Add files using upload-large-folder tool

env-llmeval/lib/python3.10/site-packages/evaluate-0.4.1.dist-info/RECORD

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env-llmeval/lib/python3.10/site-packages/evaluate-0.4.1.dist-info/WHEEL

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+Wheel-Version: 1.0
+Generator: bdist_wheel (0.41.2)
+Root-Is-Purelib: true
+Tag: py3-none-any
+

env-llmeval/lib/python3.10/site-packages/evaluate-0.4.1.dist-info/top_level.txt

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+evaluate

env-llmeval/lib/python3.10/site-packages/frozenlist/__init__.py

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+import os
+import sys
+import types
+from collections.abc import MutableSequence
+from functools import total_ordering
+from typing import Type
+
+__version__ = "1.4.1"
+
+__all__ = ("FrozenList", "PyFrozenList")  # type: Tuple[str, ...]
+
+
+NO_EXTENSIONS = bool(os.environ.get("FROZENLIST_NO_EXTENSIONS"))  # type: bool
+
+
+@total_ordering
+class FrozenList(MutableSequence):
+    __slots__ = ("_frozen", "_items")
+
+    if sys.version_info >= (3, 9):
+        __class_getitem__ = classmethod(types.GenericAlias)
+    else:
+
+        @classmethod
+        def __class_getitem__(cls: Type["FrozenList"]) -> Type["FrozenList"]:
+            return cls
+
+    def __init__(self, items=None):
+        self._frozen = False
+        if items is not None:
+            items = list(items)
+        else:
+            items = []
+        self._items = items
+
+    @property
+    def frozen(self):
+        return self._frozen
+
+    def freeze(self):
+        self._frozen = True
+
+    def __getitem__(self, index):
+        return self._items[index]
+
+    def __setitem__(self, index, value):
+        if self._frozen:
+            raise RuntimeError("Cannot modify frozen list.")
+        self._items[index] = value
+
+    def __delitem__(self, index):
+        if self._frozen:
+            raise RuntimeError("Cannot modify frozen list.")
+        del self._items[index]
+
+    def __len__(self):
+        return self._items.__len__()
+
+    def __iter__(self):
+        return self._items.__iter__()
+
+    def __reversed__(self):
+        return self._items.__reversed__()
+
+    def __eq__(self, other):
+        return list(self) == other
+
+    def __le__(self, other):
+        return list(self) <= other
+
+    def insert(self, pos, item):
+        if self._frozen:
+            raise RuntimeError("Cannot modify frozen list.")
+        self._items.insert(pos, item)
+
+    def __repr__(self):
+        return f"<FrozenList(frozen={self._frozen}, {self._items!r})>"
+
+    def __hash__(self):
+        if self._frozen:
+            return hash(tuple(self))
+        else:
+            raise RuntimeError("Cannot hash unfrozen list.")
+
+
+PyFrozenList = FrozenList
+
+
+if not NO_EXTENSIONS:
+    try:
+        from ._frozenlist import FrozenList as CFrozenList  # type: ignore
+    except ImportError:  # pragma: no cover
+        pass
+    else:
+        FrozenList = CFrozenList  # type: ignore

env-llmeval/lib/python3.10/site-packages/frozenlist/__init__.pyi

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+from typing import (
+    Generic,
+    Iterable,
+    Iterator,
+    List,
+    MutableSequence,
+    Optional,
+    TypeVar,
+    Union,
+    overload,
+)
+
+_T = TypeVar("_T")
+_Arg = Union[List[_T], Iterable[_T]]
+
+class FrozenList(MutableSequence[_T], Generic[_T]):
+    def __init__(self, items: Optional[_Arg[_T]] = None) -> None: ...
+    @property
+    def frozen(self) -> bool: ...
+    def freeze(self) -> None: ...
+    @overload
+    def __getitem__(self, i: int) -> _T: ...
+    @overload
+    def __getitem__(self, s: slice) -> FrozenList[_T]: ...
+    @overload
+    def __setitem__(self, i: int, o: _T) -> None: ...
+    @overload
+    def __setitem__(self, s: slice, o: Iterable[_T]) -> None: ...
+    @overload
+    def __delitem__(self, i: int) -> None: ...
+    @overload
+    def __delitem__(self, i: slice) -> None: ...
+    def __len__(self) -> int: ...
+    def __iter__(self) -> Iterator[_T]: ...
+    def __reversed__(self) -> Iterator[_T]: ...
+    def __eq__(self, other: object) -> bool: ...
+    def __le__(self, other: FrozenList[_T]) -> bool: ...
+    def __ne__(self, other: object) -> bool: ...
+    def __lt__(self, other: FrozenList[_T]) -> bool: ...
+    def __ge__(self, other: FrozenList[_T]) -> bool: ...
+    def __gt__(self, other: FrozenList[_T]) -> bool: ...
+    def insert(self, pos: int, item: _T) -> None: ...
+    def __repr__(self) -> str: ...
+    def __hash__(self) -> int: ...
+
+# types for C accelerators are the same
+CFrozenList = PyFrozenList = FrozenList

env-llmeval/lib/python3.10/site-packages/frozenlist/_frozenlist.pyx

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+import sys
+import types
+from collections.abc import MutableSequence
+
+
+cdef class FrozenList:
+
+    if sys.version_info >= (3, 9):
+        __class_getitem__ = classmethod(types.GenericAlias)
+    else:
+        @classmethod
+        def __class_getitem__(cls):
+            return cls
+
+    cdef readonly bint frozen
+    cdef list _items
+
+    def __init__(self, items=None):
+        self.frozen = False
+        if items is not None:
+            items = list(items)
+        else:
+            items = []
+        self._items = items
+
+    cdef object _check_frozen(self):
+        if self.frozen:
+            raise RuntimeError("Cannot modify frozen list.")
+
+    cdef inline object _fast_len(self):
+        return len(self._items)
+
+    def freeze(self):
+        self.frozen = True
+
+    def __getitem__(self, index):
+        return self._items[index]
+
+    def __setitem__(self, index, value):
+        self._check_frozen()
+        self._items[index] = value
+
+    def __delitem__(self, index):
+        self._check_frozen()
+        del self._items[index]
+
+    def __len__(self):
+        return self._fast_len()
+
+    def __iter__(self):
+        return self._items.__iter__()
+
+    def __reversed__(self):
+        return self._items.__reversed__()
+
+    def __richcmp__(self, other, op):
+        if op == 0:  # <
+            return list(self) < other
+        if op == 1:  # <=
+            return list(self) <= other
+        if op == 2:  # ==
+            return list(self) == other
+        if op == 3:  # !=
+            return list(self) != other
+        if op == 4:  # >
+            return list(self) > other
+        if op == 5:  # =>
+            return list(self) >= other
+
+    def insert(self, pos, item):
+        self._check_frozen()
+        self._items.insert(pos, item)
+
+    def __contains__(self, item):
+        return item in self._items
+
+    def __iadd__(self, items):
+        self._check_frozen()
+        self._items += list(items)
+        return self
+
+    def index(self, item):
+        return self._items.index(item)
+
+    def remove(self, item):
+        self._check_frozen()
+        self._items.remove(item)
+
+    def clear(self):
+        self._check_frozen()
+        self._items.clear()
+
+    def extend(self, items):
+        self._check_frozen()
+        self._items += list(items)
+
+    def reverse(self):
+        self._check_frozen()
+        self._items.reverse()
+
+    def pop(self, index=-1):
+        self._check_frozen()
+        return self._items.pop(index)
+
+    def append(self, item):
+        self._check_frozen()
+        return self._items.append(item)
+
+    def count(self, item):
+        return self._items.count(item)
+
+    def __repr__(self):
+        return '<FrozenList(frozen={}, {!r})>'.format(self.frozen,
+                                                      self._items)
+
+    def __hash__(self):
+        if self.frozen:
+            return hash(tuple(self._items))
+        else:
+            raise RuntimeError("Cannot hash unfrozen list.")
+
+
+MutableSequence.register(FrozenList)

env-llmeval/lib/python3.10/site-packages/frozenlist/py.typed

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+Marker

env-llmeval/lib/python3.10/site-packages/python_dateutil-2.9.0.post0.dist-info/INSTALLER

@@ -0,0 +1 @@
+pip

env-llmeval/lib/python3.10/site-packages/torch/_C/_autograd.pyi

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+from enum import Enum
+from typing import Any, Callable, List, Optional, Set
+
+import torch
+
+from ._profiler import (
+    _ProfilerEvent,
+    ActiveProfilerType,
+    ProfilerActivity,
+    ProfilerConfig,
+)
+
+# Defined in tools/autograd/init.cpp
+
+class DeviceType(Enum):
+    CPU = ...
+    CUDA = ...
+    MKLDNN = ...
+    OPENGL = ...
+    OPENCL = ...
+    IDEEP = ...
+    HIP = ...
+    FPGA = ...
+    ORT = ...
+    XLA = ...
+    MPS = ...
+    HPU = ...
+    Meta = ...
+    Vulkan = ...
+    Metal = ...
+    PrivateUse1 = ...
+
+class ProfilerEvent:
+    def cpu_elapsed_us(self, other: ProfilerEvent) -> float: ...
+    def cpu_memory_usage(self) -> int: ...
+    def cuda_elapsed_us(self, other: ProfilerEvent) -> float: ...
+    def privateuse1_elapsed_us(self, other: ProfilerEvent) -> float: ...
+    def cuda_memory_usage(self) -> int: ...
+    def device(self) -> int: ...
+    def handle(self) -> int: ...
+    def has_cuda(self) -> bool: ...
+    def is_remote(self) -> bool: ...
+    def kind(self) -> int: ...
+    def name(self) -> str: ...
+    def node_id(self) -> int: ...
+    def sequence_nr(self) -> int: ...
+    def shapes(self) -> List[List[int]]: ...
+    def thread_id(self) -> int: ...
+    def flops(self) -> float: ...
+    def is_async(self) -> bool: ...
+
+class _KinetoEvent:
+    def name(self) -> str: ...
+    def device_index(self) -> int: ...
+    def start_us(self) -> int: ...
+    def duration_us(self) -> int: ...
+    def is_async(self) -> bool: ...
+    def linked_correlation_id(self) -> int: ...
+    def shapes(self) -> List[List[int]]: ...
+    def dtypes(self) -> List[str]: ...
+    def concrete_inputs(self) -> List[Any]: ...
+    def device_type(self) -> DeviceType: ...
+    def start_thread_id(self) -> int: ...
+    def end_thread_id(self) -> int: ...
+    def correlation_id(self) -> int: ...
+    def fwd_thread_id(self) -> int: ...
+    def stack(self) -> List[str]: ...
+    def scope(self) -> int: ...
+    def sequence_nr(self) -> int: ...
+    def flops(self) -> int: ...
+    def cuda_elapsed_us(self) -> int: ...
+    def privateuse1_elapsed_us(self) -> int: ...
+
+class _ProfilerResult:
+    def events(self) -> List[_KinetoEvent]: ...
+    def legacy_events(self) -> List[List[ProfilerEvent]]: ...
+    def save(self, path: str) -> None: ...
+    def experimental_event_tree(self) -> List[_ProfilerEvent]: ...
+    def trace_start_us(self) -> int: ...
+
+class SavedTensor: ...
+
+def _enable_profiler(
+    config: ProfilerConfig,
+    activities: Set[ProfilerActivity],
+) -> None: ...
+def _prepare_profiler(
+    config: ProfilerConfig,
+    activities: Set[ProfilerActivity],
+) -> None: ...
+def _disable_profiler() -> _ProfilerResult: ...
+def _profiler_enabled() -> bool: ...
+def _add_metadata_json(key: str, value: str) -> None: ...
+def _kineto_step() -> None: ...
+def _get_sequence_nr() -> int: ...
+def kineto_available() -> bool: ...
+def _record_function_with_args_enter(name: str, *args) -> torch.Tensor: ...
+def _record_function_with_args_exit(handle: torch.Tensor) -> None: ...
+def _supported_activities() -> Set[ProfilerActivity]: ...
+def _enable_record_function(enable: bool) -> None: ...
+def _set_empty_test_observer(is_global: bool, sampling_prob: float) -> None: ...
+def _push_saved_tensors_default_hooks(
+    pack_hook: Callable[[torch.Tensor], Any],
+    unpack_hook: Callable[[Any], torch.Tensor],
+) -> None: ...
+def _pop_saved_tensors_default_hooks() -> None: ...
+def _unsafe_set_version_counter(t: torch.Tensor, prev_version: int) -> None: ...
+def _enable_profiler_legacy(config: ProfilerConfig) -> None: ...
+def _disable_profiler_legacy() -> List[List[ProfilerEvent]]: ...
+def _profiler_type() -> ActiveProfilerType: ...
+def _saved_tensors_hooks_enable() -> None: ...
+def _saved_tensors_hooks_disable(message: str) -> None: ...
+def _saved_tensors_hooks_get_disabled_error_message() -> Optional[str]: ...
+
+class CreationMeta(Enum):
+    DEFAULT = ...
+    IN_CUSTOM_FUNCTION = ...
+    MULTI_OUTPUT_NODE = ...
+    NO_GRAD_MODE = ...
+    INFERENCE_MODE = ...
+
+def _set_creation_meta(t: torch.Tensor, creation_meta: CreationMeta) -> None: ...
+def _get_creation_meta(t: torch.Tensor) -> CreationMeta: ...

env-llmeval/lib/python3.10/site-packages/torch/_C/_cpu.pyi

@@ -0,0 +1,5 @@
+from torch.types import _bool
+
+# Defined in torch/csrc/cpu/Module.cpp
+
+def _is_cpu_support_vnni() -> _bool: ...

env-llmeval/lib/python3.10/site-packages/torch/_C/_distributed_autograd.pyi

@@ -0,0 +1,26 @@
+from typing import Any, Dict, List, Set
+
+import torch
+
+# This module is defined in torch/csrc/distributed/autograd/init.cpp
+
+class DistAutogradContext:
+    def _context_id(self) -> int: ...
+    def _recv_functions(self) -> Dict[int, Any]: ...
+    def _send_functions(self) -> Dict[int, Any]: ...
+    def _known_worker_ids(self) -> Set[int]: ...
+
+def _new_context() -> DistAutogradContext: ...
+def _release_context(context_id: int) -> None: ...
+def _get_max_id() -> int: ...
+def _is_valid_context(worker_id: int) -> bool: ...
+def _retrieve_context(context_id: int) -> DistAutogradContext: ...
+def _current_context() -> DistAutogradContext: ...
+def _init(worker_id: int) -> None: ...
+def _get_debug_info() -> Dict[str, str]: ...
+def backward(
+    context_id: int,
+    roots: List[torch.Tensor],
+    retain_graph=False,
+) -> None: ...
+def get_gradients(context_id: int) -> Dict[torch.Tensor, torch.Tensor]: ...

env-llmeval/lib/python3.10/site-packages/torch/_C/_distributed_c10d.pyi

@@ -0,0 +1,478 @@
+# mypy: disable-error-code="type-arg"
+from datetime import timedelta
+from enum import Enum
+from typing import Any, Dict, List, Optional, overload, Tuple, Union
+
+from torch import Tensor
+from torch._C import ScriptObject
+from torch.futures import Future
+
+# This module is defined in torch/csrc/distributed/c10d/init.cpp
+
+_DEFAULT_FIRST_BUCKET_BYTES: int
+_DEFAULT_NO_TIMEOUT: timedelta
+_DEFAULT_PG_TIMEOUT: timedelta
+_DEFAULT_PG_NCCL_TIMEOUT: timedelta
+
+class BuiltinCommHookType(Enum):
+    ALLREDUCE = ...
+    FP16_COMPRESS = ...
+
+def _register_comm_hook(reducer: Reducer, state: Any, comm_hook: Any): ...
+def _register_builtin_comm_hook(
+    reducer: Reducer,
+    comm_hook_type: BuiltinCommHookType,
+): ...
+
+class GradBucket:
+    def index(self) -> int: ...
+    def buffer(self) -> Tensor: ...
+    def gradients(self) -> List[Tensor]: ...
+    def is_last(self) -> bool: ...
+    def set_buffer(self, tensor: Tensor) -> None: ...
+    def parameters(self) -> List[Tensor]: ...
+
+class Reducer:
+    def __init__(
+        self,
+        params: List[Tensor],
+        bucket_indices: List[List[int]],
+        per_bucket_size_limits: List[int],
+        process_group: ProcessGroup,
+        expect_sparse_gradients: List[bool] = ...,
+        bucket_bytes_cap: int = ...,  # kDefaultBucketBytesCap in reducer.hpp
+        find_unused_parameters: bool = ...,
+        gradient_as_bucket_view: bool = ...,
+        param_to_name_mapping: Dict[int, str] = ...,
+        first_bucket_types_cap: int = ...,  # kDefaultFirstBucketBytes in reducer.hpp
+    ): ...
+    def prepare_for_forward(self) -> None: ...
+    def prepare_for_backward(self, output: List[Tensor]) -> None: ...
+    def get_backward_stats(self) -> List[int]: ...
+    def _install_post_backward_futures(self, futures: List[Future]) -> None: ...
+    def _rebuild_buckets(self) -> bool: ...
+    def _get_zeros_like_grad_buckets(self) -> List[GradBucket]: ...
+    def _push_all_rebuilt_params(self) -> None: ...
+    def _set_forward_pass_work_handle(
+        self,
+        work: Work,
+        use_static_world_size: bool,
+    ): ...
+    def _get_local_used_map(self) -> Tensor: ...
+    def _set_ddp_runtime_logging_sample_rate(self, sample_rate: int) -> None: ...
+    def _set_static_graph(self) -> None: ...
+    def _run_comm_hook(self, bucket: GradBucket) -> Future: ...
+    def set_logger(self, logger: Logger) -> None: ...
+    def _remove_autograd_hooks(self) -> None: ...
+    def _check_reducer_finalized(self) -> None: ...
+    def _set_sparse_metadata(self, global_unique_ids: Dict[str, Tensor]) -> None: ...
+    def _reset_state(self) -> None: ...
+    def _update_process_group(self, new_process_group: ProcessGroup) -> None: ...
+
+class DDPLoggingData:
+    strs_map: Dict[str, str]
+    ints_map: Dict[str, int]
+
+class Logger:
+    def __init__(self, reducer: Reducer): ...
+    def set_construction_data_and_log(
+        self,
+        module_name: str,
+        device_ids: List[int],
+        output_device: int,
+        broadcast_buffers: bool,
+        has_sync_bn: bool,
+        static_graph: bool,
+    ): ...
+    def set_runtime_stats_and_log(self) -> None: ...
+    def set_error_and_log(self, error: str) -> None: ...
+    def _get_ddp_logging_data(self) -> DDPLoggingData: ...
+    def _set_comm_hook_name(self, comm_hook: str) -> None: ...
+    def _set_uneven_input_join(self) -> None: ...
+    def _set_static_graph(self) -> None: ...
+
+def get_debug_level(): ...
+def set_debug_level(): ...
+def set_debug_level_from_env(): ...
+
+class DebugLevel(Enum):
+    OFF = ...
+    INFO = ...
+    DETAIL = ...
+
+class ReduceOp:
+    def __init__(self, op: RedOpType): ...
+
+    SUM: RedOpType = ...
+    AVG: RedOpType = ...
+    PRODUCT: RedOpType = ...
+    MIN: RedOpType = ...
+    MAX: RedOpType = ...
+    BAND: RedOpType = ...
+    BOR: RedOpType = ...
+    BXOR: RedOpType = ...
+    PREMUL_SUM: RedOpType = ...
+    UNUSED: RedOpType = ...
+
+    class RedOpType(Enum): ...
+
+class BroadcastOptions:
+    rootRank: int
+    rootTensor: int
+    timeout: timedelta
+    asyncOp: bool
+
+class AllreduceOptions:
+    reduceOp: ReduceOp
+    timeout: timedelta
+
+class AllreduceCoalescedOptions(AllreduceOptions): ...
+
+class ReduceOptions:
+    reduceOp: ReduceOp
+    rootRank: int
+    rootTensor: int
+    timeout: timedelta
+
+class AllgatherOptions:
+    timeout: timedelta
+    asyncOp: bool
+
+class GatherOptions:
+    rootRank: int
+    timeout: timedelta
+
+class ScatterOptions:
+    rootRank: int
+    timeout: timedelta
+    asyncOp: bool
+
+class ReduceScatterOptions:
+    reduceOp: ReduceOp
+    timeout: timedelta
+    asyncOp: bool
+
+class BarrierOptions:
+    device_ids: List[int]
+    timeout: timedelta
+
+class AllToAllOptions:
+    timeout: timedelta
+
+class Store:
+    def set(self, key: str, value: str): ...
+    def get(self, key: str) -> bytes: ...
+    def add(self, key: str, value: int) -> int: ...
+    def compare_set(
+        self,
+        key: str,
+        expected_value: str,
+        desired_value: str,
+    ) -> bytes: ...
+    def delete_key(self, key: str) -> bool: ...
+    def num_keys(self) -> int: ...
+    def set_timeout(self, timeout: timedelta): ...
+    @overload
+    def wait(self, keys: List[str]): ...
+    @overload
+    def wait(self, keys: List[str], timeout: timedelta): ...
+
+class FileStore(Store):
+    def __init__(self, path: str, numWorkers: int = ...): ...
+
+class HashStore(Store):
+    def __init__(self): ...
+
+class TCPStore(Store):
+    def __init__(
+        self,
+        host_name: str,
+        port: int,
+        world_size: Optional[int] = ...,
+        is_master: bool = ...,
+        timeout: timedelta = ...,
+        wait_for_workers: bool = ...,
+        multi_tenant: bool = ...,
+        master_listen_fd: Optional[int] = ...,
+        use_libuv: Optional[bool] = ...,
+    ): ...
+    @property
+    def host(self) -> str: ...
+    @property
+    def port(self) -> int: ...
+
+class PrefixStore(Store):
+    def __init__(self, prefix: str, store: Store): ...
+    @property
+    def underlying_store(self) -> Store: ...
+
+class Work:
+    def is_completed(self) -> bool: ...
+    def is_success(self) -> bool: ...
+    def exception(self) -> Any: ...
+    def wait(self, timeout: timedelta = ...) -> bool: ...
+    def source_rank(self) -> int: ...
+    def _source_rank(self) -> int: ...
+    def result(self) -> List[Tensor]: ...
+    def synchronize(self): ...
+    def boxed(self) -> ScriptObject: ...
+    @staticmethod
+    def unbox(obj: ScriptObject) -> Work: ...
+
+class ProcessGroup:
+    class Options: ...
+
+    def __init__(self): ...
+    def rank(self) -> int: ...
+    def size(self) -> int: ...
+    @overload
+    def broadcast(
+        self,
+        tensors: List[Tensor],
+        opts=...,
+    ) -> Work: ...
+    @overload
+    def broadcast(
+        self,
+        tensor: Tensor,
+        root: int,
+    ) -> Work: ...
+    @overload
+    def allreduce(
+        self,
+        tensors: List[Tensor],
+        opts: AllreduceOptions = ...,
+    ) -> Work: ...
+    @overload
+    def allreduce(
+        self,
+        tensors: List[Tensor],
+        op=...,
+    ) -> Work: ...
+    @overload
+    def allreduce(
+        self,
+        tensor: Tensor,
+        op=...,
+    ) -> Work: ...
+    def allreduce_coalesced(
+        self,
+        tensors: List[Tensor],
+        opts=...,
+    ) -> Work: ...
+    @overload
+    def reduce(
+        self,
+        tensors: List[Tensor],
+        opts=...,
+    ) -> Work: ...
+    @overload
+    def reduce(
+        self,
+        tensor: Tensor,
+        root: int,
+        op=...,
+    ) -> Work: ...
+    @overload
+    def allgather(
+        self,
+        output_tensors: List[List[Tensor]],
+        input_tensors: List[Tensor],
+        opts=...,
+    ) -> Work: ...
+    @overload
+    def allgather(
+        self,
+        output_tensors: List[Tensor],
+        input_tensor: Tensor,
+    ) -> Work: ...
+    def _allgather_base(
+        self,
+        output: Tensor,
+        input: Tensor,
+        opts=...,
+    ) -> Work: ...
+    def allgather_coalesced(
+        self,
+        output_lists: List[List[Tensor]],
+        input_list: List[Tensor],
+        opts=...,
+    ) -> Work: ...
+    @overload
+    def gather(
+        self,
+        output_tensors: List[List[Tensor]],
+        input_tensors: List[Tensor],
+        opts=...,
+    ) -> Work: ...
+    @overload
+    def gather(
+        self,
+        output_tensors: List[Tensor],
+        input_tensor: Tensor,
+        root: int,
+    ) -> Work: ...
+    @overload
+    def scatter(
+        self,
+        output_tensors: List[Tensor],
+        input_tensors: List[List[Tensor]],
+        opts=...,
+    ) -> Work: ...
+    @overload
+    def scatter(
+        self,
+        output_tensor: Tensor,
+        input_tensors: List[Tensor],
+        root: int,
+    ) -> Work: ...
+    @overload
+    def reduce_scatter(
+        self,
+        output_tensors: List[Tensor],
+        input_tensors: List[List[Tensor]],
+        opts=...,
+    ) -> Work: ...
+    @overload
+    def reduce_scatter(
+        self,
+        output_tensors: Tensor,
+        input_tensor: List[Tensor],
+    ) -> Work: ...
+    def _reduce_scatter_base(
+        self,
+        outputTensor: Tensor,
+        inputTensor: Tensor,
+    ) -> Work: ...
+    @overload
+    def alltoall_base(
+        self,
+        output_tensor: Tensor,
+        input_tensor: Tensor,
+        output_split_sizes: List[int],
+        input_split_sizes: List[int],
+        opts=...,
+    ) -> Work: ...
+    @overload
+    def alltoall_base(
+        self,
+        output: Tensor,
+        input: Tensor,
+        output_split_sizes: List[int],
+        input_split_sizes: List[int],
+    ) -> Work: ...
+    @overload
+    def alltoall(
+        self,
+        output_tensor: List[Tensor],
+        input_tensor: List[Tensor],
+        opts=...,
+    ) -> Work: ...
+    @overload
+    def alltoall(
+        self,
+        output: List[Tensor],
+        input: List[Tensor],
+    ) -> Work: ...
+    def send(
+        self,
+        tensors: List[Tensor],
+        dstRank: int,
+        tag: int,
+    ) -> Work: ...
+    def recv(
+        self,
+        tensors: List[Tensor],
+        srcRank: int,
+        tag: int,
+    ) -> Work: ...
+    def recv_anysource(self, tensors: List[Tensor], tag: int) -> Work: ...
+    def barrier(self, opts=...) -> Work: ...
+    def boxed(self) -> ScriptObject: ...
+    @staticmethod
+    def unbox(obj: ScriptObject) -> ProcessGroup: ...
+
+class ProcessGroupRoundRobin(ProcessGroup): ...
+
+def _round_robin_process_groups(
+    process_groups: List[ProcessGroup],
+) -> ProcessGroupRoundRobin: ...
+
+class ProcessGroupGloo(ProcessGroup):
+    class Device: ...
+    class Options: ...
+
+    def __init__(
+        self,
+        store: Store,
+        rank: int,
+        size: int,
+        timeout: timedelta,
+    ): ...
+    @staticmethod
+    def create_device(hostname="", interface="") -> Device: ...
+    @staticmethod
+    def create_default_device() -> Device: ...
+
+class _ProcessGroupWrapper(ProcessGroup):
+    def __init__(self, pg: ProcessGroup, gloo_pg: ProcessGroupGloo): ...
+    wrapped_pg: ProcessGroup
+
+class ProcessGroupNCCL(ProcessGroup):
+    class Options: ...
+
+    def __init__(
+        self,
+        store: Store,
+        rank: int,
+        size: int,
+        timeout: timedelta,
+    ): ...
+    def _group_start(self) -> None: ...
+    def _group_end(self) -> None: ...
+
+class ProcessGroupUCC(ProcessGroup):
+    def __init__(
+        self,
+        store: Store,
+        rank: int,
+        size: int,
+        timeout: timedelta,
+    ): ...
+
+class ProcessGroupMPI(ProcessGroup):
+    def __init__(
+        self,
+        rank: int,
+        size: int,
+        pgComm: int,
+    ): ...
+    @staticmethod
+    def create(ranks: List[int]) -> ProcessGroupMPI: ...
+
+def _compute_bucket_assignment_by_size(
+    tensors: List[Tensor],
+    bucket_size_limits: List[int],
+    expect_sparse_gradient: List[bool] = ...,
+    tensor_indices: List[int] = ...,
+) -> Tuple[List[List[int]], List[int]]: ...
+def _broadcast_coalesced(
+    process_group: ProcessGroup,
+    tensors: List[Tensor],
+    buffer_size: int,
+    src: int,
+): ...
+def _test_python_store(store: Store): ...
+def _verify_params_across_processes(
+    process_group: ProcessGroup,
+    params: List[Tensor],
+    logger: Optional[Logger],
+): ...
+def _make_nccl_premul_sum(factor: Union[float, List[Tensor]]) -> ReduceOp: ...
+
+class Backend:
+    def __init__(
+        self,
+        rank: int,
+        size: int,
+    ): ...

