return False
+        if self.kwarg_types != other.kwarg_types:
+            return False
+        if self.kwarg_order != other.kwarg_order:
+            return False
+        if self.nontensor_args != other.nontensor_args:
+            return False
+        if self.nontensor_kwargs != other.nontensor_kwargs:
+            return False
+        return True
+
+# make a new function where all non-tensor arguments in 'args' have been partially
+# applied, and all tensor arguments remain.
+# used to trace functions when some arguments are not tensors
+def partial_apply_nontensors(fn, args, kwargs):
+    inputs = SplitInputs(args, kwargs)
+
+    def new_fn(*tensors_):
+        tensors = iter(tensors_)
+        full_args = [args[i] if s == 's' else next(tensors) for i, s in enumerate(inputs.arg_types)]
+        full_kwargs = {k: kwargs[k] if s == 's' else next(tensors) for k, s in inputs.kwarg_types.items()}
+        return fn(*full_args, **full_kwargs)
+
+    return new_fn, inputs
+
+# create a trace function from input fn
+def create_traced_fn(self, fn, cache_traced_fn=False):
+    def traced_fn(*inputs, **kwargs):
+        # `check_trace` is set to False because check_trace is run with @no_grad
+        # Also, `check_against_reference` already does all the checks
+        # against python function
+        fn_tensors, split_inputs = partial_apply_nontensors(fn, inputs, kwargs)
+        if not cache_traced_fn or not hasattr(traced_fn, 'traced'):
+            traced = torch.jit.trace(fn_tensors, split_inputs.all_tensors, check_trace=False)
+            self.assertExportImport(traced.graph, split_inputs.all_tensors)
+            output = traced(*split_inputs.all_tensors)
+            if cache_traced_fn:
+                traced_fn.traced = traced
+                traced_fn.split_inputs = split_inputs
+        else:
+            # Guard to check that nontensor inputs are the same as during tracing
+            self.assertTrue(traced_fn.split_inputs.nontensors_match(split_inputs))
+            output = traced_fn.traced(*split_inputs.all_tensors)
+            traced = traced_fn.traced
+        # skip type annotate function attributes for now, see: https://github.com/python/mypy/issues/2087
+        traced_fn.last_graph = traced.graph_for(*split_inputs.all_tensors)  # type: ignore[attr-defined]
+        traced_fn.graph = traced.graph  # type: ignore[attr-defined]
+        return output
+    return traced_fn
+
+# known to be failing in script
+EXCLUDE_SCRIPT = {
+    'test_norm_fro_default',
+    'test_norm_fro_cpu',
+    'test_norm_nuc',
+    'test_norm_fro',
+    'test_norm_nuc_batched',
+
+    # aten op has additional cudnn argument
+    'test_nn_unfold',
+
+    # flaky test - TODO fix
+    'test_nn_ctc_loss',
+
+    # unknown builtin op
+    'test_nn_fold',
+
+    # jit doesn't support sparse tensors.
+    'test_to_sparse',
+    'test_to_sparse_dim',
+}
+
+# generates a script function and set of example inputs
+# from a specified test in the format of nn_functional_tests
+def get_nn_functional_compiled_fn_and_inputs(name, self_size, args, variant_name='', *extra_args):
+    test_name = 'test_nn_' + name
+
+    if variant_name != '':
+        test_name = test_name + '_' + variant_name
+
+    no_grad = variant_name == 'inplace'
+
+    self_variable = create_input((self_size,))[0][0]
+    kwargs = None
+
+    # need to record this because methods can change the size (e.g. unsqueeze)
+    args_variable, kwargs_variable = create_input(args)
+
+    self_tensor = deepcopy(self_variable.data)
+    args_tensor = deepcopy(unpack_variables(args_variable))
+
+    f_args_variable = (self_variable,) + args_variable
+    f_args_tensor = (self_tensor,) + args_tensor
+    with torch._jit_internal._disable_emit_hooks():
+        script_fn, inputs = gen_script_fn_and_args(name, "nn_functional", *f_args_variable)
+    return script_fn, inputs
+
+
+# additional modules test
+# TODO: delete this list once we make all nn_tests work
+additional_module_tests = [
+    {
+        'module_name': 'Bilinear',
+        'constructor_args': (S, S, M),
+        'input_size': (S, S),
+        'extra_args': ((S, S),)
+    },
+    {
+        'module_name': 'RNNCell',
+        'constructor_args': (S, S),
+        'input_size': (S, S),
+    },
+    {
+        'module_name': 'LSTMCell',
+        'constructor_args': (S, S),
+        'input_size': (S, S),
+    },
+    {
+        'module_name': 'GRUCell',
+        'constructor_args': (S, S),
+        'input_size': (S, S),
+    },
+    {
+        'module_name': 'MultiheadAttention',
+        'constructor_args': (128, 8),
+        'input_size': (10, 8, 128),
+        'extra_args': (torch.randn(10, 8, 128), torch.randn(10, 8, 128)),
+        'slowTest': True
+    },
+    {
+        'module_name': 'Transformer',
+        'constructor_args': (1, 1, 1, 1, 2),
+        'input_size': (3, 1, 1),
+        'extra_args': (torch.randn(1, 1, 1),),
+        'slowTest': True
+    }
+]
+
+EXCLUDE_SCRIPT_MODULES = {
+    'test_nn_AdaptiveAvgPool2d_tuple_none',
+    'test_nn_AdaptiveAvgPool3d_tuple_none',
+    'test_nn_AdaptiveMaxPool2d_tuple_none',
+    'test_nn_AdaptiveMaxPool3d_tuple_none',
+
+    # Doesn't use future division, so this is not supported
+    'test_nn_CrossMapLRN2d',
+    # Derivative for aten::_scaled_dot_product_flash_attention_backward is not implemented
+    'test_nn_TransformerDecoderLayer_gelu_activation',
+    'test_nn_TransformerDecoderLayer_relu_activation',
+    'test_nn_TransformerEncoderLayer_gelu_activation',
+    'test_nn_TransformerEncoderLayer_relu_activation',
+    'test_nn_Transformer_multilayer_coder',
+}
+
+script_method_template = '''
+def forward({}):
+    return {}
+'''
+
+def create_script_module(self, nn_module, constructor_args, *args, **kwargs):
+    def script_module(*args, **kwargs):
+        formals, tensors, actuals = get_script_args(args)
+
+        method_args = ', '.join(['self'] + actuals)
+        call_args_str = ', '.join(actuals)
+        call = f"self.submodule({call_args_str})"
+        script = script_method_template.format(method_args, call)
+
+        submodule_constants = []
+        if kwargs.get('is_constant'):
+            submodule_constants = ['submodule']
+
+        # Create module to use the script method
+        class TheModule(torch.jit.ScriptModule):
+            __constants__ = submodule_constants
+
+            def __init__(self):
+                super().__init__()
+                self.submodule = nn_module(*constructor_args)
+
+        def make_module(script):
