Source code for torch.overrides
"""
Python implementation of ``__torch_function__``
While most of the torch API and handling for ``__torch_function__`` happens
at the C++ level, some of the torch API is written in Python so we need
python-level handling for ``__torch_function__`` overrides as well. The main
developer-facing functionality in this file are handle_torch_function and
has_torch_function. See torch/functional.py and test/test_overrides.py
for usage examples.
Note
----
heavily inspired by NumPy's ``__array_function__`` (see:
https://github.com/pytorch/pytorch/issues/24015 and
https://www.numpy.org/neps/nep-0018-array-function-protocol.html
)
If changing this file in a way that can affect ``__torch_function__`` overhead,
please report the benchmarks in ``benchmarks/overrides_benchmark``. See the
instructions in the ``README.md`` in that directory.
"""
import __future__ # noqa: F404
import collections
import contextlib
import functools
import types
import warnings
from functools import wraps
from typing import Any, Callable, Dict, Iterable, List, Set, Tuple, Type
import torch
from torch._C import (
_add_docstr,
_get_function_stack_at,
_has_torch_function,
_has_torch_function_unary,
_has_torch_function_variadic,
_is_torch_function_mode_enabled,
_len_torch_function_stack,
_pop_torch_function_stack,
_push_on_torch_function_stack,
)
__all__ = [
"get_ignored_functions",
"get_overridable_functions",
"get_testing_overrides",
"handle_torch_function",
"has_torch_function",
"resolve_name",
"is_tensor_like",
"is_tensor_method_or_property",
"wrap_torch_function",
"enable_reentrant_dispatch",
]
def _disable_user_warnings(
func: Callable,
regex: str = ".*is deprecated, please use.*",
module: str = "torch",
) -> Callable:
"""
Decorator that temporarily disables ``UserWarning``s for the given ``module`` if the warning message matches the
given ``regex`` pattern.
Arguments
---------
func : function
Function to disable the warnings for.
regex : str
A regex pattern compilable by ``re.compile``. This is used to match the ``UserWarning`` message.
module : str
The python module to which the filtering should be restricted.
Returns
-------
function
The wrapped function.
"""
@wraps(func)
def wrapper(*args, **kwargs):
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore", category=UserWarning, message=regex, module=module
)
return func(*args, **kwargs)
return wrapper
[docs]@functools.lru_cache(None)
@_disable_user_warnings
def get_ignored_functions() -> Set[Callable]:
"""
Return public functions that cannot be overridden by ``__torch_function__``.
Returns
-------
Set[Callable]
A tuple of functions that are publicly available in the torch API but cannot
be overridden with ``__torch_function__``. Mostly this is because none of the
arguments of these functions are tensors or tensor-likes.
Examples
--------
>>> torch.Tensor.as_subclass in torch.overrides.get_ignored_functions()
True
>>> torch.add in torch.overrides.get_ignored_functions()
False
"""
Tensor = torch.Tensor
return {
torch.typename,
torch.is_tensor,
torch.is_storage,
torch.set_default_tensor_type,
torch.set_default_device,
torch.get_default_device,
torch.set_rng_state,
torch.get_rng_state,
torch.manual_seed,
torch.initial_seed,
torch.seed,
torch.save,
torch.load,
torch.set_printoptions,
torch.fork,
torch.get_default_dtype,
torch.get_num_interop_threads,
torch.get_num_threads,
torch.init_num_threads,
torch.import_ir_module,
torch.import_ir_module_from_buffer,
torch.is_anomaly_enabled,
torch.is_anomaly_check_nan_enabled,
torch.is_grad_enabled,
torch.merge_type_from_type_comment,
torch.parse_ir,
torch.parse_schema,
torch.parse_type_comment,
torch.set_anomaly_enabled,
torch.set_flush_denormal,
torch.set_num_interop_threads,
torch.set_num_threads,
torch.wait,
torch.as_tensor,
torch.from_numpy,
torch.get_device,
torch.tensor,
torch.default_generator,
torch.has_cuda,
torch.has_cudnn,
torch.has_lapack,
torch.device,
torch.dtype,
torch.finfo,
torch.has_mkl,
torch.has_mps,
torch.has_mkldnn,
torch.has_openmp,
torch.iinfo,
torch.memory_format,
torch.qscheme,
torch.set_grad_enabled,
torch.no_grad,
torch.enable_grad,
torch.inference_mode,
torch.is_inference_mode_enabled,
torch.layout,
torch.align_tensors,
torch.arange,
torch.as_strided,
torch.bartlett_window,
torch.blackman_window,
torch.broadcast_shapes,
torch.can_cast,
torch.compile,
torch.cudnn_affine_grid_generator,
torch.cudnn_batch_norm,
torch.cudnn_convolution,
torch.cudnn_convolution_transpose,
torch.cudnn_convolution_relu,
torch.cudnn_convolution_add_relu,
torch.cudnn_grid_sampler,
torch.cudnn_is_acceptable,
torch.empty,
torch.empty_permuted,
torch.empty_strided,
torch.empty_quantized,
torch.export.export,
torch.export.load,
torch.export.register_dataclass,
torch.export.save,
torch.eye,
torch.fft.fftfreq,
torch.fft.rfftfreq,
torch.from_file,
torch.full,
torch.fill,
torch.hamming_window,
torch.hann_window,
torch.kaiser_window,
torch.linspace,
torch.logspace,
torch.mkldnn_adaptive_avg_pool2d,
torch.mkldnn_convolution,
torch.mkldnn_max_pool2d,
torch.mkldnn_max_pool3d,
torch.mkldnn_linear_backward_weights,
torch.mkldnn_rnn_layer,
torch.normal,
torch.ones,
torch.promote_types,
torch.rand,
torch.randn,
torch.randint,
torch.randperm,
torch.range,
torch.result_type,
torch.scalar_tensor,
torch.sparse_coo_tensor,
torch.sparse_compressed_tensor,
torch.sparse_csr_tensor,
torch.sparse_csc_tensor,
torch.sparse_bsr_tensor,
torch.sparse_bsc_tensor,
torch.sym_constrain_range,
torch.sym_constrain_range_for_size,
torch.tril_indices,
torch.triu_indices,
torch.vander,
torch.zeros,
torch._jit_internal.boolean_dispatch,
torch.nn.functional.assert_int_or_pair,
torch.nn.functional.upsample,
torch.nn.functional.upsample_bilinear,
torch.nn.functional.upsample_nearest,
torch.nn.functional.has_torch_function,
torch.nn.functional.has_torch_function_unary,
torch.nn.functional.has_torch_function_variadic,
torch.nn.functional.handle_torch_function,
torch.nn.functional.sigmoid,
torch.nn.functional.hardsigmoid,
torch.nn.functional.tanh,
torch.nn.functional._canonical_mask,
torch.nn.functional._none_or_dtype,
# Doesn't actually take or return tensor arguments
torch.nn.init.calculate_gain,
# These are deprecated; don't test them
torch.nn.init.uniform,
torch.nn.init.normal,
torch.nn.init.constant,
torch.nn.init.eye,
torch.nn.init.dirac,
torch.nn.init.xavier_uniform,
torch.nn.init.xavier_normal,
torch.nn.init.kaiming_uniform,
torch.nn.init.kaiming_normal,
torch.nn.init.orthogonal,
torch.nn.init.sparse,
torch.nested.to_padded_tensor,
has_torch_function,
handle_torch_function,
torch.set_autocast_enabled,
torch.is_autocast_enabled,
torch.set_autocast_dtype,
torch.get_autocast_dtype,
torch.clear_autocast_cache,
torch.set_autocast_cpu_enabled,
torch.is_autocast_cpu_enabled,
torch.set_autocast_xla_enabled,
torch.is_autocast_xla_enabled,
torch.set_autocast_ipu_enabled,
torch.is_autocast_ipu_enabled,
torch.set_autocast_cpu_dtype,
torch.get_autocast_cpu_dtype,
torch.set_autocast_ipu_dtype,
torch.get_autocast_ipu_dtype,
torch.get_autocast_gpu_dtype,
torch.set_autocast_gpu_dtype,
torch.get_autocast_xla_dtype,
torch.set_autocast_xla_dtype,
torch.autocast_increment_nesting,
torch.autocast_decrement_nesting,
torch.is_autocast_cache_enabled,
torch.set_autocast_cache_enabled,
torch.nn.functional.hardswish,
torch.is_vulkan_available,
torch.are_deterministic_algorithms_enabled,
torch.use_deterministic_algorithms,
torch.is_deterministic_algorithms_warn_only_enabled,
torch.set_deterministic_debug_mode,
torch.get_device_module,
torch.get_deterministic_debug_mode,
torch.set_float32_matmul_precision,
torch.get_float32_matmul_precision,
torch.unify_type_list,
torch.is_warn_always_enabled,
torch.set_warn_always,
torch.vitals_enabled,
torch.set_vital,
torch.read_vitals,
torch.vmap,
torch.cond,
torch.frombuffer,
torch.asarray,
torch._functional_sym_constrain_range,
torch._make_dep_token,
Tensor.__delitem__,
Tensor.__dir__,
Tensor.__getattribute__,
Tensor.__init__,
Tensor.__iter__,
Tensor.__init_subclass__,
Tensor.__delattr__,
Tensor.__setattr__,
Tensor.__torch_function__,
Tensor.__torch_dispatch__,
Tensor.__new__,
Tensor.__class__,
Tensor.__subclasshook__,
Tensor.__hash__,
Tensor.as_subclass,
Tensor.eig,
Tensor.lstsq,
Tensor.reinforce,
Tensor.new,
Tensor.new_tensor,
Tensor.new_empty,
Tensor.new_empty_strided,
Tensor.new_zeros,
Tensor.new_ones,
Tensor.new_full,
Tensor._make_subclass,
Tensor.solve,
Tensor.symeig,
Tensor.stride,
Tensor.unflatten,
Tensor.to_sparse_coo,
Tensor.to_sparse_csr,
Tensor.to_sparse_csc,
Tensor.to_sparse_bsr,
Tensor.to_sparse_bsc,
Tensor._to_sparse,
Tensor._to_sparse_csr,
Tensor._to_sparse_csc,
Tensor._to_sparse_bsr,
Tensor._to_sparse_bsc,
Tensor._typed_storage,
Tensor._reduce_ex_internal,
Tensor._fix_weakref,
Tensor._view_func,
Tensor._view_func_unsafe,
Tensor._rev_view_func_unsafe,
Tensor._make_wrapper_subclass,
Tensor._python_dispatch.__get__,
Tensor._has_symbolic_sizes_strides.__get__,
Tensor._conj,
Tensor._conj_physical,
Tensor._lazy_clone,
Tensor._neg_view,
Tensor._is_zerotensor,
Tensor._is_all_true,
Tensor._is_any_true,
Tensor._addmm_activation,
Tensor.to_padded_tensor,
Tensor._use_count,
}
@functools.lru_cache(None)
def get_default_nowrap_functions() -> Set[Callable]:
"""
Return public functions that do not wrap in a subclass when invoked by
the default ``Tensor.__torch_function__`` that preserves subclasses. Typically,
these functions represent field accesses (i.e., retrieving a Tensor that
is stored somewhere on the Tensor) as opposed to computation. Users of
these functions expect object identity to be preserved over multiple accesses
(e.g., ``a.grad is a.grad``) which cannot be upheld if we're wrapping on
the fly every time (furthermore, the tensor stored here might already be
the subclass, in which case wrapping really ought not to happen).
Not ALL property accessors have this property; for example ``Tensor.T`` actually
just creates a new transposed tensor on the fly, and so we SHOULD interpose on
these calls (you need to check the implementation of the function to see if
this is the case or not). Additionally, if a property accessor doesn't return a Tensor,
it doesn't have to be on this list (though it is harmless if it is).
"""
Tensor = torch.Tensor
return {
Tensor._base.__get__,
Tensor.grad.__get__,
Tensor._grad.__get__,
}
[docs]@functools.lru_cache(None)
@_disable_user_warnings
def get_testing_overrides() -> Dict[Callable, Callable]:
"""Return a dict containing dummy overrides for all overridable functions
Returns
-------
Dict[Callable, Callable]
A dictionary that maps overridable functions in the PyTorch API to
lambda functions that have the same signature as the real function
and unconditionally return -1. These lambda functions are useful
for testing API coverage for a type that defines ``__torch_function__``.