env-llmeval/lib/python3.10/site-packages/torch/_C/_distributed_rpc_testing.pyi

@@ -0,0 +1,35 @@
+from typing import Dict, List
+
+import torch
+
+from ._distributed_c10d import Store
+from ._distributed_rpc import _TensorPipeRpcBackendOptionsBase, TensorPipeAgent
+
+# This module is defined in torch/csrc/distributed/rpc/testing/init.cpp
+
+class FaultyTensorPipeRpcBackendOptions(_TensorPipeRpcBackendOptionsBase):
+    def __init__(
+        self,
+        num_worker_threads: int,
+        rpc_timeout: float,
+        init_method: str,
+        messages_to_fail: List[str],
+        messages_to_delay: Dict[str, float],
+        num_fail_sends: int,
+    ): ...
+    num_send_recv_threads: int
+    messages_to_fail: List[str]
+    messages_to_delay: Dict[str, float]
+    num_fail_sends: int
+
+class FaultyTensorPipeAgent(TensorPipeAgent):
+    def __init__(
+        self,
+        store: Store,
+        name: str,
+        rank: int,
+        world_size: int,
+        options: FaultyTensorPipeRpcBackendOptions,
+        reverse_device_maps: Dict[str, Dict[torch.device, torch.device]],
+        devices: List[torch.device],
+    ): ...

env-llmeval/lib/python3.10/site-packages/torch/_C/_functorch.pyi

@@ -0,0 +1,71 @@
+from enum import Enum
+from typing import Optional, Tuple
+
+from torch import Tensor
+
+# Defined in torch/csrc/functorch/init.cpp
+
+def _set_dynamic_layer_keys_included(included: bool) -> None: ...
+def get_unwrapped(tensor: Tensor) -> Tensor: ...
+def is_batchedtensor(tensor: Tensor) -> bool: ...
+def is_functionaltensor(tensor: Tensor) -> bool: ...
+def is_functorch_wrapped_tensor(tensor: Tensor) -> bool: ...
+def is_gradtrackingtensor(tensor: Tensor) -> bool: ...
+def maybe_get_bdim(tensor: Tensor) -> int: ...
+def maybe_get_level(tensor: Tensor) -> int: ...
+def unwrap_if_dead(tensor: Tensor) -> Tensor: ...
+def _unwrap_for_grad(tensor: Tensor, level: int) -> Tensor: ...
+def _wrap_for_grad(tensor: Tensor, level: int) -> Tensor: ...
+def _unwrap_batched(tensor: Tensor, level: int) -> Tuple[Tensor, Optional[int]]: ...
+def current_level() -> int: ...
+def _add_batch_dim(tensor: Tensor, bdim: int, level: int) -> Tensor: ...
+def set_single_level_autograd_function_allowed(allowed: bool) -> None: ...
+def get_single_level_autograd_function_allowed() -> bool: ...
+def _unwrap_functional_tensor(tensor: Tensor, reapply_views: bool) -> Tensor: ...
+def _wrap_functional_tensor(tensor: Tensor, level: int) -> Tensor: ...
+
+# Defined in aten/src/ATen/functorch/Interpreter.h
+class TransformType(Enum):
+    Torch: TransformType = ...
+    Vmap: TransformType = ...
+    Grad: TransformType = ...
+    Jvp: TransformType = ...
+    Functionalize: TransformType = ...
+
+class RandomnessType(Enum):
+    Error: TransformType = ...
+    Same: TransformType = ...
+    Different: TransformType = ...
+
+class CInterpreter:
+    def key(self) -> TransformType: ...
+    def level(self) -> int: ...
+
+class CGradInterpreterPtr:
+    def __init__(self, interpreter: CInterpreter): ...
+    def lift(self, Tensor) -> Tensor: ...
+    def prevGradMode(self) -> bool: ...
+
+class CJvpInterpreterPtr:
+    def __init__(self, interpreter: CInterpreter): ...
+    def lift(self, Tensor) -> Tensor: ...
+    def prevFwdGradMode(self) -> bool: ...
+
+class CFunctionalizeInterpreterPtr:
+    def __init__(self, interpreter: CInterpreter): ...
+    def key(self) -> TransformType: ...
+    def level(self) -> int: ...
+    def functionalizeAddBackViews(self) -> bool: ...
+
+class CVmapInterpreterPtr:
+    def __init__(self, interpreter: CInterpreter): ...
+    def key(self) -> TransformType: ...
+    def level(self) -> int: ...
+    def batchSize(self) -> int: ...
+    def randomness(self) -> RandomnessType: ...
+
+class DynamicLayer: ...
+
+def peek_interpreter_stack() -> CInterpreter: ...
+def pop_dynamic_layer_stack() -> DynamicLayer: ...
+def push_dynamic_layer_stack(dl: DynamicLayer) -> int: ...

env-llmeval/lib/python3.10/site-packages/torch/_C/_itt.pyi

@@ -0,0 +1,5 @@
+# Defined in torch/csrc/itt.cpp
+def is_available() -> None: ...
+def rangePush(message: str) -> None: ...
+def rangePop() -> None: ...
+def mark(message: str) -> None: ...

env-llmeval/lib/python3.10/site-packages/torch/_C/_lazy.pyi

@@ -0,0 +1,28 @@
+from typing import List
+
+from torch import Tensor
+
+# defined in torch/csrc/lazy/python/init.cpp
+def _mark_step(device: str, devices: List[str], wait: bool): ...
+def _wait_device_ops(devices: List[str]): ...
+def _reset_metrics(): ...
+def _counter_names() -> List[str]: ...
+def _counter_value(name: str) -> int: ...
+def _metrics_report() -> str: ...
+def _get_graph_hash(tensors: List[Tensor]) -> str: ...
+def _sync_multi(
+    tensors: List[Tensor],
+    devices: List[str],
+    wait: bool = True,
+    sync_ltc_data: bool = True,
+): ...
+def _get_tensor_id(tensor: Tensor) -> int: ...
+def _get_tensors_text(tensors: List[Tensor]) -> str: ...
+def _get_tensors_dot(tensors: List[Tensor]) -> str: ...
+def _get_tensors_backend(tensors: List[Tensor]) -> str: ...
+def _get_force_fallback() -> str: ...
+def _set_force_fallback(newval: str): ...
+def _clear_ir_cache(): ...
+def _dump_ir_cache(filename: str): ...
+def _set_reuse_ir(val: bool): ...
+def _get_default_device_type(): ...

env-llmeval/lib/python3.10/site-packages/torch/_C/_lazy_ts_backend.pyi

@@ -0,0 +1,11 @@
+# defined in torch/csrc/lazy/python/init.cpp
+
+from typing import Any, List, Tuple
+
+from torch import Tensor
+
+def _init(): ...
+def _get_tensors_ts_device_data_node(
+    tensors: List[Tensor],
+) -> Tuple[List[int], List[Any]]: ...
+def _run_cached_graph(hash_str: str, graph_inputs: List[Any]) -> List[Tensor]: ...

env-llmeval/lib/python3.10/site-packages/torch/_C/_monitor.pyi

@@ -0,0 +1,44 @@
+# Defined in torch/csrc/monitor/python_init.cpp
+
+import datetime
+from enum import Enum
+from typing import Callable, Dict, List, Union
+
+class Aggregation(Enum):
+    VALUE = ...
+    MEAN = ...
+    COUNT = ...
+    SUM = ...
+    MAX = ...
+    MIN = ...
+
+class Stat:
+    name: str
+    count: int
+    def __init__(
+        self,
+        name: str,
+        aggregations: List[Aggregation],
+        window_size: int,
+        max_samples: int = -1,
+    ) -> None: ...
+    def add(self, v: float) -> None: ...
+    def get(self) -> Dict[Aggregation, float]: ...
+
+class Event:
+    name: str
+    timestamp: datetime.datetime
+    data: Dict[str, Union[int, float, bool, str]]
+    def __init__(
+        self,
+        name: str,
+        timestamp: datetime.datetime,
+        data: Dict[str, Union[int, float, bool, str]],
+    ) -> None: ...
+
+def log_event(e: Event) -> None: ...
+
+class EventHandlerHandle: ...
+
+def register_event_handler(handler: Callable[[Event], None]) -> EventHandlerHandle: ...
+def unregister_event_handler(handle: EventHandlerHandle) -> None: ...

env-llmeval/lib/python3.10/site-packages/torch/_C/_profiler.pyi

@@ -0,0 +1,238 @@
+from enum import Enum
+from typing import Any, Dict, List, Literal, Optional, Tuple, Union
+
+from torch._C import device, dtype, layout
+from typing_extensions import TypeAlias
+
+# defined in torch/csrc/profiler/python/init.cpp
+
+class RecordScope(Enum):
+    FUNCTION = ...
+    BACKWARD_FUNCTION = ...
+    TORCHSCRIPT_FUNCTION = ...
+    KERNEL_FUNCTION_DTYPE = ...
+    CUSTOM_CLASS = ...
+    BUILD_FEATURE = ...
+    LITE_INTERPRETER = ...
+    USER_SCOPE = ...
+    STATIC_RUNTIME_OP = ...
+    STATIC_RUNTIME_MODEL = ...
+
+class ProfilerState(Enum):
+    Disable = ...
+    CPU = ...
+    CUDA = ...
+    NVTX = ...
+    ITT = ...
+    KINETO = ...
+    KINETO_GPU_FALLBACK = ...
+    KINETO_PRIVATEUSE1_FALLBACK = ...
+    KINETO_PRIVATEUSE1 = ...
+
+class ActiveProfilerType(Enum):
+    NONE = ...
+    LEGACY = ...
+    KINETO = ...
+    NVTX = ...
+    ITT = ...
+
+class ProfilerActivity(Enum):
+    CPU = ...
+    CUDA = ...
+    MTIA = ...
+    PrivateUse1 = ...
+
+class _EventType(Enum):
+    TorchOp = ...
+    Backend = ...
+    Allocation = ...
+    OutOfMemory = ...
+    PyCall = ...
+    PyCCall = ...
+    Kineto = ...
+
+class _ExperimentalConfig:
+    def __init__(
+        self,
+        profiler_metrics: List[str] = ...,
+        profiler_measure_per_kernel: bool = ...,
+        verbose: bool = ...,
+        performance_events: List[str] = ...,
+        enable_cuda_sync_events: bool = ...,
+    ) -> None: ...
+
+class ProfilerConfig:
+    def __init__(
+        self,
+        state: ProfilerState,
+        report_input_shapes: bool,
+        profile_memory: bool,
+        with_stack: bool,
+        with_flops: bool,
+        with_modules: bool,
+        experimental_config: _ExperimentalConfig,
+    ) -> None: ...
+
+class _ProfilerEvent:
+    start_tid: int
+    start_time_ns: int
+    children: List[_ProfilerEvent]
+
+    # TODO(robieta): remove in favor of `self.typed`
+    extra_fields: Union[
+        _ExtraFields_TorchOp,
+        _ExtraFields_Backend,
+        _ExtraFields_Allocation,
+        _ExtraFields_OutOfMemory,
+        _ExtraFields_PyCall,
+        _ExtraFields_PyCCall,
+        _ExtraFields_Kineto,
+    ]
+
+    @property
+    def typed(
+        self,
+    ) -> Union[
+        Tuple[Literal[_EventType.TorchOp], _ExtraFields_TorchOp],
+        Tuple[Literal[_EventType.Backend], _ExtraFields_Backend],
+        Tuple[Literal[_EventType.Allocation], _ExtraFields_Allocation],
+        Tuple[Literal[_EventType.OutOfMemory], _ExtraFields_OutOfMemory],
+        Tuple[Literal[_EventType.PyCall], _ExtraFields_PyCall],
+        Tuple[Literal[_EventType.PyCCall], _ExtraFields_PyCCall],
+        Tuple[Literal[_EventType.Kineto], _ExtraFields_Kineto],
+    ]: ...
+    @property
+    def name(self) -> str: ...
+    @property
+    def tag(self) -> _EventType: ...
+    @property
+    def id(self) -> int: ...
+    @property
+    def parent(self) -> Optional[_ProfilerEvent]: ...
+    @property
+    def correlation_id(self) -> int: ...
+    @property
+    def end_time_ns(self) -> int: ...
+    @property
+    def duration_time_ns(self) -> int: ...
+
+class _TensorMetadata:
+    impl_ptr: Optional[int]
+    storage_data_ptr: Optional[int]
+    id: Optional[int]
+
+    @property
+    def allocation_id(self) -> Optional[int]: ...
+    @property
+    def layout(self) -> layout: ...
+    @property
+    def device(self) -> device: ...
+    @property
+    def dtype(self) -> dtype: ...
+    @property
+    def sizes(self) -> List[int]: ...
+    @property
+    def strides(self) -> List[int]: ...
+
+Scalar: TypeAlias = Union[int, float, bool, complex]
+Input: TypeAlias = Optional[Union[_TensorMetadata, List[_TensorMetadata], Scalar]]
+
+class _ExtraFields_TorchOp:
+    name: str
+    sequence_number: int
+    allow_tf32_cublas: bool
+
+    @property
+    def inputs(self) -> List[Input]: ...
+    @property
+    def scope(self) -> RecordScope: ...
+
+class _ExtraFields_Backend: ...
+
+class _ExtraFields_Allocation:
+    ptr: int
+    id: Optional[int]
+    alloc_size: int
+    total_allocated: int
+    total_reserved: int
+
+    @property
+    def allocation_id(self) -> Optional[int]: ...
+    @property
+    def device(self) -> device: ...
+
+class _ExtraFields_OutOfMemory: ...
+
+class _PyFrameState:
+    line_number: int
+    function_name: str
+
+    @property
+    def file_name(self) -> str: ...
+
+class _NNModuleInfo:
+    @property
+    def self_ptr(self) -> int: ...
+    @property
+    def cls_ptr(self) -> int: ...
+    @property
+    def cls_name(self) -> str: ...
+    @property
+    def parameters(
+        self,
+    ) -> List[Tuple[str, _TensorMetadata, Optional[_TensorMetadata]]]: ...
+
+class _OptimizerInfo:
+    @property
+    def parameters(
+        self,
+    ) -> List[
+        Tuple[
+            # Parameter
+            _TensorMetadata,
+            #
+            # Gradient (if present during optimizer.step())
+            Optional[_TensorMetadata],
+            #
+            # Optimizer state for Parameter as (name, tensor) pairs
+            List[Tuple[str, _TensorMetadata]],
+        ]
+    ]: ...
+
+class _ExtraFields_PyCCall:
+    @property
+    def caller(self) -> _PyFrameState: ...
+
+class _ExtraFields_PyCall:
+    @property
+    def callsite(self) -> _PyFrameState: ...
+    @property
+    def caller(self) -> _PyFrameState: ...
+    @property
+    def module(self) -> Optional[_NNModuleInfo]: ...
+    @property
+    def optimizer(self) -> Optional[_OptimizerInfo]: ...
+
+class _ExtraFields_Kineto: ...
+
+def _add_execution_trace_observer(output_file_path: str) -> bool: ...
+def _remove_execution_trace_observer() -> None: ...
+def _enable_execution_trace_observer() -> None: ...
+def _disable_execution_trace_observer() -> None: ...
+def _set_record_concrete_inputs_enabled_val(val: bool) -> None: ...
+def _set_fwd_bwd_enabled_val(val: bool) -> None: ...
+def _set_cuda_sync_enabled_val(val: bool) -> None: ...
+
+class CapturedTraceback: ...
+
+def gather_traceback(python: bool, script: bool, cpp: bool) -> CapturedTraceback: ...
+
+# The Dict has name, filename, line
+def symbolize_tracebacks(
+    to_symbolize: List[CapturedTraceback],
+) -> List[List[Dict[str, str]]]: ...
+
+class _RecordFunctionFast:
+    def __init__(self, name: str) -> None: ...
+    def __enter__(self) -> None: ...
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None: ...

env-llmeval/lib/python3.10/site-packages/torch/_refs/_conversions.py

@@ -0,0 +1,118 @@
+import torch
+import torch._prims_common as utils
+
+# Utilities should come BEFORE this import
+from torch._decomp import register_decomposition
+
+from torch._prims_common import TensorLikeType
+from torch._prims_common.wrappers import out_wrapper
+from torch._refs import _broadcast_shapes
+
+# Data conversion references.
+#
+# Note: this module breaks the usual _refs to torch naming scheme where
+# _refs.foo.bar is a ref for torch.foo.bar.  The following definitions are not
+# part of _refs/__init__.py to avoid name clashes with Python builtin types
+# (like int).
+
+__all__ = [
+    # dtypes
+    "bfloat16",
+    "bool",
+    "byte",
+    "cdouble",
+    "cfloat",
+    "chalf",
+    "char",
+    "double",
+    "float",
+    "half",
+    "int",
+    "long",
+    "short",
+    # misc
+    "complex",
+    "polar",
+]
+
+
+def _make_conversion_method(name: str, dtype: torch.dtype):
+    def fn(
+        self: TensorLikeType, memory_format: torch.memory_format = torch.preserve_format
+    ) -> TensorLikeType:
+        return self.to(dtype, memory_format=memory_format)  # type: ignore[call-overload]
+
+    fn.__name__ = name
+    return fn
+
+
+bfloat16 = _make_conversion_method("bfloat16", torch.bfloat16)
+
+bool = _make_conversion_method("bool", torch.bool)
+
+byte = _make_conversion_method("byte", torch.uint8)
+
+cdouble = _make_conversion_method("cdouble", torch.cdouble)
+
+cfloat = _make_conversion_method("cfloat", torch.cfloat)
+
+chalf = _make_conversion_method("chalf", torch.complex32)
+
+char = _make_conversion_method("char", torch.int8)
+
+double = _make_conversion_method("double", torch.double)
+
+float = _make_conversion_method("float", torch.float)
+
+half = _make_conversion_method("half", torch.half)
+
+int = _make_conversion_method("int", torch.int)
+
+long = _make_conversion_method("long", torch.long)
+
+short = _make_conversion_method("short", torch.short)
+
+
+@register_decomposition(torch._ops.ops.aten.complex)
+# Note: complex has type promotion tests disabled due to different semantics.
+# exact_dtype is for compat with complex_check_dtype from core.
+@out_wrapper(exact_dtype=True)
+def complex(real: TensorLikeType, imag: TensorLikeType) -> TensorLikeType:
+    allowed_dtypes = (torch.float32, torch.float64, torch.float16)
+    torch._check(
+        real.dtype in allowed_dtypes and imag.dtype in allowed_dtypes,
+        lambda: (
+            f"Expected both inputs to be Half, Float or Double tensors but got "
+            f"{real.dtype} and {imag.dtype}"
+        ),
+    )
+    torch._check(
+        real.dtype == imag.dtype,
+        lambda: (
+            f"Expected object of scalar type {real.dtype} but got "
+            f"scalar type {imag.dtype} for second argument"
+        ),
+    )
+    result_dtype = utils.corresponding_complex_dtype(real.dtype)  # type: ignore[arg-type]
+    common_shape = _broadcast_shapes(real.shape, imag.shape)
+    result = real.new_empty(
+        common_shape,
+        dtype=result_dtype,
+        layout=real.layout,
+        device=real.device,
+        # pin_memory=real.is_pinned(),  # NYI
+    )
+    result.real = real
+    result.imag = imag
+    return result
+
+
+@register_decomposition(torch._ops.ops.aten.polar)
+# Note: polar has type promotion tests disabled due to different semantics.
+# exact_dtype is for compat with complex_check_dtype from core.
+@out_wrapper(exact_dtype=True)
+def polar(abs: TensorLikeType, angle: TensorLikeType) -> TensorLikeType:
+    result = torch.complex(abs, angle)
+    result.real = abs * torch.cos(angle)
+    result.imag = abs * torch.sin(angle)
+    return result

