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This can occur when using the `install` workflow. e.g. $ python setup.py install && python -c "import torch" This error can generally be solved using the `develop` workflow $ python setup.py develop && python -c "import torch" # This should succeed or by running Python from a different directory. )_C)rqNrBase> GeneratorDisableTorchFunctionDisableTorchFunctionSubclass TensorBasecZ| t}||vry|j||j}t|D]o}t ||}t |dd}t j |s2|j|sDtjj||t||qy)Nrwrq) setaddrwdirrinspectismodule startswithrr setdefault _import_extension_to_sys_modules)modulememo module_namermember member_names rrrs <5D T>  oo K ?DVT*F!&*b9K'K,B,B;,O &&{F;0>  ?robjcZt|tjr|jSt |ddxsd}d}t |dr |j }nIt |dr |j}n0|jjxsd}|jj }|dvr|S|d|S)a String representation of the type of an object. This function returns a fully qualified string representation of an object's type. 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Args: obj (Object): Object to test )r_storage_classesrs rrGrG`s 9( ((r torch.devicecttdrGtjj}|j|St j gjSt jdS)z?Gets the default ``torch.Tensor`` to be allocated on ``device``device_contextcpu)r_GLOBAL_DEVICE_CONTEXTrdeviceindexrr)rs rr?r?lsV%'78'66== << #M<<#** *||E""rrcttdr%tj}||jddd|d}|t_yddlm}||}|j |t_y)aSets the default ``torch.Tensor`` to be allocated on ``device``. This does not affect factory function calls which are called with an explicit ``device`` argument. Factory calls will be performed as if they were passed ``device`` as an argument. To only temporarily change the default device instead of setting it globally, use ``with torch.device(device):`` instead. The default device is initially ``cpu``. If you set the default tensor device to another device (e.g., ``cuda``) without a device index, tensors will be allocated on whatever the current device for the device type, even after :func:`torch.cuda.set_device` is called. .. warning:: This function imposes a slight performance cost on every Python call to the torch API (not just factory functions). If this is causing problems for you, please comment on https://github.com/pytorch/pytorch/issues/92701 .. note:: This doesn't affect functions that create tensors that share the same memory as the input, like: :func:`torch.from_numpy` and :func:`torch.frombuffer` Args: device (device or string): the device to set as default Example:: >>> # xdoctest: +SKIP("requires cuda, changes global state") >>> torch.get_default_device() device(type='cpu') >>> torch.set_default_device('cuda') # current device is 0 >>> torch.get_default_device() device(type='cuda', index=0) >>> torch.set_default_device('cuda') >>> torch.cuda.set_device('cuda:1') # current device is 1 >>> torch.get_default_device() device(type='cuda', index=1) >>> torch.set_default_device('cuda:1') >>> torch.get_default_device() device(type='cuda', index=1) rNr) DeviceContext)rrr__exit__torch.utils._devicer __enter__)rrrs rrSrS|smb%'78/>>  %  # #D$ 5 ~ -;) 6&v.  ",:)rrcdt|tr t|}tj|y)a .. warning:: This function is deprecated as of PyTorch 2.1, please use :func:`torch.set_default_dtype()` and :func:`torch.set_default_device()` as alternatives. Sets the default ``torch.Tensor`` type to floating point tensor type ``t``. This type will also be used as default floating point type for type inference in :func:`torch.tensor`. The default floating point tensor type is initially ``torch.FloatTensor``. Args: t (type or string): the floating point tensor type or its name Example:: >>> # xdoctest: +SKIP("Other tests may have changed the default type. Can we reset it?") >>> torch.tensor([1.2, 3]).dtype # initial default for floating point is torch.float32 torch.float32 >>> torch.set_default_tensor_type(torch.DoubleTensor) >>> torch.tensor([1.2, 3]).dtype # a new floating point tensor torch.float64 N)rrrr_set_default_tensor_type)rs rrTrTs&4!S  ""rc.tj|y)a Sets the default floating point dtype to :attr:`d`. Supports floating point dtype as inputs. Other dtypes will cause torch to raise an exception. When PyTorch is initialized its default floating point dtype is torch.float32, and the intent of set_default_dtype(torch.float64) is to facilitate NumPy-like type inference. The default floating point dtype is used to: 1. Implicitly determine the default complex dtype. When the default floating type is float16, the default complex dtype is complex32. For float32, the default complex dtype is complex64. For float64, it is complex128. For bfloat16, an exception will be raised because there is no corresponding complex type for bfloat16. 