Examples
--------
>>> import inspect
>>> my_add = torch.overrides.get_testing_overrides()[torch.add]
>>> inspect.signature(my_add)
<Signature (input, other, out=None)>
"""
# Every function in the PyTorchAPI that can be overriden needs an entry
# in this dict.
#
# Optimally we would use inspect to get the function signature and define
# the lambda function procedurally but that is blocked by generating
# function signatures for native kernels that can be consumed by inspect.
# See Issue #28233.
Tensor = torch.Tensor
ret: Dict[Callable, Callable] = {
torch.abs: lambda input, out=None: -1,
torch.absolute: lambda input, out=None: -1,
torch.adaptive_avg_pool1d: lambda input, output_size: -1,
torch.adaptive_max_pool1d: lambda inputs, output_size: -1,
torch.acos: lambda input, out=None: -1,
torch.adjoint: lambda input: -1,
torch.arccos: lambda input, out=None: -1,
torch.acosh: lambda input, out=None: -1,
torch.arccosh: lambda input, out=None: -1,
torch.add: lambda input, other, out=None: -1,
torch.addbmm: lambda input, batch1, batch2, alpha=1, beta=1, out=None: -1,
torch.addcdiv: lambda input, tensor1, tensor2, value=1, out=None: -1,
torch.addcmul: lambda input, tensor1, tensor2, value=1, out=None: -1,
torch.addmm: lambda input, mat1, mat2, beta=1, alpha=1, out=None: -1,
torch.addmv: lambda input, mat, vec, beta=1, alpha=1, out=None: -1,
torch.addr: lambda input, vec1, vec2, beta=1, alpha=1, out=None: -1,
torch.affine_grid_generator: lambda theta, size, align_corners: -1,
torch.all: lambda input, dim=None: -1,
torch.allclose: lambda input, other, trol=1e-05, atol=1e-08, equal_nan=False: -1,
torch.alpha_dropout: lambda input, p, train, inplace=False: -1,
torch.amax: lambda input, dim=None: -1,
torch.amin: lambda input, dim=None: -1,
torch.aminmax: lambda input, dim=None, keepdim=False, out=None: -1,
torch.angle: lambda input, out=None: -1,
torch.any: lambda input, dim=None, keepdim=False, out=None: -1,
torch.argmax: lambda input: -1,
torch.argmin: lambda input: -1,
torch.argsort: lambda input, dim=None: -1,
torch.asin: lambda input, out=None: -1,
torch._assert_async: lambda input, msg: -1,
torch.arcsin: lambda input, out=None: -1,
torch.asinh: lambda input, out=None: -1,
torch.arcsinh: lambda input, out=None: -1,
torch.atan: lambda input, out=None: -1,
torch.arctan: lambda input, out=None: -1,
torch.atan2: lambda input, other, out=None: -1,
torch.arctan2: lambda input, other, out=None: -1,
torch.atanh: lambda input, out=None: -1,
torch.arctanh: lambda input, out=None: -1,
torch.atleast_1d: lambda *tensors: -1,
torch.atleast_2d: lambda *tensors: -1,
torch.atleast_3d: lambda *tensors: -1,
torch.avg_pool1d: lambda input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True: -1,
torch.baddbmm: lambda input, batch1, batch2, alpha=1, beta=1, out=None: -1,
torch.batch_norm: lambda input, weight, bias, running_mean, running_var, training, momentum, eps, cudnn_enabled: -1,
torch.batch_norm_backward_elemt: lambda grad_out, input, mean, invstd, weight, sum_dy, sum_dy_xmu, count_tensor: -1,
torch.batch_norm_backward_reduce: lambda grad_out, input, mean, invstd, weight, input_g, weight_g, bias_g: -1,
torch.batch_norm_elemt: lambda input, weight, bias, mean, invstd, eps: -1,
torch.batch_norm_gather_stats: lambda input, mean, invstd, running_mean, running_var, momentum, eps, count: -1,
torch.batch_norm_gather_stats_with_counts: lambda input, mean, invstd, running_mean, running_var, momentum, eps, count: -1,
torch.batch_norm_stats: lambda input, eps: -1,
torch.batch_norm_update_stats: lambda input, running_mean, running_var, momentum: -1,
torch.bernoulli: lambda input, generator=None, out=None: -1,
torch.bilinear: lambda input1, input2, weight, bias: -1,
torch.binary_cross_entropy_with_logits: (
lambda input, target, weight=None, size_average=None, reduce=None, reduction="mean", pos_weight=None: -1
),
torch.bincount: lambda input, weights=None, minlength=0: -1,
torch.binomial: lambda count, prob, generator=None: -1,
torch.bitwise_and: lambda input, other, out=None: -1,
torch.bitwise_not: lambda input, out=None: -1,
torch.bitwise_or: lambda input, other, out=None: -1,
torch.bitwise_xor: lambda input, other, out=None: -1,
torch.bitwise_left_shift: lambda input, other, out=None: -1,
torch.bitwise_right_shift: lambda input, other, out=None: -1,
torch.block_diag: lambda *tensors: -1,
torch.bmm: lambda input, mat2, out=None: -1,
torch.broadcast_tensors: lambda *tensors: -1,
torch.broadcast_to: lambda self, size: -1,
torch.bucketize: lambda input, boundaries, out_int32=False, right=False, out=None: -1,
torch.cartesian_prod: lambda *tensors: -1,
torch.cat: lambda tensors, dim=0, out=None: -1,
torch.concat: lambda tensors, dim=0, out=None: -1, # alias for torch.cat
torch.concatenate: lambda tensors, dim=0, out=None: -1, # alias for torch.concatenate
torch.cdist: lambda x1, x2, p=2.0, compute_mode="use_mm_for_euclid_dist_if_necessary": -1,
torch.ceil: lambda input, out=None: -1,
torch.celu: lambda input, alpha=1.0, inplace=False: -1,
torch.chain_matmul: lambda *matrices, out=None: -1,
torch.channel_shuffle: lambda input, groups: -1,
torch.cholesky: lambda input, upper=False, out=None: -1,
torch.linalg.cholesky: lambda input, out=None: -1,
torch.linalg.cholesky_ex: lambda input, check_errors=False, out=None: -1,
torch.cholesky_inverse: lambda input, upper=False, out=None: -1,
torch.cholesky_solve: lambda input1, input2, upper=False, out=None: -1,
torch.choose_qparams_optimized: lambda input, numel, n_bins, ratio, bit_width: -1,
torch.chunk: lambda input, chunks, dim=0: -1,
torch.clamp: lambda input, min=None, max=None, out=None: -1,
torch.clip: lambda input, min=None, max=None, out=None: -1,
torch.clamp_min: lambda input, min, out=None: -1,
torch.clamp_max: lambda input, max, out=None: -1,
torch.column_stack: lambda tensors, out=None: -1,
torch.cov: lambda input, correction=1, fweights=None, aweights=None: -1,
torch.clone: lambda input: -1,
torch.combinations: lambda input, r=2, with_replacement=False: -1,
torch.complex: lambda real, imag: -1,
torch.copysign: lambda input, other, out=None: -1,
torch.polar: lambda abs, ang: -1,
torch.linalg.cond: lambda input, ord=None: -1,
torch.conj: lambda input, out=None: -1,
torch.conj_physical: lambda input, out=None: -1,
torch.resolve_conj: lambda input, out=None: -1,
torch.resolve_neg: lambda input, out=None: -1,
torch.constant_pad_nd: lambda input, pad, value=0: -1,
torch.conv1d: lambda input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1: -1,
torch.conv2d: lambda input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1: -1,
torch.conv3d: lambda input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1: -1,
torch.convolution: lambda input, weight, bias, stride, padding, dilation, transposed, output_adding, groups: -1,
torch.conv_tbc: lambda input, weight, bias, pad=0: -1,
torch.conv_transpose1d: lambda input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1: -1,
torch.conv_transpose2d: lambda input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1: -1,
torch.conv_transpose3d: lambda input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1: -1,
torch.corrcoef: lambda input: -1,
torch.cos: lambda input, out=None: -1,
torch.cosine_embedding_loss: lambda input1, input2, target, margin=0, size_average=None, reduce=None, reduction="mean": -1,
torch.cosh: lambda input, out=None: -1,
torch.cosine_similarity: lambda x1, x2, dim=1, eps=1e-8: -1,
torch.count_nonzero: lambda input: -1,
torch.cross: lambda input, other, dim=None, out=None: -1,
torch.linalg.cross: lambda input, other, dim=-1, out=None: -1,
torch.ctc_loss: (
lambda log_probs, targets, input_lengths, target_lengths, blank=0, reduction="mean", zero_infinity=False: -1
),
torch.cummax: lambda input, dim, out=None: -1,
torch.cummin: lambda input, dim, out=None: -1,
torch.cumprod: lambda input, dim, out=None, dtype=None: -1,
torch.cumsum: lambda input, dim, out=None, dtype=None: -1,
torch.cumulative_trapezoid: lambda y, x=None, dim=-1: -1,
torch.logcumsumexp: lambda input, dim, out=None: -1,
torch.deg2rad: lambda input, out=None: -1,
torch.dequantize: lambda input: -1,
torch.det: lambda input: -1,
torch.linalg.det: lambda input: -1, # alias for torch.det # type: ignore[attr-defined]
torch.detach: lambda input: -1,
torch.diag: lambda input, diagonal=0, out=None: -1,
torch.diag_embed: lambda input, diagonal=0, out=None: -1,
torch.diagflat: lambda input, offset=0: -1,
torch.diff: lambda input, n=1, dim=-1, prepend=None, append=None, out=None: -1,
torch.diagonal: lambda input, offset=0, dim1=0, dim2=1: -1,
torch.linalg.diagonal: lambda input, offset=0, dim1=-2, dim2=-1: -1,
torch.diagonal_scatter: lambda input, src, offset=0, dim1=0, dim2=1: -1,
torch.as_strided_scatter: lambda self, src, size, stride, storage_offset=None: -1,
torch.digamma: lambda input, out=None: -1,
torch.dist: lambda input, other, p=2: -1,
torch.div: lambda input, other, rounding_mode=None, out=None: -1,
torch.divide: lambda input, other, rounding_mode=None, out=None: -1,
torch.dot: lambda input, other, out=None: -1,
torch.dropout: lambda input, p, train, inplace=False: -1,
torch.dsmm: lambda input, mat2: -1,
torch.hsmm: lambda mat1, mat2: -1,
torch.dsplit: lambda input, indices_or_sections: -1,
torch.dstack: lambda tensors, out=None: -1,
torch.linalg.eig: lambda input, out=None: -1,
torch.linalg.eigvals: lambda input, out=None: -1,
torch.linalg.eigh: lambda input, UPLO="L", out=None: -1,
torch.linalg.eigvalsh: lambda input, UPLO="L", out=None: -1,
torch.einsum: lambda equation, *operands: -1,
torch.embedding: (
lambda input, weight, padding_idx=None, max_norm=None, norm_type=2.0, scale_grad_by_freq=False, sparse=False: -1 # noqa: B950
),
torch.embedding_bag: (
lambda input, weight, offsets, max_norm=None, norm_type=2, scale_grad_by_freq=False, mode="mean", sparse=False, per_sample_weights=None, padding_idx=None: -1 # noqa: B950
),
torch.empty_like: lambda input, dtype=None, layout=None, device=None, requires_grad=False: -1,
torch.eq: lambda input, other, out=None: -1,
torch.equal: lambda input, other: -1,
torch.erf: lambda input, out=None: -1,
torch.erfc: lambda input, out=None: -1,
torch.erfinv: lambda input, out=None: -1,
torch.exp: lambda input, out=None: -1,
torch.exp2: lambda input, out=None: -1,
torch.expm1: lambda input, out=None: -1,
torch.fake_quantize_per_channel_affine: lambda input, scale, zero_point, axis, quant_min, quant_max: -1,
torch.fake_quantize_per_tensor_affine: lambda input, scale, zero_point, quant_min, quant_max: -1,
torch.fused_moving_avg_obs_fake_quant: (
lambda x, observer_on, fake_quant_on, averaging_const, running_min, running_max, scale, zero_point, quant_min, quant_max, ch_axis, per_row_fake_quant=False, symmetric_quant=False: -1 # noqa: B950
),
torch.fbgemm_linear_fp16_weight: lambda input, packed_weight, bias: -1,
torch.fbgemm_linear_fp16_weight_fp32_activation: lambda input, packed_weight, bias: -1,
torch.fbgemm_linear_int8_weight: lambda input, weight, packed, col_offsets, weight_scale, weight_zero_point, bias: -1, # noqa: B950