env-llmeval/lib/python3.10/site-packages/torch/_refs/fft.py

@@ -0,0 +1,590 @@
+import math
+
+from typing import Iterable, List, Literal, NamedTuple, Optional, Sequence, Tuple, Union
+
+import torch
+import torch._prims as prims
+import torch._prims_common as utils
+from torch._decomp import register_decomposition
+from torch._prims_common import DimsType, ShapeType, TensorLikeType
+from torch._prims_common.wrappers import _maybe_convert_to_dtype, out_wrapper
+
+__all__ = [
+    # Transforms
+    "fft",
+    "fft2",
+    "fftn",
+    "hfft",
+    "hfft2",
+    "hfftn",
+    "rfft",
+    "rfft2",
+    "rfftn",
+    "ifft",
+    "ifft2",
+    "ifftn",
+    "ihfft",
+    "ihfft2",
+    "ihfftn",
+    "irfft",
+    "irfft2",
+    "irfftn",
+    # Helpers
+    "fftshift",
+    "ifftshift",
+]
+
+NormType = Union[None, Literal["forward", "backward", "ortho"]]
+_NORM_VALUES = {None, "forward", "backward", "ortho"}
+aten = torch._ops.ops.aten
+
+
+def _apply_norm(
+    x: TensorLikeType, norm: NormType, signal_numel: int, forward: bool
+) -> TensorLikeType:
+    """Apply normalization to the un-normalized FFT result"""
+    torch._check(norm in _NORM_VALUES, lambda: f"Invalid normalization mode: {norm}")
+
+    if norm == "ortho":
+        return x * (1 / math.sqrt(signal_numel))
+
+    normalize = (not forward and (norm is None or norm == "backward")) or (
+        forward and norm == "forward"
+    )
+    return x * (1 / signal_numel) if normalize else x
+
+
+def _promote_type_fft(
+    dtype: torch.dtype, require_complex: bool, device: torch.device
+) -> torch.dtype:
+    """Helper to promote a dtype to one supported by the FFT primitives"""
+    if dtype.is_complex:
+        return dtype
+
+    # Promote integral to default float type
+    if not dtype.is_floating_point:
+        dtype = torch.get_default_dtype()
+
+    allowed_types = [torch.float32, torch.float64]
+    maybe_support_half = device.type in ["cuda", "meta"] and not torch.version.hip
+
+    if maybe_support_half:
+        allowed_types.append(torch.float16)
+    torch._check(dtype in allowed_types, lambda: f"Unsupported dtype {dtype}")
+
+    if require_complex:
+        dtype = utils.corresponding_complex_dtype(dtype)
+
+    return dtype
+
+
+def _maybe_promote_tensor_fft(
+    t: TensorLikeType, require_complex: bool = False
+) -> TensorLikeType:
+    """Helper to promote a tensor to a dtype supported by the FFT primitives"""
+    cur_type = t.dtype
+    new_type = _promote_type_fft(cur_type, require_complex, t.device)
+    return _maybe_convert_to_dtype(t, new_type)  # type: ignore[return-value]
+
+
+def _resize_fft_input(
+    x: TensorLikeType, dims: Tuple[int, ...], sizes: Tuple[int, ...]
+) -> TensorLikeType:
+    """
+    Fixes the shape of x such that x.size(dims[i]) == sizes[i],
+    either by zero-padding, or by slicing x starting from 0.
+    """
+    assert len(dims) == len(sizes)
+    must_copy = False
+    x_sizes = x.shape
+    pad_amount = [0] * len(x_sizes) * 2
+    for i in range(len(dims)):
+        if sizes[i] == -1:
+            continue
+
+        if x_sizes[dims[i]] < sizes[i]:
+            must_copy = True
+            pad_idx = len(pad_amount) - 2 * dims[i] - 1
+            pad_amount[pad_idx] = sizes[i] - x_sizes[dims[i]]
+
+        if x_sizes[dims[i]] > sizes[i]:
+            x = x.narrow(dims[i], 0, sizes[i])
+
+    return torch.constant_pad_nd(x, pad_amount) if must_copy else x
+
+
+def _fft_c2r(
+    func_name: str,
+    input: TensorLikeType,
+    n: Optional[int],
+    dim: int,
+    norm: NormType,
+    forward: bool,
+) -> TensorLikeType:
+    """Common code for performing any complex to real FFT (irfft or hfft)"""
+    input = _maybe_promote_tensor_fft(input, require_complex=True)
+    dims = (utils.canonicalize_dim(input.ndim, dim, wrap_scalar=False),)
+    last_dim_size = n if n is not None else 2 * (input.shape[dim] - 1)
+    torch._check(
+        last_dim_size >= 1,
+        lambda: f"Invalid number of data points ({last_dim_size}) specified",
+    )
+
+    if n is not None:
+        input = _resize_fft_input(input, dims=dims, sizes=(last_dim_size // 2 + 1,))
+
+    if forward:
+        input = torch.conj(input)
+
+    output = prims.fft_c2r(input, dim=dims, last_dim_size=last_dim_size)
+    return _apply_norm(output, norm=norm, signal_numel=last_dim_size, forward=forward)
+
+
+def _fft_r2c(
+    func_name: str,
+    input: TensorLikeType,
+    n: Optional[int],
+    dim: int,
+    norm: NormType,
+    forward: bool,
+    onesided: bool,
+) -> TensorLikeType:
+    """Common code for performing any real to complex FFT (rfft or ihfft)"""
+    torch._check(
+        not input.dtype.is_complex,
+        lambda: f"{func_name} expects a floating point input tensor, but got {input.dtype}",
+    )
+    input = _maybe_promote_tensor_fft(input)
+    dims = (utils.canonicalize_dim(input.ndim, dim, wrap_scalar=False),)
+    dim_size = n if n is not None else input.shape[dim]
+    torch._check(
+        dim_size >= 1, lambda: f"Invalid number of data points ({dim_size}) specified"
+    )
+
+    if n is not None:
+        input = _resize_fft_input(input, dims, (n,))
+
+    ret = prims.fft_r2c(input, dim=dims, onesided=onesided)
+    ret = _apply_norm(ret, norm, dim_size, forward)
+    return ret if forward else torch.conj(ret)
+
+
+def _fft_c2c(
+    func_name: str,
+    input: TensorLikeType,
+    n: Optional[int],
+    dim: int,
+    norm: NormType,
+    forward: bool,
+) -> TensorLikeType:
+    """Common code for performing any complex to complex FFT (fft or ifft)"""
+    torch._check(
+        input.dtype.is_complex,
+        lambda: f"{func_name} expects a complex input tensor, but got {input.dtype}",
+    )
+    dims = (utils.canonicalize_dim(input.ndim, dim, wrap_scalar=False),)
+    dim_size = n if n is not None else input.shape[dim]
+    torch._check(
+        dim_size >= 1, lambda: f"Invalid number of data points ({dim_size}) specified"
+    )
+
+    if n is not None:
+        input = _resize_fft_input(input, dims, (n,))
+
+    ret = prims.fft_c2c(input, dim=dims, forward=forward)
+    return _apply_norm(ret, norm, dim_size, forward)
+
+
+@register_decomposition(aten.fft_fft)
+@out_wrapper()
+def fft(
+    input: TensorLikeType,
+    n: Optional[int] = None,
+    dim: int = -1,
+    norm: NormType = None,
+) -> TensorLikeType:
+    if input.dtype.is_complex:
+        return _fft_c2c("fft", input, n, dim, norm, forward=True)
+    else:
+        return _fft_r2c("fft", input, n, dim, norm, forward=True, onesided=False)
+
+
+@register_decomposition(aten.fft_ifft)
+@out_wrapper()
+def ifft(
+    input: TensorLikeType,
+    n: Optional[int] = None,
+    dim: int = -1,
+    norm: NormType = None,
+) -> TensorLikeType:
+    if input.dtype.is_complex:
+        return _fft_c2c("ifft", input, n, dim, norm, forward=False)
+    else:
+        return _fft_r2c("ifft", input, n, dim, norm, forward=False, onesided=False)
+
+
+@register_decomposition(aten.fft_rfft)
+@out_wrapper()
+def rfft(
+    input: TensorLikeType,
+    n: Optional[int] = None,
+    dim: int = -1,
+    norm: NormType = None,
+) -> TensorLikeType:
+    return _fft_r2c("rfft", input, n, dim, norm, forward=True, onesided=True)
+
+
+@register_decomposition(aten.fft_irfft)
+@out_wrapper()
+def irfft(
+    input: TensorLikeType,
+    n: Optional[int] = None,
+    dim: int = -1,
+    norm: NormType = None,
+) -> TensorLikeType:
+    return _fft_c2r("irfft", input, n, dim, norm, forward=False)
+
+
+@register_decomposition(aten.fft_hfft)
+@out_wrapper()
+def hfft(
+    input: TensorLikeType,
+    n: Optional[int] = None,
+    dim: int = -1,
+    norm: NormType = None,
+) -> TensorLikeType:
+    return _fft_c2r("hfft", input, n, dim, norm, forward=True)
+
+
+@register_decomposition(aten.fft_ihfft)
+@out_wrapper()
+def ihfft(
+    input: TensorLikeType,
+    n: Optional[int] = None,
+    dim: int = -1,
+    norm: NormType = None,
+) -> TensorLikeType:
+    return _fft_r2c("ihfft", input, n, dim, norm, forward=False, onesided=True)
+
+
+class _ShapeAndDims(NamedTuple):
+    shape: Tuple[int, ...]
+    dims: Tuple[int, ...]
+
+
+def _canonicalize_fft_shape_and_dim_args(
+    input: TensorLikeType, shape: Optional[ShapeType], dim: Optional[DimsType]
+) -> _ShapeAndDims:
+    """Convert the shape and dim arguments into a canonical form where neither are optional"""
+    input_dim = input.ndim
+    input_sizes = input.shape
+
+    if dim is not None:
+        if not isinstance(dim, Sequence):
+            dim = (dim,)
+        ret_dims = utils.canonicalize_dims(input_dim, dim, wrap_scalar=False)
+
+        # Check dims are unique
+        torch._check(
+            len(set(ret_dims)) == len(ret_dims), lambda: "FFT dims must be unique"
+        )
+
+    if shape is not None:
+        if not isinstance(shape, Sequence):
+            shape = (shape,)
+
+        # Has shape, might have dim
+        torch._check(
+            dim is None or len(dim) == len(shape),
+            lambda: "When given, dim and shape arguments must have the same length",
+        )
+        transform_ndim = len(shape)
+
+        torch._check(
+            transform_ndim <= input_dim,
+            lambda: f"Got shape with {transform_ndim} values but input tensor "
+            f"only has {input_dim} dimensions.",
+        )
+
+        # If shape is given, dims defaults to the last len(shape) dimensions
+        if dim is None:
+            ret_dims = tuple(range(input_dim - transform_ndim, input_dim))
+
+        # Translate any -1 values in shape to the default length
+        ret_shape = tuple(
+            s if s != -1 else input_sizes[d] for (s, d) in zip(shape, ret_dims)
+        )
+    elif dim is None:
+        # No shape, no dim
+        ret_dims = tuple(range(input_dim))
+        ret_shape = tuple(input_sizes)
+    else:
+        # No shape, has dim
+        ret_shape = tuple(input_sizes[d] for d in ret_dims)
+
+    for n in ret_shape:
+        torch._check(n > 0, lambda: f"Invalid number of data points ({n}) specified")
+
+    return _ShapeAndDims(shape=ret_shape, dims=ret_dims)
+
+
+def _prod(xs: Iterable[int]) -> int:
+    """Compute product of a list"""
+    prod = 1
+    for x in xs:
+        prod *= x
+    return prod
+
+
+def _fftn_c2c(
+    function_name: str,
+    input: TensorLikeType,
+    shape: Tuple[int, ...],
+    dim: Tuple[int, ...],
+    norm: NormType,
+    forward: bool,
+) -> TensorLikeType:
+    """Common code for n-dimensional complex to complex FFTs (fftn or ifftn)"""
+    torch._check(
+        input.dtype.is_complex,
+        lambda: f"{function_name} expects a complex input tensor, "
+        f"but got {input.dtype}",
+    )
+    x = _resize_fft_input(input, dim, shape)
+    output = prims.fft_c2c(x, dim=dim, forward=forward)
+    return _apply_norm(output, norm=norm, signal_numel=_prod(shape), forward=forward)
+
+
+@register_decomposition(aten.fft_fftn)
+@out_wrapper()
+def fftn(
+    input: TensorLikeType,
+    s: Optional[ShapeType] = None,
+    dim: Optional[DimsType] = None,
+    norm: NormType = None,
+) -> TensorLikeType:
+    (shape, dim) = _canonicalize_fft_shape_and_dim_args(input, s, dim)
+    x = _maybe_promote_tensor_fft(input, require_complex=True)
+    return _fftn_c2c("fftn", x, shape, dim, norm, forward=True)
+
+
+@register_decomposition(aten.fft_ifftn)
+@out_wrapper()
+def ifftn(
+    input: TensorLikeType,
+    s: Optional[ShapeType] = None,
+    dim: Optional[DimsType] = None,
+    norm: NormType = None,
+) -> TensorLikeType:
+    (shape, dim) = _canonicalize_fft_shape_and_dim_args(input, s, dim)
+    x = _maybe_promote_tensor_fft(input, require_complex=True)
+    return _fftn_c2c("ifftn", x, shape, dim, norm, forward=False)
+
+
+@register_decomposition(aten.fft_rfftn)
+@out_wrapper()
+def rfftn(
+    input: TensorLikeType,
+    s: Optional[ShapeType] = None,
+    dim: Optional[DimsType] = None,
+    norm: NormType = None,
+) -> TensorLikeType:
+    torch._check(
+        not input.dtype.is_complex,
+        lambda: f"rfftn expects a real-valued input tensor, but got {input.dtype}",
+    )
+    shape, dim = _canonicalize_fft_shape_and_dim_args(input, s, dim)
+    input = _maybe_promote_tensor_fft(input, require_complex=False)
+    input = _resize_fft_input(input, dim, shape)
+    out = prims.fft_r2c(input, dim=dim, onesided=True)
+    return _apply_norm(out, norm=norm, signal_numel=_prod(shape), forward=True)
+
+
+@register_decomposition(aten.fft_ihfftn)
+@out_wrapper()
+def ihfftn(
+    input: TensorLikeType,
+    s: Optional[ShapeType] = None,
+    dim: Optional[DimsType] = None,
+    norm: NormType = None,
+) -> TensorLikeType:
+    torch._check(
+        not input.dtype.is_complex,
+        lambda: f"ihfftn expects a real-valued input tensor, but got {input.dtype}",
+    )
+    shape, dim = _canonicalize_fft_shape_and_dim_args(input, s, dim)
+    torch._check(len(shape) > 0, lambda: "ihfftn must transform at least one axis")
+    input = _maybe_promote_tensor_fft(input, require_complex=False)
+    input = _resize_fft_input(input, dim, shape)
+
+    tmp = prims.fft_r2c(input, dim=dim[-1:], onesided=True)
+
+    if len(dim) == 1:
+        tmp = _apply_norm(tmp, norm=norm, signal_numel=shape[0], forward=False)
+        return prims.conj(tmp)
+
+    tmp = prims.conj_physical(tmp)
+    tmp = prims.fft_c2c(tmp, dim=dim[:-1], forward=False)
+    return _apply_norm(tmp, norm=norm, signal_numel=_prod(shape), forward=False)
+
+
+class _CanonicalizeC2rReturn(NamedTuple):
+    shape: Tuple[int, ...]
+    dim: Tuple[int, ...]
+    last_dim_size: int
+
+
+def _canonicalize_fft_c2r_shape_and_dim_args(
+    fname: str,
+    input: TensorLikeType,
+    s: Optional[ShapeType],
+    dim: Optional[DimsType],
+) -> _CanonicalizeC2rReturn:
+    """Canonicalize shape and dim arguments for n-dimensional c2r transforms,
+    as well as calculating the last_dim_size which is shape[dim[-1]] for the output"""
+    (shape, dim) = _canonicalize_fft_shape_and_dim_args(input, s, dim)
+    torch._check(len(shape) > 0, lambda: f"{fname} must transform at least one axis")
+
+    if s is None or s[-1] == -1:
+        last_dim_size = 2 * (input.shape[dim[-1]] - 1)
+    else:
+        last_dim_size = shape[-1]
+
+    torch._check(
+        last_dim_size >= 1,
+        lambda: f"Invalid number of data points ({last_dim_size}) specified",
+    )
+
+    shape_list = list(shape)
+    shape_list[-1] = last_dim_size // 2 + 1
+    return _CanonicalizeC2rReturn(
+        shape=tuple(shape_list), dim=dim, last_dim_size=last_dim_size
+    )
+
+
+@register_decomposition(aten.fft_irfftn)
+@out_wrapper()
+def irfftn(
+    input: TensorLikeType,
+    s: Optional[ShapeType] = None,
+    dim: Optional[DimsType] = None,
+    norm: NormType = None,
+) -> TensorLikeType:
+    shape, dim, last_dim_size = _canonicalize_fft_c2r_shape_and_dim_args(
+        "irfftn", input, s, dim
+    )
+    input = _maybe_promote_tensor_fft(input, require_complex=True)
+    input = _resize_fft_input(input, dim, shape)
+    out = prims.fft_c2r(input, dim=dim, last_dim_size=last_dim_size)
+    return _apply_norm(out, norm, _prod(out.shape[d] for d in dim), forward=False)
+
+
+@register_decomposition(aten.fft_hfftn)
+@out_wrapper()
+def hfftn(
+    input: TensorLikeType,
+    s: Optional[ShapeType] = None,
+    dim: Optional[DimsType] = None,
+    norm: NormType = None,
+) -> TensorLikeType:
+    shape, dim, last_dim_size = _canonicalize_fft_c2r_shape_and_dim_args(
+        "hfftn", input, s, dim
+    )
+    input = _maybe_promote_tensor_fft(input, require_complex=True)
+    input = _resize_fft_input(input, dim, shape)
+
+    tmp = prims.fft_c2c(input, dim=dim[:-1], forward=True) if len(dim) > 1 else input
+    tmp = _apply_norm(tmp, norm, _prod(shape[:-1]), forward=True)
+    tmp = prims.conj_physical(tmp)
+    out = prims.fft_c2r(tmp, dim=dim[-1:], last_dim_size=last_dim_size)
+    return _apply_norm(out, norm, last_dim_size, forward=True)
+
+
+@register_decomposition(aten.fft_fft2)
+@out_wrapper()
+def fft2(
+    input: TensorLikeType,
+    s: Optional[ShapeType] = None,
+    dim: Optional[DimsType] = (-2, -1),
+    norm: NormType = None,
+) -> TensorLikeType:
+    return torch.fft.fftn(input, s=s, dim=dim, norm=norm)
+
+
+@register_decomposition(aten.fft_ifft2)
+@out_wrapper()
+def ifft2(
+    input: TensorLikeType,
+    s: Optional[ShapeType] = None,
+    dim: Optional[DimsType] = (-2, -1),
+    norm: NormType = None,
+) -> TensorLikeType:
+    return torch.fft.ifftn(input, s=s, dim=dim, norm=norm)
+
+
+@register_decomposition(aten.fft_rfft2)
+@out_wrapper()
+def rfft2(
+    input: TensorLikeType,
+    s: Optional[ShapeType] = None,
+    dim: Optional[DimsType] = (-2, -1),
+    norm: NormType = None,
+) -> TensorLikeType:
+    return torch.fft.rfftn(input, s=s, dim=dim, norm=norm)
+
+
+@register_decomposition(aten.fft_irfft2)
+@out_wrapper()
+def irfft2(
+    input: TensorLikeType,
+    s: Optional[ShapeType] = None,
+    dim: Optional[DimsType] = (-2, -1),
+    norm: NormType = None,
+) -> TensorLikeType:
+    return torch.fft.irfftn(input, s=s, dim=dim, norm=norm)
+
+
+@register_decomposition(aten.fft_hfft2)
+@out_wrapper()
+def hfft2(
+    input: TensorLikeType,
+    s: Optional[ShapeType] = None,
+    dim: Optional[DimsType] = (-2, -1),
+    norm: NormType = None,
+) -> TensorLikeType:
+    return torch.fft.hfftn(input, s=s, dim=dim, norm=norm)
+
+
+@register_decomposition(aten.fft_ihfft2)
+@out_wrapper()
+def ihfft2(
+    input: TensorLikeType,
+    s: Optional[ShapeType] = None,
+    dim: Optional[DimsType] = (-2, -1),
+    norm: NormType = None,
+) -> TensorLikeType:
+    return torch.fft.ihfftn(input, s=s, dim=dim, norm=norm)
+
+
+def _default_alldims(dim: Optional[DimsType], x: TensorLikeType) -> List[int]:
+    """Convert Optional[DimsType] to a simple list, defaulting to all dimensions"""
+    if dim is None:
+        return list(range(x.ndim))
+    elif not isinstance(dim, Sequence):
+        return [dim]
+    else:
+        return list(dim)
+
+
+@register_decomposition(aten.fft_fftshift)
+def fftshift(input: TensorLikeType, dim: Optional[DimsType] = None) -> TensorLikeType:
+    dims = _default_alldims(dim, input)
+    shift = [input.shape[d] // 2 for d in dims]
+    return torch.roll(input, shift, dims)
+
+
+@register_decomposition(aten.fft_ifftshift)
+def ifftshift(input: TensorLikeType, dim: Optional[DimsType] = None) -> TensorLikeType:
+    dims = _default_alldims(dim, input)
+    shift = [(input.shape[d] + 1) // 2 for d in dims]
+    return torch.roll(input, shift, dims)