2. Infer the dtype for tensors constructed using Python floats or complex Python numbers. See examples below. 3. Determine the result of type promotion between bool and integer tensors and Python floats and complex Python numbers. Args: d (:class:`torch.dtype`): the floating point dtype to make the default. Example: >>> # xdoctest: +SKIP("Other tests may have changed the default type. Can we reset it?") >>> # initial default for floating point is torch.float32 >>> # Python floats are interpreted as float32 >>> torch.tensor([1.2, 3]).dtype torch.float32 >>> # initial default for floating point is torch.complex64 >>> # Complex Python numbers are interpreted as complex64 >>> torch.tensor([1.2, 3j]).dtype torch.complex64 >>> torch.set_default_dtype(torch.float64) >>> # Python floats are now interpreted as float64 >>> torch.tensor([1.2, 3]).dtype # a new floating point tensor torch.float64 >>> # Complex Python numbers are now interpreted as complex128 >>> torch.tensor([1.2, 3j]).dtype # a new complex tensor torch.complex128 >>> torch.set_default_dtype(torch.float16) >>> # Python floats are now interpreted as float16 >>> torch.tensor([1.2, 3]).dtype # a new floating point tensor torch.float16 >>> # Complex Python numbers are now interpreted as complex128 >>> torch.tensor([1.2, 3j]).dtype # a new complex tensor torch.complex32 N)r_set_default_dtype)ds rset_default_dtyper&sd!rF warn_onlyrr(c2tj||y)aSets whether PyTorch operations must use "deterministic" algorithms. That is, algorithms which, given the same input, and when run on the same software and hardware, always produce the same output. When enabled, operations will use deterministic algorithms when available, and if only nondeterministic algorithms are available they will throw a :class:`RuntimeError` when called. .. note:: This setting alone is not always enough to make an application reproducible. Refer to :ref:`reproducibility` for more information. .. note:: :func:`torch.set_deterministic_debug_mode` offers an alternative interface for this feature. The following normally-nondeterministic operations will act deterministically when ``mode=True``: * :class:`torch.nn.Conv1d` when called on CUDA tensor * :class:`torch.nn.Conv2d` when called on CUDA tensor * :class:`torch.nn.Conv3d` when called on CUDA tensor * :class:`torch.nn.ConvTranspose1d` when called on CUDA tensor * :class:`torch.nn.ConvTranspose2d` when called on CUDA tensor * :class:`torch.nn.ConvTranspose3d` when called on CUDA tensor * :class:`torch.nn.ReplicationPad2d` when attempting to differentiate a CUDA tensor * :func:`torch.bmm` when called on sparse-dense CUDA tensors * :func:`torch.Tensor.__getitem__` when attempting to differentiate a CPU tensor and the index is a list of tensors * :func:`torch.Tensor.index_put` with ``accumulate=False`` * :func:`torch.Tensor.index_put` with ``accumulate=True`` when called on a CPU tensor * :func:`torch.Tensor.put_` with ``accumulate=True`` when called on a CPU tensor * :func:`torch.Tensor.scatter_add_` when called on a CUDA tensor * :func:`torch.gather` when called on a CUDA tensor that requires grad * :func:`torch.index_add` when called on CUDA tensor * :func:`torch.index_select` when attempting to differentiate a CUDA tensor * :func:`torch.repeat_interleave` when attempting to differentiate a CUDA tensor * :func:`torch.Tensor.index_copy` when called on a CPU or CUDA tensor * :func:`torch.Tensor.scatter` when `src` type is Tensor and called on CUDA tensor * :func:`torch.Tensor.scatter_reduce` when ``reduce='sum'`` or ``reduce='mean'`` and called on CUDA tensor The following normally-nondeterministic operations will throw a :class:`RuntimeError` when ``mode=True``: * :class:`torch.nn.AvgPool3d` when attempting to differentiate a CUDA tensor * :class:`torch.nn.AdaptiveAvgPool2d` when attempting to differentiate a CUDA tensor * :class:`torch.nn.AdaptiveAvgPool3d` when attempting to differentiate a CUDA tensor * :class:`torch.nn.MaxPool3d` when attempting to differentiate a CUDA tensor * :class:`torch.nn.AdaptiveMaxPool2d` when attempting to differentiate a CUDA tensor * :class:`torch.nn.FractionalMaxPool2d` when