+            module = TheModule()
+            # check __repr__
+            str(module)
+            module.define(script)
+            return module
+
+        module = make_module(script)
+        if self:
+            self.assertExportImportModule(module, tensors)
+            module(*args)
+        # skip type annotate function attributes for now, see: https://github.com/python/mypy/issues/2087
+        create_script_module.last_graph = module.graph  # type: ignore[attr-defined]
+        return module
+    return script_module
+
+def check_alias_annotation(method_name, args, kwargs, *, aten_name, func_type='method'):
+    formals, tensors, actuals = get_script_args(args)
+    call = get_call(method_name, func_type, actuals, kwargs)
+    script = script_template.format(', '.join(formals), call)
+    CU = torch.jit.CompilationUnit(script)
+    # to clean up IR
+    torch._C._jit_pass_inline(CU.the_method.graph)
+    torch._C._jit_pass_constant_propagation(CU.the_method.graph)
+    torch._C._jit_check_alias_annotation(CU.the_method.graph, tuple(tensors), aten_name)
+
+def get_nn_module_name_from_kwargs(**kwargs):
+    if 'module_name' in kwargs:
+        return kwargs['module_name']
+    elif 'fullname' in kwargs:
+        return kwargs['fullname']
+    elif 'constructor' in kwargs:
+        return kwargs['constructor'].__name__
+
+def get_nn_mod_test_name(**kwargs):
+    if 'fullname' in kwargs:
+        test_name = kwargs['fullname']
+    else:
+        test_name = get_nn_module_name_from_kwargs(**kwargs)
+        if 'desc' in kwargs:
+            test_name = f"{test_name}_{kwargs['desc']}"
+    return f'test_nn_{test_name}'
+
+def get_nn_module_class_from_kwargs(**kwargs):
+    name = get_nn_module_name_from_kwargs(**kwargs)
+    index = name.find("_")
+    if index == -1:
+        return name
+    else:
+        return name[0:name.find("_")]
+
+def try_get_nn_module_compiled_mod_and_inputs(*args, **kwargs):
+    name = get_nn_module_name_from_kwargs(**kwargs)
+
+    if 'desc' in kwargs and 'eval' in kwargs['desc']:
+        # eval() is not supported, so skip these tests
+        return
+
+    test_name = name
+    if 'desc' in kwargs:
+        test_name = f"{test_name}_{kwargs['desc']}"
+    test_name = get_nn_mod_test_name(**kwargs)
+
+    if test_name in EXCLUDE_SCRIPT_MODULES:
+        return
+    if 'constructor' in kwargs:
+        nn_module = kwargs['constructor']
+    else:
+        nn_module = getattr(torch.nn, name)
+
+    if "FunctionalModule" in str(nn_module):
+        return
+
+    if 'constructor_args_fn' in kwargs:
+        constructor_args = kwargs['constructor_args_fn']()
+    else:
+        constructor_args = kwargs.get('constructor_args', ())
+
+    # Set up inputs from tuple of sizes or constructor fn
+    input_dtype = torch.double
+    if 'input_fn' in kwargs:
+        input = kwargs['input_fn']()
+        if isinstance(input, torch.Tensor):
+            input = (input,)
+
+        if all(tensor.is_complex() for tensor in input):
+            input_dtype = torch.cdouble
+    else:
+        input = (kwargs['input_size'],)
+
+    # Extra parameters to forward()
+    if 'extra_args' in kwargs:
+        input = input + kwargs['extra_args']
+
+    if 'target_size' in kwargs:
+        input = input + (kwargs['target_size'],)
+    elif 'target_fn' in kwargs:
+        if torch.is_tensor(input):
+            input = (input,)
+        input = input + (kwargs['target_fn'](),)
+
+    args_variable, kwargs_variable = create_input(input, dtype=input_dtype)
+    f_args_variable = deepcopy(unpack_variables(args_variable))
+    out_var = deepcopy(f_args_variable)
+
+    args, mod = f_args_variable, create_script_module(None, nn_module, constructor_args, *f_args_variable)(*f_args_variable)
+
+    return mod, out_var
+
+
+def get_all_nn_module_tests():
+    return module_tests + new_module_tests + additional_module_tests

venv/lib/python3.10/site-packages/torch/testing/_internal/jit_utils.py

@@ -0,0 +1,893 @@
+# mypy: ignore-errors
+
+# Torch
+from torch.autograd import Variable
+from torch.autograd.function import _nested_map
+from torch.jit.annotations import BroadcastingList2, BroadcastingList3  # noqa: F401
+
+from torch.onnx import OperatorExportTypes
+import torch
+import torch.cuda
+import torch.jit
+import torch.jit._logging
+import torch.jit.frontend
+import torch.jit.quantized
+import zipfile
+import functools
+
+# Testing utils
+from torch.testing import FileCheck
+from torch.testing._internal.common_utils import IS_WINDOWS, \
+    freeze_rng_state, enable_profiling_mode_for_profiling_tests, ProfilingMode, TEST_BAILOUTS, \
+    is_iterable_of_tensors
+from torch.testing._internal.common_jit import JitCommonTestCase
+from torch.testing._internal.common_utils import enable_profiling_mode  # noqa: F401
+
+# Standard library
+from contextlib import contextmanager
+from functools import reduce
+from io import StringIO
+from collections import defaultdict
+
+import importlib.util
+import inspect
+import io
+import math
+import os
+import pickle
+import sys
+import tempfile
+import textwrap
+from importlib.abc import Loader
+from typing import Any, Dict, List, Tuple, Union
+
+RUN_CUDA = torch.cuda.is_available()
+RUN_CUDA_MULTI_GPU = RUN_CUDA and torch.cuda.device_count() > 1
+RUN_CUDA_HALF = RUN_CUDA
+# HIP supports half, no version check necessary
+if torch.cuda.is_available() and not torch.version.hip:
+    CUDA_VERSION = torch._C._cuda_getCompiledVersion()
+    for d in range(torch.cuda.device_count()):
+        major = torch.cuda.get_device_capability(d)[0]
+        if (major < 6):
+            RUN_CUDA_HALF = False
+
+def execWrapper(code, glob, loc):
+    exec(code, glob, loc)
+
+def do_input_map(fn, input):
+    return _nested_map(lambda t: isinstance(t, torch.Tensor), fn)(input)
+
+def clear_class_registry():
+    torch._C._jit_clear_class_registry()
+    torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
+    torch.jit._state._clear_class_state()
+
+def get_execution_plan(graph_executor_state):
+    execution_plans = list(graph_executor_state.execution_plans.values())
+    num_plans = len(execution_plans)
+    if num_plans != 1:
+        raise RuntimeError('This test assumes this GraphExecutor should '
+                           f'only have one execution plan, got: {num_plans}')
+    return execution_plans[0]
+
+class _AssertRaisesRegexWithHighlightContext:
+    """
+    A context manager that is useful for checking that error messages highlight
+    the correct part of the source code.
