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This means that when this is imported no CUDA calls shall be made, including torch.cuda.device_count(), etc. torch.testing._internal.common_cuda.py can freely initialize CUDA context when imported. N)MappingSequence)closingcontextmanager)deepcopy) dataclass)Enum)partialwraps)productchain)Path)mean)AnyTypeVarCallable)IterableIterator) MagicMock)Tensor) ScriptDict ScriptList)get_writable_path)open_source_signpost) ModuleDict ModuleList ParameterDict ParameterList Sequentialregister_custom_op_symbolicunregister_custom_op_symbolic) make_tensor)_unwrap_dtensor_for_comparison BooleanPairNonePairnot_close_error_metas NumberPairPairTensorLikePair)get_all_dtypes)_check_module_exists) cpp_extension)!_is_privateuse1_backend_availableTFic"]tRt^pt^t^t^tRtR#) ProfilingModeN)__name__ __module__ __qualname____firstlineno__LEGACYSIMPLE PROFILING__static_attributes__r1e/home/luca/screen-checking/.venv/lib/python3.14/site-packages/torch/testing/_internal/common_utils.pyr0r0ps F FIr:r0c2]tRt^utRtRtRtRtRtRt Rt Rt R #) HardwareClassificationuHardware classification metadata for test classes. Test classes declare a ``hw_classification`` class attribute to indicate the kind of hardware their tests require. When ``--hw-classification`` is passed, only tests whose classification matches one of the requested values are executed. When the flag is not specified, all test discovery and execution paths remain unchanged. Currently there are three hardware classification categories: * ``GENERIC`` – tests that exercise shared, device-agnostic logic (e.g. Dynamo dispatcher, FX passes, and other framework internals that do not depend on a particular accelerator). These test classes typically do **not** use :func:`~torch.testing._internal.common_utils.instantiate_device_type_tests`. * ``ACCELERATOR`` – tests that verify behavior which must hold across every accelerator (e.g. operator semantics, memory profiling). These test classes **are** instantiated via :func:`~torch.testing._internal.common_utils.instantiate_device_type_tests`. * ``CPU``, ``CUDA``, ``MPS``, ``XPU`` – tests tied to a specific device. Use sparingly, and only for device-specific behavior. These replace ``@onlyCPU``, ``@onlyCUDA``, and similar decorators Usage:: class TestFoo(TestCase): hw_classification = HardwareClassification.GENERIC def test_bar(self): ... Run only GENERIC and ACCELERATOR tests: python test/test_torch.py --hw-classification GENERIC ACCELERATOR generic acceleratorcpucudampsxpur1N) r2r3r4r5__doc__GENERIC ACCELERATORCPUCUDAMPSXPUr9r1r:r;r=r=us(%LGK C D C Cr:r=chV^8dQhR\\P,R\R,/#) test_case_clsreturnN)typeunittestTestCaser=)formats"r; __annotate__rTs,  ))* d" r:c\VRR4pVfR#\V\4'g&\VP RVP R24hV#)hw_classificationN.z3.hw_classification must be a HardwareClassification)getattr isinstancer= TypeErrorr3r2)rN requirements& r;get_hw_classificationr\se-)TestEnvironment.&s$+[C  E+2D Er:cR#Fr1r1r:r;rr(ser:c4TpRpVe\P!V4pV!Wr4pRpVfV!4pT;'gTpV'd&Ve"Wb8wdV'gV\PV&V\ 49d\ RV R24hV\ 4V&V#)NFzduplicate definition of flag ''osgetenvrrepro_env_varsglobalsAssertionError) reenv_varrinclude_in_repro enabled_fn implied_by_fnenabledrimplieds &&&&&& r;def_flagTestEnvironment.def_flag s  ))G,K 6G  #oG((G !47;MW^6AO * *7 3 79  #A$q!IJ J! $r:cV#rr1) maybe_val_strs&r;rrSs}r:cVfTM\P!V4pV!V4pV'dWR8wdV\PV&V\ 49d\ RV R24hV\ 4V&V#)Nz!duplicate definition of setting 'rr)rerrrparse_fnvalues&&&&& r; def_settingTestEnvironment.def_settingMsj#? 