torch.fbgemm_linear_int8_weight_fp32_activation: (
lambda input, weight, packed, col_offsets, weight_scale, weight_zero_point, bias: -1
),
torch.fbgemm_linear_quantize_weight: lambda input: -1,
torch.fbgemm_pack_gemm_matrix_fp16: lambda input: -1,
torch.fbgemm_pack_quantized_matrix: lambda input, a, b: -1,
torch.feature_alpha_dropout: lambda input, p, train: -1,
torch.feature_dropout: lambda input, p, train: -1,
torch.fft.ifft: lambda input, n=None, dim=-1, norm=None: -1,
torch.fft.rfft: lambda input, n=None, dim=-1, norm=None: -1,
torch.fft.irfft: lambda input, n=None, dim=-1, norm=None: -1,
torch.fft.hfft: lambda input, n=None, dim=-1, norm=None: -1,
torch.fft.ihfft: lambda input, n=None, dim=-1, norm=None: -1,
torch.fft.hfft2: lambda input, s=None, dim=(-2, -1), norm=None: -1,
torch.fft.ihfft2: lambda input, s=None, dim=(-2, -1), norm=None: -1,
torch.fft.hfftn: lambda input, s=None, dim=-1, norm=None: -1,
torch.fft.ihfftn: lambda input, s=None, dim=-1, norm=None: -1,
torch.fft.fftn: lambda input, s=None, dim=None, norm=None: -1,
torch.fft.ifftn: lambda input, s=None, dim=None, norm=None: -1,
torch.fft.rfftn: lambda input, s=None, dim=None, norm=None: -1,
torch.fft.irfftn: lambda input, s=None, dim=None, norm=None: -1,
torch.fft.fft2: lambda input, s=None, dim=(-2, -1), norm=None: -1,
torch.fft.ifft2: lambda input, s=None, dim=(-2, -1), norm=None: -1,
torch.fft.rfft2: lambda input, s=None, dim=(-2, -1), norm=None: -1,
torch.fft.irfft2: lambda input, s=None, dim=(-2, -1), norm=None: -1,
torch.fft.fftshift: lambda input, dim=None: -1,
torch.fft.ifftshift: lambda input, dim=None: -1,
torch.fft.fft: lambda input, n=None, dim=-1, norm=None: -1,
torch.fix: lambda input, out=None: -1,
torch.flatten: lambda input, start_dim=0, end_dim=-1: -1,
torch.flip: lambda input, dims: -1,
torch.fliplr: lambda input: -1,
torch.flipud: lambda input: -1,
torch.frobenius_norm: lambda input, dim=None, keepdim=False, out=None: -1,
torch.floor: lambda input, out=None: -1,
torch.floor_divide: lambda input, other: -1,
torch.float_power: lambda input, exponent, out=None: -1,
torch.fmod: lambda input, other, out=None: -1,
torch.frac: lambda input, out=None: -1,
torch.frexp: lambda input, out=None: -1,
torch.full_like: lambda input, fill_value, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False: -1, # noqa: B950
torch._functional_assert_async: lambda input, msg, dep_token: -1,
torch.lu_unpack: lambda LU_data, LU_pivots, unpack_data=True, unpack_pivots=True: -1,
torch.gather: lambda input, dim, index, out=None, sparse_grad=False: -1,
torch.gcd: lambda input, other, out=None: -1,
torch.ge: lambda input, other, out=None: -1,
torch.greater_equal: lambda input, other, out=None: -1,
torch.geqrf: lambda input, out=None: -1,
torch.i0: lambda input, out=None: -1,
torch.inner: lambda input, other, out=None: -1,
torch.outer: lambda input, vec2, out=None: -1,
torch.ger: lambda input, vec2, out=None: -1, # alias for torch.outer
torch.gradient: lambda input, spacing=None, dim=None, edge_order=1: -1,
torch.grid_sampler: lambda input, grid, interpolation_mode, padding_mode, align_corners: -1,
torch.grid_sampler_2d: lambda input, grid, interpolation_mode, padding_mode, align_corners: -1,
torch.grid_sampler_3d: lambda input, grid, interpolation_mode, padding_mode, align_corners: -1,
torch.group_norm: lambda input, num_groups, weight=None, bias=None, eps=1e-05, cudnn_enabled=True: -1,
torch.gru: lambda input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first: -1,
torch.gru_cell: lambda input, hx, w_ih, w_hh, b_ih=None, b_hh=None: -1,
torch.gt: lambda input, other, out=None: -1,
torch.greater: lambda input, other, out=None: -1,
torch.hardshrink: lambda input, lambd=0.5: -1,
torch.heaviside: lambda input, values, out=None: -1,
torch.hinge_embedding_loss: lambda input, target, margin=1.0, size_average=None, reduce=None, reduction="mean": -1, # noqa: B950
torch.histc: lambda input, bins=100, min=0, max=0, out=None: -1,
torch.histogram: lambda input, bins=100, min=None, max=None, weight=None, density=False, out=None: -1,
torch.histogramdd: lambda input, bins, range=None, weight=None, density=False: -1,
torch.linalg.householder_product: lambda input, tau: -1,
torch.hspmm: lambda mat1, mat2, out=None: -1,
torch.hsplit: lambda input, indices_or_sections: -1,
torch.hstack: lambda tensors, out=None: -1,
torch.hypot: lambda input, other, out=None: -1,
torch.igamma: lambda input, other, out=None: -1,
torch.igammac: lambda input, other, out=None: -1,
torch.imag: lambda input, out=None: -1,
torch.index_add: lambda input, dim, index, source: -1,
torch.index_copy: lambda input, dim, index, source: -1,
torch.index_put: lambda input, indices, values, accumulate=False: -1,
torch.index_select: lambda input, dim, index, out=None: -1,
torch.index_fill: lambda input, dim, index, value: -1,
torch.index_reduce: lambda input, dim, index, source, reduce, include_input=True: -1,
torch.isfinite: lambda tensor: -1,
torch.isin: lambda e, te, assume_unique=False, invert=False: -1,
torch.isinf: lambda tensor: -1,
torch.isreal: lambda tensor: -1,
torch.isposinf: lambda input, out=None: -1,
torch.isneginf: lambda input, out=None: -1,
torch.instance_norm: (
lambda input, running_mean, running_var, weight, bias, use_input_stats, momentum, eps, cudnn_enabled: -1
),
torch.int_repr: lambda input: -1,
torch.inverse: lambda input, out=None: -1,
torch.linalg.inv: lambda input, out=None: -1,
torch.linalg.inv_ex: lambda input, check_errors=False, out=None: -1,
torch.is_complex: lambda input: -1,
torch.is_conj: lambda input: -1,
torch.is_neg: lambda input: -1,
torch.is_distributed: lambda input: -1,
torch.is_inference: lambda input: -1,
torch.is_floating_point: lambda input: -1,
torch.is_nonzero: lambda input: -1,
torch.is_same_size: lambda input, other: -1,
torch.is_signed: lambda input: -1,
torch.isclose: lambda input, other, rtol=1e-05, atol=1e-08, equal_nan=False: -1,
torch.isnan: lambda input: -1,
torch.istft: (
lambda input, n_fft, hop_length=None, win_length=None, window=None, center=True, normalized=False, onesided=None, length=None, return_complex=False: -1 # noqa: B950
),
torch.kl_div: lambda input, target, size_average=None, reduce=None, reduction="mean", log_target=False: -1,
torch.kron: lambda input, other: -1,
torch.kthvalue: lambda input, k, dim=None, keepdim=False, out=None: -1,
torch.linalg.ldl_factor_ex: lambda input, hermitian=False, check_errors=False, out=None: -1,
torch.linalg.ldl_factor: lambda input, hermitian=False, out=None: -1,
torch.linalg.ldl_solve: lambda LD, pivots, B, hermitian=False, out=None: -1,
torch.layer_norm: lambda input, normalized_shape, weight=None, bias=None, esp=1e-05, cudnn_enabled=True: -1,
torch.lcm: lambda input, other, out=None: -1,
torch.ldexp: lambda input, other, out=None: -1,
torch.le: lambda input, other, out=None: -1,
torch.less_equal: lambda input, other, out=None: -1,
torch.lerp: lambda input, end, weight, out=None: -1,
torch.lgamma: lambda input, out=None: -1,
torch.lobpcg: lambda input, k=None, B=None, X=None, n=None, iK=None, niter=None, tol=None, largest=None, method=None, tracker=None, ortho_iparams=None, ortho_fparams=None, ortho_bparams=None: -1, # noqa: B950
torch.log: lambda input, out=None: -1,
torch.log_softmax: lambda input, dim, dtype=None: -1,
torch.log10: lambda input, out=None: -1,
torch.log1p: lambda input, out=None: -1,
torch.log2: lambda input, out=None: -1,
torch.logaddexp: lambda input, other, out=None: -1,
torch.logaddexp2: lambda input, other, out=None: -1,
torch.logdet: lambda input: -1,
torch.xlogy: lambda x, y, out=None: -1,
torch.logical_and: lambda input, other, out=None: -1,
torch.logical_not: lambda input, out=None: -1,
torch.logical_or: lambda input, other, out=None: -1,
torch.logical_xor: lambda input, other, out=None: -1,
torch.logit: lambda input, eps=None: -1,
torch.logsumexp: lambda input, names, keepdim=False, out=None: -1,
torch.lstm: lambda data, batch_sizes, hx, params, has_biases, num_layers, dropout, train, bidirectional: -1,
torch.lstm_cell: lambda input, hx, w_ih, w_hh, b_ih=None, b_hh=None: -1,
torch.lt: lambda input, other, out=None: -1,
torch.less: lambda input, other, out=None: -1,
torch.lu: lambda A, pivot=True, get_infos=False, out=None: -1,
torch.lu_solve: lambda b, LU_data, LU_pivots, out=None: -1,
torch.margin_ranking_loss: lambda input1, input2, target, margin=0, size_average=None, reduce=None, reduction="mean": -1, # type: ignore[attr-defined] # noqa: B950
torch.masked_fill: lambda input, mask, value: -1,
torch.masked_scatter: lambda input, mask, source: -1,
torch.masked_select: lambda input, mask, out=None: -1,
torch.matmul: lambda input, other, out=None: -1,
torch.linalg.lu: lambda input, pivot=True, out=None: -1,
torch.linalg.lu_factor: lambda input, pivot=True, out=None: -1,
torch.linalg.lu_factor_ex: lambda input, pivot=True, check_errors=False, out=None: -1,
torch.linalg.lu_solve: lambda LU, pivots, B, left=True, adjoint=False, out=None: -1,
torch.linalg.matmul: lambda input, other, out=None: -1, # alias for torch.matmul
torch.matrix_power: lambda input, n: -1,
torch.linalg.matrix_power: lambda input, n, out=None: -1,
torch.linalg.matrix_rank: lambda input, tol=None, hermitian=False: -1,
torch.linalg.multi_dot: lambda tensors, out=None: -1,
torch.matrix_exp: lambda input: -1,
torch.linalg.matrix_exp: lambda input: -1,
torch.max: lambda input, out=None: -1,
torch.maximum: lambda input, other, out=None: -1,
torch.fmax: lambda input, other, out=None: -1,
torch.max_pool1d: lambda input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False: -1,
torch.max_pool2d: lambda input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False: -1,
torch.max_pool3d: lambda input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False: -1,
torch.max_pool1d_with_indices: (
lambda input, kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=False: -1
),
torch.mean: lambda input, dim=None: -1,
torch.nanmean: lambda input, dim=None, keepdim=False, dtype=None, out=None: -1,
torch.median: lambda input, dim=None: -1,
torch.nanmedian: lambda input, dim=None: -1,
torch.meshgrid: lambda *tensors, **kwargs: -1,
torch.min: lambda input, out=None: -1,
torch.minimum: lambda input, other, out=None: -1,
torch.fmin: lambda input, other, out=None: -1,
torch.miopen_batch_norm: (
lambda input, weight, bias, running_mean, running_var, training, exponential_average_factor, epsilon: -1
),
torch.miopen_convolution: lambda input, weight, bias, padding, stride, dilation, groups, benchmark, deterministic: -1, # noqa: B950
torch.miopen_convolution_add_relu: lambda input, weight, z, alpha, bias, stride, padding, dilation, groups: -1,
torch.miopen_convolution_relu: lambda input, weight, bias, stride, padding, dilation, groups: -1,
torch.miopen_convolution_transpose: (
lambda input, weight, bias, padding, output_padding, stride, dilation, groups, benchmark, deterministic: -1
),