+    """
+
+    def __init__(self, test_case, exception, regex, highlight):
+        self.test_case = test_case
+        self.exception_type = exception
+        self.regex = regex
+        self.highlight = highlight
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, type, value, traceback):
+        with self.test_case.assertRaisesRegex(self.exception_type, self.regex):
+            if type:
+                raise value
+
+        if self.highlight:
+            FileCheck().check_source_highlighted(self.highlight).run(str(value))
+
+        return True
+
+FUSION_GROUP = "prim::TensorExprGroup"
+
+class JitTestCase(JitCommonTestCase):
+    _do_cuda_memory_leak_check = True
+    _restored_warnings = False
+
+    class capture_stdout(list):
+        """
+        Replace sys.stdout with a temporary StringIO
+        """
+        def __enter__(self):
+            self.sys_stdout = sys.stdout
+            self.stringio = StringIO()
+            sys.stdout = self.stringio
+            return self
+
+        def __exit__(self, *args):
+            self.append(str(self.stringio.getvalue()))
+            del self.stringio
+            sys.stdout = self.sys_stdout
+
+    class capture_stderr(list):
+        """
+        Replace sys.stderr with a temporary StringIO
+        """
+        def __enter__(self):
+            self.sys_stderr = sys.stderr
+            self.stringio = StringIO()
+            sys.stderr = self.stringio
+            return self
+
+        def __exit__(self, *args):
+            self.append(str(self.stringio.getvalue()))
+            del self.stringio
+            sys.stderr = self.sys_stderr
+
+    def setHooks(self):
+        torch._C._jit_set_emit_hooks(self.emitModuleHook, self.emitFunctionHook)
+
+    def clearHooks(self):
+        torch._C._jit_set_emit_hooks(None, None)
+
+    def setUp(self):
+        super().setUp()
+        # unittest overrides all warning filters and forces all of them to show up
+        # after we install our own to silence those coming from inside PyTorch.
+        # This will ensure that our filter still takes precedence.
+        if not JitTestCase._restored_warnings:
+            torch.jit.TracerWarning.ignore_lib_warnings()
+            JitTestCase._restored_warnings = True
+        self.setHooks()
+
+    def tearDown(self):
+        super().tearDown()
+        # needs to be cleared because python might be unloaded before
+        # the callback gets destructed
+        self.clearHooks()
+        clear_class_registry()
+
+    def assertAllFused(self, graph, except_for=()):
+
+        # note this helper collects nodes on 'fast path' only
+        # i.e. the true blocks of specialized checks
+        def get_nodes_and_parents_recursively(block, kind, acc):
+            for node in block.nodes():
+                if node.kind() == kind:
+                    acc[block].append(node)
+                elif node.kind() == 'prim::DifferentiableGraph':
+                    get_nodes_and_parents_recursively(node.g('Subgraph'), kind, acc)
+                elif node.kind() == 'prim::If' and (node.inputs().__next__().node().kind() == 'aten::all' or
+                                                    node.inputs().__next__().node().kind() == 'prim::TypeCheck' or
+                                                    node.inputs().__next__().node().kind() == 'prim::RequiresGradCheck'):
+                    get_nodes_and_parents_recursively(node.blocks().__next__(), kind, acc)
+                else:
+                    for inner_block in node.blocks():
+                        get_nodes_and_parents_recursively(inner_block, kind, acc)
+
+        allowed_nodes = {'prim::Constant', FUSION_GROUP, 'prim::BailoutTemplate',
+                         'prim::TupleConstruct', 'prim::If', 'prim::TypeCheck', 'prim::RequiresGradCheck'} | set(except_for)
+
+        fusion_groups : Dict[torch._C.Block, List[torch._C.Node]] = defaultdict(list)
+        get_nodes_and_parents_recursively(graph, FUSION_GROUP, fusion_groups)
+        self.assertTrue(len(fusion_groups) == 1, f'got {graph}')
+        (graph, fusion_nodes) = next(iter(fusion_groups.items()))
+        # the block contains one FUSION_GROUP and the rest of nodes are `allowed_nodes`
+        self.assertTrue(len(fusion_nodes) == 1, f'got {graph}')
+        self.assertTrue(all(node.kind() in allowed_nodes for node in graph.nodes()),
+                        f'got {graph}')
+
+    def _isHookExceptionOk(self, e):
+        se = str(e)
+        allowed = ("Could not export Python function",
+                   "closures are not exportable")
+        for a in allowed:
+            if a in se:
+                return True
+        return False
+
+    def _compared_saved_loaded(self, m):
+        def extract_files(buffer):
+            # crack open the zip format to get at the main module code
+            archive = zipfile.ZipFile(buffer)
+            # check that we have no duplicate names
+            self.assertEqual(len(set(archive.namelist())), len(archive.namelist()))
+            files = list(filter(lambda x: x.startswith('archive/code/'), archive.namelist()))
+            # unwrap all the code files into strings
+            code_files_str = filter(lambda x: x.endswith('.py'), files)
+            code_files_stream = (archive.open(f) for f in code_files_str)
+            code_files = ("".join([line.decode() for line in file]) for file in code_files_stream)
+
+            # unpickled all the debug files
+            debug_files_str = filter(lambda f: f.endswith('.debug_pkl'), files)
+            debug_files_stream = (archive.open(f) for f in debug_files_str)
+            debug_files = (pickle.load(f) for f in debug_files_stream)
+            return code_files, debug_files
+
+        # disable the hook while we parse code, otherwise we will re-enter the hook
+        with torch._jit_internal._disable_emit_hooks():
+            try:
+                # short-circuit if this is an empty function or module
+                if len(m.code) == 0:
+                    return
+                if isinstance(m, torch._C.ScriptModule):
+                    if len(m._method_names()) == 0:
+                        return
+
+                # save the module to a buffer
+                buffer = io.BytesIO()
+                torch.jit.save(m, buffer)
+                # copy the data in the buffer so we can restore it later. This
+                # is because py2 and py3 have different semantics with zipfile
+                # and it's easier to just work with a fresh copy each time.