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For example, parametrizing a test across the set of ops will result in a test function per op. The decision of how to parametrize / what to parametrize over is intended to be implemented by each derived class. In the details, the decorator adds a 'parametrize_fn' property to the test function. This function is intended to be called later by one of: * Device-specific test instantiation via instantiate_device_type_tests(). Note that for this case there is no need to explicitly parametrize over device type, as that is handled separately. * Device-agnostic parametrized test instantiation via instantiate_parametrized_tests(). If the decorator is applied to a test function that already has a 'parametrize_fn' property, a new composite 'parametrize_fn' will be created that generates tests with the product of the parameters generated by the old and new parametrize_fns. This allows for convenient composability of decorators. c \h)a Parametrizes the given test function across whatever dimension is specified by the derived class. Tests can be parametrized over any arbitrary dimension or combination of dimensions, such as all ops, all modules, or all ops + their associated dtypes. Args: test (fn): Test function to parametrize over generic_cls (class): Generic test class object containing tests (e.g. TestFoo) device_cls (class): Device-specialized test class object (e.g. TestFooCPU); set to None if the tests are not part of a device-specific set Returns: Generator object returning 4-tuples of: test (fn): Parametrized test function; must support a device arg and args for any params test_name (str): Parametrized suffix for the test (e.g. opname_int64); will be appended to the base name of the test param_kwargs (dict): Param kwargs to pass to the test (e.g. {'op': 'add', 'dtype': torch.int64}) decorator_fn (callable): Callable[[Dict], List] for list of decorators to apply given param_kwargs )NotImplementedError)rr generic_cls device_clss&&&&r;_parametrize_test#_TestParametrizer._parametrize_testns ("!r:c\VR4'd+VPpVPp\W#4VnV#VPVnV#)parametrize_fn)r&r^r[compose_parametrize_fns)rrold_parametrize_fnnew_parametrize_fns&& r;__call___TestParametrizer.__call__sR 2' ( (!#!2!2 !%!7!7  78J _B  !% 6 6B  r:r1N) r2r3r4r5rDr[rbr9rrs@r;rVrV[s$",r:rVcVV3RlpV#)a Returns a parametrize_fn that parametrizes over the product of the parameters handled by the given parametrize_fns. Each given parametrize_fn should each have the signature f(test, generic_cls, device_cls). The test names will be a combination of the names produced by the parametrize_fns in "_" order. This order is done to match intuition for constructed names when composing multiple decorators; the names will be built in top to bottom order when stacking parametrization decorators. Args: old_parametrize_fn (callable) - First parametrize_fn to compose. new_parametrize_fn (callable) - Second