torch.miopen_depthwise_convolution: (
lambda input, weight, bias, padding, stride, dilation, groups, benchmark, deterministic: -1
),
torch.miopen_rnn: (
lambda input, weight, weight_stride0, hx, cx, mode, hidden_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state: -1 # noqa: B950
),
torch.mm: lambda input, mat2, out=None: -1,
torch.mode: lambda input, dim=-1, keepdim=False, out=None: -1,
torch.movedim: lambda input, source, destination: -1,
torch.moveaxis: lambda input, source, destination: -1,
torch.msort: lambda input, descending=False, out=None: -1,
torch.mul: lambda input, other, out=None: -1,
torch.multiply: lambda input, other, out=None: -1,
torch.multinomial: lambda input, num_samples, replacement=False, out=None: -1,
torch.mv: lambda input, vec, out=None: -1,
torch.mvlgamma: lambda input, p: -1,
torch.narrow: lambda input, dim, start, length: -1,
torch.nan_to_num: lambda input, nan=0.0, posinf=None, neginf=None, out=None: -1,
torch.native_batch_norm: lambda input, weight, bias, running_mean, running_var, training, momentum, eps: -1,
torch._native_batch_norm_legit: lambda input, weight, bias, training, momentum, eps: -1,
torch.native_dropout: lambda input, p, train: -1,
torch.native_layer_norm: lambda input, normalized_shape, weight=None, bias=None, eps=1e-05: -1,
torch.native_group_norm: lambda input, weight, bias, N, C, HxW, group, eps: -1,
torch.native_norm: lambda input, p=2, dim=None, keepdim=False, dtype=None: -1,
torch.native_channel_shuffle: lambda input, groups: -1,
torch.ne: lambda input, other, out=None: -1,
torch.not_equal: lambda input, other, out=None: -1,
torch.neg: lambda input, out=None: -1,
torch.negative: lambda input, out=None: -1,
torch.nextafter: lambda input, other, out=None: -1,
torch.nn.functional.adaptive_avg_pool2d: lambda input, output_size: -1,
torch.nn.functional.adaptive_avg_pool3d: lambda input, output_size: -1,
torch.nn.functional.adaptive_max_pool1d: lambda input, output_size, return_indices=False: -1,
torch.nn.functional.adaptive_max_pool1d_with_indices: lambda input, output_size, return_indices=False: -1,
torch.nn.functional.adaptive_max_pool2d: lambda input, output_size, return_indices=False: -1,
torch.nn.functional.adaptive_max_pool2d_with_indices: lambda input, output_size, return_indices=False: -1,
torch.nn.functional.adaptive_max_pool3d: lambda input, output_size, return_indices=False: -1,
torch.nn.functional.adaptive_max_pool3d_with_indices: lambda input, output_size, return_indices=False: -1,
torch.nn.functional.affine_grid: lambda theta, size, align_corners=None: -1,
torch.nn.functional.alpha_dropout: lambda input, p=0.5, training=False, inplace=False: -1,
torch.nn.functional.avg_pool2d: (
lambda input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True, divisor_override=None: -1 # noqa: B950
),
torch.nn.functional.avg_pool3d: (
lambda input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True, divisor_override=None: -1 # noqa: B950
),
torch.nn.functional.batch_norm: (
lambda input, running_mean, running_var, weight=None, bias=None, training=False, momentum=0.1, eps=1e-05: -1
),
torch.nn.functional.bilinear: lambda input1, input2, weight, bias=None: -1,
torch.nn.functional.binary_cross_entropy: (
lambda input, target, weight=None, size_average=None, reduce=None, reduction="mean": -1
),
torch.nn.functional.binary_cross_entropy_with_logits: (
lambda input, target, weight=None, size_average=None, reduce=None, reduction="mean", pos_weight=None: -1
),
torch.nn.functional.celu: lambda input, alpha=1.0, inplace=False: -1,
torch.nn.functional.cosine_embedding_loss: (
lambda input1, input2, target, margin=0, size_average=None, reduce=None, reduction="mean": -1
),
torch.nn.functional.cross_entropy: (
lambda input, target, weight=None, size_average=None, ignore_index=-100, reduce=None, reduction="mean", label_smoothing=0.0: -1 # noqa: B950
),
torch.nn.functional.ctc_loss: (
lambda log_probs, targets, input_lengths, target_lengths, blank=0, reduction="mean", zero_infinity=False: -1
),
torch.nn.functional.dropout: lambda input, p=0.5, training=True, inplace=False: -1,
torch.nn.functional.dropout1d: lambda input, p=0.5, training=True, inplace=False: -1,
torch.nn.functional.dropout2d: lambda input, p=0.5, training=True, inplace=False: -1,
torch.nn.functional.dropout3d: lambda input, p=0.5, training=True, inplace=False: -1,
torch.nn.functional.elu: lambda input, alpha=1.0, inplace=False: -1,
torch.nn.functional.embedding: (
lambda input, weight, padding_idx=None, max_norm=None, norm_type=2.0, scale_grad_by_freq=False, sparse=False: -1 # noqa: B950
),
torch.nn.functional.embedding_bag: (
lambda input, weight, offsets=None, max_norm=None, norm_type=2, scale_grad_by_freq=False, mode="mean", sparse=False, per_sample_weights=None, include_last_offset=False, padding_idx=None: -1 # noqa: B950
),
torch.nn.functional.feature_alpha_dropout: lambda input, p=0.5, training=False, inplace=False: -1,
torch.nn.functional.fold: lambda input, output_size, kernel_size, dilation=1, padding=0, stride=1: -1,
torch.nn.functional.fractional_max_pool2d: (
lambda input, kernel_size, output_size=None, output_ratio=None, return_indices=False, _random_samples=None: -1 # noqa: B950
),
torch.nn.functional.fractional_max_pool2d_with_indices: (
lambda input, kernel_size, output_size=None, output_ratio=None, return_indices=False, _random_samples=None: -1 # noqa: B950
),
torch.nn.functional.fractional_max_pool3d: (
lambda input, kernel_size, output_size=None, output_ratio=None, return_indices=False, _random_samples=None: -1 # noqa: B950
),
torch.nn.functional.fractional_max_pool3d_with_indices: (
lambda input, kernel_size, output_size=None, output_ratio=None, return_indices=False, _random_samples=None: -1 # noqa: B950
),
torch.nn.functional.gaussian_nll_loss: lambda input, target, var, full=False, eps=1e-06, reduction="mean": -1,
torch.nn.functional.gelu: lambda input, approximate="none": -1,
torch.nn.functional.glu: lambda input, dim=-1: -1,
torch.nn.functional.grid_sample: lambda input, grid, mode="bilinear", padding_mode="zeros", align_corners=None: -1, # noqa: B950
torch.nn.functional.group_norm: lambda input, num_groups, weight=None, bias=None, eps=1e-05: -1,
torch.nn.functional.gumbel_softmax: lambda logits, tau=1, hard=False, eps=1e-10, dim=-1: -1,
torch.nn.functional.hardshrink: lambda input, lambd=0.5: -1,
torch.nn.functional.hardtanh: lambda input, min_val=-1.0, max_val=1.0, inplace=False: -1,
torch.nn.functional.hinge_embedding_loss: (
lambda input, target, margin=1.0, size_average=None, reduce=None, reduction="mean": -1
),
torch.nn.functional.instance_norm: (
lambda input, running_mean=None, running_var=None, weight=None, bias=None, use_input_stats=True, momentum=0.1, eps=1e-05: -1 # noqa: B950
),
torch.nn.functional.interpolate: (
lambda input, size=None, scale_factor=None, mode="nearest", align_corners=None, recompute_scale_factor=None, antialias=False: -1 # noqa: B950
),
torch.nn.functional.kl_div: lambda input, target, size_average=None, reduce=None, reduction="mean", log_target=False: -1, # noqa: B950
torch.nn.functional.l1_loss: lambda input, target, size_average=None, reduce=None, reduction="mean": -1,
torch.nn.functional.layer_norm: lambda input, normalized_shape, weight=None, bias=None, eps=1e-05: -1,
torch.nn.functional.leaky_relu: lambda input, negative_slope=0.01, inplace=False: -1,
torch.nn.functional.linear: lambda input, weight, bias=None: -1,
torch.nn.functional.local_response_norm: lambda input, size, alpha=0.0001, beta=0.75, k=1.0: -1,
torch.nn.functional.log_softmax: lambda input, dim=None, _stacklevel=3, dtype=None: -1,
torch.nn.functional.logsigmoid: lambda input: -1,
torch.nn.functional.lp_pool1d: lambda input, norm_type, kernel_size, stride=None, ceil_mode=False: -1,
torch.nn.functional.lp_pool2d: lambda input, norm_type, kernel_size, stride=None, ceil_mode=False: -1,
torch.nn.functional.lp_pool3d: lambda input, norm_type, kernel_size, stride=None, ceil_mode=False: -1,
torch.nn.functional.margin_ranking_loss: (
lambda input1, input2, target, margin=0, size_average=None, reduce=None, reduction="mean": -1
),
torch.nn.functional.max_pool1d: (
lambda input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False, return_indices=False: -1
),
torch.nn.functional.max_pool1d_with_indices: (
lambda input, kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=False: -1
),
torch.nn.functional.max_pool2d: (
lambda input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False, return_indices=False: -1
),
torch.nn.functional.max_pool2d_with_indices: (
lambda input, kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=False: -1
),
torch.nn.functional.max_pool3d: (
lambda input, kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=False: -1
),
torch.nn.functional.max_pool3d_with_indices: (
lambda input, kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=False: -1
),
torch.nn.functional.max_unpool1d: lambda input, indices, kernel_size, stride=None, padding=0, output_size=None: -1, # noqa: B950
torch.nn.functional.max_unpool2d: lambda input, indices, kernel_size, stride=None, padding=0, output_size=None: -1, # noqa: B950
torch.nn.functional.max_unpool3d: lambda input, indices, kernel_size, stride=None, padding=0, output_size=None: -1, # noqa: B950
torch.nn.functional.mse_loss: lambda input, target, size_average=None, reduce=None, reduction="mean": -1,
torch.nn.functional.multi_head_attention_forward: (
lambda query, key, value, embed_dim_to_check, num_heads, in_proj_weight, in_proj_bias, bias_k, bias_v, add_zero_attn, dropout_p, out_proj_weight, out_proj_bias, training=True, key_padding_mask=None, need_weights=True, attn_mask=None, use_separate_proj_weight=False, q_proj_weight=None, k_proj_weight=None, v_proj_weight=None, static_k=None, static_v=None, average_attn_weights=None, is_causal=False: -1 # noqa: B950
),
torch.nn.functional.multi_margin_loss: (
lambda input, target, p=1, margin=1.0, weight=None, size_average=None, reduce=None, reduction="mean": -1
),
torch.nn.functional.multilabel_margin_loss: (
lambda input, target, size_average=None, reduce=None, reduction="mean": -1
),
torch.nn.functional.multilabel_soft_margin_loss: (
lambda input, target, weight=None, size_average=None, reduce=None, reduction="mean": -1
),
torch.nn.functional.nll_loss: (
lambda input, target, weight=None, size_average=None, ignore_index=-100, reduce=None, reduction="mean": -1
),
torch.nn.functional.normalize: lambda input, p=2, dim=1, eps=1e-12, out=None: -1,
torch.nn.functional.one_hot: lambda tensor, num_classes=-1: -1,
torch.nn.functional.pad: lambda input, pad, mode="constant", value=0: -1,
torch.nn.functional.pairwise_distance: lambda x1, x2, p=2.0, eps=1e-06, keepdim=False: -1,
torch.nn.functional.poisson_nll_loss: (
lambda input, target, log_input=True, full=False, size_average=None, eps=1e-08, reduce=None, reduction="mean": -1 # noqa: B950
),
torch.nn.functional.prelu: lambda input, weight: -1,