env-llmeval/lib/python3.10/site-packages/torch/_refs/linalg/__init__.py

@@ -0,0 +1,276 @@
+from functools import partial
+
+from typing import List, Optional, Tuple, Union
+
+import torch
+
+import torch._prims as prims
+
+import torch._prims_common as utils
+import torch._refs as refs
+import torch._refs.linalg as linalg
+from torch import Tensor
+from torch._prims_common import (
+    check_fp_or_complex,
+    check_is_matrix,
+    Dim,
+    DimsType,
+    ELEMENTWISE_TYPE_PROMOTION_KIND,
+    NumberType,
+    TensorLikeType,
+)
+from torch._prims_common.wrappers import (
+    _maybe_convert_to_dtype,
+    elementwise_type_promotion_wrapper,
+    out_wrapper,
+)
+
+
+__all__ = ["diagonal", "matrix_norm", "norm", "svd", "svdvals", "vector_norm", "vecdot"]
+
+
+def _check_norm_dtype(dtype: Optional[torch.dtype], x_dtype: torch.dtype, fn_name: str):
+    """
+    Checks related to the dtype kwarg in `linalg.*norm` functions
+    """
+    if dtype is not None:
+        torch._check(
+            utils.is_float_dtype(dtype) or utils.is_complex_dtype(dtype),
+            lambda: f"{fn_name}: dtype should be floating point or complex. Got {dtype}",
+        )
+        torch._check(
+            utils.is_complex_dtype(dtype) == utils.is_complex_dtype(x_dtype),
+            lambda: "{fn_name}: dtype should be {d} for {d} inputs. Got {dtype}".format(
+                fn_name=fn_name,
+                d="complex" if utils.is_complex_dtype(x_dtype) else "real",
+                dtype=dtype,
+            ),
+        )
+        torch._check(
+            utils.get_higher_dtype(dtype, x_dtype) == dtype,
+            lambda: f"{fn_name}: the dtype of the input ({x_dtype}) should be convertible "
+            "without narrowing to the specified dtype ({dtype})",
+        )
+
+
+# Utilities should come BEFORE this import
+from torch._decomp import register_decomposition
+
+
+def diagonal(
+    input: TensorLikeType,
+    *,
+    offset: int = 0,
+    dim1: int = -2,
+    dim2: int = -1,
+) -> TensorLikeType:
+    return torch.diagonal(input, offset=offset, dim1=dim1, dim2=dim2)
+
+
+@register_decomposition(torch._ops.ops.aten.linalg_vector_norm)
+@out_wrapper(exact_dtype=True)
+def vector_norm(
+    x: TensorLikeType,
+    ord: float = 2.0,
+    dim: Optional[DimsType] = None,
+    keepdim: bool = False,
+    *,
+    dtype: Optional[torch.dtype] = None,
+) -> Tensor:
+    # Checks
+    check_fp_or_complex(x.dtype, "linalg.vector_norm")
+
+    if isinstance(dim, Dim):
+        dim = [dim]  # type: ignore[assignment]
+
+    if x.numel() == 0 and (ord < 0.0 or ord == float("inf")):
+        torch._check(
+            dim is not None and len(dim) != 0,
+            lambda: f"linalg.vector_norm cannot compute the {ord} norm on an empty tensor "
+            "because the operation does not have an identity",
+        )
+        shape = x.shape
+        assert dim is not None  # mypy does not seem to be able to see through check?
+        for d in dim:
+            torch._check(
+                shape[d] != 0,
+                lambda: f"linalg.vector_norm cannot compute the {ord} norm on the "
+                f"dimension {d} because this dimension is empty and the "
+                "operation does not have an identity",
+            )
+    _check_norm_dtype(dtype, x.dtype, "linalg.vector_norm")
+
+    computation_dtype, result_dtype = utils.reduction_dtypes(
+        x, utils.REDUCTION_OUTPUT_TYPE_KIND.COMPLEX_TO_FLOAT, dtype
+    )
+
+    to_result_dtype = partial(_maybe_convert_to_dtype, dtype=result_dtype)
+
+    # Implementation
+    if ord == 0.0:
+        return torch.sum(torch.ne(x, 0.0), dim=dim, keepdim=keepdim, dtype=result_dtype)
+    elif ord == float("inf"):
+        return to_result_dtype(torch.amax(torch.abs(x), dim=dim, keepdim=keepdim))  # type: ignore[return-value,arg-type]
+    elif ord == float("-inf"):
+        return to_result_dtype(torch.amin(torch.abs(x), dim=dim, keepdim=keepdim))  # type: ignore[return-value,arg-type]
+    else:
+        # From here on the computation dtype is important as the reduction is non-trivial
+        x = _maybe_convert_to_dtype(x, computation_dtype)  # type: ignore[assignment]
+        reduce_sum = partial(torch.sum, dim=dim, keepdim=keepdim)
+
+        if not (ord % 2.0 == 0.0 and utils.is_float_dtype(x.dtype)):
+            x = torch.abs(x)
+        return to_result_dtype(torch.pow(reduce_sum(torch.pow(x, ord)), 1.0 / ord))  # type: ignore[return-value]
+
+
+def _backshift_permutation(dim0, dim1, ndim):
+    # Auxiliary function for matrix_norm
+    # Computes the permutation that moves the two given dimensions to the back
+    ret = [i for i in range(ndim) if i != dim0 and i != dim1]
+    ret.extend((dim0, dim1))
+    return ret
+
+
+def _inverse_permutation(perm):
+    # Given a permutation, returns its inverse. It's equivalent to argsort on an array
+    return [i for i, j in sorted(enumerate(perm), key=lambda i_j: i_j[1])]
+
+
+# CompositeImplicitAutograd
+@out_wrapper(exact_dtype=True)
+def matrix_norm(
+    A: TensorLikeType,
+    ord: Union[float, str] = "fro",
+    dim: DimsType = (-2, -1),
+    keepdim: bool = False,
+    *,
+    dtype: Optional[torch.dtype] = None,
+) -> TensorLikeType:
+    # shape
+    check_is_matrix(A, "linalg.matrix_norm")
+    # dim
+    dim = utils.canonicalize_dims(A.ndim, dim)
+    if isinstance(dim, Dim):
+        dim = (dim,)  # type: ignore[assignment]
+    torch._check(
+        len(dim) == 2, lambda: "linalg.matrix_norm: dim must be a 2-tuple. Got {dim}"
+    )
+    torch._check(
+        dim[0] != dim[1],
+        lambda: "linalg.matrix_norm: dims must be different. Got ({dim[0]}, {dim[1]})",
+    )
+    # dtype arg
+    _check_norm_dtype(dtype, A.dtype, "linalg.matrix_norm")
+
+    if isinstance(ord, str):
+        # ord
+        torch._check(
+            ord in ("fro", "nuc"),
+            lambda: "linalg.matrix_norm: Order {ord} not supported.",
+        )
+        # dtype
+        check_fp_or_complex(
+            A.dtype, "linalg.matrix_norm", allow_low_precision_dtypes=ord != "nuc"
+        )
+
+        if ord == "fro":
+            return vector_norm(A, 2, dim, keepdim, dtype=dtype)
+        else:  # ord == "nuc"
+            if dtype is not None:
+                A = _maybe_convert_to_dtype(A, dtype)  # type: ignore[assignment]
+            perm = _backshift_permutation(dim[0], dim[1], A.ndim)
+            result = torch.sum(svdvals(prims.transpose(A, perm)), -1, keepdim)
+            if keepdim:
+                inv_perm = _inverse_permutation(perm)
+                result = prims.transpose(torch.unsqueeze(result, -1), inv_perm)
+            return result
+    else:
+        # ord
+        abs_ord = abs(ord)
+        torch._check(
+            abs_ord in (2, 1, float("inf")),
+            lambda: "linalg.matrix_norm: Order {ord} not supported.",
+        )
+        # dtype
+        check_fp_or_complex(
+            A.dtype, "linalg.matrix_norm", allow_low_precision_dtypes=ord != 2
+        )
+
+        max_min = partial(torch.amax if ord > 0.0 else torch.amin, keepdim=keepdim)
+
+        if abs_ord == 2.0:
+            if dtype is not None:
+                A = _maybe_convert_to_dtype(A, dtype)  # type: ignore[assignment]
+            perm = _backshift_permutation(dim[0], dim[1], A.ndim)
+            result = max_min(svdvals(prims.transpose(A, perm)), dim=-1)
+            if keepdim:
+                inv_perm = _inverse_permutation(perm)
+                result = prims.transpose(torch.unsqueeze(result, -1), inv_perm)
+            return result
+        else:  # 1, -1, inf, -inf
+            dim0, dim1 = dim
+            if abs_ord == float("inf"):
+                dim0, dim1 = dim1, dim0
+            if not keepdim and (dim0 < dim1):
+                dim1 -= 1
+            return max_min(
+                vector_norm(A, 1.0, dim=dim0, keepdim=keepdim, dtype=dtype), dim1
+            )
+
+
+# CompositeImplicitAutograd
+@out_wrapper(exact_dtype=True)
+def norm(
+    A: TensorLikeType,
+    ord: Optional[Union[float, str]] = None,
+    dim: Optional[DimsType] = None,
+    keepdim: bool = False,
+    *,
+    dtype: Optional[torch.dtype] = None,
+) -> TensorLikeType:
+    if dim is not None:
+        if isinstance(dim, Dim):
+            dim = (dim,)  # type: ignore[assignment]
+        torch._check(
+            len(dim) in (1, 2),
+            lambda: "linalg.norm: If dim is specified, it must be of length 1 or 2. Got {dim}",
+        )
+    elif ord is not None:
+        torch._check(
+            A.ndim in (1, 2),
+            lambda: "linalg.norm: If dim is not specified but ord is, the input must be 1D or 2D. Got {A.ndim}D",
+        )
+
+    if ord is not None and (
+        (dim is not None and len(dim) == 2) or (dim is None and A.ndim == 2)
+    ):
+        if dim is None:
+            dim = (0, 1)
+        return matrix_norm(A, ord, dim, keepdim, dtype=dtype)
+    else:
+        if ord is None:
+            ord = 2.0
+        return vector_norm(A, ord, dim, keepdim, dtype=dtype)
+
+
+# CompositeImplicitAutograd
+@out_wrapper("U", "S", "Vh", exact_dtype=True)
+def svd(A: TensorLikeType, full_matrices: bool = True) -> Tuple[Tensor, Tensor, Tensor]:
+    return prims.svd(A, full_matrices=full_matrices)
+
+
+# CompositeImplicitAutograd
+@out_wrapper(exact_dtype=True)
+def svdvals(A: TensorLikeType) -> Tensor:
+    return svd(A, full_matrices=False)[1]
+
+
+# CompositeImplicitAutograd
+@out_wrapper()
+@elementwise_type_promotion_wrapper(
+    type_promoting_args=("x", "y"),
+    type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT,
+)
+def vecdot(x: Tensor, y: Tensor, dim: int = -1) -> Tensor:
+    check_fp_or_complex(x.dtype, "linalg.vecdot")
+    return (x.conj() * y).sum(dim=dim)

env-llmeval/lib/python3.10/site-packages/torch/_refs/nn/__init__.py

@@ -0,0 +1,3 @@
+from typing import List
+
+__all__: List[str] = []