attempting to differentiate a CUDA tensor * :class:`torch.nn.FractionalMaxPool3d` when attempting to differentiate a CUDA tensor * :class:`torch.nn.MaxUnpool1d` * :class:`torch.nn.MaxUnpool2d` * :class:`torch.nn.MaxUnpool3d` * :func:`torch.nn.functional.interpolate` when attempting to differentiate a CUDA tensor and one of the following modes is used: - ``linear`` - ``bilinear`` - ``bicubic`` - ``trilinear`` * :class:`torch.nn.ReflectionPad1d` when attempting to differentiate a CUDA tensor * :class:`torch.nn.ReflectionPad2d` when attempting to differentiate a CUDA tensor * :class:`torch.nn.ReflectionPad3d` when attempting to differentiate a CUDA tensor * :class:`torch.nn.ReplicationPad1d` when attempting to differentiate a CUDA tensor * :class:`torch.nn.ReplicationPad3d` when attempting to differentiate a CUDA tensor * :class:`torch.nn.NLLLoss` when called on a CUDA tensor * :class:`torch.nn.CTCLoss` when attempting to differentiate a CUDA tensor * :class:`torch.nn.EmbeddingBag` when attempting to differentiate a CUDA tensor when ``mode='max'`` * :func:`torch.Tensor.put_` when ``accumulate=False`` * :func:`torch.Tensor.put_` when ``accumulate=True`` and called on a CUDA tensor * :func:`torch.histc` when called on a CUDA tensor * :func:`torch.bincount` when called on a CUDA tensor and ``weights`` tensor is given * :func:`torch.kthvalue` with called on a CUDA tensor * :func:`torch.median` with indices output when called on a CUDA tensor * :func:`torch.nn.functional.grid_sample` when attempting to differentiate a CUDA tensor * :func:`torch.cumsum` when called on a CUDA tensor when dtype is floating point or complex * :func:`torch.Tensor.scatter_reduce` when ``reduce='prod'`` and called on CUDA tensor * :func:`torch.Tensor.resize_` when called with a quantized tensor In addition, several operations fill uninitialized memory when this setting is turned on and when :attr:`torch.utils.deterministic.fill_uninitialized_memory` is turned on. See the documentation for that attribute for more information. A handful of CUDA operations are nondeterministic if the CUDA version is 10.2 or greater, unless the environment variable ``CUBLAS_WORKSPACE_CONFIG=:4096:8`` or ``CUBLAS_WORKSPACE_CONFIG=:16:8`` is set. See the CUDA documentation for more details: ``_ If one of these environment variable configurations is not set, a :class:`RuntimeError` will be raised from these operations when called with CUDA tensors: * :func:`torch.mm` * :func:`torch.mv` * :func:`torch.bmm` Note that deterministic operations tend to have worse performance than nondeterministic operations. .. note:: This flag does not detect or prevent nondeterministic behavior caused by calling an inplace operation on a tensor with an internal memory overlap or by giving such a tensor as the :attr:`out` argument for an operation. In these cases, multiple writes of different data may target a single memory location, and the order of writes is not guaranteed. Args: mode (:class:`bool`): If True, makes potentially nondeterministic operations switch to a deterministic algorithm or throw a runtime error. If False, allows nondeterministic operations. Keyword args: warn_only (:class:`bool`, optional): If True, operations that do not have a deterministic implementation will throw a warning instead of an error. Default: ``False`` Example:: >>> # xdoctest: +SKIP >>> torch.use_deterministic_algorithms(True) # Forward mode nondeterministic error >>> torch.randn(10, device='cuda').kthvalue(1) ... RuntimeError: kthvalue CUDA does not have a deterministic implementation... # Backward mode nondeterministic error >>> torch.nn.AvgPool3d(1)(torch.randn(3, 4, 5, 6, requires_grad=True).cuda()).sum().backward() ... RuntimeError: avg_pool3d_backward_cuda does not have a deterministic implementation... r'N)r_set_deterministic_algorithms)rr(s rrfrfsV$$TY?rc*tjS)zReturns True if the global deterministic flag is turned on. Refer to :func:`torch.use_deterministic_algorithms` documentation for more details. )r_get_deterministic_algorithmsrrrr8r8  + + --rc*tjS)zReturns True if the global deterministic flag is set to warn only. Refer to :func:`torch.use_deterministic_algorithms` documentation for more details. )r'_get_deterministic_algorithms_warn_onlyrrrrFrFs  5 5 77r debug_modect|tjtfst dt |t|tr&|dk(rd}n|dk(rd}n|dk(rd}nt