torch.nn.functional.relu: lambda input, inplace=False: -1,
torch.nn.functional.relu6: lambda input, inplace=False: -1,
torch.nn.functional.rms_norm: lambda input, normalized_shape, weight=None, eps=1e-6: -1,
torch.nn.functional.rrelu: lambda input, lower=0.125, upper=0.3333333333333333, training=False, inplace=False: -1, # noqa: B950
torch.nn.functional.selu: lambda input, inplace=False: -1,
torch.nn.functional.silu: lambda input, inplace=False: -1,
torch.nn.functional.mish: lambda input, inplace=False: -1,
torch.nn.functional.scaled_dot_product_attention: lambda query, key, value, attn_mask=None, dropout_p=0.0: -1,
torch.nn.functional.smooth_l1_loss: lambda input, target, size_average=None, reduce=None, reduction="mean", beta=1.0: -1, # noqa: B950
torch.nn.functional.huber_loss: lambda input, target, reduction="mean", delta=1.0: -1,
torch.nn.functional.soft_margin_loss: lambda input, target, size_average=None, reduce=None, reduction="mean": -1, # noqa: B950
torch.nn.functional.softmax: lambda input, dim=None, _stacklevel=3, dtype=None: -1,
torch.nn.functional.softmin: lambda input, dim=None, _stacklevel=3, dtype=None: -1,
torch.nn.functional.softplus: lambda input, beta=1, threshold=20: -1,
torch.nn.functional.softshrink: lambda input, lambd=0.5: -1,
torch.nn.functional.softsign: lambda input: -1,
torch.nn.functional.tanhshrink: lambda input: -1,
torch.nn.functional.threshold: lambda input, threshold, value, inplace=False: -1,
torch.nn.functional.triplet_margin_loss: (
lambda anchor, positive, negative, margin=1.0, p=2, eps=1e-06, swap=False, size_average=None, reduce=None, reduction="mean": -1 # noqa: B950
),
torch.nn.functional.triplet_margin_with_distance_loss: (
lambda anchor, positive, negative, *, distance_function=None, margin=1.0, swap=False, reduction="mean": -1
),
torch.nn.functional.unfold: lambda input, kernel_size, dilation=1, padding=0, stride=1: -1,
torch.nn.init.uniform_: lambda tensor, a=0.0, b=1.0, generator=None: -1,
torch.nn.init.normal_: lambda tensor, mean=0.0, std=1.0, generator=None: -1,
torch.nn.init.constant_: lambda tensor, val: -1,
torch.nn.init.kaiming_uniform_: lambda tensor, a=0, mode="fan_in", nonlinearity="leaky_relu", generator=None: -1, # noqa: B950
torch.nonzero: lambda input, as_tuple=False: -1,
torch.nonzero_static: lambda input, *, size, fill_value=-1: -1,
torch.argwhere: lambda input: -1,
torch.norm: lambda input, p="fro", dim=None, keepdim=False, out=None, dtype=None: -1,
torch.linalg.norm: lambda input, ord=None, dim=None, keepdim=False, out=None, dtype=None: -1,
torch.linalg.vector_norm: lambda input, ord=2, dim=None, keepdim=False, out=None, dtype=None: -1,
torch.linalg.matrix_norm: lambda input, ord="fro", dim=(
-2,
-1,
), keepdim=False, out=None, dtype=None: -1,
torch.norm_except_dim: lambda v, pow=2, dim=0: -1,
torch.nuclear_norm: lambda input, p="fro", dim=None, keepdim=False, out=None, dtype=None: -1,
torch.numel: lambda input: -1,
torch.orgqr: lambda input, tau: -1,
torch.ormqr: lambda input, input2, input3, left=True, transpose=False: -1,
torch.pairwise_distance: lambda x1, x2, p=2.0, eps=1e-06, keepdim=False: -1,
torch.permute: lambda self, dim: -1,
torch.pca_lowrank: lambda input, q=None, center=True, niter=2: -1,
torch.pdist: lambda input, p=2: -1,
torch.pinverse: lambda input, rcond=1e-15: -1,
torch.linalg.pinv: lambda input, rcond=1e-15, hermitian=False: -1,
torch.pixel_shuffle: lambda input, upscale_factor: -1,
torch.pixel_unshuffle: lambda input, downscale_factor: -1,
torch.poisson: lambda input, generator=None: -1,
torch.poisson_nll_loss: lambda input, target, log_input, full, eps, reduction: -1,
torch.polygamma: lambda input, n, out=None: -1,
torch.positive: lambda input, out=None: -1,
torch.prelu: lambda input, weight: -1,
torch.ones_like: lambda input, dtype=None, layout=None, device=None, requires_grad=False: -1,
torch.pow: lambda input, exponent, out=None: -1,
torch.prod: lambda input, dtype=None: -1,
torch.put: lambda input, index, source, accumulate=False: -1,
torch.q_per_channel_axis: lambda input: -1,
torch.q_per_channel_scales: lambda input: -1,
torch.q_per_channel_zero_points: lambda input: -1,
torch.q_scale: lambda input: -1,
torch.q_zero_point: lambda input: -1,
torch.qr: lambda input, some=True, out=None: -1,
torch.linalg.qr: lambda input, mode="reduced", out=None: -1,
torch.quantile: lambda input, q, dim=None, keepdim=False, interpolation="linear", out=None: -1,
torch.nanquantile: lambda input, q, dim=None, keepdim=False, interpolation="linear", out=None: -1,
torch.quantize_per_channel: lambda input, scales, zero_points, axis, dtype: -1,
torch.quantize_per_tensor: lambda input, scale, zero_point, dtype: -1,
torch.quantize_per_tensor_dynamic: lambda input, dtype, reduce_range: -1,
torch.quantized_batch_norm: lambda input, weight, bias, mean, var, eps, output_scale, output_zero_point: -1,
torch.quantized_gru_cell: (
lambda input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh: -1 # noqa: B950
),
torch.quantized_lstm_cell: (
lambda input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh: -1 # noqa: B950
),
torch.quantized_max_pool1d: (
lambda input, kernel_size, stride=(), padding=(0,), dilation=(
1,
), ceil_mode=False: -1
),
torch.quantized_max_pool2d: (
lambda input, kernel_size, stride=(), padding=(0, 0), dilation=(
1,
1,
), ceil_mode=False: -1
),
torch.quantized_max_pool3d: (
lambda input, kernel_size, stride=(), padding=(0, 0, 0), dilation=(
1,
1,
1,
), ceil_mode=False: -1
),
torch.quantized_rnn_relu_cell: (
lambda input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh: -1 # noqa: B950
),
torch.quantized_rnn_tanh_cell: (
lambda input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh: -1 # noqa: B950
),
torch.rad2deg: lambda input, out=None: -1,
torch.rand_like: lambda input, dtype=None, layout=None, device=None, requires_grad=False: -1,
torch.randint_like: lambda input, high, dtype=None, layout=torch.strided, device=None, requires_grad=False: -1,
torch.randn_like: lambda input, dtype=None, layout=None, device=None, requires_grad=False: -1,
torch.ravel: lambda input: -1,
torch.real: lambda input, out=None: -1,
torch.vdot: lambda input, other, out=None: -1,
torch.linalg.vecdot: lambda input, other, dim=-1, out=None: -1,
torch.view_as_real: lambda input: -1,
torch.view_as_complex: lambda input: -1,
torch.reciprocal: lambda input, out=None: -1,
torch.relu: lambda input, inplace=False: -1,
torch.remainder: lambda input, other, out=None: -1,
torch.renorm: lambda input, p, dim, maxnorm, out=None: -1,
torch.repeat_interleave: lambda input, dim=None: -1,
torch.reshape: lambda input, shape: -1,
torch.rms_norm: lambda input, normalized_shape, weight=None, eps=1e-6: -1,
torch.rnn_relu: lambda input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first: -1, # noqa: B950
torch.rnn_relu_cell: lambda input, hx, w_ih, w_hh, b_ih=None, b_hh=None: -1,
torch.rnn_tanh: lambda input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first: -1, # noqa: B950
torch.rnn_tanh_cell: lambda input, hx, w_ih, w_hh, b_ih=None, b_hh=None: -1,
torch.roll: lambda input, shifts, dims=None: -1,
torch.rot90: lambda input, k=1, dims=(0, 1): -1,
torch.round: lambda input, out=None: -1,
torch.row_stack: lambda tensors, out=None: -1, # alias for torch.vstack
torch._rowwise_prune: (lambda weight, mask, compressed_indices_dtype: -1),
torch.rrelu: lambda input, lower=1.0 / 8, upper=1.0 / 3, training=False, inplace=False: -1,
torch.rsqrt: lambda input, out=None: -1,
torch.rsub: lambda input, other, alpha=1: -1,
torch.saddmm: lambda input, mat1, mat2, beta=1, alpha=1, out=None: -1,
torch.scatter: lambda input, dim, index, src: -1,
torch.scatter_add: lambda input, dim, index, src: -1,
torch.scatter_reduce: lambda input, dim, index, src, reduce, include_self=True: -1,
torch.searchsorted: lambda sorted_sequence, input, out_int32=False, right=False, out=None: -1,
torch._segment_reduce: lambda data, reduce="max", lengths=None, indices=None, offsets=None, axis=0, unsafe=False: -1, # noqa: B950
torch.select: lambda input, dim, index: -1,
torch.select_scatter: lambda input, src, dim, index: -1,
torch.slice_inverse: lambda input, src, dim=0, start=None, end=None, step=1: -1,
torch.slice_scatter: lambda input, src, dim=0, start=None, end=None, step=1: -1,
torch.selu: lambda input, inplace=False: -1,
torch.sigmoid: lambda input, out=None: -1,
torch.sign: lambda input, out=None: -1,
torch.signbit: lambda input, out=None: -1,
torch.sgn: lambda input, out=None: -1,
torch.sin: lambda input, out=None: -1,
torch.sinc: lambda input, out=None: -1,
torch.sinh: lambda input, out=None: -1,
torch.slogdet: lambda input: -1,
torch.linalg.slogdet: lambda input: -1,
torch.smm: lambda input, mat2: -1,
torch.spmm: lambda input, mat2: -1,
torch.softmax: lambda input, dim, dtype=None: -1,
torch.linalg.solve: lambda A, B, left=True, out=None: -1,
torch.linalg.solve_ex: lambda A, B, left=True, check_errors=False, out=None: -1,
torch.sort: lambda input, dim=-1, descending=False, *, stable=False, out=None: -1,
torch.split: lambda tensor, split_size_or_sections, dim=0: -1,
torch.split_with_sizes: lambda tensor, split_size_or_sections, dim=0: -1,
torch.sqrt: lambda input, out=None: -1,
torch.square: lambda input, out=None: -1,
torch.squeeze: lambda input, dim=None, out=None: -1,
torch.sspaddmm: lambda input, mat1, mat2, beta=1, alpha=1, out=None: -1,
torch.stack: lambda tensors, dim=0, out=None: -1,
torch.std: lambda input, dim=None: -1,
torch.std_mean: lambda input, dim=None: -1,
torch.stft: (
lambda input, n_fft, hop_length=None, win_length=None, window=None, center=True, pad_mode="reflect", normalized=False, onesided=True, return_complex=None: -1 # noqa: B950
),
torch.sub: lambda input, other, out=None: -1,
torch.subtract: lambda input, other, out=None: -1,
torch.sum: lambda input, dim=None: -1,
torch.sym_float: lambda input: -1,
torch.sym_int: lambda input: -1,
torch.sym_max: lambda a, b: -1,
torch.sym_min: lambda a, b: -1,
torch.sym_not: lambda input: -1,
torch.sym_ite: lambda a, b, c: -1,
torch._sym_sqrt: lambda input: -1,
torch._sym_cos: lambda input: -1,
torch._sym_cosh: lambda input: -1,
torch._sym_sin: lambda input: -1,
torch._sym_sinh: lambda input: -1,
torch._sym_tan: lambda input: -1,
torch._sym_tanh: lambda input: -1,
torch._sym_asin: lambda input: -1,
torch._sym_acos: lambda input: -1,
torch._sym_atan: lambda input: -1,
torch.nansum: lambda input, dim=None: -1,
torch.svd: lambda input, some=True, compute_uv=True, out=None: -1,
torch.svd_lowrank: lambda input, q=6, niter=2, M=None: -1,
torch.linalg.svd: lambda input, full_matrices=True, out=None: -1,
torch.linalg.svdvals: lambda input, out=None: -1,
torch.swapaxes: lambda input, dim0, dim1: -1,
torch.swapdims: lambda input, axis0, axis1: -1,
torch.special.airy_ai: lambda input: -1,
torch.special.bessel_j0: lambda input: -1,
torch.special.bessel_j1: lambda input: -1,
torch.special.bessel_y0: lambda input: -1,
torch.special.bessel_y1: lambda input: -1,
torch.special.chebyshev_polynomial_t: lambda input, n, out=None: -1,