+                buffer_copy = buffer.getvalue()
+
+                code_files, debug_files = extract_files(buffer)
+
+            except RuntimeError as e:
+                if not self._isHookExceptionOk(e):
+                    raise
+                else:
+                    return
+
+            # import the model again (from a the copy we made of the original)
+            buffer2 = io.BytesIO(buffer_copy)
+            imported = torch.jit.load(buffer2)
+
+            # save it again
+            saved_module_buffer_2 = io.BytesIO()
+            torch.jit.save(imported, saved_module_buffer_2)
+
+            saved_module_buffer_2.seek(0)
+            code_files_2, debug_files_2 = extract_files(saved_module_buffer_2)
+
+            for a, b in zip(code_files, code_files_2):
+                self.assertMultiLineEqual(a, b)
+
+            if isinstance(m, torch._C.ScriptModule):
+                self.assertTrue(torch._C._ivalue_tags_match(m, imported._c))
+
+
+    def emitFunctionHook(self, func):
+        # func has invalid names for export, skip the jitter check
+        if func.name == "<lambda>" or "aten::" in func.name:
+            return
+        self._compared_saved_loaded(func)
+
+    def emitModuleHook(self, module):
+        self._compared_saved_loaded(module)
+
+
+    def getExportImportCopyWithPacking(self, m, also_test_file=True, map_location=None):
+        buffer = io.BytesIO()
+        m.apply(lambda s: s._pack() if s._c._has_method('_pack') else None)
+        torch.jit.save(m, buffer)
+        m.apply(lambda s: s._unpack() if s._c._has_method('_unpack') else None)
+        buffer.seek(0)
+        imported = torch.jit.load(buffer, map_location=map_location)
+        imported.apply(lambda s: s._unpack() if s._c._has_method('_unpack') else None)
+
+        if not also_test_file:
+            return imported
+
+        # Ideally we would like to not have to manually delete the file, but NamedTemporaryFile
+        # opens the file, and it cannot be opened multiple times in Windows. To support Windows,
+        # close the file after creation and try to remove it manually
+        f = tempfile.NamedTemporaryFile(delete=False)
+        try:
+            f.close()
+            imported.save(f.name)
+            result = torch.jit.load(f.name, map_location=map_location)
+        finally:
+            os.unlink(f.name)
+
+        result.apply(lambda s: s._unpack() if s._c._has_method('_unpack') else None)
+        return result
+
+    def assertGraphContains(self, graph, kind, consider_subgraphs=False):
+
+        if consider_subgraphs:
+            strgraph = str(graph)
+            count = strgraph.count(kind) - strgraph.count(f'with {kind}')
+            self.assertTrue(count > 0)
+            return
+
+        def nodes(block):
+            out = []
+            for node in block.nodes():
+                if node.kind() == kind:
+                    out.append(node)
+                for block in node.blocks():
+                    out += nodes(block)
+            return out
+
+        out_nodes = nodes(graph)
+        self.assertTrue(len(out_nodes) > 0)
+
+    def assertGraphContainsExactly(self, graph, kind, num_kind_nodes, consider_subgraphs=False):
+        def perform_assert(graph, kind, actual, expected, consider_subgraphs):
+            if actual == expected:
+                return
+            subgraph = 'including' if consider_subgraphs else 'excluding'
+            raise AssertionError(
+                f'{graph}\nError: graph contains {actual} {kind} nodes ({subgraph} subgraphs) but expected {expected}')
+
+        if consider_subgraphs:
+            strgraph = str(graph)
+            count = strgraph.count(kind) - strgraph.count(f'with {kind}')
+            perform_assert(graph, kind, count, num_kind_nodes,
+                           consider_subgraphs)
+            return
+
+        def nodes(block):
+            out = []
+            for node in block.nodes():
+                if node.kind() == kind:
+                    out.append(node)
+                for block in node.blocks():
+                    out += nodes(block)
+            return out
+
+        out_nodes = nodes(graph)
+        perform_assert(graph, kind, len(out_nodes), num_kind_nodes,
+                       consider_subgraphs)
+
+    def assertExpectedONNXGraph(self, g, *args, **kwargs):
+        g = torch.onnx._optimize_trace(g, operator_export_type=OperatorExportTypes.ONNX)
+        self.assertExpectedGraph(g, *args, **kwargs)
+
+    def assertExpectedGraph(self, trace, *args, **kwargs):
+        if isinstance(trace, torch._C.Graph):
+            graph = trace
+        else:
+            graph = trace.graph()
+
+        torch._C._jit_pass_lint(graph)
+        torch._C._jit_pass_dce(graph)
+        torch._C._jit_pass_lint(graph)
+        graph = torch._C._jit_pass_canonicalize(graph)
+        torch._C._jit_pass_lint(graph)
+        self.assertExpected(str(graph), *args, **kwargs)
+
+    def run_pass(self, name, trace):
+        if isinstance(trace, torch._C.Graph):
+            graph = trace
+            set_graph = False
+        else:
+            set_graph = True
+            graph = trace.graph()
+
+        torch._C._jit_pass_lint(graph)
+        result = getattr(torch._C, '_jit_pass_' + name)(graph)
+        if result is not None and not isinstance(result, bool):
+            graph = result
+        torch._C._jit_pass_lint(graph)
+
+        if set_graph:
+            trace.set_graph(graph)
+        return graph
+
+    def get_frame_vars(self, frames_up):
+        frame = inspect.currentframe()
+        if not frame:
+            raise RuntimeError("failed to inspect frame")
+        i = 0
+        while i < frames_up + 1:
+            frame = frame.f_back
+            if not frame:
+                raise RuntimeError("failed to get frame")
+            i += 1
+        defined_vars: Dict[str, Any] = {}
+        defined_vars.update(frame.f_locals)
+        defined_vars.update(frame.f_globals)
+        return defined_vars
+
+    def assertRaisesRegexWithHighlight(self, exception, regex, highlight):
+        return _AssertRaisesRegexWithHighlightContext(self, exception, regex, highlight)
+
+    def checkScriptRaisesRegex(self, script, inputs, exception, regex,
+                               name=None, outputs=None, capture_output=False,
+                               frames_up=1, profiling=ProfilingMode.PROFILING):
+        """
+        Checks that a given function will throw the correct exception,
+        when executed with normal python, the string frontend, and the
+        AST frontend. Logic taken from `checkScript` (see comments there
+        for details)
+        """
+        with enable_profiling_mode_for_profiling_tests():
+            # Normal Python
+            with self.assertRaisesRegex(exception, regex):
+                if isinstance(script, str):
+                    frame = self.get_frame_vars(frames_up)
+                    the_locals: Dict[str, Any] = {}
+                    execWrapper(script, glob=frame, loc=the_locals)
+                    frame.update(the_locals)
+
+                    python_fn = frame[name]
+                else:
+                    python_fn = script
+
+                python_fn(*inputs)
+
+            # String frontend
+            with self.assertRaisesRegex(exception, regex):
+                if isinstance(script, str):
+                    cu = torch.jit.CompilationUnit(script, _frames_up=frames_up)
+                    string_frontend = getattr(cu, name)
+                else:
+                    source = textwrap.dedent(inspect.getsource(script))
+                    cu = torch.jit.CompilationUnit(source, _frames_up=frames_up)
+                    string_frontend = getattr(cu, script.__name__)
+
+                string_frontend(*inputs)
+
+            # Python AST frontend
+            if not isinstance(script, str):
+                with self.assertRaisesRegex(exception, regex):
+                    ge = torch.jit.script(python_fn)
+                    ge(*inputs)
+
+    def checkBailouts(self, model, inputs, expected):
+        state = model.get_debug_state()
+        plan = get_execution_plan(state)
+        num_bailouts = plan.code.num_bailouts()
+        for i in range(0, num_bailouts):
+            plan.code.request_bailout(i)
+            bailout_outputs = model(*inputs)
+            self.assertEqual(bailout_outputs, expected)
+
+    def checkScript(self,
+                    script,
+                    inputs,
+                    name='func',
+                    optimize=True,
+                    inputs_requires_grad=False,
+                    capture_output=False,
+                    frames_up=1,
+                    profiling=ProfilingMode.PROFILING,
+                    atol=None,
+                    rtol=None):
+        """
+        Checks that a given script generates the same output as the Python
+        version using the given inputs.