d||dk(rtjd y |dk(rtjd d y |dk(rtjd y t d |)aSets the debug mode for deterministic operations. .. note:: This is an alternative interface for :func:`torch.use_deterministic_algorithms`. Refer to that function's documentation for details about affected operations. Args: debug_mode(str or int): If "default" or 0, don't error or warn on nondeterministic operations. If "warn" or 1, warn on nondeterministic operations. If "error" or 2, error on nondeterministic operations. z'debug_mode must be str or int, but got defaultrwarnr errorzQinvalid value of debug_mode, expected one of `default`, `warn`, `error`, but got FTr'z:invalid value of debug_mode, expected 0, 1, or 2, but got N) rrrrr6r RuntimeErrorrr*)r0s rrUrUs j8<<"5 6A$zBRASTUU*c"  "J 6 !J 7 "J,,6<9  Q ((/ q ((> q ((.H U  rcXtjrtjryyy)zReturns the current value of the debug mode for deterministic operations. Refer to :func:`torch.set_deterministic_debug_mode` documentation for more details. r r5r)rr,r/rrrr@r@s%  '') 5 5 7rc*tjS)zReturns the current value of float32 matrix multiplication precision. Refer to :func:`torch.set_float32_matmul_precision` documentation for more details. )r_get_float32_matmul_precisionrrrrBrBr-r precisionc.tj|y)aSets the internal precision of float32 matrix multiplications. Running float32 matrix multiplications in lower precision may significantly increase performance, and in some programs the loss of precision has a negligible impact. Supports three settings: * "highest", float32 matrix multiplications use the float32 datatype (24 mantissa bits with 23 bits explicitly stored) for internal computations. * "high", float32 matrix multiplications either use the TensorFloat32 datatype (10 mantissa bits explicitly stored) or treat each float32 number as the sum of two bfloat16 numbers (approximately 16 mantissa bits with 14 bits explicitly stored), if the appropriate fast matrix multiplication algorithms are available. Otherwise float32 matrix multiplications are computed as if the precision is "highest". See below for more information on the bfloat16 approach. * "medium", float32 matrix multiplications use the bfloat16 datatype (8 mantissa bits with 7 bits explicitly stored) for internal computations, if a fast matrix multiplication algorithm using that datatype internally is available. Otherwise float32 matrix multiplications are computed as if the precision is "high". When using "high" precision, float32 multiplications may use a bfloat16-based algorithm that is more complicated than simply truncating to some smaller number mantissa bits (e.g. 10 for TensorFloat32, 7 for bfloat16 explicitly stored). Refer to [Henry2019]_ for a complete description of this algorithm. To briefly explain here, the first step is to realize that we can perfectly encode a single float32 number as the sum of three bfloat16 numbers (because float32 has 23 mantissa bits while bfloat16 has 7 explicitly stored, and both have the same number of exponent bits). This means that the product of two float32 numbers can be exactly given by the sum of nine products of bfloat16 numbers. We can then trade accuracy for speed by dropping some of these products. The "high" precision algorithm specifically keeps only the three most significant products, which conveniently excludes all of the products involving the last 8 mantissa bits of either input. This means that we can represent our inputs as the sum of two bfloat16 numbers rather than three. Because bfloat16 fused-multiply-add (FMA) instructions are typically >10x faster than float32 ones, it's faster to do three multiplications and 2 additions with bfloat16 precision than it is to do a single multiplication with float32 precision. .. [Henry2019] http://arxiv.org/abs/1904.06376 .. note:: This does not change the output dtype of float32 matrix multiplications, it controls how the internal computation of the matrix multiplication is performed. .. note:: This does not change the precision of convolution operations. Other flags, like `torch.backends.cudnn.allow_tf32`, may control the precision of convolution operations. .. note:: This flag currently only affects one native device type: CUDA. If "high" or "medium" are set then the TensorFloat32 datatype will be used when computing float32 matrix multiplications, equivalent to setting `torch.backends.cuda.matmul.allow_tf32 = True`. When "highest" (the default) is set then the float32 datatype is used for internal computations, equivalent to setting `torch.backends.cuda.matmul.allow_tf32 = False`. Args: precision(str): can be set to "highest" (default), "high", or "medium" (see above). N)r_set_float32_matmul_precision)r:s rrVrVs~$$Y/rrc.tj|y)aWhen this flag is False (default) then some PyTorch warnings may only appear once per process. This helps avoid excessive warning information. Setting it to True causes these warnings to always appear, which may be helpful when debugging. Args: b (:class:`bool`): If True, force warnings to always be emitted If False, set to the default behaviour N)r_set_warnAlways)rs rrYrY1sqrc*tjS)zReturns True if the global warn_always flag is turned on. Refer to :func:`torch.set_warn_always` documentation for more details. )r_get_warnAlwaysrrrrIrI>s    rr<messagec8t|tjtfst dt |ddlm}||ryt|trt|trJ|d}n&t|s t dt|}||)Nzcond must be a bool, but got r) expect_truezExpected cond to be True, but got False. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)zmessage must be a callable) rrr r2r6r%torch.fx.experimental.symbolic_shapesrC issubclassrWarningcallabler) error_typer<rArCmessage_evaluateds r _check_withrJMs dX]]G4 57T |DEEA4 j) ,Z G5TT T   89 9 N & ''rc&tt||y)aThrows error containing an optional message if the specified condition is False. Error type: ``RuntimeError`` C++ equivalent: ``TORCH_CHECK`` Args: cond (:class:`bool`): If False, throw error message (Callable, optional): Callable that returns either a string or an object that has a ``__str__()`` method to be used as the error message. Default: ``None`` N)rJr6r<rAs rrrms dG,r)rct|dk\|ddlm}|||t||k|ddlm}|||yy)a{Checks that a given integer is a valid size (i.e., is non-negative). You should use this over ``_check(i >= 0)`` because it can prevent ``GuardOnDataDependentSymNode`` exceptions by opting yourself into alternate semantics for ``guard_size_oblivious`` tests that treat values 0 and 1 equivalently to all other values. When max is not None, this specifies an upper bound equivalent to ``_check(i <= max)``. This bound is also subject to alternate semantics: in ``guard_size_oblivious`` tests, we assume that a constant max bound is treated equivalently to all other values. Symbolic max bounds are not yet supported. NB: Do NOT use this in contexts where a -1 size would be valid (indicating to infer the size from context, or if you should wrap-around or truncate). Only use this if the only valid value is an honest to goodness size. r)_advise_is_sizeN)_advise_is_bounded)rrDrNrO)irArrNrOs r_check_is_sizerQsB$ 167EA qCx!L1c" rc&tt||y)aThrows error containing an optional message if the specified condition is False. Error type: ``IndexError`` C++ equivalent: ``TORCH_CHECK_INDEX`` Args: cond (:class:`bool`): If False, throw error message (Callable, optional): Callable that returns either a string or an object that has a ``__str__()`` method to be used as the error message. Default: ``None`` N)rJ IndexErrorrLs r _check_indexrT D'*rc&tt||y)aThrows error containing an optional message if the specified condition is False. Error type: ``ValueError`` C++ equivalent: ``TORCH_CHECK_VALUE`` Args: cond (:class:`bool`): If False, throw error message (Callable, optional): Callable that returns either a string or an object that has a ``__str__()`` method to be used as the error message. Default: ``None`` N)rJrrLs r _check_valuerWrUrc&tt||y)aThrows error containing an optional message if the specified condition is False. Error type: ``TypeError`` C++ equivalent: ``TORCH_CHECK_TYPE`` Args: cond (:class:`bool`): If False, throw error message (Callable, optional): Callable that returns either a string or an object that has a ``__str__()`` method to be used as the error message. Default: ``None`` N)rJr6rLs r _check_typerYs 4)rc&tt||y)aThrows error containing an optional message if the specified condition is False. Error type: ``NotImplementedError`` C++ equivalent: ``TORCH_CHECK_NOT_IMPLEMENTED`` Args: cond (:class:`bool`): If False, throw error message (Callable, optional): Callable that returns either a string or an object that has a ``__str__()`` method to be used as the error message. 