parametrize_fn to compose. c3"\V!WV44pVFwrgrV!WaV4Fwrr\VP44PV P44pV'd\ RVP RV 24h/VCV CpRP T VR8wd V R8wdRMRV4pW3RlpV VVV3xK K R#5i)zkParametrization over the same parameter by multiple parametrization decorators is not supported. For test "z8", the following parameters are handled multiple times: z{}{}{}rKrcP\V!V44\V!V44,#r)list) param_kwargs old_dec_fn new_dec_fns&&&r;merged_decorator_fnJcompose_parametrize_fns..composite_fn..merged_decorator_fns! < 89DLAY.composite_fns+DzJK GP CX&6&xjIH*:#&'7'<'<'>#?#L#LM]MbMbMd#e #&(QQUQ^Q^P_`FFVEW(YZZ%M'7$L;K$L!#+??=:G2:MR_ceRe3km3@$B BL[ !13DFYZZJHQsCCr1)r`rarzs&& r;r_r_s");(:[, r:c2RV9d\RV R24hR#)am Validates a generated test name at instantiation time. In particular, test names must not contain '.': unittest.TestLoader.loadTestsFromName resolves dotted names by splitting on '.' and walking with getattr, so a dotted test name is discoverable in a full-suite run but breaks any attempt to load the test by name (e.g. `python test_foo.py TestClass.test_name`). rWz Test name "a" is invalid: it contains a "." character, which breaks loading the test by name (unittest.TestLoader.loadTestsFromName splits on "."). This name likely comes from a custom name_fn or an explicit subtest name that interpolates a value whose string form contains a dot (e.g. str(torch.bfloat16) == "torch.bfloat16" or a float). Use dtype_name() for dtypes, or sanitize the generated name (e.g. .replace(".", "_")).Nro)res&r;validate_test_namer~s2 d{$ P PQ Qr:c Za\\S44Fp\SV4p\VR4'gK#\ SV4V3RlpVP VSRR7FDwrErgVP RV 2p\V4V!V4F p V !V4pK V!SWVR7KF K S#)a5 Instantiates tests that have been decorated with a parametrize_fn. This is generally performed by a decorator subclass of _TestParametrizer. The generic test will be replaced on the test class by parametrized tests with specialized names. This should be used instead of instantiate_device_type_tests() if the test class contains device-agnostic tests. You can also use it as a class decorator. E.g. ``` @instantiate_parametrized_tests class TestFoo(TestCase): ... ``` Args: generic_cls (class): Generic test class object containing tests (e.g. TestFoo) r^c<a\S4V3V3Rll4p\SV4'd\RV 24h\SW4R#)c<S!V3/VBR#rr1)rrhrs&&r;instantiated_testZinstantiate_parametrized_tests..instantiate_test_helper..instantiated_testsT*\*r:zRedefinition of test N)r r&rsetattr)rnrerrhrrYs&&f& r;instantiate_test_helper?instantiate_parametrized_tests..instantiate_test_helpersH 4[5A + +{D))$'OpInfo ModuleInfo) rYrwdtype dtype_namedevicerrPr2formatted_namerfloatreplace)ridxrers&&&&r;_formatted_str_reprparametrize._formatted_str_reprLs eU[[ ) )e$ $ u|| , ,u:  %[ ! !%= ='' ' UC0 1 1V1SZ//S9:; ;VC5> !r:c RP\VPVRR7UUu.uFwr4VPWV4NK upp4#uuppi)rTstrict)rhziprr)rrvaluesavs&&& r;_default_subtest_name!parametrize._default_subtest_nameZsCxxT^^]clpIqrIq11#!Tp V PpV Pp\S4V3Rl4p V Pp T p MSp \VP4^8d \V4MV.p\V4\VP48wd<\R\V4 R\VP4 RSP R24h\\VPVR R 74p VPWuVR 7pV 3R lpWW3xEK( WVJd\!S R 24hR#5i)rurKc.