env-llmeval/lib/python3.10/site-packages/torch/_refs/nn/functional/__init__.py

@@ -0,0 +1,1174 @@
+import math
+from functools import wraps
+from typing import Callable, Optional, Union
+
+import torch
+import torch._prims as prims
+import torch._prims_common as utils
+import torch._refs as refs
+from torch._decomp import register_decomposition
+from torch._prims_common import (
+    ELEMENTWISE_TYPE_PROMOTION_KIND,
+    NumberType,
+    ShapeType,
+    TensorLike,
+    TensorLikeType,
+)
+from torch._prims_common.wrappers import (
+    elementwise_type_promotion_wrapper,
+    elementwise_unary_scalar_wrapper,
+    out_wrapper,
+)
+from torch._refs import _make_inplace
+
+__all__ = [
+    "alpha_dropout",
+    "celu",
+    "celu_",
+    "dropout",
+    "elu",
+    "elu_",
+    "gelu",
+    "glu",
+    "group_norm",
+    "hardshrink",
+    "hardtanh",
+    "hinge_embedding_loss",
+    "huber_loss",
+    "l1_loss",
+    "layer_norm",
+    "leaky_relu",
+    "log_softmax",
+    "margin_ranking_loss",
+    "mish",
+    "mish_",
+    "mse_loss",
+    "nll_loss",
+    "pairwise_distance",
+    "pdist",
+    "poisson_nll_loss",
+    "prelu",
+    "relu",
+    "relu6",
+    "selu",
+    "selu_",
+    "smooth_l1_loss",
+    "softmax",
+    "softmin",
+    "softplus",
+    "softshrink",
+    "tanhshrink",
+    "threshold",
+    "threshold_",
+    "triplet_margin_loss",
+]
+
+Tensor = torch.Tensor
+aten = torch._ops.ops.aten
+DispatchKey = torch._C.DispatchKey  # type: ignore[attr-defined]
+
+
+def _dropout_helper(
+    self: TensorLikeType,
+    val: float,
+) -> TensorLikeType:
+    """
+    Helper function for all dropout-type operators. During training,
+    some of the elements of the input tensor are randomly masked.
+
+    Returns the masked tensor of the boolean values.
+
+    """
+
+    return (
+        refs._uniform_helper(
+            self.shape, low=0.0, high=1.0, dtype=torch.float32, device=self.device
+        )
+        < val
+    )
+
+
+@register_decomposition(aten.alpha_dropout)
+def alpha_dropout(
+    self: TensorLikeType, p: float = 0.5, training: bool = False, inplace: bool = False
+) -> TensorLikeType:
+    if inplace:
+        raise NotImplementedError
+
+    if not training:
+        return self
+
+    torch._check(
+        p <= 1 and p >= 0,
+        lambda: f"dropout probability has to be between 0 and 1, but got, {p}",
+    )
+
+    if p == 1:
+        return torch.zeros_like(self)
+
+    if p == 0:
+        return self
+
+    dropout_mask = _dropout_helper(self, 1 - p)
+
+    # From paper: Self-Normalizing Neural Networks (https://arxiv.org/pdf/1706.02515.pdf)
+    # alpha = - SELU.alpha * SELU.scale, here
+    # SELU.alpha = 1.6732632423543772848170429916717 and
+    # SELU.scale = 1.0507009873554804934193349852946
+    alpha = -1.7580993408473766
+
+    a = 1.0 / math.sqrt((alpha * alpha * p + 1) * (1 - p))
+    b = torch.logical_not(dropout_mask)
+    b = b * (alpha * a) + alpha * a * p
+    dropout_mask = a * dropout_mask
+
+    return self * dropout_mask + b
+
+
+def _inplace_wrapper(fn):
+    """
+    Given a nn.functional non-linearity, implements its `inplace: bool` argument
+    """
+
+    # nb. We use the name of the first argument used in the unary references
+    @wraps(fn)
+    def _fn(a, *args, inplace=False, **kwargs):
+        if inplace:
+            torch._check(
+                "out" not in kwargs,
+                lambda: "Cannot set inplace=True and pass out= at the same time",
+            )
+            return fn(a, *args, inplace=False, out=a, **kwargs)
+        else:
+            return fn(a, *args, inplace=False, **kwargs)
+
+    return _fn
+
+
+# celu is implemented specially because it has an alpha argument
+# celu is very similar to elu
+@register_decomposition(aten.celu)
+@_inplace_wrapper
+@out_wrapper()
+@elementwise_type_promotion_wrapper(
+    type_promoting_args=("a",),
+    type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT,
+)
+def celu(
+    a: TensorLikeType, alpha: Optional[NumberType] = None, inplace: bool = False
+) -> TensorLikeType:
+    """
+    Reference implementation of torch.nn.functional.celu
+    """
+
+    if inplace:
+        raise NotImplementedError
+
+    rhs: TensorLikeType
+    if alpha is not None:
+        python_type = utils.dtype_to_type(a.dtype)
+        if not utils.is_weakly_lesser_type(type(alpha), python_type):
+            msg = f"alpha argument of type {type(alpha)} cannot be safely cast to type {python_type}!"
+            raise ValueError(msg)
+        rhs = alpha * torch.expm1(torch.true_divide(a, alpha))  # type: ignore[arg-type]
+    else:
+        rhs = torch.expm1(a)
+
+    return torch.where(a > 0, a, rhs)
+
+
+@_inplace_wrapper
+@out_wrapper()
+def dropout(
+    a: TensorLikeType, p: float = 0.5, training: bool = True, inplace: bool = False
+) -> TensorLikeType:
+    if inplace:
+        raise NotImplementedError
+
+    if not training:
+        return a
+
+    torch._check(
+        p <= 1 and p >= 0,
+        lambda: f"dropout probability has to be between 0 and 1, but got, {p}",
+    )
+
+    if p == 1:
+        return torch.zeros_like(a)
+
+    if p == 0:
+        return a
+
+    scale = 1 / (1 - p)
+    dropout_mask = _dropout_helper(a, 1 - p)
+
+    return a * dropout_mask * scale
+
+
+@register_decomposition(aten.elu)
+@_inplace_wrapper
+@out_wrapper()
+@elementwise_type_promotion_wrapper(
+    type_promoting_args=("a",),
+    type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT,
+)
+def elu(
+    a: TensorLikeType,
+    alpha: NumberType = 1.0,
+    scale: NumberType = 1.0,
+    input_scale: NumberType = 1.0,
+    inplace: bool = False,
+) -> TensorLikeType:
+    """
+    Reference implementation of torch.nn.functional.elu
+    """
+    if inplace:
+        raise NotImplementedError
+
+    # nb. This should be factored out into a can_cast aux function
+    python_type = utils.dtype_to_type(a.dtype)
+    torch._check(
+        utils.is_weakly_lesser_type(type(input_scale), python_type),
+        lambda: f"input_scale argument of type {type(input_scale)} cannot be safely cast to type {python_type}!",
+    )
+    torch._check(
+        utils.is_weakly_lesser_type(type(scale), python_type),
+        lambda: f"scale argument of type {type(scale)} cannot be safely cast to type {python_type}!",
+    )
+    torch._check(
+        utils.is_weakly_lesser_type(type(alpha), python_type),
+        lambda: f"alpha argument of type {type(alpha)} cannot be safely cast to type {python_type}!",
+    )
+
+    return torch.where(a > 0, scale * a, (alpha * scale) * torch.expm1(a * input_scale))
+
+
+@register_decomposition(aten.relu)
+@_inplace_wrapper
+@out_wrapper()
+@elementwise_type_promotion_wrapper(
+    type_promoting_args=("a",),
+    type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT,
+)
+def relu(a: TensorLikeType, inplace: bool = False) -> TensorLikeType:
+    """
+    Reference implementation of torch.nn.functional.relu
+    """
+
+    if inplace:
+        raise NotImplementedError
+
+    return torch.where(torch.le(a, 0), 0, a)
+
+
+def group_norm(
+    input: Tensor,
+    num_groups: int,
+    weight: Optional[Tensor] = None,
+    bias: Optional[Tensor] = None,
+    eps: float = 1e-5,
+) -> Tensor:
+    """
+    Reference implementation of :func:`torch.nn.functional.group_norm`.
+    """
+    torch._check(
+        input.ndim >= 2,
+        lambda: f"Expected at least 2 dimensions for input tensor but received {input.ndim}",
+    )
+
+    batch_size = input.shape[0]
+    num_channels = input.shape[1]
+    torch._check(
+        num_channels % num_groups == 0,
+        lambda: "Expected number of channels in input to be divisible by num_groups, "
+        + f"but got input of shape {input.shape} and num_groups = {num_groups}",
+    )
+
+    # input shape is (N, C, *), so we flatten all inner dimensions except (N, C)
+    flattened_inner_size = 1
+    for dim_length in input.shape[2:]:
+        flattened_inner_size *= dim_length
+
+    return torch.native_group_norm(
+        input,
+        weight,
+        bias,
+        batch_size,
+        num_channels,
+        flattened_inner_size,
+        num_groups,
+        eps,
+    )[0]
+
+
+def layer_norm(
+    input: Tensor,
+    normalized_shape: ShapeType,
+    weight: Optional[Tensor] = None,
+    bias: Optional[Tensor] = None,
+    eps: float = 1e-5,
+) -> Tensor:
+    """
+    Reference implementation of :func:`torch.nn.functional.layer_norm`.
+    """
+    return torch.native_layer_norm(input, normalized_shape, weight, bias, eps)[0]
+
+
+@register_decomposition(aten.leaky_relu)
+@_inplace_wrapper
+@out_wrapper()
+@elementwise_type_promotion_wrapper(
+    type_promoting_args=("a",),
+    type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT,
+)
+def leaky_relu(
+    a: TensorLikeType, negative_slope: float = 0.01, inplace: bool = False
+) -> TensorLikeType:
+    """
+    Reference implementation of torch.nn.functional.leaky_relu
+    """
+
+    if inplace:
+        raise NotImplementedError
+
+    python_type = utils.dtype_to_type(a.dtype)
+    if not utils.is_weakly_lesser_type(type(negative_slope), python_type):
+        msg = f"negative_slope argument of type {type(negative_slope)} cannot be safely cast to type {python_type}!"
+        raise ValueError(msg)
+    return torch.where(torch.gt(a, 0), a, torch.mul(a, negative_slope))
+
+
+@register_decomposition(aten.mish)
+@_inplace_wrapper
+@out_wrapper()
+@elementwise_type_promotion_wrapper(
+    type_promoting_args=("a",),
+    type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT,
+)
+def mish(a: TensorLikeType, inplace: bool = False) -> TensorLikeType:
+    """
+    Reference implementation of torch.nn.functional.mish
+    """
+
+    if inplace:
+        raise NotImplementedError
+    return a * torch.tanh(torch.nn.functional.softplus(a))
+
+
+@register_decomposition(aten.selu)
+@_inplace_wrapper
+@out_wrapper()
+@elementwise_type_promotion_wrapper(
+    type_promoting_args=("a",),
+    type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT,
+)
+def selu(a: TensorLikeType, inplace: bool = False) -> TensorLikeType:
+    """
+    Reference implementation of torch.nn.functional.selu
+    """
+    if inplace:
+        raise NotImplementedError
+
+    alpha = 1.6732632423543772848170429916717
+    scale = 1.0507009873554804934193349852946
+
+    rhs = alpha * torch.expm1(a)
+
+    return scale * torch.where(a > 0, a, rhs)
+
+
+# Forwarding alias: the functional variant doesn't support the out kwarg
+# CompositeImplicitAutograd - don't register decomp
+def softmax(
+    a: TensorLikeType,
+    dim: Optional[int] = None,
+    _stacklevel: int = 3,  # for compat when using TorchRefsMode(strict=True)
+    dtype: Optional[torch.dtype] = None,
+) -> TensorLikeType:
+    # The error is for compat with regular PyTorch, which has this behavior
+    # deprecated.  For PrimTorch, it's fine to drop support for deprecated
+    # behavior because it requires explicit opt in.  This error is to inform
+    # users how to update their calls.
+    torch._check(dim is not None, lambda: "implicit dim not supported, use dim=X")
+    return torch.softmax(a=a, dim=dim, dtype=dtype)  # type: ignore[call-overload]
+
+
+# CompositeImplicitAutograd - don't register decomp
+def softmin(
+    a: TensorLikeType,
+    dim: Optional[int] = None,
+    _stacklevel: int = 3,  # for compat when using TorchRefsMode(strict=True)
+    dtype: Optional[torch.dtype] = None,
+) -> TensorLikeType:
+    # The error is for compat with regular PyTorch, which has this behavior
+    # deprecated.  For PrimTorch, it's fine to drop support for deprecated
+    # behavior because it requires explicit opt in.  This error is to inform
+    # users how to update their calls.
+    torch._check(dim is not None, lambda: "implicit dim not supported, use dim=X")
+    return torch.softmax(a=-a, dim=dim, dtype=dtype)  # type: ignore[call-overload]
+
+
+# softplus is implemented specially because it has beta and threshold arguments
+@register_decomposition(aten.softplus)
+@_inplace_wrapper
+@out_wrapper()
+@elementwise_type_promotion_wrapper(
+    type_promoting_args=("a",),
+    type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT,
+)
+def softplus(
+    a: TensorLikeType,
+    beta: Optional[NumberType] = None,
+    threshold: NumberType = 20,
+    inplace: bool = False,
+) -> TensorLikeType:
+    """
+    Reference implementation of torch.nn.functional.softplus
+    """
+
+    if inplace:
+        raise NotImplementedError
+
+    rhs: TensorLikeType
+    if beta is not None:
+        python_type = utils.dtype_to_type(a.dtype)
+        if not utils.is_weakly_lesser_type(type(beta), python_type):
+            msg = f"beta argument of type {type(beta)} cannot be safely cast to type {python_type}!"
+            raise ValueError(msg)
+        scaled_input = a * beta
+        rhs = torch.true_divide(torch.log1p(torch.exp(scaled_input)), beta)  # type: ignore[arg-type]
+
+    else:
+        scaled_input = a
+        rhs = torch.log1p(torch.exp(scaled_input))
+
+    return torch.where(scaled_input > threshold, a, rhs)
+
+
+@aten.hardshrink.default.py_impl(DispatchKey.Autograd)
+@register_decomposition(aten.hardshrink)
+@out_wrapper()
+def hardshrink(a: TensorLikeType, lambd: float = 0.5):
+    # Formula for reference,
+    # hardshrink(x) = x if x > lambd
+    #               = x if x < -lambd
+    #               = 0 otherwise
+    return torch.where(torch.abs(a) <= lambd, 0, a)
+
+
+@aten.softshrink.default.py_impl(DispatchKey.Autograd)
+@register_decomposition(aten.softshrink)
+@out_wrapper()
+def softshrink(a: TensorLikeType, lambd: float = 0.5):
+    # Formula for reference,
+    # softshrink(x) = x - lambd if x > lambd
+    #               = x + lambd if x < -lambd
+    #               = 0 otherwise
+    torch._check(
+        lambd >= 0,
+        lambda: f"lambda must be greater or equal to 0, but found to be {lambd}",
+    )
+    # We implement this in one torch.where to generate better code in the backward
+    # see https://github.com/pytorch/pytorch/pull/107052#discussion_r1293748211
+    return torch.where(torch.abs(a) > lambd, a - torch.sign(a) * lambd, 0)
+
+
+# Losses
+def _reduction_int_to_str(reduction: int) -> str:
+    from torch._decomp.decompositions import Reduction
+
+    if reduction == Reduction.NONE.value:
+        return "none"
+    elif reduction == Reduction.MEAN.value:
+        return "mean"
+    elif reduction == Reduction.SUM.value:
+        return "sum"
+    else:
+        raise ValueError(f"{reduction} is not a valid value for reduction")
+
+
+def _apply_loss_reduction(loss: TensorLikeType, reduction: str) -> TensorLikeType:
+    if reduction == "sum":
+        return torch.sum(loss)
+    elif reduction == "mean":
+        return torch.mean(loss)
+    else:  # reduction == "none"
+        return loss
+
+
+def _check_reduction_value(reduction: str):
+    if reduction not in ("mean", "sum", "none"):
+        raise ValueError(f"{reduction} is not a valid value for reduction")
+
+
+# This helper function maps depreciated arguments, "size_average" and "reduce"
+# to their corresponding "reduction" string argument
+def _get_string_reduction_arg(
+    *, size_average: Optional[bool], reduce: Optional[bool]
+) -> str:
+    if size_average is None:
+        size_average = True
+    if reduce is None:
+        reduce = True
+    if size_average and reduce:
+        ret = "mean"
+    elif reduce:
+        ret = "sum"
+    else:
+        ret = "none"
+    return ret
+
+
+# CompositeImplicitAutograd - don't register decomp
+@elementwise_type_promotion_wrapper(
+    type_promoting_args=("input", "target"),
+    type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.COMPLEX_TO_FLOAT,
+)
+def l1_loss(
+    input: TensorLikeType,
+    target: TensorLikeType,
+    size_average: Optional[bool] = None,
+    reduce: Optional[bool] = None,
+    reduction: str = "mean",
+) -> TensorLikeType:
+    """
+    Reference implementation of torch.nn.functional.l1_loss
+    """
+    if size_average is not None or reduce is not None:
+        # TODO: Raise exception instead of converting value.  This is only for
+        # primTorch since it can drop support for deprecated arguments.
+        # msg = "size_average and reduce args are deprecated, please use reduction argument."
+        reduction = _get_string_reduction_arg(size_average=size_average, reduce=reduce)
+    _check_reduction_value(reduction)
+    loss = torch.abs(input - target)
+    return _apply_loss_reduction(loss, reduction)
+
+
+@elementwise_type_promotion_wrapper(
+    type_promoting_args=("input", "target"),
+    type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.COMPLEX_TO_FLOAT,
+)
+def smooth_l1_loss(
+    input: TensorLikeType,
+    target: TensorLikeType,
+    size_average: Optional[bool] = None,
+    reduce: Optional[bool] = None,
+    reduction: str = "mean",
+    beta: float = 1.0,
+) -> TensorLikeType:
+    """
+    Reference implementation of torch.nn.functional.smooth_l1_loss
+    """
+    if size_average is not None or reduce is not None:
+        # TODO: Raise exception instead of converting value.  This is only for
+        # primTorch since it can drop support for deprecated arguments.
+        # msg = "size_average and reduce args are deprecated, please use reduction argument."
+        reduction = _get_string_reduction_arg(size_average=size_average, reduce=reduce)
+    _check_reduction_value(reduction)
+
+    if beta == 0.0:
+        return torch.nn.functional.l1_loss(
+            input, target, size_average=size_average, reduce=reduce, reduction=reduction
+        )
+    else:
+        loss = torch.abs(input - target)
+        loss = torch.where(loss < beta, 0.5 * loss**2 / beta, loss - 0.5 * beta)
+        return _apply_loss_reduction(loss, reduction)
+
+
+# Forwarding alias: the functional variant doesn't support the out kwarg
+# CompositeImplicitAutograd - don't register decomp
+def log_softmax(
+    a: TensorLikeType,
+    dim: Optional[int] = None,
+    _stacklevel: int = 3,  # for compat when using TorchRefsMode(strict=True)
+    dtype: Optional[torch.dtype] = None,
+) -> TensorLikeType:
+    # The error is for compat with regular PyTorch, which has this behavior
+    # deprecated.  For PrimTorch, it's fine to drop support for deprecated
+    # behavior because it requires explicit opt in.  This error is to inform
+    # users how to update their calls.
+    torch._check(dim is not None, lambda: "implicit dim not supported, use dim=X")
+    return torch.log_softmax(a=a, dim=dim, dtype=dtype)  # type: ignore[call-overload]
+
+
+@register_decomposition(aten.margin_ranking_loss)
+def margin_ranking_loss(
+    input1: TensorLikeType,
+    input2: TensorLikeType,
+    target: TensorLikeType,
+    margin: float = 0.0,
+    reduction: str = "mean",
+) -> TensorLikeType:
+    # loss_without_reduction = max(0, −target * (input1 − input2) + margin)
+    if input1.ndim != input2.ndim or input1.ndim != target.ndim:
+        raise RuntimeError(
+            "margin_ranking_loss : All input tensors should have same dimension but got sizes: "
+            f"input1: {input1.shape}, input2: {input2.shape}, target: {target.shape} "
+        )
+    _check_reduction_value(reduction)
+    loss = torch.clamp_min(-target * (input1 - input2) + margin, 0)
+    return _apply_loss_reduction(loss, reduction)
+
+
+@elementwise_type_promotion_wrapper(
+    type_promoting_args=("input", "target"),
+    type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.COMPLEX_TO_FLOAT,
+)
+def mse_loss(
+    input: TensorLikeType,
+    target: TensorLikeType,
+    size_average: Optional[bool] = None,
+    reduce: Optional[bool] = None,
+    reduction: str = "mean",
+) -> TensorLikeType:
+    if size_average is not None or reduce is not None:
+        # TODO: Raise exception instead of converting value.  This is only for
+        # primTorch since it can drop support for deprecated arguments.
+        # msg = "size_average and reduce args are deprecated, please use reduction argument."
+        reduction = _get_string_reduction_arg(size_average=size_average, reduce=reduce)
+    _check_reduction_value(reduction)
+    loss = torch.pow(input - target, 2)
+    return _apply_loss_reduction(loss, reduction)
+
+
+@register_decomposition(aten.hinge_embedding_loss)
+def hinge_embedding_loss(
+    input: TensorLikeType,
+    target: TensorLikeType,
+    margin: float = 1.0,
+    reduction: str = "mean",
+) -> TensorLikeType:
+    # loss_without_reduction = input if y == 1
+    #                        = max(0, margin - input) if y == -1
+    _check_reduction_value(reduction)
+    margin_clamp = torch.clamp_min(margin - input, 0)
+    output_margin = torch.where(target != 1, margin_clamp, 0)
+    output_self = torch.where(target != -1, input, 0)
+    loss = output_margin + output_self
+    return _apply_loss_reduction(loss, reduction)
+
+
+def _nll_loss_nd(
+    input: TensorLikeType,
+    target: TensorLikeType,
+    weight: Optional[TensorLikeType],
+    reduction: str,
+    ignore_index: int,
+) -> TensorLikeType:
+    torch._check(
+        input.ndim > 0 and input.ndim <= 3,
+        lambda: f"Expected input dimension to be either [1, 2, 3] but received {input.ndim}.",
+    )
+
+    torch._check(
+        (input.ndim == 1) or (input.shape[0] == target.shape[0]),
+        lambda: f"Expected input batch size {input.shape[0]} to match target batch size {target.shape[0]}.",
+    )
+
+    _check_reduction_value(reduction)
+
+    flat_target = torch.flatten(target)
+    ignore_classes_mask = torch.eq(flat_target, ignore_index)
+
+    # TODO: Enable data-dependent checks with debug mode
+    # TODO: This check does not work with FakeTensor inputs; See Issue #85834
+    # Explicit cast for class_check to bool; See Issue #78071
+    """
+    from torch._subclasses.fake_tensor import FakeTensor
+    num_classes = input.shape[1] if input.ndim > 1 else input.shape[0]
+    valid_classes_mask = torch.logical_and(
+        (flat_target >= 0), (flat_target < num_classes)
+    )
+    class_check = torch.all(torch.logical_or(ignore_classes_mask, valid_classes_mask))
+    torch._check(
+        isinstance(target, FakeTensor) or bool(class_check.item()),
+        lambda: "A target class is out-of-bounds and not the ignore index.",
+    )
+    """
+
+    ignore_class_weight = torch.scalar_tensor(0, dtype=input.dtype, device=input.device)
+    class_weight = (
+        torch.scalar_tensor(1, dtype=input.dtype, device=input.device)
+        if weight is None
+        else weight[flat_target]
+    )
+    current_weight = torch.where(
+        ignore_classes_mask,
+        ignore_class_weight,
+        class_weight,
+    )
+
+    if input.ndim == 1:
+        # implicit batch size = 1
+        # input (1 batch size, C classes)
+        loss = -input[target] * current_weight
+    elif input.ndim == 2:
+        # input (N batch size, C classes)
+        batch_size = input.shape[0]
+        loss = -input[torch.arange(batch_size), target] * current_weight
+    else:
+        # 3D case (N batch size, C classe, K dimensions)
+        # input (N batch size, C classes, K)
+        batch_size = input.shape[0]
+        extent = input.shape[2]
+        numel = batch_size * extent
+        indices = torch.arange(numel)
+        bdx = indices // extent
+        kdx = indices % extent
+        loss = -input[bdx, flat_target, kdx] * current_weight
+    loss = torch.reshape(loss, target.shape)
+
+    if reduction == "none":
+        return loss
+    elif reduction == "sum":
+        return torch.sum(loss)
+    else:
+        # calculate weighted mean of the loss function
+        return torch.sum(loss) / torch.sum(current_weight)
+
+
+@register_decomposition(aten.nll_loss)
+@out_wrapper()
+@elementwise_type_promotion_wrapper(
+    type_promoting_args=("input",),
+    type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT,
+)
+def nll_loss(
+    input: TensorLikeType,
+    target: TensorLikeType,
+    weight: Optional[TensorLikeType] = None,
+    size_average: Optional[bool] = None,
+    ignore_index: int = -100,
+    reduce: Optional[bool] = None,
+    reduction: str = "mean",
+) -> TensorLikeType:
+    """
+    Reference implementation of torch.nn.functional.nll_loss
+    """
+    torch._check(
+        input.ndim > 0,
+        lambda: f"Expected input tensor to have 1 or more dimensions (got {input.ndim})",
+    )
+
+    # TODO: raise exception instead of converting value
+    # msg = "size_average and reduce args are deprecated, please use reduction argument."
+    # Convert these options for consistency with the eager mode
+    if size_average is not None or reduce is not None:
+        reduction = _get_string_reduction_arg(size_average=size_average, reduce=reduce)
+
+    # The expected behavior when the target and input have zero elements:
+    #   reduction = 'none' --- tensor([])
+    #   reduction = 'sum'  --- tensor(0.)
+    #   reduction = 'mean' --- tensor(nan)
+    # Mean reduction on empty tensors produces NaN. See the discussion in
+    # https://github.com/pytorch/pytorch/pull/64572#issuecomment-926504162
+    if input.numel() == 0 and target.numel() == 0:
+        if reduction == "none":
+            return torch.zeros_like(target)
+        elif reduction == "sum":
+            return torch.empty_like(target)
+        else:
+            return torch.full_like(target, float("nan"))
+
+    # The _nll_loss_nd helper function handles the most common cases.
+    # ndim == 1 (Single Example)
+    #   => Batch Size: 1, Input: (C), Target: ()
+    # ndim == 2 (k = 1)
+    #   => Batch Size: N, Input: (N, C), Target: (N)
+    # ndim == 3 (k > 1)
+    #   => Batch Size: N, Input: (N, C, K), Target: (N, K)
+    if input.ndim <= 3:
+        return _nll_loss_nd(input, target, weight, reduction, ignore_index)
+
+    # For ndim > 3, we reshape the input and target to 3-D case.
+    # Input (N batch-size, C classes, k-dimensions)
+    # Target (N batch-size, k-dimensions)
+    torch._check(
+        input.ndim > 0 and target.ndim > 0 and target.shape[1:] == input.shape[2:],
+        lambda: (
+            "Expected input and target to both have ndim > 0 and "
+            "target.shape[1:] == input.shape[2:], but got "
+            f"target.shape {target.shape} and input.shape {input.shape}"
+        ),
+    )
+
+    batch_size = input.shape[0]
+    num_classes = input.shape[1]
+    out_size = [batch_size] + list(target.shape[1:])
+
+    input = torch.reshape(input, [batch_size, num_classes, -1])
+    target = torch.reshape(target, [batch_size, -1])
+    if reduction != "none":
+        return _nll_loss_nd(input, target, weight, reduction, ignore_index)
+    else:
+        result = _nll_loss_nd(input, target, weight, reduction, ignore_index)
+        # reshape flattened inner-dim to original k-dimensions
+        return torch.reshape(result, out_size)
+
+
+# TODO: This ref supports int reduction and out kwarg to be compatible with ATen:
+# https://github.com/pytorch/pytorch/issues/83931
+# TODO: Could be rewritten to support complex:
+# https://github.com/pytorch/pytorch/pull/85041
+@register_decomposition(aten.huber_loss)
+@out_wrapper()
+@elementwise_type_promotion_wrapper(
+    type_promoting_args=("input", "target"),
+    type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT,
+)
+def huber_loss(
+    input: TensorLikeType,
+    target: TensorLikeType,
+    reduction: Union[str, int] = "mean",
+    delta: float = 1.0,
+) -> TensorLikeType:
+    """
+    Reference implementation of torch.nn.functional.huber_loss
+    """
+    if type(reduction) is int:
+        reduction = _reduction_int_to_str(reduction)
+    _check_reduction_value(reduction)  # type: ignore[arg-type]
+    torch._check(
+        delta > 0,
+        lambda: "huber_loss does not support non-positive values for delta.",
+    )
+    z = (input - target).abs()
+    loss = torch.where(z < delta, 0.5 * z * z, delta * (z - 0.5 * delta))
+    return _apply_loss_reduction(loss, reduction)  # type: ignore[arg-type]
+
+
+# tanhshrink does not use _make_elementwise_unary_reference because it does not support out
+@elementwise_unary_scalar_wrapper
+@elementwise_type_promotion_wrapper(
+    type_promoting_args=("a",),
+    type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
+)
+def tanhshrink(a: TensorLikeType) -> TensorLikeType:
+    """
+    Reference implementation of torch.nn.functional.tanhshrink
+    """
+    if not isinstance(a, TensorLike):
+        raise RuntimeError(
+            "Expected a tensor input for an elementwise unary operation!"
+        )
+    return a - torch.tanh(a)
+
+
+@register_decomposition(aten.threshold)
+@_inplace_wrapper
+@out_wrapper()
+@elementwise_type_promotion_wrapper(
+    type_promoting_args=("a",),
+    type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT,
+)
+def threshold(
+    a: TensorLikeType,
+    threshold: NumberType,
+    value: Union[bool, int, float],
+    inplace: bool = False,
+) -> TensorLikeType:
+    """
+    Reference implementation of torch.nn.functional.threshold
+    """
+
+    if inplace:
+        raise NotImplementedError
+
+    return torch.where(a <= threshold, value, a)
+
+
+# CompositeImplicitAutograd - don't register decomp
+# No elementwise type promotion - core op doesn't explicitly type promote
+def triplet_margin_loss(
+    anchor: TensorLikeType,
+    positive: TensorLikeType,
+    negative: TensorLikeType,
+    margin: float = 1.0,
+    p: float = 2,
+    eps: float = 1e-6,
+    swap: bool = False,
+    size_average: Optional[bool] = None,
+    reduce: Optional[bool] = None,
+    reduction: str = "mean",
+) -> TensorLikeType:
+    if size_average is not None or reduce is not None:
+        # TODO: Raise exception instead of converting value.  This is only for
+        # primTorch since it can drop support for deprecated arguments.
+        # msg = "size_average and reduce args are deprecated, please use reduction argument."
+        reduction = _get_string_reduction_arg(size_average=size_average, reduce=reduce)
+
+    # torch.nn.functional.triplet_margin_with_distance_loss has no ref defined
+    # since it's a pure Python implementation.  Use this helper instead.
+    return _triplet_margin_with_distance_loss(
+        anchor=anchor,
+        positive=positive,
+        negative=negative,
+        distance_function=lambda x, y: torch.pairwise_distance(x, y, p, eps),
+        margin=margin,
+        swap=swap,
+        reduction=reduction,
+    )
+
+
+# Pure Python impl - don't register decomp and don't add a ref.  Defined as a
+# helper here since triplet_margin_loss can be nicely implemented with it.
+def _triplet_margin_with_distance_loss(
+    anchor: TensorLikeType,
+    positive: TensorLikeType,
+    negative: TensorLikeType,
+    *,
+    distance_function: Optional[
+        Callable[[TensorLikeType, TensorLikeType], TensorLikeType]
+    ] = None,
+    margin: float = 1.0,
+    swap: bool = False,
+    reduction: str = "mean",
+) -> TensorLikeType:
+    _check_reduction_value(reduction)
+
+    a_dim = anchor.ndim
+    p_dim = positive.ndim
+    n_dim = negative.ndim
+    torch._check(
+        a_dim == p_dim and p_dim == n_dim,
+        lambda: (
+            f"The anchor, positive, and negative tensors are expected to have "
+            f"the same number of dimensions, but got: anchor {a_dim}D, "
+            f"positive {p_dim}D, and negative {n_dim}D inputs"
+        ),
+    )
+
+    if distance_function is None:
+        distance_function = torch.pairwise_distance
+
+    dist_pos = distance_function(anchor, positive)
+    dist_neg = distance_function(anchor, negative)
+    # The distance swap is described in the paper "Learning shallow
+    # convolutional feature descriptors with triplet losses" by V. Balntas, E.
+    # Riba et al.  If True, and if the positive example is closer to the
+    # negative example than the anchor is, swaps the positive example and the
+    # anchor in the loss computation.
+    if swap:
+        dist_swap = distance_function(positive, negative)
+        dist_neg = torch.minimum(dist_neg, dist_swap)
+    loss = torch.clamp_min(margin + dist_pos - dist_neg, 0)
+    return _apply_loss_reduction(loss, reduction)
+
+
+@register_decomposition(aten.hardtanh)
+@_inplace_wrapper
+@out_wrapper()
+@elementwise_unary_scalar_wrapper
+@elementwise_type_promotion_wrapper(
+    type_promoting_args=("a"),
+    type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT,
+)
+def hardtanh(
+    a: TensorLikeType,
+    min_val: NumberType = -1,
+    max_val: NumberType = 1,
+    inplace: bool = False,
+) -> TensorLikeType:
+    """
+    Reference implementation of torch.nn.functional.hardtanh
+    """
+    if inplace:
+        raise NotImplementedError
+    if utils.is_boolean_dtype(a.dtype):
+        raise RuntimeError("Bool inputs not supported for hardtanh")
+
+    # preserve legacy behavior of boundaries not causing type promotion
+    if utils.is_integer_dtype(a.dtype):
+        min_val = int(min_val)  # type: ignore[arg-type]
+        max_val = int(max_val)  # type: ignore[arg-type]
+        if not (a.dtype != torch.uint8 or (min_val >= 0 and max_val >= 0)):
+            raise RuntimeError(
+                "Cannot do hardtanh on an unsigned type with negative limits"
+            )
+    return torch.clamp(a, min_val, max_val)  # type: ignore[arg-type]
+
+
+@register_decomposition(aten.gelu)
+@out_wrapper()
+@elementwise_unary_scalar_wrapper
+@elementwise_type_promotion_wrapper(
+    type_promoting_args=("a",),
+    type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT,
+)
+def gelu(a: TensorLikeType, approximate: str = "none") -> TensorLikeType:
+    """
+    Reference implementation of torch.nn.functional.gelu
+    """
+    if not isinstance(a, TensorLike):
+        raise RuntimeError(
+            "Expected a tensor input for an elementwise unary operation!"
+        )
+    M_SQRT2 = 1.41421356237309504880
+    M_SQRT1_2 = 0.70710678118654752440
+    M_2_SQRTPI = 1.12837916709551257390
+    if approximate == "tanh":
+        kBeta = M_SQRT2 * M_2_SQRTPI * 0.5
+        kKappa = 0.044715
+        a_cube = a * a * a
+        inner = kBeta * (a + kKappa * a_cube)
+        return 0.5 * a * (1 + torch.tanh(inner))
+    elif approximate == "none":
+        kAlpha = M_SQRT1_2
+        return a * 0.5 * (1 + torch.erf(a * kAlpha))
+    else:
+        raise RuntimeError("approximate argument must be either none or tanh.")
+
+
+# CompositeImplicitAutograd - don't register decomp
+@elementwise_type_promotion_wrapper(
+    type_promoting_args=("input", "target"),
+    type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
+)
+def poisson_nll_loss(
+    input: TensorLikeType,
+    target: TensorLikeType,
+    log_input: bool = True,
+    full: bool = False,
+    size_average: Optional[bool] = None,
+    eps: float = 1e-8,
+    reduce: Optional[bool] = None,
+    reduction: str = "mean",
+) -> TensorLikeType:
+    """
+    Reference implementation of torch.nn.functional.poisson_nll_loss
+    """
+    if size_average is not None or reduce is not None:
+        # TODO: Raise exception instead of converting value.  This is only for
+        # primTorch since it can drop support for deprecated arguments.
+        # msg = "size_average and reduce args are deprecated, please use reduction argument."
+        reduction = _get_string_reduction_arg(size_average=size_average, reduce=reduce)
+    _check_reduction_value(reduction)
+    if log_input:
+        loss = torch.exp(input) - target * input
+    else:
+        loss = input - target * torch.log(input + eps)
+
+    if full:
+        stirling_term = (
+            target * torch.log(target) - target + 0.5 * torch.log(2 * torch.pi * target)
+        )
+        # avoid inplace add
+        loss = loss + stirling_term.masked_fill(target <= 1, 0)
+    return _apply_loss_reduction(loss, reduction)
+
+
+@register_decomposition(aten.prelu)
+@elementwise_type_promotion_wrapper(
+    type_promoting_args=("a", "weight"),
+    type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT,
+)
+def prelu(a: TensorLikeType, weight: TensorLikeType) -> TensorLikeType:
+    """
+    Reference implementation of torch.nn.functional.prelu
+    """
+    torch._check(
+        isinstance(a, TensorLike),
+        lambda: f"prelu: Expected `a` to be tensor, but got: {type(a)}",
+    )
+    torch._check(
+        isinstance(weight, TensorLike),
+        lambda: f"prelu: Expected `weight` to be tensor, but got: {type(weight)}",
+    )
+
+    if weight.numel() != 1:
+        torch._check(a.ndim > 0, lambda: "Not allow zero-dim input tensor.")
+        channel_size = a.shape[1] if a.ndim >= 2 else 1
+        torch._check(
+            weight.numel() == channel_size,
+            lambda: f"Mismatch of parameter numbers and input channel size. Found parameter numbers ="
+            f" {weight.numel()} and channel size = {channel_size}.",
+        )
+
+    torch._check(
+        weight.ndim == 0 or weight.ndim == 1,
+        lambda: f"prelu: Expected `weight` to be a scalar or 1D tensor, but got: "
+        f"ndim = {weight.ndim}",
+    )
+    if a.ndim == 0:
+        weight = weight[0] if weight.ndim == 1 else weight
+    else:
+        weight = prims.broadcast_in_dim(
+            weight, a.shape, tuple() if weight.ndim == 0 else (0 if a.ndim == 1 else 1,)
+        )
+
+    return torch.where(a > 0, a, a * weight)
+
+
+@register_decomposition(aten.relu6)
+@_inplace_wrapper
+@out_wrapper()
+def relu6(a: TensorLikeType, inplace: bool = False) -> TensorLikeType:
+    """
+    Reference implementation of torch.nn.functional.relu6
+    """
+    if inplace:
+        raise NotImplementedError
+
+    # See https://github.com/pytorch/pytorch/pull/81142#discussion_r918220126
+    # It may be better to use clamp here, but we use hardtanh to replicate
+    # the behavior of the existing implementation
+    return torch.nn.functional.hardtanh(a, 0, 6)
+
+
+@register_decomposition(aten.glu)
+@out_wrapper()
+@elementwise_type_promotion_wrapper(
+    type_promoting_args=("a",),
+    type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT,
+)
+def glu(a: TensorLikeType, dim: int = -1) -> TensorLikeType:
+    dim = utils.canonicalize_dims(a.ndim, dim)
+    torch._check(
+        a.shape[dim] % 2 == 0,
+        lambda: f"Halving dimension must be even, but dimension {dim} is size {a.shape[dim]}",
+    )
+    b, c = torch.tensor_split(a, 2, dim)
+
+    return b * torch.sigmoid(c)
+
+
+@register_decomposition(aten.pairwise_distance)
+@out_wrapper()
+def pairwise_distance(
+    x1: TensorLikeType,
+    x2: TensorLikeType,
+    p: NumberType = 2.0,
+    eps: NumberType = 1e-6,
+    keepdim=False,
+) -> TensorLikeType:
+    return torch.linalg.vector_norm(x1 - x2 + eps, ord=p, dim=-1, keepdim=keepdim)
+
+
+@register_decomposition(aten.pdist)
+@out_wrapper()
+@elementwise_type_promotion_wrapper(
+    type_promoting_args=("a",),
+    type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT,
+)
+def pdist(a: TensorLikeType, p: float = 2) -> TensorLikeType:
+    torch._check(a.ndim == 2, lambda: f"pdist only supports 2D tensors, got: {a.ndim}D")
+    torch._check(p >= 0, lambda: "pdist only supports non-negative p values")
+    # For p == 2 we can use an efficient implementation, but other values of p
+    # require creating a much bigger tensor for an intermediate step
+    if p == 2:
+        aTa = torch.mm(a, a.T)
+        aTa_diag = torch.diag(aTa)
+        t = torch.sqrt(torch.clamp(aTa_diag + aTa_diag.unsqueeze(-1) - 2 * aTa, min=0))
+    else:
+        t = torch.linalg.vector_norm(a.unsqueeze(1) - a, ord=p, dim=2)
+    i = torch.triu_indices(t.shape[0], t.shape[1], offset=1, device=a.device)
+    return t.flatten().index_select(0, i[0] * t.shape[0] + i[1])
+
+
+# Needed as aten.{celu_,elu_...} exist (even if they don't have the in-place kwarg)
+celu_ = _make_inplace(celu)
+elu_ = _make_inplace(elu)
+mish_ = _make_inplace(mish)
+selu_ = _make_inplace(selu)
+threshold_ = _make_inplace(threshold)