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Multiple compiled results can be associated with a frame up to ``torch._dynamo.config.recompile_limit``, which defaults to 8; at which point we will fall back to eager. Note that compile caches are per *code object*, not frame; if you dynamically create multiple copies of a function, they will all share the same code cache. Args: model (Callable): Module/function to optimize fullgraph (bool): If False (default), torch.compile attempts to discover compileable regions in the function that it will optimize. If True, then we require that the entire function be capturable into a single graph. If this is not possible (that is, if there are graph breaks), then this will raise an error. dynamic (bool or None): Use dynamic shape tracing. When this is True, we will up-front attempt to generate a kernel that is as dynamic as possible to avoid recompilations when sizes change. This may not always work as some operations/optimizations will force specialization; use TORCH_LOGS=dynamic to debug overspecialization. When this is False, we will NEVER generate dynamic kernels, we will always specialize. By default (None), we automatically detect if dynamism has occurred and compile a more dynamic kernel upon recompile. backend (str or Callable): backend to be used - "inductor" is the default backend, which is a good balance between performance and overhead - Non experimental in-tree backends can be seen with `torch._dynamo.list_backends()` - Experimental or debug in-tree backends can be seen with `torch._dynamo.list_backends(None)` - To register an out-of-tree custom backend: https://pytorch.org/docs/main/torch.compiler_custom_backends.html#registering-custom-backends mode (str): Can be either "default", "reduce-overhead", "max-autotune" or "max-autotune-no-cudagraphs" - "default" is the default mode, which is a good balance between performance and overhead - "reduce-overhead" is a mode that reduces the overhead of python with CUDA graphs, useful for small batches. Reduction of overhead can come at the cost of more memory usage, as we will cache the workspace memory required for the invocation so that we do not have to reallocate it on subsequent runs. Reduction of overhead is not guaranteed to work; today, we only reduce overhead for CUDA only graphs which do not mutate inputs. There are other circumstances where CUDA graphs are not applicable; use TORCH_LOG=perf_hints to debug. - "max-autotune" is a mode that leverages Triton or template based matrix multiplications on supported devices and Triton based convolutions on GPU. It enables CUDA graphs by default on GPU. - "max-autotune-no-cudagraphs" is a mode similar to "max-autotune" but without CUDA graphs - To see the exact configs that each mode sets you can call `torch._inductor.list_mode_options()` options (dict): A dictionary of options to pass to the backend. Some notable ones to try out are - `epilogue_fusion` which fuses pointwise ops into templates. Requires `max_autotune` to also be set - `max_autotune` which will profile to pick the best matmul configuration - `fallback_random` which is useful when debugging accuracy issues - `shape_padding` which pads matrix shapes to better align loads on GPUs especially for tensor cores - `triton.cudagraphs` which will reduce the overhead of python with CUDA graphs - `trace.enabled` which is the most useful debugging flag to turn on - `trace.graph_diagram` which will show you a picture of your graph after fusion - For inductor you can see the full list of configs that it supports by calling `torch._inductor.list_options()` disable (bool): Turn torch.compile() into a no-op for testing Example:: @torch.compile(options={"triton.cudagraphs": True}, fullgraph=True) def foo(x): return torch.sin(x) + torch.cos(x) rNz torch.compile)roz.torch.compile is not supported on Python 3.14+Py_GIL_DISABLEDr z@torch.compile is not supported on Python built with GIL disabledrTrc B| tdt|S)NzModel can't be NonerQ)r6r;)rTrKrSrrRrrs rrzcompile..fn s6}"#899# rzVEither mode or options can be specified, but both can't be specified at the same time.r2rr )rKnopythonrrS)rr_log_api_usage_oncer version_infor6r _CallablerOrPrr get_backendr rEr_dynamooptimize) rTrRrrKrrrSrrrbisect_backends `````` rr;r;} s,D?+ 7"KLL  ! !"3 4 9 N   } i/ Igun4M    G/ d   |B)5577~7 *.tWgF&wgwG == ! ! 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