torch.special.chebyshev_polynomial_u: lambda input, n, out=None: -1,
torch.special.chebyshev_polynomial_v: lambda input, n, out=None: -1,
torch.special.chebyshev_polynomial_w: lambda input, n, out=None: -1,
torch.special.digamma: lambda input: -1,
torch.special.entr: lambda input: -1,
torch.special.erf: lambda input: -1,
torch.special.erfc: lambda input: -1,
torch.special.erfcx: lambda input: -1,
torch.special.erfinv: lambda input: -1,
torch.special.exp2: lambda input: -1,
torch.special.expit: lambda input: -1,
torch.special.expm1: lambda input: -1,
torch.special.gammainc: lambda input, other, out=None: -1,
torch.special.gammaincc: lambda input, other, out=None: -1,
torch.special.gammaln: lambda input: -1,
torch.special.hermite_polynomial_h: lambda input, n, out=None: -1,
torch.special.hermite_polynomial_he: lambda input, n, out=None: -1,
torch.special.i0: lambda input: -1,
torch.special.i0e: lambda input: -1,
torch.special.i1: lambda input: -1,
torch.special.i1e: lambda input: -1,
torch.special.laguerre_polynomial_l: lambda input, n, out=None: -1,
torch.special.legendre_polynomial_p: lambda input, n, out=None: -1,
torch.special.log1p: lambda input: -1,
torch.special.log_ndtr: lambda input: -1,
torch.special.log_softmax: lambda input, dim, dtype=None: -1,
torch.special.logit: lambda input: -1,
torch.special.logsumexp: lambda input, dim, keepdim=False, out=None: -1,
torch.special.modified_bessel_i0: lambda input: -1,
torch.special.modified_bessel_i1: lambda input: -1,
torch.special.modified_bessel_k0: lambda input: -1,
torch.special.modified_bessel_k1: lambda input: -1,
torch.special.multigammaln: lambda input, p: -1,
torch.special.ndtr: lambda input: -1,
torch.special.ndtri: lambda input: -1,
torch.special.polygamma: lambda input, n, out=None: -1,
torch.special.psi: lambda input: -1,
torch.special.round: lambda input: -1,
torch.special.scaled_modified_bessel_k0: lambda input: -1,
torch.special.scaled_modified_bessel_k1: lambda input: -1,
torch.special.shifted_chebyshev_polynomial_t: lambda input, n, out=None: -1,
torch.special.shifted_chebyshev_polynomial_u: lambda input, n, out=None: -1,
torch.special.shifted_chebyshev_polynomial_v: lambda input, n, out=None: -1,
torch.special.shifted_chebyshev_polynomial_w: lambda input, n, out=None: -1,
torch.special.sinc: lambda input: -1,
torch.special.softmax: lambda input, dim, dtype=None: -1,
torch.special.spherical_bessel_j0: lambda input: -1,
torch.special.xlog1py: lambda input, other, out=None: -1,
torch.special.xlogy: lambda input, other, out=None: -1,
torch.special.zeta: lambda self, other, out=None: -1,
torch.t: lambda input: -1,
torch.take: lambda input, index: -1,
torch.take_along_dim: lambda input, indices, dim=None, out=None: -1,
torch.tan: lambda input, out=None: -1,
torch.tanh: lambda input, out=None: -1,
torch.linalg.tensorinv: lambda a, ind=2: -1,
torch.linalg.tensorsolve: lambda a, b, dims=None: -1,
torch.tensordot: lambda a, b, dims=2, out=None: -1,
torch.tensor_split: lambda input, indices_or_sections, dim=0: -1,
torch.threshold: lambda input, threshold, value, inplace=False: -1,
torch.tile: lambda input, dims: -1,
torch.topk: lambda input, k, dim=-1, descending=False, out=None: -1,
torch.trace: lambda input: -1,
torch.transpose: lambda input, dim0, dim1: -1,
torch.trapz: lambda y, x=None, dim=-1: -1,
torch.trapezoid: lambda y, x=None, dim=-1: -1,
torch.triangular_solve: lambda input, A, upper=True, transpose=False, unitriangular=False: -1,
torch.linalg.solve_triangular: lambda input, B, upper, left=True, unitriangular=False: -1,
torch.tril: lambda input, diagonal=0, out=None: -1,
torch.triplet_margin_loss: (
lambda anchor, positive, negative, margin=1.0, p=2, eps=1e-06, swap=False, size_average=None, reduce=None, reduction="mean": -1 # noqa: B950
),
torch.triu: lambda input, diagonal=0, out=None: -1,
torch.true_divide: lambda input, other: -1,
torch.trunc: lambda input, out=None: -1,
torch.unbind: lambda input, dim=0: -1,
torch.unflatten: lambda input, dim, sizes, names: -1,
torch.unique: lambda input, sorted=True, return_inverse=False, return_counts=False, dim=None: -1,
torch.unique_consecutive: lambda input, return_inverse=False, return_counts=False, dim=None: -1,
torch.unravel_index: lambda indices, shape: -1,
torch.unsafe_chunk: lambda input, chunks, dim=0: -1,
torch.unsafe_split: lambda tensor, split_size_or_sections, dim=0: -1,
torch.unsafe_split_with_sizes: lambda tensor, split_size_or_sections, dim=0: -1,
torch.unsqueeze: lambda input, dim, out=None: -1,
torch.linalg.vander: lambda x, N=None: -1,
torch.var: lambda input, dim=None: -1,
torch.var_mean: lambda input, dim=None: -1,
torch.vsplit: lambda input, indices_or_sections: -1,
torch.vstack: lambda tensors, out=None: -1,
torch.where: lambda condition, x=None, y=None: -1,
torch._wrapped_linear_prepack: lambda weight, weight_scale, weight_zero_point, bias : -1,
torch._wrapped_quantized_linear_prepacked: (
lambda input, input_scale, input_zero_point, prepacked, out_scale, out_zero_point, out_channel : -1 # noqa: B950
),
torch.zeros_like: lambda input, dtype=None, layout=None, device=None, requires_grad=False: -1,
torch._fw_primal_copy: lambda self, level: -1,
torch._make_dual_copy: lambda primal, tangent, level: -1,
torch.view_as_real_copy: lambda self: -1,
torch.view_as_complex_copy: lambda self: -1,
torch._conj_copy: lambda self: -1,
torch._neg_view_copy: lambda self: -1,
torch.as_strided_copy: lambda self, size, stride, storage_offset=None: -1,
torch._sparse_broadcast_to_copy: lambda self, size: -1,
torch.diagonal_copy: lambda self, offset=0, dim1=0, dim2=1: -1,
torch.expand_copy: lambda self, size, *, implicit=False: -1,
torch.narrow_copy: lambda self, dim, start, length: -1,
torch.permute_copy: lambda self, dims: -1,
torch._reshape_alias_copy: lambda self, size, stride: -1,
torch.select_copy: lambda self, dim, index: -1,
torch.detach_copy: lambda self: -1,
torch.slice_copy: lambda self, dim=0, start=None, end=None, step=1: -1,
torch.split_copy: lambda self, split_size, dim=0: -1,
torch.split_with_sizes_copy: lambda self, split_sizes, dim=0: -1,
torch.squeeze_copy: lambda self, dim: -1,
torch.t_copy: lambda self: -1,
torch.transpose_copy: lambda self, dim0, dim1: -1,
torch.unsqueeze_copy: lambda self, dim: -1,
torch._indices_copy: lambda self: -1,
torch._values_copy: lambda self: -1,
torch.indices_copy: lambda self: -1,
torch.values_copy: lambda self: -1,
torch.crow_indices_copy: lambda self: -1,
torch.col_indices_copy: lambda self: -1,
torch.ccol_indices_copy: lambda self: -1,
torch.row_indices_copy: lambda self: -1,
torch.unbind_copy: lambda self, dim=0: -1,
torch.view_copy: lambda self, dtype: -1,
torch.unfold_copy: lambda self, dimension, size, step: -1,
torch.alias_copy: lambda self: -1,
Tensor.__floordiv__: lambda self, other: -1,
Tensor.__rfloordiv__: lambda self, other: -1,
Tensor.__ifloordiv__: lambda self, other: -1,
Tensor.__truediv__: lambda self, other: -1,
Tensor.__rtruediv__: lambda self, other: -1,
Tensor.__itruediv__: lambda self, other: -1,
Tensor.__lshift__: lambda self, other: -1,
Tensor.__rlshift__: lambda self, other: -1,
Tensor.__ilshift__: lambda self, other: -1,
Tensor.__rshift__: lambda self, other: -1,
Tensor.__rrshift__: lambda self, other: -1,
Tensor.__irshift__: lambda self, other: -1,
Tensor.__and__: lambda self, other: -1,
Tensor.__or__: lambda self, other: -1,
Tensor.__xor__: lambda self, other: -1,
Tensor.__float__: lambda self: -1,
Tensor.__complex__: lambda self: -1,
Tensor.__array__: lambda self, dtype: -1,
Tensor.__bool__: lambda self: -1,
Tensor.__contains__: lambda self, other: -1,
Tensor.__neg__: lambda self: -1,
Tensor.__invert__: lambda self: -1,
Tensor.__mod__: lambda self, other: -1,
Tensor.__rmod__: lambda self, other: -1,
Tensor.__imod__: lambda self, other: -1,
Tensor.__array_wrap__: lambda self, array: -1,
Tensor.__getitem__: lambda self, idx: -1,
Tensor.__deepcopy__: lambda self, memo: -1,
Tensor.__int__: lambda self: -1,
Tensor.__long__: lambda self: -1,
Tensor.__index__: lambda self: -1,
Tensor.__len__: lambda self: -1,
Tensor.__format__: lambda self, format_spec: -1,
Tensor.__reduce_ex__: lambda self, proto: -1,
Tensor.__reversed__: lambda self: -1,
Tensor.__repr__: lambda self, *, tensor_contents=None: -1,
Tensor.__setitem__: lambda self, k, v: -1,
Tensor.__setstate__: lambda self, d: -1,
Tensor.T.__get__: lambda self: -1,
Tensor.H.__get__: lambda self: -1,
Tensor.mT.__get__: lambda self: -1,
Tensor.mH.__get__: lambda self: -1,
Tensor._backward_hooks.__get__: lambda self: -1,
Tensor._post_accumulate_grad_hooks.__get__: lambda self: -1,
Tensor._base.__get__: lambda self: -1,
Tensor._cdata.__get__: lambda self: -1,
Tensor.grad.__get__: lambda self: -1,
Tensor._grad.__get__: lambda self: -1,
Tensor._grad_fn.__get__: lambda self: -1,
Tensor.grad_fn.__get__: lambda self: -1,
Tensor._version.__get__: lambda self: -1,
Tensor._autocast_to_reduced_precision: lambda self, cuda_enabled, cpu_enabled, cuda_dtype, cpu_dtype: -1,
Tensor._autocast_to_full_precision: lambda self, cuda_enabled, cpu_enabled: -1,
Tensor.data.__get__: lambda self: -1,
Tensor.device.__get__: lambda self: -1,
Tensor.dtype.__get__: lambda self: -1,
Tensor.is_cuda.__get__: lambda self: -1,
Tensor.is_cpu.__get__: lambda self: -1,
Tensor.is_xla.__get__: lambda self: -1,
Tensor.is_xpu.__get__: lambda self: -1,
Tensor.is_ipu.__get__: lambda self: -1,
Tensor.is_leaf.__get__: lambda self: -1,
Tensor.retains_grad.__get__: lambda self: -1,
Tensor.is_meta.__get__: lambda self: -1,
Tensor.is_mps.__get__: lambda self: -1,
Tensor.is_mtia.__get__: lambda self: -1,
Tensor.is_nested.__get__: lambda self: -1,
Tensor.is_maia.__get__: lambda self: -1,
Tensor.is_mkldnn.__get__: lambda self: -1,
Tensor.is_quantized.__get__: lambda self: -1,
Tensor.is_sparse.__get__: lambda self: -1,
Tensor.is_sparse_csr.__get__: lambda self: -1,
Tensor.is_vulkan.__get__: lambda self: -1,
Tensor.itemsize.__get__: lambda self: -1,
Tensor.layout.__get__: lambda self: -1,
Tensor.name.__get__: lambda self: -1,
Tensor.names.__get__: lambda self: -1,
Tensor.nbytes.__get__: lambda self: -1,
Tensor.ndim.__get__: lambda self: -1,
Tensor.output_nr.__get__: lambda self: -1,
Tensor.requires_grad.__get__: lambda self: -1,
Tensor.shape.__get__: lambda self: -1,
Tensor.volatile.__get__: lambda self: -1,
Tensor.real.__get__: lambda self: -1,
Tensor.imag.__get__: lambda self: -1,
Tensor.__cuda_array_interface__.__get__: lambda self: -1,
Tensor.type: lambda self, dtype=None, non_blocking=False, **kwargs: -1,
Tensor._dimI: lambda self: -1,
Tensor._dimV: lambda self: -1,
Tensor._indices: lambda self: -1,
Tensor._is_view: lambda self: -1,
Tensor._nnz: lambda self: -1,
Tensor.crow_indices: lambda self: -1,
Tensor.col_indices: lambda self: -1,
Tensor.ccol_indices: lambda self: -1,
Tensor.row_indices: lambda self: -1,
Tensor._update_names: lambda self, names, inplace: -1,
Tensor._values: lambda self: -1,
Tensor.adjoint: lambda self: -1,