env-llmeval/lib/python3.10/site-packages/torch/_refs/special/__init__.py

@@ -0,0 +1,236 @@
+import math
+from typing import Optional, Union
+
+import torch
+import torch._prims as prims
+import torch._prims_common as utils
+import torch._refs as refs
+
+from torch import Tensor
+from torch._decomp import register_decomposition
+from torch._prims_common import (
+    ELEMENTWISE_TYPE_PROMOTION_KIND,
+    Number,
+    NumberType,
+    TensorLike,
+    TensorLikeType,
+)
+from torch._prims_common.wrappers import elementwise_type_promotion_wrapper, out_wrapper
+from torch._refs import (
+    _make_alias,
+    _make_elementwise_binary_reference,
+    _make_elementwise_unary_reference,
+)
+
+
+__all__ = [
+    "bessel_j0",
+    "bessel_j1",
+    "entr",
+    "erfcx",
+    "expit",
+    "i0e",
+    "i1",
+    "i1e",
+    "log_ndtr",
+    "logit",
+    "log_softmax",
+    "multigammaln",
+    "ndtr",
+    "ndtri",
+    "softmax",
+    "spherical_bessel_j0",
+    "xlog1py",
+    "zeta",
+]
+aten = torch._ops.ops.aten
+
+
+@_make_elementwise_unary_reference(
+    ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
+)
+def bessel_j0(a: TensorLikeType) -> TensorLikeType:
+    return prims.bessel_j0(a)
+
+
+@_make_elementwise_unary_reference(
+    ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
+)
+def bessel_j1(a: TensorLikeType) -> TensorLikeType:
+    return prims.bessel_j1(a)
+
+
+@register_decomposition(aten.special_entr)
+@out_wrapper()
+@elementwise_type_promotion_wrapper(
+    type_promoting_args=("a",),
+    type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
+)
+def entr(a: TensorLikeType) -> TensorLikeType:
+    return torch.where(
+        torch.isnan(a),
+        a,
+        torch.where(a > 0, -a * torch.log(a), torch.where(a == 0, 0, -torch.inf)),
+    )
+
+
+@register_decomposition(aten.special_erfcx)
+@out_wrapper()
+@elementwise_type_promotion_wrapper(
+    type_promoting_args=("a",),
+    type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
+)
+def erfcx(a: TensorLikeType) -> TensorLikeType:
+    return prims.erfcx(a)
+
+
+# alias for sigmoid
+expit = _make_alias(torch.sigmoid, "expit")
+
+
+@_make_elementwise_unary_reference(
+    ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
+)
+def i0e(a: TensorLikeType) -> TensorLikeType:
+    return prims.bessel_i0e(a)
+
+
+@_make_elementwise_unary_reference(
+    ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
+)
+def i1(a: TensorLikeType) -> TensorLikeType:
+    return prims.bessel_i1(a)
+
+
+@_make_elementwise_unary_reference(
+    ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
+)
+def i1e(a: TensorLikeType) -> TensorLikeType:
+    return prims.bessel_i1e(a)
+
+
+@register_decomposition(aten.special_log_ndtr)
+@out_wrapper()
+@elementwise_type_promotion_wrapper(
+    type_promoting_args=("a",),
+    type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
+)
+def log_ndtr(a: TensorLikeType) -> TensorLikeType:
+    # Note: M_SQRT1_2 is the value of 1 / √2
+    M_SQRT1_2 = 0.707106781186547524400844362104849039
+    t = a * M_SQRT1_2
+    return torch.where(
+        a < 1.0,
+        torch.log(torch.special.erfcx(-t) / 2) - t * t,
+        torch.log1p(-torch.erfc(t) / 2),
+    )
+
+
+@register_decomposition(aten.logit)
+@out_wrapper()
+@elementwise_type_promotion_wrapper(
+    type_promoting_args=("self",),
+    type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
+)
+def logit(self: TensorLikeType, eps: Optional[float] = None) -> TensorLikeType:
+    if eps is None:
+        eps = -1.0
+    lo = eps
+    hi = 1 - eps
+    self = torch.clamp(self, lo, hi)
+    return torch.log(torch.true_divide(self, torch.sub(1, self)))
+
+
+@register_decomposition(aten.special_xlog1py)
+@out_wrapper()
+@elementwise_type_promotion_wrapper(
+    type_promoting_args=("a", "b"),
+    type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
+)
+def xlog1py(a: Union[TensorLikeType, NumberType], b: Union[TensorLikeType, NumberType]):
+    torch._check(
+        isinstance(a, TensorLike) or isinstance(b, TensorLike),
+        lambda: 'Expected either argument a or b to be a Tensor"',
+    )
+
+    # Operations like eq and log do not handle scalar values, so we convert them to scalar_tensors.
+    if isinstance(a, TensorLike) and isinstance(b, Number):
+        b = refs.scalar_tensor(b, dtype=a.dtype, device=a.device)
+    elif isinstance(b, TensorLike) and isinstance(a, Number):
+        a = refs.scalar_tensor(a, dtype=b.dtype, device=b.device)
+
+    # mypy: expected "Tensor"
+    assert isinstance(a, TensorLike)
+    assert isinstance(b, TensorLike)
+    rhs = torch.where(torch.eq(a, 0), 0, torch.mul(a, torch.log1p(b)))
+    return torch.where(torch.isnan(b), float("nan"), rhs)
+
+
+@register_decomposition(aten.mvlgamma)
+@out_wrapper()
+@elementwise_type_promotion_wrapper(
+    type_promoting_args=("a",),
+    type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
+)
+def multigammaln(a: TensorLikeType, p: int) -> TensorLikeType:
+    c = 0.25 * p * (p - 1) * math.log(math.pi)
+    b = 0.5 * torch.arange(start=(1 - p), end=1, step=1, dtype=a.dtype, device=a.device)
+    return torch.sum(torch.lgamma(a.unsqueeze(-1) + b), dim=-1) + c
+
+
+@register_decomposition(aten.special_ndtr)
+@out_wrapper()
+@elementwise_type_promotion_wrapper(
+    type_promoting_args=("a",),
+    type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
+)
+def ndtr(a: TensorLikeType) -> TensorLikeType:
+    # Note: M_SQRT1_2 is the value of 1 / √2
+    M_SQRT1_2 = 0.707106781186547524400844362104849039
+    a_sqrt_2 = a * M_SQRT1_2
+    return (1 + torch.erf(a_sqrt_2)) * 0.5
+
+
+@register_decomposition(aten.special_ndtri)
+@out_wrapper()
+@elementwise_type_promotion_wrapper(
+    type_promoting_args=("a",),
+    type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
+)
+def ndtri(a: TensorLikeType) -> TensorLikeType:
+    return prims.ndtri(a)
+
+
+# Forwarding alias: the special variant doesn't support the out kwarg
+# CompositeImplicitAutograd - don't register decomp
+def log_softmax(
+    a: TensorLikeType,
+    dim: int,
+    dtype: Optional[torch.dtype] = None,
+) -> TensorLikeType:
+    return torch.log_softmax(a=a, dim=dim, dtype=dtype)  # type: ignore[call-overload]
+
+
+# Forwarding alias: the special variant doesn't support the out kwarg
+# CompositeImplicitAutograd - don't register decomp
+def softmax(
+    a: TensorLikeType,
+    dim: int,
+    dtype: Optional[torch.dtype] = None,
+) -> TensorLikeType:
+    return torch.softmax(a=a, dim=dim, dtype=dtype)  # type: ignore[call-overload]
+
+
+@_make_elementwise_unary_reference(
+    ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
+)
+def spherical_bessel_j0(a: TensorLikeType) -> TensorLikeType:
+    return prims.spherical_bessel_j0(a)
+
+
+# TODO: add docstring
+@_make_elementwise_binary_reference(
+    type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
+)
+def zeta(a: TensorLikeType, b: TensorLikeType) -> TensorLikeType:
+    return prims.zeta(a, b)

env-llmeval/lib/python3.10/site-packages/torch/_subclasses/fake_utils.py

@@ -0,0 +1,188 @@
+import functools
+import warnings
+from typing import Callable, Union
+
+import torch
+import torch.utils._pytree as pytree
+from torch._ops import OpOverload
+from torch._subclasses.fake_tensor import (
+    FakeTensorMode,
+    tree_flatten_only,
+    UnsupportedFakeTensorException,
+)
+from torch.utils._python_dispatch import TorchDispatchMode
+
+
+aten = torch._ops.ops.aten
+
+
+def outputs_alias_inputs(outputs, inputs):
+    input_storages = {
+        inp._typed_storage()._cdata
+        for inp in tree_flatten_only(torch.Tensor, inputs)
+        if torch._C._has_storage(inp)
+    }
+    return any(
+        torch._C._has_storage(out) and out._typed_storage()._cdata in input_storages
+        for out in tree_flatten_only(torch.Tensor, outputs)
+    )
+
+
+def outputs_are_inputs(outputs, inputs):
+    input_ids = {id(inp) for inp in tree_flatten_only(torch.Tensor, inputs)}
+    return any(id(out) in input_ids for out in tree_flatten_only(torch.Tensor, outputs))
+
+
+def output_alias_each_other(outputs):
+    storages = set()
+    for out in tree_flatten_only(torch.Tensor, outputs):
+        if not torch._C._has_storage(out):
+            continue
+        stor = out._typed_storage()._cdata
+        if stor in storages:
+            return True
+        storages.add(stor)
+    return False
+
+
+def is_sdpa_error(func, idx, e):
+    if (
+        (
+            func is aten._scaled_dot_product_flash_attention.default
+            or func is aten._flash_attention_forward.default
+        )
+        and idx in (6, 7)
+        and "Devices" in repr(e)
+    ):
+        return True
+    if (
+        (
+            func is aten._scaled_dot_product_efficient_attention.default
+            or func is aten._efficient_attention_forward.default
+        )
+        and idx in (2, 3)
+        and "Devices" in repr(e)
+    ):
+        return True
+    return False
+
+
+class CrossRefFakeMode(TorchDispatchMode):
+    def __init__(
+        self,
+        ignore_op_fn: Union[Callable[[OpOverload], bool], None] = None,
+        *,
+        check_strides=True,
+        check_aliasing=True,
+    ):
+        self.ignore_op_fn = (
+            ignore_op_fn if ignore_op_fn is not None else lambda fn: False
+        )
+        self.check_strides = check_strides
+        self.check_aliasing = check_aliasing
+
+    def __torch_dispatch__(self, func, types, args=(), kwargs=None):
+        kwargs = kwargs or {}
+
+        fake_r = None
+
+        # empty_like excluded for now due to sparse complex
+        # aten._to_dense.default this one is getting called with csc
+        if (
+            func
+            not in (
+                aten.lift_fresh.default,
+                aten.lift_fresh_copy.default,
+                aten.set_.source_Storage_storage_offset,
+            )
+            and not self.ignore_op_fn(func)
+            and torch.Tag.dynamic_output_shape not in func.tags
+            and torch.Tag.inplace_view not in func.tags
+            and torch.Tag.data_dependent_output not in func.tags
+        ):
+            # Do not import symbolic_shapes at the top of the module as it imports sympy and that's slow
+            from torch.fx.experimental.symbolic_shapes import ShapeEnv
+
+            try:
+                # TODO: enable_python_dispatcher() here
+                with FakeTensorMode(shape_env=ShapeEnv()) as fake_mode:
+                    fake_args, fake_kwargs = pytree.tree_map_only(
+                        torch.Tensor,
+                        functools.partial(fake_mode.from_tensor, static_shapes=True),
+                        (args, kwargs),
+                    )
+                    with warnings.catch_warnings():
+                        fake_r = func(*fake_args, **fake_kwargs)
+            except UnsupportedFakeTensorException:
+                pass
+
+        context = (
+            f"When comparing the output of {func} on FakeTensor and concrete Tensors, "
+            f"found"
+        )
+        r = func(*args, **kwargs)
+        if fake_r is not None:
+            r_flat = pytree.tree_leaves(r)
+            f_flat = pytree.tree_leaves(fake_r)
+            assert len(f_flat) == len(
+                r_flat
+            ), f"{context} mismatch in number of returns {len(f_flat)} != {len(r_flat)}"
+
+            if self.check_aliasing:
+                r_aliasing = outputs_alias_inputs(r, (args, kwargs))
+                f_aliasing = outputs_alias_inputs(fake_r, (fake_args, fake_kwargs))
+                assert (
+                    r_aliasing == f_aliasing
+                ), f"{context} mismatch in outputs_alias_inputs check {f_aliasing} != {r_aliasing}"
+
+                r_identity_eq = outputs_are_inputs(r, (args, kwargs))
+                f_identity_eq = outputs_are_inputs(fake_r, (fake_args, fake_kwargs))
+                assert (
+                    r_identity_eq == f_identity_eq
+                ), f"{context} mismatch in outputs_are_inputs check {f_identity_eq} != {r_identity_eq}"
+
+                r_output_alias_each_other = output_alias_each_other(r)
+                f_output_alias_each_other = output_alias_each_other(fake_r)
+                assert r_output_alias_each_other == f_output_alias_each_other, (
+                    f"{context} mismatch in outputs_alias_each_other check "
+                    f"{f_output_alias_each_other} != {r_output_alias_each_other}"
+                )
+
+            for idx, (r_out, fake_out) in enumerate(
+                zip(pytree.tree_leaves(r), pytree.tree_leaves(fake_r))
+            ):
+                r_is_ten = isinstance(r_out, torch.Tensor)
+                assert r_is_ten == isinstance(
+                    fake_out, torch.Tensor
+                ), f"{context} mismatched number of tensor outputs"
+                if r_is_ten:
+                    assert r_out.requires_grad == fake_out.requires_grad, (
+                        f"{context} mismatched requires_grad-ness of outputs. "
+                        f"This usually means that you have added autograd support "
+                        f"for your operator at a dispatch key other than Autograd, "
+                        f"which will lead to problems"
+                    )
+                    if torch._C._has_storage(r_out):
+                        r_offset = r_out.storage_offset()
+                        f_offset = fake_out.storage_offset()
+                        assert (
+                            r_offset == f_offset
+                        ), f"{context} mismatched storage offset"
+
+                    try:
+                        torch._prims.utils.compare_tensor_meta(
+                            r_out,
+                            fake_out,
+                            check_strides=self.check_strides,
+                            allow_rhs_unbacked=True,
+                        )
+                    except Exception as e:
+                        if is_sdpa_error(func, idx, e):
+                            continue
+                        error_message = (
+                            f"{context} mismatched tensor metadata: {e}"
+                            if len(r_flat) == 1
+                            else f"{context} mismatched tensor metadata for output[{idx}]: {e}"
+                        )
+                        raise RuntimeError(error_message) from e
+        return r