+        """
+        with torch.jit.optimized_execution(optimize):
+            with enable_profiling_mode_for_profiling_tests():
+                extra_profile_runs = any(isinstance(x, torch.Tensor) and x.requires_grad for x in inputs)
+                if isinstance(script, str):
+                    # Compile the string to a Script function
+                    # with enable_profiling_mode():
+                    cu = torch.jit.CompilationUnit(script, _frames_up=frames_up)
+
+                    # Execute the Python function so we can run it later and get its
+                    # outputs
+
+                    frame = self.get_frame_vars(frames_up)
+                    the_locals: Dict[str, Any] = {}
+                    execWrapper(script, glob=frame, loc=the_locals)
+                    frame.update(the_locals)
+
+                    python_fn = frame[name]
+                    scripted_fn = getattr(cu, name)
+                else:
+
+                    # Check the string frontend first
+                    source = textwrap.dedent(inspect.getsource(script))
+                    self.checkScript(
+                        source,
+                        inputs,
+                        script.__name__,
+                        optimize=optimize,
+                        inputs_requires_grad=inputs_requires_grad,
+                        capture_output=capture_output,
+                        profiling=profiling,
+                        frames_up=2)
+
+                    # Continue checking the Python frontend
+                    scripted_fn = torch.jit.script(script, _frames_up=1)
+                    python_fn = script
+
+                if inputs_requires_grad:
+                    recording_inputs = do_input_map(lambda t: t.detach().requires_grad_(), inputs)
+                else:
+                    recording_inputs = inputs
+
+                if capture_output:
+                    with self.capture_stdout() as script_stdout:
+                        script_outputs = scripted_fn(*recording_inputs)
+                    with self.capture_stdout() as opt_script_stdout:
+                        opt_script_outputs = scripted_fn(*recording_inputs)
+                    with self.capture_stdout() as _python_stdout:
+                        python_outputs = python_fn(*inputs)
+                    if not IS_WINDOWS:
+                        self.assertExpected(script_stdout[0], subname='stdout')
+                    self.assertEqual(python_outputs, opt_script_outputs, atol=atol, rtol=rtol)
+                else:
+                    # profiling run
+                    script_outputs = scripted_fn(*recording_inputs)
+                    if inputs_requires_grad or extra_profile_runs:
+                        opt_script_outputs = scripted_fn(*recording_inputs)
+                    # optimized run
+                    opt_script_outputs = scripted_fn(*recording_inputs)
+                    if TEST_BAILOUTS:
+                        self.checkBailouts(scripted_fn, inputs, opt_script_outputs)
+                    python_outputs = python_fn(*inputs)
+                self.assertEqual(python_outputs, script_outputs, atol=atol, rtol=rtol)
+                self.assertEqual(script_outputs, opt_script_outputs, atol=atol, rtol=rtol)
+                return scripted_fn
+
+    def checkTrace(self, func, reference_tensors, input_tensors=None,
+                   drop=None, allow_unused=False, verbose=False,
+                   inputs_require_grads=True, check_tolerance=1e-5, export_import=True,
+                   _force_outplace=False, grad_atol=None, grad_rtol=None):
+
+        # TODO: check gradients for parameters, not just inputs
+        def allSum(vs):
+            # drop allows us to remove some values from ever being used
+            # to test unused outputs
+            if drop is not None:
+                vs = vs[:-drop]
+            # we don't want all the grad for all the outputs to be the same
+            # so we multiply each by a constant
+            return sum(math.log(i + 2) * v.sum() for i, v in enumerate(vs) if v is not None)
+        if input_tensors is None:
+            input_tensors = reference_tensors
+
+        def flatten_inputs(inputs):
+            def input_reduce(input, fn, acc):
+                if isinstance(input, torch.Tensor):
+                    fn(input, acc)
+                elif isinstance(input, dict):
+                    reduce(lambda acc, key: input_reduce(input[key], fn, acc), input, acc)
+                else:
+                    reduce(lambda acc, val: input_reduce(val, fn, acc), input, acc)
+                return acc
+            return tuple(input_reduce(recording_inputs, lambda t, acc: acc.append(t), []))
+
+        nograd_inputs = reference_tensors
+        if inputs_require_grads:
+            recording_inputs = do_input_map(lambda t: t.clone().requires_grad_(), reference_tensors)
+            flattened_recording_inputs = flatten_inputs(recording_inputs)
+        else:
+            recording_inputs = reference_tensors
+
+        # `check_trace` is set to False because check_trace is run with @no_grad
+        # Also, `checkTrace` already does all the checks
+        # against python function
+        ge = torch.jit.trace(func, input_tensors, check_tolerance=check_tolerance,
+                             _force_outplace=_force_outplace, check_trace=False)
+
+        if export_import:
+            ge = self.getExportImportCopy(ge)
+
+        if verbose:
+            print(ge.graph)
+
+        # test no gradients case
+        outputs = func(*nograd_inputs)
+        outputs_ge = ge(*nograd_inputs)
+        self.assertEqual(outputs, outputs_ge)
+
+        # test gradients case
+        outputs = func(*recording_inputs)
+        if inputs_require_grads:
+            grads = torch.autograd.grad(allSum(outputs), flattened_recording_inputs,
+                                        allow_unused=allow_unused)
+
+        outputs_ge = ge(*recording_inputs)
+        if inputs_require_grads:
+            grads_ge = torch.autograd.grad(allSum(outputs_ge), flattened_recording_inputs,
+                                           allow_unused=allow_unused)
+        self.assertEqual(outputs, outputs_ge)
+        if inputs_require_grads:
+            self.assertEqual(grads, grads_ge, atol=grad_atol, rtol=grad_rtol)
+
+        # test the grad grad case
+        outputs = func(*recording_inputs)
+        l1 = allSum(outputs)
+        if inputs_require_grads:
+            grads = torch.autograd.grad(l1, flattened_recording_inputs, create_graph=True,
+                                        allow_unused=allow_unused)
+        if inputs_require_grads:
+            l2 = (allSum(grads) * l1)
+            grads2 = torch.autograd.grad(l2, flattened_recording_inputs, allow_unused=allow_unused)
+
+        if inputs_require_grads:
+            recording_inputs = do_input_map(lambda t: Variable(t, requires_grad=True), reference_tensors)
+            flattened_recording_inputs = flatten_inputs(recording_inputs)
+
+        outputs_ge = ge(*recording_inputs)
+        l1_ge = allSum(outputs_ge)
+        if inputs_require_grads:
+            grads_ge = torch.autograd.grad(
+                l1_ge, flattened_recording_inputs, create_graph=True, allow_unused=allow_unused)
+
+        if inputs_require_grads:
+            l2_ge = (allSum(grads_ge) * l1_ge)
+            grads2_ge = torch.autograd.grad(l2_ge, flattened_recording_inputs, allow_unused=allow_unused)
+
+        self.assertEqual(outputs, outputs_ge)
+        if inputs_require_grads:
+            self.assertEqual(grads, grads_ge, atol=grad_atol, rtol=grad_rtol)
+            for g2, g2_ge in zip(grads2, grads2_ge):
+                if g2 is None and g2_ge is None:
+                    continue
+                self.assertEqual(g2, g2_ge, atol=8e-4, rtol=8e-4)
+
+        return ge
+
+    def checkModule(self, nn_module, args):
+        """
+        Check that a nn.Module's results in Script mode match eager and that it
+        can be exported
+        """
+        sm = torch.jit.script(nn_module)
+
+        with freeze_rng_state():
+            eager_out = nn_module(*args)
+
+        with freeze_rng_state():
+            script_out = sm(*args)
+
+        self.assertEqual(eager_out, script_out)
+        self.assertExportImportModule(sm, args)
+
+        return sm
+
+class NoTracerWarnContextManager:
+    def __enter__(self):
+        self.prev = torch._C._jit_get_tracer_state_warn()
+        torch._C._jit_set_tracer_state_warn(False)
+
+    def __exit__(self, *args):
+        torch._C._jit_set_tracer_state_warn(self.prev)
+
+@contextmanager
+def inline_everything_mode(should_inline):
+    old = torch._C._jit_get_inline_everything_mode()
+    torch._C._jit_set_inline_everything_mode(should_inline)
+    try:
+        yield
+    finally:
+        torch._C._jit_set_inline_everything_mode(old)
+
+@contextmanager
+def set_fusion_group_inlining(inlining):
+    old = torch._C._debug_get_fusion_group_inlining()
+    torch._C._debug_set_fusion_group_inlining(inlining)
+    try:
+        yield
+    finally:
+        torch._C._debug_set_fusion_group_inlining(old)
+
+# note: not re-entrant, use unnested only
+@contextmanager
+def disable_autodiff_subgraph_inlining(enabled=True):
+    torch._C._debug_set_autodiff_subgraph_inlining(not enabled)
+    try:
+        yield
+    finally:
+        torch._C._debug_set_autodiff_subgraph_inlining(True)
+
+def _inline_everything(fn):
+    @functools.wraps(fn)
+    def wrapper(*args, **kwargs):
+        with inline_everything_mode(True):
+            fn(*args, **kwargs)
+    return wrapper
+
+# this exists for forward compatibility reasons temporarily.