#rr1rs&r;r/parametrize._parametrize_test..js"r:Nc<S!V/VB#rr1rrrs*,r; test_wrapper3parametrize._parametrize_test..test_wrapperys#T4V44r:z+Expected # values == # arg names, but got: z values and z names for test ""Tr)rcV#rr1)rrs&&r;r3parametrize._parametrize_test..decorator_fns%%r:zf: An empty arg_values was passed to @parametrize. Note that this may result from reuse of a generator.)lenrobjectr1rrYrrer rrgror2rrr ValueError)rrrYrZrrcheck_exhausted_iteratorr maybe_namersubrgen_testrhrs&f&& r;r[parametrize._parametrize_testfs t~~ ! #IB 5 5 17 8F(9 ! (* fg.. C ^^F!$J4[5!5"%J+H#H),T^^)X,ooi>G"$+L#/:F7 '8HWW%,Ss5AAA)rrN r2r3r4r5rDr8r[r9rrs@r;rrs&N%XXr:rc0a]tRtRtoRtRtRtRtVtR#) decorateIfia Decorator for applying parameter-specific conditional decoration. Composes with other test parametrizers (e.g. @modules, @ops, @parametrize, etc.). Examples:: @decorateIf(unittest.skip, lambda params: params["x"] == 2) @parametrize("x", range(5)) def test_foo(self, x): ... @parametrize("x,y", [(1, 'foo'), (2, 'bar'), (3, 'baz')]) @decorateIf( unittest.expectedFailure, lambda params: params["x"] == 3 and params["y"] == "baz" ) def test_bar(self, x, y): ... @decorateIf( unittest.expectedFailure, lambda params: params["op"].name == "add" and params["dtype"] == torch.float16 ) @ops(op_db) def test_op_foo(self, device, dtype, op): ... @decorateIf( unittest.skip, lambda params: params["module_info"].module_cls is torch.nn.Linear and params["device"] == "cpu" ) @modules(module_db) def test_module_foo(self, device, dtype, module_info): ... 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Prefer this helper over hand-rolled ``@wraps + raise SkipTest`` wrappers, which silently drop classes from discovery when applied at class scope. c<a\S\4'dS!4'dRSnSSnS#\ S4VVV3Rl4pV#)TcZ<S!4'd\P!S4hS!V/VB#rrQSkipTest)rr condition_fnrrs*,r;r0lazy_skip_if..decorator..wrappers+~~''//t&v& &r:)rYrP__unittest_skip____unittest_skip_why__r )rrrrsf r;rlazy_skip_if..decoratorsI b$  ~~'+$+1(I r '  'r:r1)rrrsff r; lazy_skip_ifrs  r:c(\RR4!V4#)c\#r)rr1r:r;r skipIfCrossRef..s 2r:z$test doesn't currently with crossrefrrs&r;skipIfCrossRefrs 24Z [\^ __r:c*a]tRtRtoRRltRtVtR#) CrossRefModeiNc.T;'g/pV!V/VBpV#rr1)rfunctypesrrrs&&&&& r;__torch_function__CrossRefMode.__torch_function__s!2 $ !& !r:r1)r1N)r2r3r4r5r r9rrs@r;rrsr:rTEST_WITH_TORCHINDUCTORPYTORCH_TEST_WITH_INDUCTORTEST_WITH_AOT_EAGERPYTORCH_TEST_WITH_AOT_EAGERTEST_WITH_TORCHDYNAMOPYTORCH_TEST_WITH_DYNAMOc*\;'g\#r)r"r$r1r:r;rrs1HH5HHr:TEST_WITHOUT_COMPILED_AUTOGRAD&PYTORCH_TEST_WITHOUT_COMPILED_AUTOGRADc\'d9\P^,R8Xd\P!R4!V4#\'dV#\P !V4#)rurz'skipping numpy 2.0+ dynamo-wrapped test)r&np __version__rQr expectedFailurers&r;xpassIfTorchDynamo_npr0sH!2c!9}}FGMM((4Lh.F.Ft.LLr:zTest requires MKLzTest requires OneDNN/MKLDNNcJ\'d\P!V4#T#r)TEST_ACLrQr.r/s&r; xfailIfACLr3-5X8 # #D )?4?r:cJ\'d\P!V4#T#r)r&rQr.r/s&r;xfailIfTorchDynamor6s-B-B8 # #D )LLr:c\\PR8d\P!V4#T#))r8 r8 version_inforQr.r/s&r;xfailIfPy312Plusr<%-0-=-=-H8 # #D )RdRr:c\\PR8d\P!V4#T#r8)r8r:r/s&r;xfailIfPy314PlusrAr=r:c\\PR8d\P!V4#T#r?r:r/s&r;xfailIfPy313AndEarlierrCs%-0-=-=-G8 # #D )QTQr:cz\'d/\'g#\'g\P!V4#T#r)IS_LINUXrrrQr.r/s&r; xfailIfLinuxrFs'-5XnnU^U^8 # #D )hdhhr:cJ\'d\P!V4#T#r) IS_WINDOWSrQr.r/s&r;xfailIfWindowsrI s-7Z8 # #D )ATAr:cj\PPe\P!V4#T#r)rwrrrQr.r/s&r; xfailIfROCmrK s(-2]]->->-J8 # #D )TPTTr:c$V^8dQhR\/#rr)rSs"r;rTrTsr:c\V\P4'dVPR8H#\V\4'dVR8H;'gVP R4#R#)r@zcpu:F)rYrwrrPrr )devs&r;_is_cpu_device_typerOsM#u||$$xx5  #se|55s~~f55 r:c0V^8dQhR\R\/#)rMrr)rr)rSs"r;rTrTseTr:cFRV9d VR,#RV9d VR,#R#)rdevicesNr1rs&&r;_device_spec_from_test_callrSs+6hFi  r:cdaa^RIp^RIp^RIHoVP!S4'dl\ \ S44FQpVPR4'gK\SV4p\V4'gK;\SV\V44KS S#VP!S4VV3Rl4pV#)uRun test normally if triton is present or if running on CPU (which falls back to openmp). 