Tensor.align_as: lambda self, other: -1,
Tensor.align_to: lambda self, order, ellipsis_idx: -1,
Tensor.apply_: lambda self, callable: -1,
Tensor.as_strided: lambda self, size, stride: -1,
Tensor.as_strided_: lambda self, size, stride: -1,
Tensor.backward: lambda self, gradient=None, retain_graph=None, create_graph=False, inputs=None: -1,
Tensor.bfloat16: lambda self, memory_format=torch.preserve_format: -1,
Tensor.bool: lambda self, memory_format=torch.preserve_format: -1,
Tensor.byte: lambda self, memory_format=torch.preserve_format: -1,
Tensor.char: lambda self, memory_format=torch.preserve_format: -1,
Tensor.cauchy_: lambda self, median=0, sigma=1, *, generator=None: -1,
Tensor.coalesce: lambda self: -1,
Tensor._coalesced_: lambda self, coalesced: -1,
Tensor.contiguous: lambda self, memory_format=torch.contiguous_format: -1,
Tensor.copy_: lambda self, src, non_blocking=False: -1,
Tensor.cpu: lambda self, memory_format=torch.preserve_format: -1,
Tensor.cuda: lambda self, memory_format=torch.preserve_format: -1,
Tensor.mtia: lambda self, memory_format=torch.preserve_format: -1,
Tensor.xpu: lambda self, memory_format=torch.preserve_format: -1,
Tensor.ipu: lambda self, memory_format=torch.preserve_format: -1,
Tensor.data_ptr: lambda self: -1,
Tensor.dense_dim: lambda self: -1,
Tensor.diagonal_scatter: lambda self, src, offset=0, dim1=0, dim2=1: -1,
Tensor.dim: lambda self: -1,
Tensor.dim_order: lambda self: -1,
Tensor.double: lambda self, memory_format=torch.preserve_format: -1,
Tensor.cdouble: lambda self, memory_format=torch.preserve_format: -1,
Tensor.element_size: lambda self: -1,
Tensor.expand: lambda self, size: -1,
Tensor.expand_as: lambda self, other: -1,
Tensor.exponential_: lambda self, lambd=1, *, generator=None: -1,
Tensor.fill_: lambda self, value: -1,
Tensor.fill_diagonal_: lambda self, value: -1,
Tensor.float: lambda self, memory_format=torch.preserve_format: -1,
Tensor.cfloat: lambda self, memory_format=torch.preserve_format: -1,
Tensor.geometric_: lambda self, p, *, generator=None: -1,
Tensor.get_device: lambda self: -1,
Tensor.half: lambda self, memory_format=torch.preserve_format: -1,
Tensor.chalf: lambda self, memory_format=torch.preserve_format: -1,
Tensor.has_names: lambda self: -1,
Tensor.indices: lambda self: -1,
Tensor.int: lambda self, memory_format=torch.preserve_format: -1,
Tensor.is_coalesced: lambda self: -1,
Tensor.is_contiguous: lambda self: -1,
Tensor.is_inference: lambda self: -1,
Tensor.is_pinned: lambda self: -1,
Tensor.is_set_to: lambda self, tensor: -1,
Tensor.is_shared: lambda self: -1,
Tensor.item: lambda self: -1,
Tensor.log_normal_: lambda self, mean=1, std=2, *, generator=None: -1,
Tensor.log_softmax: lambda self, dim: -1,
Tensor.long: lambda self, memory_format=torch.preserve_format: -1,
Tensor.map_: lambda self, tensor, callable: -1,
Tensor.map2_: lambda self, x, y, callable: -1,
Tensor.mm: lambda self, mat2: -1,
Tensor.module_load: lambda self, other, assign=False: -1,
Tensor.narrow_copy: lambda self, dimension, start, length: -1,
Tensor.ndimension: lambda self: -1,
Tensor.nelement: lambda self: -1,
Tensor._nested_tensor_size: lambda self: -1,
Tensor._nested_tensor_storage_offsets: lambda self: -1,
Tensor._nested_tensor_strides: lambda self: -1,
Tensor.normal_: lambda self: -1,
Tensor.numpy: lambda self: -1,
Tensor.permute: lambda self, dim: -1,
Tensor.pin_memory: lambda self: -1,
Tensor.put_: lambda self, indices, tensor, accumulate=False: -1,
Tensor.qscheme: lambda self: -1,
Tensor.random_: lambda self, from_=0, to=None, *, generator=None: -1,
Tensor.record_stream: lambda self, stream: -1,
Tensor.refine_names: lambda self, names: -1,
Tensor.register_hook: lambda self, hook: -1,
Tensor.register_post_accumulate_grad_hook: lambda self, hook: -1,
Tensor.rename: lambda self, name: -1,
Tensor.repeat: lambda self, *size: -1,
Tensor.requires_grad_: lambda self, requires_grad=True: -1,
Tensor.reshape_as: lambda self, other: -1,
Tensor.resize: lambda self, *size: -1,
Tensor.resize_: lambda self, size: -1,
Tensor.resize_as: lambda self, other: -1,
Tensor.resize_as_sparse_: lambda self, other: -1,
Tensor.retain_grad: lambda self: -1,
Tensor.set_: lambda self, source=None, storage_offset=0, size=None, stride=None: -1,
Tensor.select_scatter: lambda self, src, dim, index: -1,
Tensor.share_memory_: lambda self: -1,
Tensor.short: lambda self, memory_format=torch.preserve_format: -1,
Tensor.size: lambda self: -1,
Tensor.slice_scatter: lambda self, src, dim=0, start=None, end=None, step=1: -1,
Tensor.sparse_dim: lambda self: -1,
Tensor.sparse_mask: lambda self, mask: -1,
Tensor._sparse_mask_projection: lambda self, mask, accumulate_matches=False: -1,
Tensor.sparse_resize_: lambda self, size1, size2, dense_dim: -1,
Tensor.sparse_resize_and_clear_: lambda self, size1, size2, dense_dim: -1,
Tensor.sspaddmm: lambda self, mat1, mat2, beta=1, alpha=1, out=None: -1,
Tensor.storage: lambda self: -1,
Tensor.untyped_storage: lambda self: -1,
Tensor.storage_offset: lambda self: -1,
Tensor.storage_type: lambda self: -1,
Tensor.sum_to_size: lambda self, size: -1,
Tensor.tile: lambda self, *reps: -1,
Tensor.to: lambda self, dtype, non_blocking=False, copy=False, memory_format=torch.preserve_format: -1,
Tensor.to_dense: lambda self, dtype=None, *, masked_grad=None: -1,
Tensor._to_dense: lambda self, dtype=None, masked_grad=None: -1,
Tensor.to_sparse: lambda self: -1,
Tensor.tolist: lambda self: -1,
Tensor.to_mkldnn: lambda self: -1,
Tensor.type_as: lambda self, other: -1,
Tensor.unfold: lambda self, dimension, size, step: -1,
Tensor.uniform_: lambda self, from_=0, to=1: -1,
Tensor.values: lambda self: -1,
Tensor.view: lambda self, shape: -1,
Tensor.view_as: lambda self, other: -1,
Tensor.zero_: lambda self: -1,
Tensor.__dlpack__: lambda self, stream=None: -1,
Tensor.__dlpack_device__: lambda self: -1,
torch.linalg.lstsq: lambda self, b, cond=None, driver=None: -1,
} # fmt: skip
privateuse1_backend_name = (
torch.utils.backend_registration._privateuse1_backend_name
)
if hasattr(Tensor, privateuse1_backend_name):
ret[getattr(Tensor, privateuse1_backend_name)] = (
lambda self, device=None, non_blocking=False, **kwargs: -1
)
ret[getattr(Tensor, f"is_{privateuse1_backend_name}").__get__] = lambda self: -1
ret2 = {}
ignored = get_ignored_functions()
for k, v in ret.items():
# Generate methods like __add__ and add_ by default from add
names = [
k.__name__, # Default method
k.__name__ + "_", # Inplace variant
"__" + k.__name__ + "__", # Dunder method
"__i" + k.__name__ + "__", # Inplace dunder method
"__r" + k.__name__ + "__", # Reverse dunder method
]
if k.__name__.startswith("bitwise_"):
# bitwise_<op> have dunder methods of the form __<op>__
# And so on.
subname = k.__name__[len("bitwise_") :]
names.extend(
["__" + subname + "__", "__i" + subname + "__", "__r" + subname + "__"]
)
for name in names:
func = getattr(Tensor, name, None)
if callable(func) and func not in ret and func not in ignored:
ret2[func] = v
ret.update(ret2)
return ret
[docs]def wrap_torch_function(dispatcher: Callable):
"""Wraps a given function with ``__torch_function__`` -related functionality.
Parameters
----------
dispatcher: Callable
A callable that returns an iterable of Tensor-likes passed into the function.
Note
----
This decorator may reduce the performance of your code. Generally, it's enough to express
your code as a series of functions that, themselves, support __torch_function__. If you
find yourself in the rare situation where this is not the case, e.g. if you're wrapping a
low-level library and you also need it to work for Tensor-likes, then this function is available.
Examples
--------
>>> def dispatcher(a): # Must have the same signature as func
... return (a,)
>>> @torch.overrides.wrap_torch_function(dispatcher)
>>> def func(a): # This will make func dispatchable by __torch_function__
... return a + 0
"""
def inner(func):
@functools.wraps(func)
def wrapped(*args, **kwargs):
relevant_args = dispatcher(*args, **kwargs)
if has_torch_function(relevant_args):
return handle_torch_function(wrapped, relevant_args, *args, **kwargs)
return func(*args, **kwargs)
return wrapped
return inner
def _get_overloaded_args(
relevant_args: Iterable[Any],
get_type_fn: Callable[[Any], Type] = None,
) -> List[Any]:
"""Returns a list of arguments on which to call __torch_function__.
Checks arguments in relevant_args for __torch_function__ implementations,
storing references to the arguments and their types in overloaded_args and
overloaded_types in order of calling precedence. Only distinct types are
considered. If a type is a subclass of another type it will have higher
precedence, otherwise the precedence order is the same as the order of
arguments in relevant_args, that is, from left-to-right in the argument list.
The precedence-determining algorithm implemented in this function is
described in `NEP-0018`_.
See torch::append_overloaded_arg for the equivalent function in the C++
implementation.
Parameters
----------
relevant_args : iterable of array-like
Iterable of array-like arguments to check for __torch_function__
methods.
get_type_fn : callable, optional
Function to call on each argument in relevant_args to get its type.
Returns
-------
overloaded_args : list
Arguments from relevant_args on which to call __torch_function__
methods, in the order in which they should be called.
.. _NEP-0018:
https://numpy.org/neps/nep-0018-array-function-protocol.html
"""
if get_type_fn is None:
get_type_fn = type
# If torch function is not enabled, there are no overloaded types
if not torch._C._is_torch_function_enabled():
return []
# Runtime is O(num_arguments * num_unique_types)
overloaded_types: Set[Type] = set()
overloaded_args: List[Any] = []
for arg in relevant_args:
arg_type = get_type_fn(arg)
# We only collect arguments if they have a unique type, which ensures
# reasonable performance even with a long list of possibly overloaded
# arguments.
#
# NB: Important to exclude _disabled_torch_function_impl, otherwise
# https://github.com/pytorch/pytorch/issues/64687
if (
arg_type not in overloaded_types
and hasattr(arg_type, "__torch_function__")
and arg_type.__torch_function__ != torch._C._disabled_torch_function_impl
):
# Create lists explicitly for the first type (usually the only one
# done) to avoid setting up the iterator for overloaded_args.
if overloaded_types:
overloaded_types.add(arg_type)
# By default, insert argument at the end, but if it is
# subclass of another argument, insert it before that argument.
# This ensures "subclasses before superclasses".
index = len(overloaded_args)
for i, old_arg in enumerate(overloaded_args):
if issubclass(arg_type, get_type_fn(old_arg)):
index = i
break
overloaded_args.insert(index, arg)
else:
overloaded_types = {arg_type}
overloaded_args = [arg]
return overloaded_args
[docs]def handle_torch_function(
public_api: Callable,
relevant_args: Iterable[Any],
*args,
**kwargs,
) -> Any:
"""Implement a function with checks for ``__torch_function__`` overrides.