+# TODO(suo) remove
+def _tmp_donotuse_dont_inline_everything(fn):
+    @functools.wraps(fn)
+    def wrapper(*args, **kwargs):
+        with inline_everything_mode(False):
+            fn(*args, **kwargs)
+    return wrapper
+
+# make it easy to quicky define/trace a function for these tests
+def _trace(*args, **kwargs):
+    def wrapper(func):
+        return torch.jit.trace(func, args, **kwargs)
+    return wrapper
+
+
+def enable_cpu_fuser(fn):
+    def wrapper(*args, **kwargs):
+        torch._C._jit_override_can_fuse_on_cpu_legacy(True)
+        torch._C._jit_override_can_fuse_on_cpu(True)
+        torch._C._jit_set_te_must_use_llvm_cpu(False)
+        try:
+            fn(*args, **kwargs)
+        finally:
+            torch._C._jit_override_can_fuse_on_cpu_legacy(False)
+            torch._C._jit_override_can_fuse_on_cpu(False)
+            torch._C._jit_set_te_must_use_llvm_cpu(True)
+    return wrapper
+
+
+def enable_cpu_fuser_if(cond):
+    if cond:
+        return enable_cpu_fuser
+    else:
+        def noop_fuser(fn):
+            def wrapper(*args, **kwargs):
+                return fn(*args, **kwargs)
+            return wrapper
+        return noop_fuser
+
+def get_forward(c):
+    return c._get_method('forward')
+
+def get_forward_graph(c):
+    return c._get_method('forward').graph
+
+def get_module_method(m, module, method):
+    return m._c.getattr(module)._get_method(method)
+
+def attrs_with_prefix(module, prefix):
+    return [x for x, _ in module._modules._c.items()
+            if x.startswith(prefix)]
+
+def warmup_backward(f, *args):
+    profiling_count = 3
+    results = []
+    for i in range(profiling_count):
+        if len(args) > 0:
+            r = torch.autograd.grad(f, *args)
+            results.append(r)
+        else:
+            f.backward(retain_graph=True)
+
+    return results
+
+# TODO: Remove me once https://bugs.python.org/issue42666 is resolved
+def make_global(*args):
+    for arg in args:
+        setattr(sys.modules[arg.__module__], arg.__name__, arg)
+
+# Helper function to eval Python3 code without causing a syntax error for
+# this file under py2
+def _get_py3_code(code, fn_name):
+    with tempfile.TemporaryDirectory() as tmp_dir:
+        script_path = os.path.join(tmp_dir, 'script.py')
+        with open(script_path, 'w') as f:
+            f.write(code)
+        spec = importlib.util.spec_from_file_location(fn_name, script_path)
+        module = importlib.util.module_from_spec(spec)
+        loader = spec.loader
+        assert isinstance(loader, Loader)  # Assert type to meet MyPy requirement
+        loader.exec_module(module)
+        fn = getattr(module, fn_name)
+        return fn
+
+class TensorExprTestOptions:
+    def __init__(self):
+        self.old_profiling_executor = torch._C._jit_set_profiling_executor(True)
+        self.old_profiling_mode = torch._C._get_graph_executor_optimize(True)
+
+        self.old_cpu_fuser_state = torch._C._jit_can_fuse_on_cpu()
+        self.old_gpu_fuser_state = torch._C._jit_can_fuse_on_gpu()
+        torch._C._jit_override_can_fuse_on_cpu(True)
+        torch._C._jit_override_can_fuse_on_gpu(True)
+        self.texpr_fuser_state = torch._C._jit_texpr_fuser_enabled()
+        torch._C._jit_set_texpr_fuser_enabled(True)
+        self.old_fusion_inlining = torch._C._debug_get_fusion_group_inlining()
+        torch._C._debug_set_fusion_group_inlining(False)
+        self.old_te_must_use_llvm_cpu = torch._C._jit_get_te_must_use_llvm_cpu()
+        torch._C._jit_set_te_must_use_llvm_cpu(False)
+
+    def restore(self):
+        torch._C._jit_set_profiling_executor(self.old_profiling_executor)
+        torch._C._get_graph_executor_optimize(self.old_profiling_mode)
+
+        torch._C._jit_set_texpr_fuser_enabled(self.texpr_fuser_state)
+        torch._C._jit_override_can_fuse_on_gpu(self.old_gpu_fuser_state)
+        torch._C._jit_override_can_fuse_on_cpu(self.old_cpu_fuser_state)
+        torch._C._debug_set_fusion_group_inlining(self.old_fusion_inlining)
+        torch._C._jit_set_te_must_use_llvm_cpu(self.old_te_must_use_llvm_cpu)
+
+def clone_inputs(args):
+    inputs: List[Union[torch.Tensor, List[torch.Tensor]]] = []
+
+    for arg in args:
+        if isinstance(arg, torch.Tensor):
+            inputs.append(arg.detach().clone())
+        elif is_iterable_of_tensors(arg):
+            inputs.append([t.detach().clone() for t in arg])
+        else:
+            inputs.append(arg)
+
+    return inputs
+
+def get_traced_sample_variant_pairs(device, dtype, op):
+    # tuples of (variant, sample)
+    outputs: List[Tuple[Any, Any]] = []
+