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Can be applied to a test method or an entire test class.N) has_tritonrc<S!4'd S!V/VB#\W4pVfV'd\V^,RR4pVe\V4'd S!V/VB#^RIpVeRV: R2MRpVP!RV 24R# \dBpR\ T4P 49d^RIpTP!RT: RT 24hRp?ii;i) N device_typerzTriton not available (device=z): z (device=rxrKzTriton not available)rSrXrOrrlowerrxfail)rrspecrqr device_inforU test_funcs*, r;r+xfailIfNoAcceleratorTriton..wrapper1s <<d-f- -*48 .decorator..wrapper]s'(("++C00''r:expected fn to be a type, got T)rYrPr rr&rrrrrsf r;r$skipIfTorchDynamo..decorator[sl"d## 2Y ( ( N"d## #A$r(!LM M #'B '*B $ r:)rYrrrrsf r;skipIfTorchDynamorrQs+ c3  IJJ$ r:z.test doesn't currently work with torchinductorcaaVV3RlpV#)c<a\S\4'g\S4VVV3Rl4pV#\S\4'g\R\S4 24hS'dRSnSSnS#)cT<S'd\P!S4hS!V/VBR#rr rr conditionrrs*,r;r7skipIfTorchInductor..decorator..wrappers$"++C00''r:rnTrYrPr rrrrrrwrsf r;r&skipIfTorchInductor..decoratorqsi"d## 2Y ( ( N"d## #A$r(!LM M #'B '*B $ r:r1rrwrsff r;skipIfTorchInductorr~os$ r:cf\V\4'g\R\V4 24hRpV#)zF Usage: @runWithoutCompiledAutograd(msg) def test_blah(self): ... zexpected msg to be str, got c0a\S4V3Rl4pV#)c<\PPP4;_uu_4S!V/VBRRR4R# +'giR#;ir)rw_dynamocompiled_autograd_disable)rrrs*,r;r>runWithoutCompiledAutograd..decorator..wrappers80099;;d%f%<;;;s A A r rrsf r;r-runWithoutCompiledAutograd..decorators t &  &r:)rYrrrPrqs& r;runWithoutCompiledAutogradrs5 c3  ;DI;GHH r:cha\S4\Jd\R\S4 24hV3RlpV#)z8 Decorator for running tests serially. Requires pytest z#expected condition to be bool, got cn<\'d(S'd \PPV4#V#r) has_pytestrmarkserialrrws&r;rserialTest..decorators$ :);;%%b) ) r:)rPrr)rwrsf r; serialTestrs6  Id"B4 ?BSTUU r:c$RpVfV#V!V4#)cRVnV#r) dynamo_strictrns&r;r)unMarkDynamoStrictTest..decorators! r:r1)rnrs& r;unMarkDynamoStrictTestrs {~r:c,aV3RlpVfV#V!V4#)z Marks the test as 'strict'. In strict mode, we reset before and after the test, and run without suppress errors. Args: - nopython: if we should run torch._dynamo.optimize with nopython={True/False}. c<a\P!V4'dRVnSVnV#Vo\ S4V3Rl4pV#)Tc<\PP4\PP RR4;_uu_4S!V/VBRRR4\PP4R# +'giL0;i)$torch._dynamo.config.suppress_errorsFN)rwrresetrQmockpatchrs*,r;r8markDynamoStrictTest..decorator..wrappersX MM   !$$%KUSSD#F#T MM   !TSs  A99 B )rr_rdynamo_strict_nopythonr ) cls_or_funcrrnopythons& @r;r'markDynamoStrictTest..decoratorsJ ??; ' '(,K %19K .   r "  " r:r1)rrrs&f r;markDynamoStrictTestrs! %%r:c@\T\;'d\R7#))rrw)r~rr"rgs&r;skipRocmIfTorchInductorrs 3.2\2\E\ ]]r:caV3RlpV#)c <a\S\4'g\S4VV3Rl4pV#\S\4'g\R\S4 24h\\ P 8XdRSnSSnS#)c<\'g \R4h\\P8Xd\P !S4hS!V/VBR#)rN)rrr0r6rQr rls*,r;r;skipIfLegacyJitExecutor..decorator..wrappers@%~()EFF!]%9%99"++C00''r:rnT) rYrPr rrr0r6rrrosf r;r*skipIfLegacyJitExecutor..decoratorst"d## 2Y ( (N"d## #A$r(!LM M ]11 1#'B '*B $ r:r1rqsf r;skipIfLegacyJitExecutorrs* r:crV^8dQhR\R\3,R,R\R\3,/#)rMr.NrO)rr)rSs"r;rTrTs3##cT!