See torch::autograd::handle_torch_function for the equivalent of this
function in the C++ implementation.
Arguments
---------
public_api : function
Function exposed by the public torch API originally called like
``public_api(*args, **kwargs)`` on which arguments are now being
checked.
relevant_args : iterable
Iterable of arguments to check for __torch_function__ methods.
args : tuple
Arbitrary positional arguments originally passed into ``public_api``.
kwargs : tuple
Arbitrary keyword arguments originally passed into ``public_api``.
Returns
-------
object
Result from calling ``implementation`` or an ``__torch_function__``
method, as appropriate.
Raises
------
TypeError : if no implementation is found.
Example
-------
>>> def func(a):
... if has_torch_function_unary(a):
... return handle_torch_function(func, (a,), a)
... return a + 0
"""
# Check for __torch_function__ methods.
overloaded_args = _get_overloaded_args(relevant_args)
# overloaded_args already have unique types.
types = tuple(map(type, overloaded_args))
# Check for __torch_function__ mode.
if _is_torch_function_mode_enabled():
# if we're here, the mode must be set to a TorchFunctionStackMode
# this unsets it and calls directly into TorchFunctionStackMode's torch function
with _pop_mode_temporarily() as mode:
result = mode.__torch_function__(public_api, types, args, kwargs)
if result is not NotImplemented:
return result
# Call overrides
for overloaded_arg in overloaded_args:
# This call needs to become a classmethod call in the future.
# See https://github.com/pytorch/pytorch/issues/63767
torch_func_method = overloaded_arg.__torch_function__
if (
hasattr(torch_func_method, "__self__")
and torch_func_method.__self__ is overloaded_arg
and torch_func_method is not torch._C._disabled_torch_function_impl
):
warnings.warn(
"Defining your `__torch_function__ as a plain method is deprecated and "
"will be an error in future, please define it as a classmethod.",
DeprecationWarning,
)
# Use `public_api` instead of `implementation` so __torch_function__
# implementations can do equality/identity comparisons.
result = torch_func_method(public_api, types, args, kwargs)
if result is not NotImplemented:
return result
func_name = f"{public_api.__module__}.{public_api.__name__}"
msg = (
f"no implementation found for '{func_name}' on types that implement "
f"__torch_function__: {[type(arg) for arg in overloaded_args]}"
)
if _is_torch_function_mode_enabled():
msg += f" nor in mode {_get_current_function_mode()}"
raise TypeError(msg)
has_torch_function = _add_docstr(
_has_torch_function,
r"""Check for __torch_function__ implementations in the elements of an iterable
or if a __torch_function__ mode is enabled. Considers exact ``Tensor`` s
and ``Parameter`` s non-dispatchable. Use this to guard a call to
:func:`handle_torch_function`; don't use it to test if something
is Tensor-like, use :func:`is_tensor_like` instead.
Arguments
---------
relevant_args : iterable
Iterable or arguments to check for __torch_function__ methods.
Returns
-------
bool
True if any of the elements of relevant_args have __torch_function__
implementations, False otherwise.
See Also
________
torch.is_tensor_like
Checks if something is a Tensor-like, including an exact ``Tensor``.
""",
)
has_torch_function_unary = _add_docstr(
_has_torch_function_unary,
r"""Special case of `has_torch_function` for single inputs.
Instead of:
`has_torch_function((t,))`
call:
`has_torch_function_unary(t)`
which skips unnecessary packing and unpacking work.
""",
)
has_torch_function_variadic = _add_docstr(
_has_torch_function_variadic,
r"""Special case of `has_torch_function` that skips tuple creation.
This uses the METH_FASTCALL protocol introduced in Python 3.7
Instead of:
`has_torch_function((a, b))`
call:
`has_torch_function_variadic(a, b)`
which skips unnecessary packing and unpacking work.
""",
)
@functools.lru_cache(None)
def _get_overridable_functions() -> (
Tuple[Dict[Any, List[Callable]], Dict[Callable, str]]
):
overridable_funcs = collections.defaultdict(list)
index = {}
tested_namespaces = [
("torch", torch, torch.__all__),
("torch.functional", torch.functional, torch.functional.__all__),
("torch.nn.functional", torch.nn.functional, dir(torch.nn.functional)),
("torch.nn.init", torch.nn.init, dir(torch.nn.init)),
("torch.Tensor", torch.Tensor, dir(torch.Tensor)),
("torch.linalg", torch.linalg, dir(torch.linalg)),
("torch.fft", torch.fft, dir(torch.fft)),
("torch.special", torch.special, dir(torch.special)),
]
for namespace_str, namespace, ns_funcs in tested_namespaces:
for func_name in ns_funcs:
ignore = False
# ignore private functions or functions that are deleted in torch.__init__
if namespace is not torch.Tensor:
if func_name.startswith("__"):
continue
elif func_name.startswith("_"):
ignore = True
elif func_name.endswith("_"):
ignore = True
elif not func_name[0].islower():
ignore = True
elif func_name == "unique_dim":
continue
else:
func = getattr(namespace, func_name)
if getattr(object, func_name, None) == func:
continue
if func_name == "__weakref__":
continue
func = getattr(namespace, func_name)
if namespace is torch.Tensor and getattr(object, func_name, None) == func:
continue
# ignore re-exported modules
if isinstance(func, types.ModuleType):
continue
# ignore __future__ imports
if isinstance(func, __future__._Feature):
continue
if not callable(func) and hasattr(func, "__get__"):
index[func.__get__] = f"{namespace_str}.{func_name}.__get__"
index[func.__set__] = f"{namespace_str}.{func_name}.__set__"
if ignore:
continue
if func.__get__ in get_ignored_functions():
msg = (
"{}.{} is in the tuple returned by torch._overrides.get_ignored_functions "
"but still has an explicit override"
)
assert func.__get__ not in get_testing_overrides(), msg.format(
namespace, func.__name__
)
continue
else:
overridable_funcs[func].append(func.__get__)
continue
if not callable(func):
continue
index[func] = f"{namespace_str}.{func_name}"
if ignore:
continue
# cannot be overriden by __torch_function__
if func in get_ignored_functions():
msg = (
"{}.{} is in the tuple returned by torch._overrides.get_ignored_functions "
"but still has an explicit override"
)
assert func not in get_testing_overrides(), msg.format(
namespace, func.__name__
)
continue
overridable_funcs[namespace].append(func)
return overridable_funcs, index
[docs]@_disable_user_warnings
def get_overridable_functions() -> Dict[Any, List[Callable]]:
"""List functions that are overridable via __torch_function__
Returns
-------
Dict[Any, List[Callable]]
A dictionary that maps namespaces that contain overridable functions
to functions in that namespace that can be overridden.
"""
return _get_overridable_functions()[0]
[docs]@_disable_user_warnings
def resolve_name(f):
"""Get a human readable string name for a function passed to
__torch_function__
Arguments
---------
f : Callable
Function to resolve the name of.
Returns
-------
str
Name of the function; if eval'ed it should give back the input
function.
"""
if isinstance(f, (torch._ops.OpOverload, torch._ops.OpOverloadPacket)):
return str(f)
return _get_overridable_functions()[1].get(f)
@functools.lru_cache(None)
def _get_tensor_methods() -> Set[Callable]:
"""Returns a set of the overridable methods on ``torch.Tensor``"""
overridable_funcs = get_overridable_functions()
methods = set(overridable_funcs[torch.Tensor])
return methods
[docs]@_disable_user_warnings
def is_tensor_method_or_property(func: Callable) -> bool:
"""
Returns True if the function passed in is a handler for a
method or property belonging to ``torch.Tensor``, as passed
into ``__torch_function__``.
.. note::
For properties, their ``__get__`` method must be passed in.
This may be needed, in particular, for the following reasons:
1. Methods/properties sometimes don't contain a `__module__` slot.
2. They require that the first passed-in argument is an instance
of ``torch.Tensor``.
Examples
--------
>>> is_tensor_method_or_property(torch.Tensor.add)
True
>>> is_tensor_method_or_property(torch.add)
False
"""
return func in _get_tensor_methods() or func.__name__ == "__get__"
[docs]def is_tensor_like(inp):
"""
Returns ``True`` if the passed-in input is a Tensor-like.
Currently, this occurs whenever there's a ``__torch_function__``
attribute on the type of the input.
Examples
--------
A subclass of tensor is generally a Tensor-like.
>>> class SubTensor(torch.Tensor): ...
>>> is_tensor_like(SubTensor([0]))
True
Built-in or user types aren't usually Tensor-like.
>>> is_tensor_like(6)
False
>>> is_tensor_like(None)
False
>>> class NotATensor: ...
>>> is_tensor_like(NotATensor())
False
But, they can be made Tensor-like by implementing __torch_function__.
>>> class TensorLike:
... @classmethod
... def __torch_function__(cls, func, types, args, kwargs):
... return -1
>>> is_tensor_like(TensorLike())
True
"""
return type(inp) is torch.Tensor or hasattr(inp, "__torch_function__")
class TorchFunctionMode:
"""
A ``TorchFunctionMode`` allows you to override the meaning of all
``__torch_function__`` overrideable functions within a dynamic scope,
without having to actually create a tensor subclass or manually
monkey-patch functions in the PyTorch API. Some common situations
where you should use a mode:
* You want to override the meaning of factory functions, or other
functions that do not otherwise take a tensor as an argument
(these cannot be overridden with tensor subclasses).
* You want to override the behavior of all functions without needing
to wrap your inputs in tensor subclasses; e.g., if you are just
interested in logging intermediate computations.
* You want to control the order of execution of various tensor
subclasses explicitly, rather than implicitly via the return of
``NotImplemented``.
Independent subclasses of :class:`TorchFunctionMode` are compositional:
modes can be pushed onto a stack using ``with MyMode():``.
When you call functions in the PyTorch API inside your
``__torch_function__`` implementation, by default, they will forward on to
the next mode on the mode stack. If you want recursively call back into
your current ``__torch_function__`` implementation, either explicitly
invoke ``self.__torch_function__(...)``, or use the context manager
``enable_torch_function_mode(self, replace=self.inner)`` to make PyTorch
API self-referential (beware of infinite loops, in this case!)
"""
inner: "TorchFunctionMode"
# Force metaclass to generate constructor at the base of the hierarchy
def __init__(self) -> None:
pass
def __torch_function__(self, func, types, args=(), kwargs=None):
raise NotImplementedError
def __enter__(self):
_push_mode(self)
return self
def __exit__(self, exc_type, exc_val, exc_tb):
_pop_mode()
@classmethod
def push(cls, *args, **kwargs):
warnings.warn(
"`Mode.push()` is no longer necessary and can be replaced with just `with Mode()`"
)
instance = cls(*args, **kwargs)
return instance
def _get_current_function_mode():
stack_len = _len_torch_function_stack()
return _get_function_stack_at(stack_len - 1) if stack_len > 0 else None
def _get_current_function_mode_stack():
stack_len = _len_torch_function_stack()
return [_get_function_stack_at(i) for i in range(stack_len)]
def _push_mode(mode):
_push_on_torch_function_stack(mode)
def _pop_mode():
old = _pop_torch_function_stack()
return old
@contextlib.contextmanager
def _pop_mode_temporarily():
old = _pop_mode()
try:
yield old
finally:
_push_mode(old)
class BaseTorchFunctionMode(TorchFunctionMode):
def __torch_function__(self, func, types, args=(), kwargs=None):
if kwargs is None:
kwargs = {}
return func(*args, **kwargs)
@contextlib.contextmanager
def enable_reentrant_dispatch():
# NB: this can't simply be
# `enable_reentrant_dispatch = torch._C._RestorePythonTLSSnapshot`
# because:
# 1. torch._C._RestorePythonTLSSnapshot is unavailable when this file
# initially gets imported. Probably an import order thing.
# 2. enable_reentrant_dispatch is technically public API; assigning
# it the object would change the __module__ to look private.
with torch._C._RestorePythonTLSSnapshot():
try:
yield
finally:
pass