+    samples = op.sample_inputs(device, dtype)
+
+    # Acquires variants to test
+    func = op.get_op()
+    method = op.get_method()
+    variants = {
+        # TODO: inplace tests currently fail, fix and add inplace variant
+        'function': func, 'method': method,
+    }
+
+    # TODO: find better way to standardize on op registration itself..
+    has_fake_function = op.name in ["resize_", 'resize_as_']
+
+    if has_fake_function:
+        variants = {'method': getattr(torch.Tensor, op.name)}
+
+    # In eager mode, these ops can take (Tensor, bool) args; but in
+    # JIT they can only take (Tensor, Scalar), and bool is not a
+    # scalar in the JIT type system. So to test these in JIT, the bool
+    # is converted to an int for the test.
+    ops_with_unsupported_bool_args = [
+        {
+            "name": "div_floor_rounding",
+            "arg_idx": [0],
+        },
+        {
+            "name": "div_no_rounding_mode",
+            "arg_idx": [0],
+        },
+        {
+            "name": "div_trunc_rounding",
+            "arg_idx": [0],
+        },
+        {
+            "name": "index_fill",
+            "arg_idx": [2],
+        },
+        {
+            "name": "full_like",
+            "arg_idx": [0],
+        },
+        {
+            "name": "mul",
+            "arg_idx": [0],
+        },
+        {
+            "name": "new_full",
+            "arg_idx": [1],
+        },
+    ]
+
+    # doesn't support tracing
+    if has_fake_function:
+        return outputs
+
+    for sample in samples:
+        for variant in variants.values():
+            if variant is None:
+                continue
+
+            if is_lambda(variant):
+                continue
+
+            matching_ops = filter(lambda x: op.formatted_name == x["name"], ops_with_unsupported_bool_args)
+            for op_data in matching_ops:
+                for idx in op_data["arg_idx"]:
+                    args = list(sample.args)
+                    if len(sample.args) > idx and isinstance(sample.args[idx], bool):
+                        args[idx] = int(args[idx])
+                    sample.args = tuple(args)
+
+            outputs.append((variant, sample))
+
+    return outputs
+
+# types.LambdaType gave false positives
+def is_lambda(lamb):
+    LAMBDA = lambda: 0  # noqa: E731
+    return isinstance(lamb, type(LAMBDA)) and lamb.__name__ == LAMBDA.__name__

venv/lib/python3.10/site-packages/torch/testing/_internal/logging_utils.py

@@ -0,0 +1,208 @@
+# mypy: ignore-errors
+
+import torch._dynamo.test_case
+import unittest.mock
+import os
+import contextlib
+import torch._logging
+import torch._logging._internal
+from torch._dynamo.utils import LazyString
+import logging
+import io
+
+@contextlib.contextmanager
+def preserve_log_state():
+    prev_state = torch._logging._internal._get_log_state()
+    torch._logging._internal._set_log_state(torch._logging._internal.LogState())
+    try:
+        yield
+    finally:
+        torch._logging._internal._set_log_state(prev_state)
+        torch._logging._internal._init_logs()
+
+def log_settings(settings):
+    exit_stack = contextlib.ExitStack()
+    settings_patch = unittest.mock.patch.dict(os.environ, {"TORCH_LOGS": settings})
+    exit_stack.enter_context(preserve_log_state())
+    exit_stack.enter_context(settings_patch)
+    torch._logging._internal._init_logs()
+    return exit_stack
+
+def log_api(**kwargs):
+    exit_stack = contextlib.ExitStack()
+    exit_stack.enter_context(preserve_log_state())
+    torch._logging.set_logs(**kwargs)
+    return exit_stack
+
+
+def kwargs_to_settings(**kwargs):
+    INT_TO_VERBOSITY = {10: "+", 20: "", 40: "-"}
+
+    settings = []
+
+    def append_setting(name, level):
+        if isinstance(name, str) and isinstance(level, int) and level in INT_TO_VERBOSITY:
+            settings.append(INT_TO_VERBOSITY[level] + name)
+            return
+        else:
+            raise ValueError("Invalid value for setting")
+
+    for name, val in kwargs.items():
+        if isinstance(val, bool):
+            settings.append(name)
+        elif isinstance(val, int):
+            append_setting(name, val)
+        elif isinstance(val, dict) and name == "modules":
+            for module_qname, level in val.items():
+                append_setting(module_qname, level)
+        else:
+            raise ValueError("Invalid value for setting")
+
+    return ",".join(settings)
+
+
+# Note on testing strategy:
+# This class does two things:
+# 1. Runs two versions of a test:
+#    1a. patches the env var log settings to some specific value
+#    1b. calls torch._logging.set_logs(..)