# c3h#r:caaaaa^RIHoHoHoSfR#RVVV3Rllo\P !S4RVV3Rll4pV#)z| Decorator function to create a dynamo test case. A function annotate with this decorator takes as input a unittest object. )CompileCounterroptimize_assertc\V4#r)make_dynamo_testrs&r;r"make_dynamo_test..s *2.r:cNV^8dQhR\R\R\3,RR/#)rMrr.rON)rr)rSs"r;rT&make_dynamo_test..__annotate__s/ S#X   r:c<aaaRVVV3RllpS!4pV!4S!4S!V4!V4pV!4S!4R#)cV^8dQhRR/#rr1)rSs"r;rT=make_dynamo_test..standard_test..__annotate__s  t r:c<S!S3/SBR#rr1)rrrsr;dummy6make_dynamo_test..standard_test..dummys t v r:Nr1) rrrractualopt_fnrrrs fff r; standard_test'make_dynamo_test..standard_tests:    !   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Migrate to skipIfRocm.c@<aaRS 2o\S4VV3Rl4pV#)rcX<\'d\P!S4hS!V/VB#rrrQr rrrrs*,r;r1skipIfRocm_BUGGY..dec_fn..wrapperz s)~''//4*6**r:rrrrrsf @r;dec_fn skipIfRocm_BUGGY..dec_fnw s,u% r +  + r:r1rrrs&d r;skipIfRocm_BUGGYru   d| Mr:c$V^8dQhR\/#)rM device_indexr)rSs"r;rTrT sFF#Fr:cT\PPV4P#r)rwrAryrz)rs&r;getRocmArchNamer s :: + +L 9 E EEr:c>V^8dQhR\\R3,/#rMarch.rr)rSs"r;rTrT s,,%S/,r:ca\PP'gR#\4o\;QJdV3RlV4F 'gK R# R#!V3RlV44#)Fc3,<"TF qS9xK R#5irr1)rr,rocmArchs& r;r"isRocmArchAnyOf.. s+dH}dT)rwrrrany)rrs&@r;isRocmArchAnyOfr sG ==    H 3+d+33+3+3+d+ ++r:c>V^8dQhR\\R3,/#rr)rSs"r;rTrT ssCxr:c*a\V3RlRS 24#)c6<\;'d \S4#r)rrrsr;r skipIfRocmArch.. s88?4#88r:zskipIfRocm: test skipped on rrsfr;skipIfRocmArchr s 8 &tf- r:c0a\S4V3Rl4pV#)c\<\'d S!V/VBR#\P!R4h)z+test currently only works on the ROCm stackNrrs*,r;rrunOnRocm..wrapper s' >   ##$QR Rr:rrsf r; runOnRocmr s" 2YSS Nr:c>V^8dQhR\\R3,/#rr)rSs"r;rTrT s  c3h r:caV3RlpV#)c4<a\S4VV3Rl4pV#)c<\'d-\S4'gRS 2p\P!V4hS!V.VO5/VB#)zskipIfRocm: test only runs on )rrrQr )rrrrrrs&*, r;wrap_fn.runOnRocmArch..dec_fn..wrap_fn sD~od&;&;9$@''//d,T,V, ,r:r)rrrsf r;rrunOnRocmArch..dec_fn s r -  - r:r1)rrsf r; runOnRocmArchr  s Mr:cJ\'d\P!V4#T#r)IS_S390XrQr.r/s&r; xfailIfS390Xr  r4r:caV3RlpV#)cD<S'd\P!V4#V#r)rQr.)rrws&r;rxfailIf..wrapper s ++D1 1Kr:r1)rwrsf r;xfailIfr s Nr:z,test doesn't currently work on the XPU stackc>\RRV 24pVe V!V4#T#)c\#rTEST_XPUr1r:r;rskipIfXpu.. sXr: skipIfXpu: rrs&$ r; skipIfXpur s*-SE/BCI".9T?=I=r:c4aV3RlpV'd V!V4#V#)zOOld skipIfXpu that silently drops classes from discovery. Migrate to skipIfXpu.c@<aaRS 2o\S4VV3Rl4pV#)rcX<\'d\P!S4hS!V/VB#r)rrQr rs*,r;r0skipIfXpu_BUGGY..dec_fn..wrapper s)x''//4*6**r:rrsf @r;rskipIfXpu_BUGGY..dec_fn s,se$ r +  + r:r1rs&d r;skipIfXpu_BUGGYr rr:cFaaaRo\S\4'd\'dRSnSSnS#\ P !S4pRVP9oS'g'\P!RSP R2^R7\S4VVV3Rl4pV#)z$test doesn't currently work with MPSTrzskipIfMPS applied to zq which has no 'device' parameter. Consider using device-generic tests with instantiate_device_type_tests instead.rcB<S'dnV'd V^,MRpVeV\VRR4;'g\VRR4p\V\4'dVR8Xd\P!S4hM"\ 'd\P!S4hS!V/VB#)ruNrWrrB)rXrYrrQr TEST_MPS)rrslfrWrhas_device_argrs*, r;rskipIfMPS..wrapper s !$q'tC%c=$?74D k3//K54H"++F33 X##F+ +4"6""r:) rYrPr rrr signature parametersr$r%r4r )rsigrr"rsf @@r; skipIfMPSr' s 3F"d 8#'B '-B $   B C/N  #BOO#45^ ^  2Y # # Nr:c(\RR4!V4#)c\#r)TEST_HPUr1r:r;rskipIfHpu.. sr:$test doesn't currently work with HPUrrs&r; skipIfHpur- s (*P QRT UUr:c0a\S4V3Rl4pV#)zOOld skipIfHpu that silently drops classes from discovery. Migrate to skipIfHpu.c\<\'d\P!R4hS!V/VBR#)r,N)r*rQr rs*,r;r skipIfHpu_BUGGY..wrapper s' 8##$JK K   r:rrsf r;skipIfHpu_BUGGYr1 s  2Y  Nr:cFV^8dQhR\\\3,/#r)rr)rSs"r;rTrT s..c3h.r:c@^RIHpV!4pV^,V^,3#)ru)_get_torch_rocm_version)#torch.testing._internal.common_cudar4)r4 rocm_versions r;getRocmVersionr7 s K*,L O\!