env-llmeval/lib/python3.10/site-packages/torch/_subclasses/functional_tensor.py

@@ -0,0 +1,552 @@
+import contextlib
+from abc import ABC, abstractmethod
+from typing import Any, Callable, ContextManager, Tuple
+
+import torch
+import torch.utils._pytree as pytree
+from torch._C import _functionalization_reapply_views_tls as _reapply_views
+from torch.utils._python_dispatch import return_and_correct_aliasing, TorchDispatchMode
+
+not_implemented_log = torch._logging.getArtifactLogger(__name__, "not_implemented")
+
+
+class FunctionalTensor(torch.Tensor):
+    """
+    Functional tensors represent tensors that will remove mutations
+    from a program. If you perform a mutable operation on a functional tensor,
+    it will re-dispatch to the functional variant of that operation.
+
+    Historically, functionalization is implemented in C++ in the dispatcher.
+    This class is a lightweight python shim around the C++ functionalization logic.
+
+    FunctionalTensor is required to be used with a corresponding
+    FunctionalTensormode active, because it relies
+    on using the mode for dispatch (which can properly handle factory functions).
+    """
+
+    elem: torch.Tensor
+    # Indicates to our torch_dispatch dispatching infra that
+    # this is an "infra" mode with lower dispatching precedence.
+    _mode_key = torch._C._TorchDispatchModeKey.FUNCTIONAL
+
+    # Note: The reason we add these extra keys to our FunctionalTensor subclass
+    # is to mirror the behavior of C++ functionalization (we can choose to change this
+    # later, as long as it doesn't break anything).
+    # FunctionalTensorWrapper copies **all** dispatch keys from the inner tensor
+    # to the wrapper, excluding functorch and python dispatch keys.
+    # Here I'm trying to re-use the keyset the functorch wrapper subclasses copy,
+    # except that they don't include ZeroTensor so I'm manually adding it in.
+    _extra_dispatch_keys = torch._C._additional_keys_to_prop_for_wrapper_tensors.add(
+        torch._C.DispatchKey.ZeroTensor
+    )
+
+    # These are all aten ops that correspond to metadata queries.
+    # We want FunctionalTensor to be able to handle them directly.
+    metadata_fns = [
+        torch.ops.aten.is_contiguous.default,  # type: ignore[has-type]
+        torch.ops.aten.is_contiguous.memory_format,  # type: ignore[has-type]
+        torch.ops.aten.is_strides_like_format.default,  # type: ignore[has-type]
+        torch.ops.aten.is_non_overlapping_and_dense.default,  # type: ignore[has-type]
+        torch.ops.aten.size.default,  # type: ignore[has-type]
+        torch.ops.aten.sym_size.default,  # type: ignore[has-type]
+        torch.ops.aten.stride.default,  # type: ignore[has-type]
+        torch.ops.aten.sym_stride.default,  # type: ignore[has-type]
+        torch.ops.aten.storage_offset.default,  # type: ignore[has-type]
+        torch.ops.aten.sym_storage_offset.default,  # type: ignore[has-type]
+        torch.ops.aten.numel.default,  # type: ignore[has-type]
+        torch.ops.aten.sym_numel.default,  # type: ignore[has-type]
+        torch.ops.aten.dim.default,  # type: ignore[has-type]
+    ]
+
+    def __new__(cls, elem):
+        assert torch._is_functional_tensor(elem)
+
+        # In general, we'd like our functional tensor subclass to only be in charge of functionalization,
+        # and defer to the inner subclass for all other functionality.
+        # Example: If our inner tensor is a ZeroTensor, we would want to defer running the ZeroTensor fallback
+        # until after we redispatch to our inner ZeroTensor.
+        # However, there are a few keys that we need to mirror between the inner and outer tensors.
+        #   Conjugate
+        #   Negative
+        # Why? These keys are used to test metadata queries, like `.is_conj()` and `.is_neg()`.
+        # We **need** calls to is_conj() to return the same thing on the outer and inner tensors,
+        # Because user code / framework code that branches like so needs to do the same thing
+        # when it sees the outer FunctionalTensor:
+        #     if (x.is_conj()) {
+        #         return at::view_as_real(x.resolve_conj());
+        #     } else {
+        #         return at::view_as_real(x);
+        #     }
+        extra_dispatch_keys = (
+            FunctionalTensor._extra_dispatch_keys & torch._C._dispatch_keys(elem)
+        )
+
+        out = torch.Tensor._make_wrapper_subclass(  # type: ignore[arg-type, attr-defined]
+            # TODO: right now, _make_wrapper_subclass's dynamic shape interaction is not great.
+            # Calling the overload that has kwargs causes us to go down the first overload path,
+            # which will **always** specialize sizes.
+            # We should probably eventually fix this so that the first overload can just handle dynamic shapes.
+            cls,
+            elem.shape,  # sizes
+            elem.stride(),  # strides
+            elem.storage_offset(),  # storage_offset
+            None,  # memory_format
+            elem.dtype,  # dtype
+            elem.layout,  # layout
+            elem.device,  # device
+            False,  # pin_memory
+            elem.requires_grad,  # requires_grad
+            "sizes",  # dispatch_sizes_strides_policy
+            False,  # dispatch_device
+            False,  # dispatch_layout
+            extra_dispatch_keys,  # _extra_dispatch_keys
+        )
+        out.elem = elem
+        return out
+
+    # Need to disable default torch_function. Why?
+    # Default torch_function will always wrap outputs into a subclass if they aren't already a subclass.
+    # We actually.. don't want to do this sometimes, see Note [FunctionalTensorMode inputs are sometimes plain tensors]
+    __torch_function__ = torch._C._disabled_torch_function_impl
+
+    def __torch_dispatch__(self, func, types, args=(), kwargs=None):
+        unrecognized_types = [
+            t
+            for t in types
+            if t not in [torch.Tensor, torch._subclasses.FakeTensor, FunctionalTensor]
+        ]
+        if unrecognized_types:
+            not_implemented_log.debug(
+                "FunctionalTensor unrecognized subclass(es): %s", unrecognized_types
+            )
+            return NotImplemented
+
+        if kwargs is None:
+            kwargs = {}
+
+        # FunctionalTensor needs to plumb all metadata requests to the inner tensor.
+        # In theory we don't have to do this - but if we want to service metadata requests here,
+        # we need to carefully make sure all metadata is accurate (including metadata mutations)
+        if func in FunctionalTensor.metadata_fns:
+
+            def unwrap(x):
+                return x.elem
+
+            assert len(args) == 1 and isinstance(args[0], FunctionalTensor)
+            assert len(kwargs) == 0
+            # All metadata accesses should be plumbed to the inner tensor, that way we don't have to worry
+            # about the problem of keeping metadata in sync between the wrapper and inner tensor.
+            # This also alleviates us from having to manually handle metadata mutations on the wrapper.
+            return func(args[0].elem)
+        # Originally I tried to implement my subclass without giving it a torch_dispatch, but I gave up:
+        # - _make_wrapper_subclass requires a __torch_dispatch__
+        # - If we want to use _make_subclass(), we have a problem: the subclass will share a TensorImpl with the inner tensor,
+        #   which is of type FunctionalTensorWrapper! We explicitly do not want our wrapper to be a FunctionalTensorWrapper.
+        # - If we use the default tensor.__new__(), we have another problem: it returns inner_tensor.alias(),
+        #   which causes every subclass created above autograd to have autograd view metadata
+        #   (in addition to also being a FunctionalTensorWrapper).
+        raise RuntimeError(
+            "Attempting to use FunctionalTensor on its own. Instead, please use it with a corresponding FunctionalTensorMode()"
+        )
+
+    def __repr__(self):
+        return f"FunctionalTensor({repr(self.elem)})"
+
+    @staticmethod
+    def to_functional(x):
+        # We will do the wrapping for the user.
+        assert not torch._is_functional_tensor(x)
+        # The only autograd metadata we care about on the FunctionalTensor is:
+        # - requires_grad (so autograd runs)
+        # - is_leaf (so that mutations on graph inputs that are not leaves are allowed by the autograd engine)
+        #   this is handled by FunctionalTensor.to_functional
+        x_functional = torch._to_functional_tensor(x)
+        # Technically the FunctionalTensormode here is unnecessary,
+        # but it avoids spurious NotImplemented logs during `ProxyTorchDispatchMode` tracing.
+        # _mirror_autograd_meta_to queries tensor sizes,
+        # and otherwise the sym_size() call will go to the proxy mode before hitting
+        # FunctionalTensor.__torch_dispatch__
+        with FunctionalTensorMode():
+            torch._mirror_autograd_meta_to(x, x_functional)  # type: ignore[attr-defined]
+            out = FunctionalTensor(x_functional)
+            torch._mirror_autograd_meta_to(x_functional, out)  # type: ignore[attr-defined]
+        return out
+
+    def from_functional(self):
+        torch._sync(self)
+        return torch._from_functional_tensor(self.elem)
+
+    def replace_(self, output) -> None:
+        torch._functionalize_replace(self.elem, output)
+
+    def commit_update(self) -> None:
+        torch._functionalize_commit_update(self.elem)
+
+    def sync(self) -> None:
+        torch._functionalize_sync(self.elem)
+
+    def mark_mutation_hidden_from_autograd(self) -> None:
+        torch._functionalize_mark_mutation_hidden_from_autograd(self.elem)
+
+
+class FunctionalTensorMode(TorchDispatchMode):
+    def __init__(self):
+        self.is_on_stack = False
+        self.enter_stack = []
+        # Indicates to our torch_dispatch dispatching infra that
+        # this is an "infra" mode with lower dispatching precedence.
+        self._mode_key = torch._C._TorchDispatchModeKey.FUNCTIONAL
+        # This will be turned off later for pre-dispatch functionalization
+        self.decompose_composite_implicit_ops = True
+
+    # No-op if FunctionalTensorMode is already in use
+    def __enter__(self):
+        if (
+            torch._C._get_dispatch_mode(torch._C._TorchDispatchModeKey.FUNCTIONAL)
+            is None
+        ):
+            self.enter_stack.append(True)
+
+            return super().__enter__()
+        else:
+            self.enter_stack.append(False)
+            return self
+
+    def __exit__(self, a, b, c):
+        is_on_stack = self.enter_stack.pop()
+        if is_on_stack:
+            super().__exit__(a, b, c)
+
+    def __torch_dispatch__(self, func, types, args=(), kwargs=None):
+        if kwargs is None:
+            kwargs = {}
+
+        unrecognized_types = [
+            t
+            for t in types
+            if not issubclass(t, torch._subclasses.FakeTensor)
+            and t not in [torch.Tensor, FunctionalTensor]
+        ]
+        if unrecognized_types:
+            not_implemented_log.debug(
+                "FunctionalTensor unrecognized subclass(es): %s", unrecognized_types
+            )
+            return NotImplemented
+
+        if (
+            func not in FunctionalTensor.metadata_fns
+            and self.decompose_composite_implicit_ops
+            # Not all funcs from __torch_dispatch__ are actual dispatcher ops,
+            # e.g. prim.device
+            and torch._C._dispatch_has_kernel(func.name())
+        ):
+            with self:
+                # Decomposes CompositeImplicitAutograd ops
+                r = func.decompose(*args, **kwargs)
+                if r is not NotImplemented:
+                    return r
+
+        def assert_is_functional(x):
+            assert torch._is_functional_tensor(x)
+
+        def wrap(x):
+            # Only wrap our outputs in subclasses if the inner functionalization call
+            # also wrapped outputs into FunctionalTensorWrappers.
+            # When can this happen? e.g. `torch.div(2, 2)`
+            assert not isinstance(x, FunctionalTensor)
+            if isinstance(x, torch.Tensor) and torch._is_functional_tensor(x):
+                return FunctionalTensor(x)
+            return x
+
+        any_functional_inputs = False
+
+        def unwrap(x):
+            any_functional_inputs = True
+            return x.elem
+
+        from torch._higher_order_ops.auto_functionalize import (
+            can_auto_functionalize,
+            do_auto_functionalize,
+        )
+
+        if can_auto_functionalize(
+            func
+        ) and not torch._C._dispatch_has_kernel_for_dispatch_key(
+            func.name(), torch._C.DispatchKey.Functionalize
+        ):
+            return do_auto_functionalize(func, args, kwargs)
+
+        args_unwrapped, kwargs_unwrapped = pytree.tree_map_only(
+            FunctionalTensor, unwrap, (args, kwargs)
+        )
+
+        # Expectation: functionalization should not **already** be enabled above our mode.
+        # Why would that be bad? when we return a FunctionalTensor here, we don't want functionalization
+        # to run above this mode and further wrap that output in **another** C++ FunctionalTensorWrapper.
+        is_included = torch._C._dispatch_tls_is_dispatch_key_included(
+            torch._C.DispatchKey.Functionalize
+        )
+        is_excluded = torch._C._dispatch_tls_is_dispatch_key_excluded(
+            torch._C.DispatchKey.Functionalize
+        )
+        assert is_excluded or not is_included
+        include_to_set = (
+            torch._C._dispatch_tls_local_include_set()
+            | torch._C.DispatchKeySet(torch._C.DispatchKey.Functionalize)
+        )
+        exclude_to_set = (
+            torch._C._dispatch_tls_local_exclude_set().remove(
+                torch._C.DispatchKey.Functionalize
+            )
+            - FunctionalTensor._extra_dispatch_keys
+        )
+        # All we want to do here is re-use the existing C++ functionalization logic.
+        # This requires swizzling our TLS dispatch keys so that the Functionalize key is active.
+        with torch._C._ForceDispatchKeyGuard(include_to_set, exclude_to_set):
+            try:
+                # By default for python functionalization (for AOTAutograd), we reapply views.
+                old_apply_views = torch._functionalize_enable_reapply_views(True)  # type: ignore[attr-defined]
+                outs_unwrapped = func(*args_unwrapped, **kwargs_unwrapped)
+                outs_wrapped = pytree.tree_map_only(torch.Tensor, wrap, outs_unwrapped)
+            finally:
+                torch._disable_functionalization()
+                torch._functionalize_enable_reapply_views(old_apply_views)  # type: ignore[attr-defined]
+
+        is_included = torch._C._dispatch_tls_is_dispatch_key_included(
+            torch._C.DispatchKey.Functionalize
+        )
+        is_excluded = torch._C._dispatch_tls_is_dispatch_key_excluded(
+            torch._C.DispatchKey.Functionalize
+        )
+        assert is_excluded or not is_included
+
+        if (
+            # If no outputs are our functional subclass, then don't try to fix up aliasing
+            not any(
+                isinstance(x, FunctionalTensor)
+                for x in pytree.tree_leaves(outs_wrapped)
+            )
+            # Since lift_fresh lifts its argument into a functional tensor, we can skip the
+            # aliasing correction step. Otherwise, we would be setting the storage of a
+            # lifted tensor to that of an unlifted tensor.
+            # Ref: https://github.com/pytorch/pytorch/issues/111506
+            or func == torch.ops.aten.lift_fresh.default
+        ):
+            return outs_wrapped
+        # Wrapper tensor subclasses do not have correct aliasing info! Use this util to manually correct the output aliasing.
+        # inplace ops like `aten.add_()` are expected to return inputs **directly**, instead of creating fresh tensor objects.
+        # Use this util to figure out the right thing to return.
+        # If none of our inputs were wrapped, then we have no FunctionalTensor outputs that we need to fix up storages for.
+        return return_and_correct_aliasing(func, args, kwargs, outs_wrapped)
+
+
+@contextlib.contextmanager
+def maybe_disable_functional_mode():
+    maybe_func_mode = torch._C._unset_dispatch_mode(
+        torch._C._TorchDispatchModeKey.FUNCTIONAL
+    )
+    try:
+        yield
+    finally:
+        if maybe_func_mode is not None:
+            torch._C._set_dispatch_mode(maybe_func_mode)
+
+
+# TODO: clean up the redundancy here,
+# unify on a single context manager for all mode keys.
+@contextlib.contextmanager
+def unset_functional_temporarily():
+    old = torch._C._unset_dispatch_mode(torch._C._TorchDispatchModeKey.FUNCTIONAL)
+    try:
+        yield old
+    finally:
+        if old is not None:
+            torch._C._set_dispatch_mode(old)
+
+
+# This is similar to torch.func.functionalize, but:
+# - It uses FunctionalTensorMode, and FunctionalTensor (a python subclass).
+#   One important advantage to using this mode is that it will let us
+#   run functionalization underneath __torch_dispatch__,
+#   which we need in AOTAutograd.
+# - Doing so means that it does not automatically compose with other
+#   functorch transforms, since these transforms always run above __torch_dispatch__.
+#   That's why this util lives here, and not in functorch.
+def dispatch_functionalize(func):
+    # TODO: pull these from aot autograd
+    def to_fun(t):
+        if isinstance(t, torch.Tensor):
+            return FunctionalTensor.to_functional(t)
+        return t
+
+    def from_fun(t):
+        if not isinstance(t, FunctionalTensor):
+            # quick sanity assert
+            if isinstance(t, torch.Tensor):
+                assert not torch._is_functional_tensor(t)
+            return t
+        torch._sync(t)
+        return torch._from_functional_tensor(t.elem)
+
+    def inner(*args, **kwargs):
+        func_args = pytree.tree_map_only(torch.Tensor, to_fun, args)
+        func_kwargs = pytree.tree_map_only(torch.Tensor, to_fun, kwargs)
+
+        flattened_wrapped_args = pytree.arg_tree_leaves(*func_args)
+        flattened_wrapped_kwargs = pytree.arg_tree_leaves(**func_kwargs)
+
+        disable_above = torch._C._ExcludeDispatchKeyGuard(
+            torch._C.DispatchKeySet(torch._C.DispatchKey.Functionalize)
+        )
+        with disable_above, FunctionalTensorMode():
+            func_outputs = func(*func_args, **func_kwargs)
+            outputs = pytree.tree_map_only(FunctionalTensor, from_fun, func_outputs)
+
+            return outputs
+
+    return inner
+
+
+class BaseFunctionalizeAPI(ABC):
+    @abstractmethod
+    def wrap_tensors(self, args: Tuple[Any]) -> Tuple[Any]:
+        pass
+
+    @abstractmethod
+    def unwrap_tensors(self, args: Tuple[Any]) -> Tuple[Any]:
+        pass
+
+    @abstractmethod
+    def functionalize(self, inner_f: Callable) -> Callable:
+        pass
+
+    @abstractmethod
+    def redispatch_to_next(self) -> ContextManager:
+        pass
+
+    @abstractmethod
+    def replace(self, input_tensor, output_tensor) -> None:
+        pass
+
+    @abstractmethod
+    def commit_update(self, tensor) -> None:
+        pass
+
+    @abstractmethod
+    def sync(self, tensor) -> None:
+        pass
+
+    @abstractmethod
+    def mark_mutation_hidden_from_autograd(self, tensor) -> None:
+        pass
+
+
+class PythonFunctionalizeAPI(BaseFunctionalizeAPI):
+    def wrap_tensors(self, args: Tuple[Any]) -> Tuple[Any]:
+        return torch.utils._pytree.tree_map_only(
+            FunctionalTensor, FunctionalTensor.to_functional, args
+        )
+
+    def unwrap_tensors(self, args: Tuple[Any]) -> Tuple[Any]:
+        return torch.utils._pytree.tree_map_only(
+            FunctionalTensor, FunctionalTensor.from_functional, args
+        )
+
+    def functionalize(self, inner_f: Callable) -> Callable:
+        return dispatch_functionalize(inner_f)
+
+    def redispatch_to_next(self) -> ContextManager:
+        return unset_functional_temporarily()
+
+    def replace(self, input_tensor, output_tensor) -> None:
+        assert isinstance(input_tensor, FunctionalTensor)
+        assert not isinstance(output_tensor, FunctionalTensor)
+        input_tensor.replace_(output_tensor)
+
+    def commit_update(self, tensor) -> None:
+        assert isinstance(tensor, FunctionalTensor)
+        tensor.commit_update()
+
+    def sync(self, tensor) -> None:
+        assert isinstance(tensor, FunctionalTensor)
+        tensor.sync()
+
+    def mark_mutation_hidden_from_autograd(self, tensor) -> None:
+        assert isinstance(tensor, FunctionalTensor)
+        tensor.mark_mutation_hidden_from_autograd()
+
+
+class CppFunctionalizeAPI(BaseFunctionalizeAPI):
+    def wrap_tensors(self, args: Tuple[Any]) -> Tuple[Any]:
+        from torch._functorch.eager_transforms import _wrap_all_tensors_to_functional
+
+        return _wrap_all_tensors_to_functional(args, level=0)
+
+    def unwrap_tensors(self, args: Tuple[Any]) -> Tuple[Any]:
+        from torch._functorch.eager_transforms import (
+            _unwrap_all_tensors_from_functional,
+        )
+
+        return _unwrap_all_tensors_from_functional(args, reapply_views=_reapply_views())
+
+    def functionalize(self, inner_f: Callable) -> Callable:
+        return torch.func.functionalize(inner_f)
+
+    def redispatch_to_next(self) -> ContextManager:
+        return torch._C._ExcludeDispatchKeyGuard(
+            torch._C.DispatchKeySet(torch._C.DispatchKey.Functionalize)
+        )
+
+    def replace(self, input_tensor, output_tensor) -> None:
+        torch._functionalize_replace(input_tensor, output_tensor)
+
+    def commit_update(self, tensor) -> None:
+        torch._functionalize_commit_update(tensor)
+
+    def sync(self, tensor) -> None:
+        torch._functionalize_sync(tensor)
+
+    def mark_mutation_hidden_from_autograd(self, tensor) -> None:
+        torch._functionalize_mark_mutation_hidden_from_autograd(tensor)
+
+
+class FunctorchFunctionalizeAPI(BaseFunctionalizeAPI):
+    def __init__(self, interpreter):
+        self.interpreter = interpreter
+
+    def wrap_tensors(self, args: Tuple[Any]) -> Tuple[Any]:
+        from torch._functorch.eager_transforms import _wrap_all_tensors_to_functional
+
+        return _wrap_all_tensors_to_functional(args, level=self.interpreter.level())
+
+    def unwrap_tensors(self, args: Tuple[Any]) -> Tuple[Any]:
+        from torch._functorch.eager_transforms import (
+            _unwrap_all_tensors_from_functional,
+        )
+
+        return _unwrap_all_tensors_from_functional(
+            args, reapply_views=self.interpreter.functionalize_add_back_views()
+        )
+
+    def functionalize(self, inner_f: Callable) -> Callable:
+        return torch.func.functionalize(
+            inner_f,
+            remove="mutations_and_views"
+            if self.interpreter.functionalize_add_back_views()
+            else "mutations",
+        )
+
+    def redispatch_to_next(self) -> ContextManager:
+        return self.interpreter.lower()
+
+    def replace(self, input_tensor, output_tensor) -> None:
+        torch._functionalize_replace(input_tensor, output_tensor)
+
+    def commit_update(self, tensor) -> None:
+        torch._functionalize_commit_update(tensor)
+
+    def sync(self, tensor) -> None:
+        torch._functionalize_sync(tensor)
+
+    def mark_mutation_hidden_from_autograd(self, tensor) -> None:
+        torch._functionalize_mark_mutation_hidden_from_autograd(tensor)