+# 2. patches the emit method of each setup handler to gather records
+# that are emitted to each console stream
+# 3. passes a ref to the gathered records to each test case for checking
+#
+# The goal of this testing in general is to ensure that given some settings env var
+# that the logs are setup correctly and capturing the correct records.
+def make_logging_test(**kwargs):
+    def wrapper(fn):
+        def test_fn(self):
+
+            torch._dynamo.reset()
+            records = []
+            # run with env var
+            if len(kwargs) == 0:
+                with self._handler_watcher(records):
+                    fn(self, records)
+            else:
+                with log_settings(kwargs_to_settings(**kwargs)), self._handler_watcher(records):
+                    fn(self, records)
+
+            # run with API
+            torch._dynamo.reset()
+            records.clear()
+            with log_api(**kwargs), self._handler_watcher(records):
+                fn(self, records)
+
+
+        return test_fn
+
+    return wrapper
+
+def make_settings_test(settings):
+    def wrapper(fn):
+        def test_fn(self):
+            torch._dynamo.reset()
+            records = []
+            # run with env var
+            with log_settings(settings), self._handler_watcher(records):
+                fn(self, records)
+
+        return test_fn
+
+    return wrapper
+
+class LoggingTestCase(torch._dynamo.test_case.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+        cls._exit_stack.enter_context(
+            unittest.mock.patch.dict(os.environ, {"___LOG_TESTING": ""})
+        )
+        cls._exit_stack.enter_context(
+            unittest.mock.patch("torch._dynamo.config.suppress_errors", True)
+        )
+        cls._exit_stack.enter_context(
+            unittest.mock.patch("torch._dynamo.config.verbose", False)
+        )
+
+    @classmethod
+    def tearDownClass(cls):
+        cls._exit_stack.close()
+        torch._logging._internal.log_state.clear()
+        torch._logging._init_logs()
+
+    def getRecord(self, records, m):
+        record = None
+        for r in records:
+            # NB: not r.msg because it looks like 3.11 changed how they
+            # structure log records
+            if m in r.getMessage():
+                self.assertIsNone(
+                    record,
+                    msg=LazyString(
+                        lambda: f"multiple matching records: {record} and {r} among {records}"
+                    ),
+                )
+                record = r
+        if record is None:
+            self.fail(f"did not find record with {m} among {records}")
+        return record
+
+    # This patches the emit method of each handler to gather records
+    # as they are emitted
+    def _handler_watcher(self, record_list):
+        exit_stack = contextlib.ExitStack()
+
+        def emit_post_hook(record):
+            nonlocal record_list
+            record_list.append(record)
+
+        # registered logs are the only ones with handlers, so patch those
+        for log_qname in torch._logging._internal.log_registry.get_log_qnames():
+            logger = logging.getLogger(log_qname)
+            num_handlers = len(logger.handlers)
+            self.assertLessEqual(
+                num_handlers,
+                2,
+                "All pt2 loggers should only have at most two handlers (debug artifacts and messages above debug level).",
+            )
+
+            self.assertGreater(num_handlers, 0, "All pt2 loggers should have more than zero handlers")
+
+            for handler in logger.handlers:
+                old_emit = handler.emit
+
+                def new_emit(record):
+                    old_emit(record)
+                    emit_post_hook(record)
+
+                exit_stack.enter_context(
+                    unittest.mock.patch.object(handler, "emit", new_emit)
+                )
+
+        return exit_stack
+
+
+def logs_to_string(module, log_option):
+    """Example:
+    logs_to_string("torch._inductor.compile_fx", "post_grad_graphs")
+    returns the output of TORCH_LOGS="post_grad_graphs" from the
+    torch._inductor.compile_fx module.
+    """
+    log_stream = io.StringIO()
+    handler = logging.StreamHandler(stream=log_stream)
+
+    @contextlib.contextmanager
+    def tmp_redirect_logs():
+        try:
+            logger = torch._logging.getArtifactLogger(module, log_option)
+            logger.addHandler(handler)
+            yield
+        finally:
+            logger.removeHandler(handler)
+
+    def ctx_manager():
+        exit_stack = log_settings(log_option)
+        exit_stack.enter_context(tmp_redirect_logs())
+        return exit_stack
+
+    return log_stream, ctx_manager

venv/lib/python3.10/site-packages/torch/testing/_internal/opinfo/__init__.py

@@ -0,0 +1,4 @@
+# mypy: ignore-errors
+
+import torch.testing._internal.opinfo.core
+import torch.testing._internal.opinfo.definitions

venv/lib/python3.10/site-packages/torch/testing/_internal/opinfo/definitions/__init__.py

@@ -0,0 +1,27 @@
+# mypy: ignore-errors
+
+from typing import List
+
+from torch.testing._internal.opinfo.core import OpInfo
+from torch.testing._internal.opinfo.definitions import (
+    _masked,
+    fft,
+    linalg,
+    signal,
+    special,
+)
+
+# Operator database
+op_db: List[OpInfo] = [
+    *fft.op_db,
+    *linalg.op_db,
+    *signal.op_db,
+    *special.op_db,
+    *_masked.op_db,
+]
+
+python_ref_db: List[OpInfo] = [
+    *fft.python_ref_db,
+    *linalg.python_ref_db,
+    *special.python_ref_db,
+]