_ --r:c0aV3Rlp\VRS R24#)c<\'gR#\4pVRJ;'gSRJ;'gV\S48#FNrr7rrocm_version_tuplers r; _should_skip/skipIfRocmVersionLessThan.._should_skip sH~+- $ & 3 3$ 3 3!E'N2 r:zROCm version less than z requiredrrr>sf r;skipIfRocmVersionLessThanrA s!   (?y &R SSr:c0aV3Rlp\VRS R24#)c<\'gR#\4pVRJ;'dSRJ;'dV\S48#r:r;r<s r;r>.skipIfRocmVersionAtLeast.._should_skip sI~+- d * 5 5t# 5 5"eGn4 r:zROCm version at least z: known failurerr@sf r;skipIfRocmVersionAtLeastrE s!   (>wi&W XXr:c(\RR4!V4#)c\'*#r)TEST_WITH_MIOPEN_SUGGEST_NHWCr1r:r;r,skipIfNotMiopenSuggestNHWC.. s11r:z:test doesn't currently work without MIOpen NHWC activationrrs&r;skipIfNotMiopenSuggestNHWCrJ s 1D    r:z0test doesn't currently work on the Windows stackc>\RRV 24pVe V!V4#T#)c\#r)rHr1r:r;rskipIfWindows..# sZr:zskipIfWindows: rrs&$ r; skipIfWindowsrN" s*/?3%1HII".9T?=I=r:c>\RRV 24pVe V!V4#T#)cZ\;'d\PP4#r)rHrwrCrxr1r:r;r"skipIfWindowsXPU..( s 77uyy5577r:zskipIfWindowsXPU: rrs&$ r;skipIfWindowsXPUrR& s/7;McU9SI#.9T?=I=r:z$requires cuda-python (cuda.bindings)c \PP4;'dK\PP4R8;'d"\PP 4^8p\PP 4p\ V^, 4FPp\ V^,V4F,p\PP W#4'dK*RpM V'dKPM \V'*R4#)Fz cuda p2p access is not available)rTru)rwrArxget_device_capabilityrrcan_device_access_peerskip_but_pass_in_sandcastle_if)cuda_p2p_access_available num_devicesr-js r;requires_cuda_p2p_accessr[. s ! + + JJ , , .& 8 + + JJ # # % * **))+K ;? #q1uk*A::44Q::,1)+)(  $ * %%* r:c0a\S4V3Rl4pV#)c<\PPP4pS!V/VB\PPPV4R# \PPPT4i;ir)rwbackendsrApreferred_linalg_libraryrr_preferred_backendrs*, r;_fn.setLinalgBackendsToDefaultFinally.._fnE sb"^^00IIK M    NN   8 89K LENN   8 89K L A+B rrrbsf r;!setLinalgBackendsToDefaultFinallyrfD s" 2YMM Jr:c0a\S4V3Rl4pV#)c><\PPP4pS!V/VB\PPPV4\PPP 4'd]\P P 4\P P4\P P4R#R# \PPPT4\PPP 4'd\\P P 4\P P4\P P4ii;ir) rwr^rApreferred_blas_libraryis_builtr_cuda_resetCublasWorkspaceSize _cuda_resetCublasLtWorkspaceSize_cuda_clearCublasWorkspacesr`s*, r;rb,setBlasBackendsToDefaultFinally.._fnR s"^^00GGI 7    NN   6 67I J~~""++--77999;446. NN   6 67I J~~""++--77999;446.s C((B4Frresf r;setBlasBackendsToDefaultFinallyroQ s  2Y 7 7 Jr:c0a\S4V3Rl4pV#)c<\PPP4pS!V/VB\PPPV4R# \PPPT4i;ir)rwr^rApreferred_rocm_fa_libraryr`s*, r;rb,setSdpaBackendsToDefaultFinally.._fn` sb"^^00JJL N    NN   9 9:L MENN   9 9:L Mrdrresf r;setSdpaBackendsToDefaultFinallyrt_ s" 2YNN Jr:cTa]tRtRtoRRRR/Rlt]R4tRtR tR t R t Vt R #) DeterministicGuardik warn_onlyFfill_uninitialized_memoryTc*WnW nW0nR#r) deterministicrwrx)rrzrwrxs&&$$r;r8DeterministicGuard.__init__l s*")B&r:cV!\P!4\P!4\PPP R7#))rwrx)rw$are_deterministic_algorithms_enabled-is_deterministic_algorithms_warn_only_enabledutilsrzrxrs&r;_current_state!DeterministicGuard._current_stateq s:  6 6 8IIK&+kk&?&?&Y&Y  r:c\P!VPVPR7VP\P PnR#)rwN)rwuse_deterministic_algorithmsrzrwrxrr;s&r;_updateDeterministicGuard._updatey s4 **4+=+=X>B>\>\ !!;r:cPVP4VnVP4R#r)r_restorerr;s&r; __enter__DeterministicGuard.__enter__} s++-  r:c:VPP4R#r)rrrexception_typeexception_value tracebacks&&&&r;__exit__DeterministicGuard.