env-llmeval/lib/python3.10/site-packages/torch/_subclasses/meta_utils.py

@@ -0,0 +1,730 @@
+import contextlib
+import warnings
+import weakref
+from typing import ContextManager, List, Optional, Tuple, TYPE_CHECKING
+
+import torch
+from torch._C._functorch import (
+    _unwrap_functional_tensor,
+    _wrap_functional_tensor,
+    current_level,
+    peek_interpreter_stack,
+    TransformType,
+)
+from torch._guards import Source
+
+from torch.multiprocessing.reductions import StorageWeakRef
+from torch.utils._python_dispatch import (
+    is_traceable_wrapper_subclass,
+    transform_subclass,
+)
+from torch.utils.weak import WeakIdRef
+
+if TYPE_CHECKING:
+    # Import the following modules during type checking to enable code intelligence features,
+    # Do not import unconditionally, as they import sympy and importing sympy is very slow
+    from torch.fx.experimental.symbolic_shapes import SymbolicContext
+
+DimList = List
+
+
+def safe_is_leaf(t):
+    try:
+        return t.is_leaf
+    except RuntimeError:
+        # inference mode can trigger this
+        return False
+
+
+def safe_grad(t):
+    with warnings.catch_warnings():
+        warnings.filterwarnings("ignore", "The .grad attribute of a Tensor")
+        return t.grad
+
+
+def assert_eq(a, b):
+    assert a == b, f"{a} != {b}"
+
+
+def assert_metadata_eq(assert_eq, m1, m2, *, skip_symbolic=False):
+    def go(m1, m2):
+        assert_eq(m1.dtype, m2.dtype)
+        if not skip_symbolic:
+            assert_eq(m1.shape, m2.shape)
+        assert_eq(m1.requires_grad, m2.requires_grad)
+        assert_eq(m1.is_leaf, m2.is_leaf)
+        assert_eq(m1.grad_fn is None, m2.grad_fn is None)
+        assert_eq(m1.is_sparse, m2.is_sparse)
+        assert_eq(m1.is_inference(), m2.is_inference())
+        assert_eq(m1.is_conj(), m2.is_conj())
+        assert_eq(m1.is_neg(), m2.is_neg())
+        assert_eq(safe_grad(m1) is not None, safe_grad(m2) is not None)
+        if safe_grad(m1) is not None:
+            go(safe_grad(m1), safe_grad(m2))
+        if m1.is_sparse:
+            assert_eq(m1.dense_dim(), m2.dense_dim())
+            assert_eq(m1.sparse_dim(), m2.sparse_dim())
+            assert_eq(m1.is_coalesced(), m2.is_coalesced())
+        else:
+            if not skip_symbolic:
+                assert_eq(m1.stride(), m2.stride())
+                assert_eq(m1.storage_offset(), m2.storage_offset())
+            assert_eq(m1._is_view(), m2._is_view())
+            if m1._is_view():
+                go(m1._base, m2._base)
+        # TODO: test if is resizable (no direct query for this atm)
+        # TODO: audit AutogradMeta to see if it matches
+        # TODO: test forward AD
+
+    return go(m1, m2)
+
+
+# This is a class for converting multiple tensors into meta tensors which
+# share the same view/storage structure.  The operation model is you allocate
+# one of these, and then call it repeatedly on all the tensors you want to
+# convert.  It's important to use the same object for tensors you want to
+# share storage because this is how we correlate shared storages to the same
+# meta storages. This class will hold weak references to cached tenosrs
+# and tensor storages.
+class MetaConverter:
+    def __init__(self):
+        self.storage_memo = {}
+        self.tensor_memo: weakref.WeakValueDictionary = weakref.WeakValueDictionary()
+        self.maybe_storages_to_delete = []
+        self.check_expired_frequency = 128
+        self.check_expired_count = 0
+        self.hit = 0
+        self.miss = 0
+        self.del_hook = None
+        self.arg_cnt = 0
+
+    def successful(self):
+        return self.hit > 0 and self.miss == 0
+
+    def check_for_expired_weak_storages(self):
+        new_li = []
+        stor_to_delete = []
+        for obj in self.maybe_storages_to_delete:
+            if not obj.expired():
+                new_li.append(obj)
+            else:
+                stor_to_delete.append(obj)
+        for obj in stor_to_delete:
+            self.storage_memo.pop(obj, None)
+        self.maybe_storages_to_delete = new_li
+
+        # if for some reason we have aquired many storages which have not expired
+        # even though a tensor with their storage has expired (aliasing or otherwise)
+        # check for expired storages less often so as to bound the amount of work we
+        # do checking for expired storages
+        self.check_expired_frequency = max(
+            self.check_expired_frequency, len(self.maybe_storages_to_delete)
+        )
+
+    def get_tensor_memo(self, t):
+        return self.tensor_memo.get(WeakIdRef(t), None)
+
+    def set_tensor_memo(self, t, v):
+        # hold a weak ref to self, otherwise it will be kept alive
+        # by the del_ten closure
+        self_weak_ref = weakref.ref(self)
+        if t.is_sparse or t.is_mkldnn:
+            weak_st = None
+        else:
+            weak_st = StorageWeakRef(t._typed_storage())
+        tensor_ref_key = WeakIdRef(t)
+
+        def del_ten():
+            # tensor outlives the converter
+            self_ref = self_weak_ref()
+            if self_ref is None:
+                return
+            # on shutdown, tensor_ref_key may not be in memo
+            self_ref.tensor_memo.pop(tensor_ref_key, None)
+            if weak_st and weak_st.expired():
+                self_ref.storage_memo.pop(weak_st, None)
+            elif weak_st is not None:
+                # [expired-storages]
+                # NB: even though the tensor has died,
+                # the deallocation of its storage can take longer,
+                # even when the storage has no other uses/views.
+                # In this case, the StorageWeakRef object will be kept alive
+                # longer than it needs to be, however the storage itself
+                # will be deallocated. We retain the possibly dead storages
+                # and periodically check if any of them are expired and
+                # can be freed.
+                self_ref.maybe_storages_to_delete.append(weak_st)
+
+        weakref.finalize(t, del_ten)
+        self.tensor_memo[tensor_ref_key] = v
+
+    # NB: doesn't actually return a storage, because meta storage is
+    # not supported
+    def meta_storage(self, s, callback):
+        # NB: TypedStorage is freshly allocated and cannot be used as hash
+        # key index.
+
+        # Use a Weak Ref to s in order to not leak memory
+        swr = StorageWeakRef(s)
+        if swr not in self.storage_memo:
+            self.storage_memo[swr] = callback(
+                lambda: torch.empty(s.size(), dtype=torch.uint8, device="meta")
+            ).untyped_storage()
+        return self.storage_memo[swr]
+
+    # This function assumes that it's possible to do the conversion
+    # NB: name here is used in a conventional way by Dynamo; it corresponds
+    # precisely to the Source.name() of the tensor we're fakeifying and
+    # corresponds to a valid Python expression.  When we construct sub-names
+    # as part of this process, we will maintain this invariant!  (Even though
+    # other users of this may not need it this property to be upheld.)
+    def meta_tensor(
+        self,
+        t,
+        shape_env=None,
+        callback=lambda t: t(),
+        source: Optional[Source] = None,
+        symbolic_context: Optional["SymbolicContext"] = None,
+    ):
+        from torch._subclasses.fake_tensor import FakeTensor
+
+        if source is None:
+            from torch._dynamo.source import ConstantSource
+
+            # TODO: make a dedicated UnknownSource for this?
+            source = ConstantSource(
+                f"__meta_utils_unknown_tensor{len(self.tensor_memo)}"
+            )
+
+        # This indicates you set no_dispatch() before calling into this
+        # function.  This is an error: we may be creating fake tensors and
+        # will perform operations on them which need fake tensor mode to
+        # be active.  You will segfault if you are in a no_dispatch() block.
+        assert not torch._C._dispatch_tls_local_exclude_set().has(
+            torch._C.DispatchKey.Python
+        )
+        arg_cnt = self.arg_cnt
+        self.arg_cnt += 1
+
+        # When we make as_strided calls, we end up generating a guard
+        # that the new as_strided tensor is in bounds for the old storage
+        # for the base (since as_strided calls can "bust" out of their
+        # bounding box.)  This guard is unnecessary: if a user is able
+        # to provide us a tensor with the view base setup this way, we
+        # don't need to produce a guard, because the fact that they
+        # were able to produce the view base means its in bounds.
+        #
+        # Now, ordinarily, this guard would be harmless.  However, the
+        # generated guard refers to variables bound on the base variable.
+        # At the moment, Dynamo doesn't actually guard on x._base, because
+        # according to Voz this results in a lot of spurious invalidations,
+        # and also if the user doesn't directly make use of _base, its
+        # pointless anyway (because programs should be parametric over
+        # whether or not the input tensor is a view or not--unless you're
+        # mutating the input, but that's a whole 'nother ballgame).  So
+        # for expediency, we suppress these guards so we don't have to
+        # deal with this (yet, anyway.)
+        #
+        # NB: An old version of this code suppressed guards for ALL operations
+        # happening during meta conversion, not just as_strided calls.
+        # This is too aggressive: we do duck sizing and 0/1 simplification
+        # as we allocate variables, and we do need to register guards for
+        # these cases.
+        maybe_suppress = contextlib.nullcontext
+        if shape_env is not None:
+            maybe_suppress = shape_env.suppress_guards
+
+        def sym_sizes_strides_storage_offset(
+            t, src
+        ) -> Tuple[Tuple[int, ...], Tuple[int, ...], int]:
+            if shape_env is not None:
+                if isinstance(t, FakeTensor) and t.fake_mode.shape_env is shape_env:
+                    # Don't reallocate the sizes; the shape envs are the same,
+                    # so reuse the old sizes/strides/etc
+                    return (t.size(), t.stride(), t.storage_offset())
+                else:
+                    return shape_env.create_symbolic_sizes_strides_storage_offset(
+                        t,
+                        src,
+                        # Assume that the set of dims that are dynamic are the same between
+                        # the wrapper tensor and any inner tensors.
+                        # We can revisit this if this assumption does not hold
+                        # for any important subclasses later.
+                        symbolic_context=symbolic_context,
+                    )
+            else:
+                assert symbolic_context is None
+            return (t.size(), t.stride(), t.storage_offset())
+
+        # see expired-storages
+        self.check_expired_count += 1
+        if self.check_expired_count >= self.check_expired_frequency:
+            self.check_for_expired_weak_storages()
+            self.check_expired_count = 0
+
+        if self.get_tensor_memo(t) is None:
+            with torch.inference_mode(t.is_inference()):
+                if t.is_sparse:
+                    is_leaf = safe_is_leaf(t)
+                    r = callback(
+                        lambda: torch.ops.aten._sparse_coo_tensor_with_dims(
+                            t.sparse_dim(),
+                            t.dense_dim(),
+                            t.shape,
+                            dtype=t.dtype,
+                            layout=torch.sparse_coo,
+                            device="meta",
+                        )
+                    )
+                    assert safe_is_leaf(r), "the callback you passed in doesn't detach"
+                    # Note [is_coalesced is dispatched]
+                    # Strangely enough, is_coalesced() is a dispatched operator,
+                    # which means that it will get caught by fake tensor mode.
+                    # Ordinarily this would error, but there's some logic in
+                    # fake tensor ensure this doesn't happen.
+                    r._coalesced_(t.is_coalesced())
+                    if t.requires_grad:
+                        r.requires_grad = True
+                    if t.requires_grad and not is_leaf:
+                        with torch.enable_grad():
+                            r = r.clone()
+                            r._coalesced_(t.is_coalesced())
+                elif t.is_mkldnn:
+                    is_leaf = safe_is_leaf(t)
+                    sizes, strides, _storage_offset = sym_sizes_strides_storage_offset(
+                        t, source
+                    )
+                    r = callback(
+                        lambda: torch.empty_strided(
+                            sizes, strides, dtype=t.dtype, device="meta"
+                        )
+                    )
+                    assert safe_is_leaf(r), "the callback you passed in doesn't detach"
+                    if t.requires_grad:
+                        r.requires_grad = True
+                    if t.requires_grad and not is_leaf:
+                        with torch.enable_grad():
+                            r = r.clone()
+                elif t._is_view():
+                    # Construct views in two steps: recursively meta-fy their
+                    # base, and then create view(s) off that.  NB: doing it
+                    # directly from storage is WRONG because this won't cause
+                    # version counters to get shared.
+                    assert t._is_view()
+
+                    from torch._dynamo.source import AttrSource
+                    from torch.fx.experimental.symbolic_shapes import (
+                        DimDynamic,
+                        StatelessSymbolicContext,
+                    )
+
+                    if shape_env and not t.is_nested and not t._base.is_nested:
+                        base_symbolic_context = StatelessSymbolicContext(
+                            dynamic_sizes=[DimDynamic.STATIC] * t._base.dim(),
+                            constraint_sizes=[None] * t._base.dim(),
+                        )
+                    else:
+                        base_symbolic_context = None
+                    base = self.meta_tensor(
+                        t._base,
+                        shape_env,
+                        callback,
+                        source=AttrSource(source, "_base"),
+                        symbolic_context=base_symbolic_context,
+                    )
+
+                    def is_c_of_r(complex_dtype, real_dtype):
+                        return (
+                            utils.is_complex_dtype(complex_dtype)
+                            and utils.corresponding_real_dtype(complex_dtype)
+                            == real_dtype
+                        )
+
+                    # In some situations, MetaConverter may be called in a
+                    # context where autograd is disabled.  For the _is_view
+                    # assert to pass, we have to setup the autograd view
+                    # metadata anyway.  Do this by reenabling the
+                    # ADInplaceOrView key.  This is kind of a hack.
+                    old_exclude = torch._C._dispatch_tls_is_dispatch_key_excluded(
+                        torch._C.DispatchKey.ADInplaceOrView
+                    )
+                    torch._C._dispatch_tls_set_dispatch_key_excluded(
+                        torch._C.DispatchKey.ADInplaceOrView, False
+                    )
+                    try:
+                        if base.dtype == t.dtype:
+                            pass
+                        elif is_c_of_r(base.dtype, t.dtype):
+                            base = torch.view_as_real(base)
+                        elif is_c_of_r(t.dtype, base.dtype):
+                            base = torch.view_as_complex(base)
+                        else:
+                            # This is not guaranteed to succeed.  If it fails, it
+                            # means there is another dtype-converting view function
+                            # that hasn't been handled here
+                            base = base.view(t.dtype)
+
+                        # This is very tricky.  Naively, you might expect this
+                        # to hold:
+                        #
+                        #   if t.requires_grad and not safe_is_leaf(t)
+                        #       assert t._base.requires_grad
+                        #
+                        # But it's not true!  As you can see in the following
+                        # program:
+                        #
+                        #   x = torch.zeros(4)
+                        #   y = x.view(1, 4)
+                        #   y.requires_grad = True
+                        #   z = y.view(1, 1, 4)
+                        #   assert z._base is x
+                        #
+                        # So we may have to do *two* views out of the base to
+                        # recreate this situation.
+                        def _view_from_base(base, t):
+                            if t.is_nested:
+                                # Nested tensors do not support as_strided, and
+                                # hence,always have _view_func available.
+                                #
+                                # The unsafe version of _view_func omits
+                                # checking whether the base passed in has the same
+                                # metadata as the original base the view_func
+                                # was originally executed with. (1) It is OK here,
+                                # because we're calling it on the meta-ified base,
+                                # so the metadata is guaranteed to be the same.
+                                # (2) It is necessary because we don't actually
+                                # want to guard on the base's metadata here.
+                                return t._view_func_unsafe(base)
+                            else:
+                                (
+                                    sizes,
+                                    strides,
+                                    storage_offset,
+                                ) = sym_sizes_strides_storage_offset(t, source)
+                                return base.as_strided(sizes, strides, storage_offset)
+
+                        if safe_is_leaf(t):
+                            # Leaf views that track view metadata are created by
+                            # creating a view inside a no_grad block
+                            with torch.no_grad(), maybe_suppress():
+                                r = _view_from_base(base, t)
+                            # As it's a leaf, we can directly assign requires_grad
+                            r.requires_grad = t.requires_grad
+                        else:
+                            if t._base.requires_grad == t.requires_grad:
+                                # Easy case, just run the view op
+                                with torch.enable_grad(), maybe_suppress():
+                                    r = _view_from_base(base, t)
+
+                                # NB: We don't actaully faithfully replicate
+                                # autograd connectivity, but that doesn't matter
+                                # today. See following for more info:
+                                # https://gist.github.com/soulitzer/e03f015b314c3f5fcf80888c69390913
+                            else:
+                                # Obscure case.  Create a leaf view and give it the
+                                # correct requires_grad, then do the final view.
+                                # NB: Can't have a non-leaf without requiring grad!
+                                assert t.requires_grad
+                                with torch.no_grad():
+                                    mid = base.view(base.shape)
+                                mid.requires_grad = t.requires_grad
+                                with torch.enable_grad(), maybe_suppress():
+                                    r = _view_from_base(mid, t)
+                        # The CreationMeta influences whether or not inplace
+                        # mutation is an error or not.  So we need to make
+                        # sure we properly propagate this as well.
+                        torch._C._autograd._set_creation_meta(
+                            r, torch._C._autograd._get_creation_meta(t)
+                        )
+                    finally:
+                        torch._C._dispatch_tls_set_dispatch_key_excluded(
+                            torch._C.DispatchKey.ADInplaceOrView, old_exclude
+                        )
+
+                else:
+                    is_leaf = safe_is_leaf(t)
+                    if not t.is_nested:
+                        # Nested tensor subclasses have special logic for
+                        # creating symbolic size/strides/storage_offset
+                        (
+                            sizes,
+                            strides,
+                            storage_offset,
+                        ) = sym_sizes_strides_storage_offset(t, source)
+
+                    def empty_create(inner_t, inner_src):
+                        (
+                            inner_sizes,
+                            inner_strides,
+                            inner_storage_offset,
+                        ) = sym_sizes_strides_storage_offset(inner_t, inner_src)
+                        return torch.empty_strided(
+                            inner_sizes,
+                            inner_strides,
+                            dtype=inner_t.dtype,
+                            device="meta",
+                        )
+
+                    # If we have a subclass that desugars into dense tensors,
+                    # perform our callback on each inner tensor.
+                    if is_traceable_wrapper_subclass(t):
+                        # Note: transform_subclass will use __tensor_unflatten__ to generate
+                        # a fresh subclass wrapper, which is why sizes/strides are not passed in
+                        # to the creation function here.
+                        # We assume that if the inner tensors of the subclass are given symbolic sizes,
+                        # their sizes will be used to construct the (symbolic) sizes of the wrapper tensor.
+                        from torch._dynamo.source import AttrSource
+
+                        if t.is_nested:
+                            # Avoid circular import
+                            from torch._dynamo.source import (
+                                TensorProperty,
+                                TensorPropertySource,
+                            )
+
+                            # For nested tensors, manually do transform_subclass
+                            # so we can insert some special processing on ctx
+                            attrs, ctx = t.__tensor_flatten__()
+                            transformed_tensors_dict = {}
+                            orig_shape_env = None
+                            for attr in attrs:
+                                inner_t = getattr(t, attr)
+                                if orig_shape_env is None:
+                                    orig_shape_env = (
+                                        inner_t.fake_mode.shape_env
+                                        if isinstance(inner_t, FakeTensor)
+                                        else None
+                                    )
+                                transformed_tensors_dict[attr] = callback(
+                                    lambda: empty_create(
+                                        inner_t, AttrSource(source, attr)
+                                    )
+                                )
+                            # We expect JaggedTensor to have a 'ragged_size' in
+                            # its context
+                            assert isinstance(ctx, dict)
+                            assert "ragged_size" in ctx
+                            assert isinstance(t._size[1], torch.SymInt)
+                            if orig_shape_env is shape_env:
+                                # It's already fake and the shape envs line up, reuse the old size
+                                # Do not assert singleton_int; it may already
+                                # be a variable
+                                ctx["ragged_size"] = t._size[1]
+                            else:
+                                assert t._size[1].node.singleton_int() is not None
+                                # Replace the eager ragged size with our freshly
+                                # allocated jagged size that has a source
+                                ctx["ragged_size"] = shape_env.create_symintnode(
+                                    shape_env.create_symbol(
+                                        t._size[1],
+                                        TensorPropertySource(
+                                            source, TensorProperty.SIZE, 1
+                                        ),
+                                    ),
+                                    hint=t._size[1],
+                                )
+                            r = type(t).__tensor_unflatten__(
+                                transformed_tensors_dict, ctx
+                            )
+                        else:
+                            r = transform_subclass(
+                                t,
+                                lambda attr, inner_t: callback(
+                                    lambda: empty_create(
+                                        inner_t,
+                                        AttrSource(source, attr),
+                                    )
+                                ),
+                            )
+                    else:
+                        r = callback(
+                            lambda: torch.empty_strided(
+                                sizes,
+                                strides,
+                                dtype=t.dtype,
+                                device="meta",
+                            )
+                        )
+                    assert safe_is_leaf(r), "the callback you passed in doesn't detach"
+                    if t.requires_grad:
+                        r.requires_grad = t.requires_grad
+                        if not is_leaf:
+                            # Fake up some autograd history.
+                            with torch.enable_grad():
+                                # preserve_format is the default, but we want to
+                                # emphasize how important it is to preserve
+                                # format here
+                                r = r.clone(memory_format=torch.preserve_format)
+
+                    # Graph-Break for wrapped tensors
+                    if torch._C._functorch.is_functorch_wrapped_tensor(t):
+                        return NotImplemented
+
+                    s = t.untyped_storage()
+                    swr = StorageWeakRef(s)
+                    if swr not in self.storage_memo and (
+                        r.is_nested
+                        or (
+                            r.stride() == strides
+                            and r.storage_offset() == storage_offset
+                        )
+                    ):
+                        # You're normal and happy, install the fresh storage into the memo
+                        self.storage_memo[swr] = r.untyped_storage()
+                    else:
+                        # You're in crazy town; somehow you gave us a tensor
+                        # that wasn't a view, but had nonzero storage offset,
+                        # nontrivial strides (such that clone() couldn't
+                        # preserve them), or already aliases with another
+                        # tensor's storage.  The most typical way to end
+                        # up here is with set_.  So use set_ to bludgeon this
+                        # in.
+                        r_s = self.meta_storage(s, callback=callback)
+                        # NB: In principle, this should always work, but there
+                        # is some subtle difference in the autograd metadata
+                        # that means we will backprop the set_ call, even if
+                        # r is declared as an input to grad.
+                        # See https://github.com/pytorch/pytorch/issues/87956
+                        # for the reproducer.
+                        # NB: The in_kernel_invocation_manager here is necessary
+                        # for fake tensor.  If we run the set_ call with fake
+                        # tensor on, r will improperly report that it is NOT a
+                        # meta tensor but a cpu tensor, and then the set_ call
+                        # will fail due to device mismatch.  no_dispatch() is
+                        # not enough, because the fake tensor will still claim
+                        # to be a CPU tensor and you'll end up in the CPU
+                        # kernel.  Arguably this is a hack; a cleaner way to
+                        # solve this is to have a FakeStorage concept which
+                        # would report it's CPU device--no problem now!  But
+                        # this is difficult to do because we don't have storage
+                        # subclasses.  Relevant test is
+                        # DynamicShapesFunctionTests::test_add_dynamic_shapes in
+                        # test/dynamo/test_dynamic_shapes.py
+                        maybe_fake_mgr: ContextManager[None] = contextlib.nullcontext()
+                        from torch._subclasses.fake_tensor import (
+                            in_kernel_invocation_manager,
+                            maybe_get_fake_mode,
+                        )
+
+                        mb_fake_mode = maybe_get_fake_mode(r)
+                        if mb_fake_mode is not None:
+                            maybe_fake_mgr = in_kernel_invocation_manager(mb_fake_mode)
+                        with maybe_fake_mgr, torch.no_grad():
+                            r.set_(r_s, storage_offset, sizes, strides)
+
+                if safe_grad(t) is not None:
+                    from torch._dynamo.source import AttrSource
+
+                    r.grad = self.meta_tensor(
+                        safe_grad(t),
+                        shape_env,
+                        callback,
+                        source=AttrSource(source, "grad"),
+                        symbolic_context=symbolic_context,
+                    )
+                torch._C._set_conj(r, t.is_conj())
+                torch._C._set_neg(r, t.is_neg())
+            # This can be skipped if necessary for performance reasons
+            assert_metadata_eq(assert_eq, t, r, skip_symbolic=True)
+            self.set_tensor_memo(t, r)
+
+        return self.get_tensor_memo(t)
+
+    def __call__(
+        self,
+        t,
+        shape_env=None,
+        *,
+        callback=lambda t: t(),
+        source=None,
+        symbolic_context=None,
+    ):
+        # TODO: zero tensors?  We appear to have eliminated them by
+        # excluding complex for now
+
+        if isinstance(t, torch.Tensor) or is_traceable_wrapper_subclass(t):
+            if t.device.type != "xla" and any(
+                [
+                    t.is_sparse_csr,
+                    t.layout in [torch.sparse_csc, torch.sparse_bsr, torch.sparse_bsc],
+                    t.is_quantized,
+                    t._is_view() and t._base is not None and t._base.is_sparse,
+                    torch._is_functional_tensor(t),
+                    t.device.type in ("lazy"),
+                    # We need a way to test if a tensor is batched but there
+                    # is no official APi to do it
+                    # torch._C._is_batched(t),
+                ]
+            ):
+                # TODO: sparse should support meta
+                # NB technically to('meta') does work but our logging
+                # instrumentation will see the meta conversions and the
+                # tests all break so we just exclude this.  In any case
+                # the to conversion isn't really right anyhow.
+
+                if torch._is_functional_tensor(t) and t.device.type != "lazy":
+                    if t._is_view():
+                        raise RuntimeError(
+                            "Cannot safely fakify a view because this process drops the view information right now."
+                        )
+
+                    st = peek_interpreter_stack()
+                    assert (
+                        st is None or st.key() == TransformType.Functionalize
+                    ), "Expect st to be either None or have Functionalize transform key."
+                    if st is None:
+                        # the case of AOTAutograd
+                        torch._sync(t)
+                        unwrap_t = torch._from_functional_tensor(t)
+                        with torch._dispatch.python.suspend_functionalization():
+                            fake_t = self.meta_tensor(
+                                unwrap_t,
+                                shape_env=shape_env,
+                                callback=callback,
+                                source=source,
+                                symbolic_context=symbolic_context,
+                            )
+                        out = torch._to_functional_tensor(fake_t)
+                        torch._mirror_autograd_meta_to(fake_t, out)
+                        return out
+                    else:
+                        # torch.func.functionalize
+                        reapply_views = torch._C._functionalization_reapply_views_tls()
+                        unwrap_t = _unwrap_functional_tensor(t, reapply_views)
+                        pop_st_ctx = (
+                            torch._functorch.pyfunctorch.temporarily_pop_interpreter_stack()
+                        )
+                        with pop_st_ctx:
+                            fake_t = self.meta_tensor(
+                                unwrap_t,
+                                shape_env=shape_env,
+                                callback=callback,
+                                source=source,
+                                symbolic_context=symbolic_context,
+                            )
+                        return _wrap_functional_tensor(fake_t, current_level())
+                self.miss += 1
+                return NotImplemented
+            else:
+                self.hit += 1
+                r = self.meta_tensor(
+                    t,
+                    shape_env=shape_env,
+                    callback=callback,
+                    source=source,
+                    symbolic_context=symbolic_context,
+                )
+                if type(t) is torch.nn.Parameter:
+                    # NB: Cannot directly use Parameter constructor
+                    # because that would force a detach, not desirable
+                    r._is_param = True
+                return r
+        elif torch.overrides.is_tensor_like(t):
+            self.miss += 1
+            return NotImplemented
+        else:
+            # non-Tensor types don't count as hit or miss
+            return t
+
+
+import torch._prims_common as utils

env-llmeval/lib/python3.10/site-packages/torch/_subclasses/schema_check_mode.py

@@ -0,0 +1,196 @@
+from collections import namedtuple
+from copy import deepcopy
+from itertools import combinations
+
+import torch
+from torch.fx.operator_schemas import normalize_function
+from torch.testing._internal.jit_utils import clone_inputs
+from torch.utils import _pytree as pytree
+from torch.utils._python_dispatch import TorchDispatchMode
+from torch.utils._pytree import tree_map
+
+# Named Tuples used within SchemaCheckMode
+Mutation = namedtuple("Mutation", ["op_name", "arg_name"])
+Aliasing = namedtuple("Aliasing", ["op_name", "arg_name", "output_number"])
+
+# Simplified naming for C++ classes
+SchemaArgument = torch._C._SchemaArgument
+SchemaArgType = torch._C._SchemaArgType
+SchemaInfo = torch._C._SchemaInfo
+
+# This TorchDispatchMode Subclass is used to verify op schemas
+# This TorchDispatchMode Scubclass currently:
+#  - Records the called ops
+#  - Checks for mutations on all inputs
+#  - Checks for aliasing on all inputs
+
+
+class SchemaCheckMode(TorchDispatchMode):
+    def __init__(self):
+        # Information recorded for testing purposes. For example:
+        #  - incorrect schemas
+        #  - overly conservative schemas
+        self.ops = []
+        self.mutated = []
+        self.aliasing = []
+
+    def reset_cache(self):
+        self.ops.clear()
+        self.mutated.clear()
+        self.aliasing.clear()
+
+    def display_ops(self):
+        print(*self.ops, sep=",")
+
+    def __torch_dispatch__(self, func, types, args=(), kwargs=None):
+        def bitwise_equal(lhs, rhs):
+            if lhs.is_quantized:
+                # TODO: This is only OK if can't have NaN quantized; idk if
+                # this is actually true
+                return torch.equal(lhs, rhs)
+            else:
+                return torch.allclose(lhs, rhs, equal_nan=True)
+
+        def has_mutated(before, after, md):
+            are_tensors = type(before) == torch.Tensor and type(after) == torch.Tensor
+            if (
+                are_tensors
+                and before.layout != torch.sparse_csr
+                and after.layout != torch.sparse_csr
+            ):
+                return not (
+                    before.size() == after.size()
+                    and bitwise_equal(before, after)
+                    and md[0] == after.stride()
+                    and md[1] == after._typed_storage()._cdata
+                )
+            return False
+
+        def has_aliased(lhs, rhs):
+            try:
+                return torch._C._overlaps(lhs, rhs)
+            except Exception as exception:
+                if str(exception).startswith("Cannot inspect value of type "):
+                    return False
+                else:
+                    raise exception
+
+        def standardize_name(name):
+            return name if name != "self" else "input"
+
+        def unwrap(e):
+            if isinstance(e, torch.Tensor) and not type(e) == torch.Tensor:
+                try:
+                    return e.elem
+                except AttributeError as t:
+                    return e
+            return e
+
+        def parse_metadata(e):
+            if isinstance(e, torch.Tensor):
+                if not type(e) == torch.Tensor:
+                    try:
+                        current = e.elem
+                        return (
+                            deepcopy(current.stride()),
+                            current._typed_storage()._cdata,
+                        )
+                    except AttributeError as t:
+                        return None
+                # Sparse CSR tensors do not have strides or storage
+                elif e.layout != torch.sparse_csr:
+                    return (deepcopy(e.stride()), e._typed_storage()._cdata)
+            return None
+
+        self.ops.append(func._schema.name)
+
+        # Clone and process arguments and outputs
+        pre_arguments = normalize_function(
+            func, args, kwargs, normalize_to_only_use_kwargs=True
+        ).kwargs
+
+        c_p_args = dict(zip(pre_arguments.keys(), clone_inputs(pre_arguments.values())))
+        cloned_arguments = {
+            name: tree_map(unwrap, c_p_args.get(name)) for name in c_p_args
+        }
+        cloned_metadata = {
+            name: [
+                parse_metadata(a) for a in pytree.tree_leaves(pre_arguments.get(name))
+            ]
+            for name in pre_arguments
+        }
+
+        out = func(*args, **kwargs)
+        arguments = {
+            name: tree_map(unwrap, pre_arguments.get(name)) for name in pre_arguments
+        }
+        tuple_out = out if isinstance(out, tuple) else (out,)
+        tuple_out = tree_map(unwrap, tuple_out)
+
+        schema_info = SchemaInfo(func._schema)
+        schema_info.add_argument_values(pre_arguments)
+
+        # Process arguments with outputs
+        for i in range(len(func._schema.arguments)):
+            arg = func._schema.arguments[i]
+            name = standardize_name(arg.name)
+            if arguments.get(name) is not None:
+                before = cloned_arguments.get(name)
+                md = cloned_metadata.get(name)
+                after = arguments.get(name)
+                for j in range(len(tuple_out)):
+                    # aten::_unsafe_view is intended to have incorrect aliasing notation (hence unsafe)
+                    unsafe_ops = ("aten::_unsafe_view", "aten::unsafe_split")
+                    if (
+                        has_aliased(tuple_out[j], after)
+                        and func._schema.name not in unsafe_ops
+                    ):
+                        if not schema_info.may_contain_alias(
+                            SchemaArgument(SchemaArgType.output, j),
+                            SchemaArgument(SchemaArgType.input, i),
+                        ):
+                            raise RuntimeError(
+                                f"Argument {name} is not defined to alias output but was aliasing"
+                            )
+                        else:
+                            self.aliasing.append(
+                                Aliasing(func._schema.name, name, f"output_{j}")
+                            )
+                    if after is tuple_out[j] and isinstance(after, torch.Tensor):
+                        # Only mutable ops e.g. (add_, add.out) are allowed to directly return inputs.
+                        if not schema_info.is_mutable(
+                            SchemaArgument(SchemaArgType.input, i)
+                        ) and func not in [
+                            torch.ops.aten.lift.default,
+                            torch.ops.aten.lift_fresh.default,
+                        ]:
+                            raise RuntimeError(
+                                f"""\
+Dispatcher operators below autograd are not allowed to directly return inputs.
+However, we found that `outputs[{str(j)}] is {name}"""
+                            )
+                if any(
+                    has_mutated(a, b, c)
+                    for a, b, c in zip(
+                        pytree.tree_leaves(before), pytree.tree_leaves(after), md
+                    )
+                ):
+                    if not schema_info.is_mutable(
+                        SchemaArgument(SchemaArgType.input, i)
+                    ):
+                        raise RuntimeError(
+                            f"Argument {name} is not defined as mutable but was mutated"
+                        )
+                    else:
+                        self.mutated.append(Mutation(func._schema.name, name))
+
+        # Aliasing between outputs
+        for i, j in combinations(range(len(func._schema.returns)), 2):
+            if has_aliased(tuple_out[i], tuple_out[j]):
+                if not schema_info.may_contain_alias(
+                    SchemaArgument(SchemaArgType.output, i),
+                    SchemaArgument(SchemaArgType.output, j),
+                ):
+                    raise RuntimeError(f"Outputs {i} and {j} alias unexpectedly")
+
+        return out