__exit__ s r:)rrzrxrwN) r2r3r4r5r8 classmethodrrrrr9rrs@r;rvrvk sEC5CTXC   ]  r:rvc2a]tRtRtoRtRtRtRtVtR#)AlwaysWarnTypedStorageRemovali cl\V\4'g\R\V4 24hWnR#)z%expected always_warn to be bool, got N)rYrrrP always_warn)rrs&&r;r8&AlwaysWarnTypedStorageRemoval.__init__ s.+t,, #HkIZH[!\] ]&r:c\PP4Vn\PP VP 4R#r)rwstorage&_get_always_warn_typed_storage_removalalways_warn_restore&_set_always_warn_typed_storage_removalrr;s&r;r'AlwaysWarnTypedStorageRemoval.__enter__ s/#(==#W#W#Y  <'>?r:)rrNrrs@r;rr s$2@@r:rc2a]tRtRtoRtRtRtRtVtR#)SwapTensorsGuardi cWnR#r)use_swap_tensors)rrs&&r;r8SwapTensorsGuard.__init__ s 0r:c\PP4VnVPe,\PP VP4R#R#r)rw __future__$get_swap_module_params_on_conversionswap_tensors_restorer$set_swap_module_params_on_conversionr;s&r;rSwapTensorsGuard.__enter__ sD$)$4$4$Y$Y$[!  ,    A A$BWBW X -r:cX\PPVP4R#r)rwrrrrs&&&&r;rSwapTensorsGuard.__exit__ s ==d>W>WXr:)rrNrrs@r;rr s1Y YYr:rc0a\S4V3Rl4pV#)c 2<\\P!4\P!4R7;_uu_4!RR4pV!4;_uu_4S!V/VBRRR4RRR4R# +'giL;i +'giR#;i)rc0a]tRtRtoRtRtRtRtVtR#)HwrapDeterministicFlagAPITest..wrapper..CuBLASConfigGuardi CUBLAS_WORKSPACE_CONFIGc\PPVP4VnR\PVP&R#)z:4096:8Nrr rcublas_var_namecublas_config_restorer;s&r;rRwrapDeterministicFlagAPITest..wrapper..CuBLASConfigGuard.__enter__ s113@T@T1UD.7@BJJt334r:c\PPVP4pVPf$Ve\PVPR#R#VP\PVP&R#rr)rrrrcur_cublas_configs&&&& r;rQwrapDeterministicFlagAPITest..wrapper..CuBLASConfigGuard.__exit__ sa(* t7K7K(L%119,8 " 4+?+? @9<@;U;U 4#7#78r:)rN) r2r3r4r5rrrr9rrs@r;CuBLASConfigGuardr s";AVVr:rN)rvrwr}r~)rrrrs*, r;r-wrapDeterministicFlagAPITest..wrapper s{ ::<MMOQQ V V#$$D#F#%#QQ"%$#QQQs#B A2 B2 B =B B rrsf r;wrapDeterministicFlagAPITestr s  2Y$$( Nr:caV3RlpV#)c4<a\S4VV3Rl4pV#)cx<\S4;_uu_4S!V/VBRRR4R# +'giR#;ir)r)rrrswaps*,r;r4wrapSwapTensorsTest..dec_fn..wrapper s*!$''D#F#('''s ( 9 r)rrrsf r;r#wrapSwapTensorsTest..dec_fn s r $  $r:r1)rrsf r;wrapSwapTensorsTestr s Mr:c8aa]tRtRtoV3RltRtRtVtV;t#)ri c0<\SV`4WnR#r)rr8 swap_values)rrrs&&r;r8 swap.__init__ s &r:c#j"VPFp\V4!V4RV 2/R3xK R#5i)swap_c.#rr1rs&r;r(swap._parametrize_test.. sQSr:N)rr)rrrYrZrs&&&& r;r[swap._parametrize_test s4$$D%d+D1U4&>2|S S%s13)r) r2r3r4r5r8r[r9rrrs@@r;rr s'TTr:rc\pV'd.\P!\P!^^.44\ P!V'*R4!V4# \ dRpL1i;i)rMFz*PyTorch was compiled without numpy support) TEST_NUMPYrw from_numpyr,arrayrorQr)r numpy_supports& r;skipIfCompiledWithoutNumpyr s_M "   RXXq!f- . ??},.Z [\^ __ "!M "s,A A.-A.caaVV3RlpV#)c<S!VS4#rr1)rrrs&r;run_test_function)_test_function..run_test_function s$r:r1)rrrsff r;_test_functionr s r:c(\RR4!V4#)cT\PPP'*#r)rwr^xnnpackrr1r:r;r!skipIfNoXNNPACK.. sENN**222r:zIXNNPACK must be enabled for these tests. Please build with USE_XNNPACK=1.rrs&r;skipIfNoXNNPACKr s 2S    r:c(\RR4!V4#)c@\PP'*#r)rwr has_lapackr1r:r;r skipIfNoLapack.. sEHH$7$7 7r:zPyTorch compiled without Lapackrrs&r;skipIfNoLapackr s 79Z [\^ __r:c.\P!R4#)a,Wraps the decorator to hide the import of the `core`. Args: op_name: Check if this op is registered in `core._REGISTERED_OPERATORS`. message: message to fail with. Usage: @skipIfNotRegistered('MyOp', 'MyOp is not linked!') This will check if 'MyOp' is in the caffe2.python.core z"Pytorch is compiled without Caffe2)rQr )op_namemessages&&r;skipIfNotRegisteredr s === >>r:c(\RR4!V4#)c\'*#r) TEST_SCIPYr1r:r;rskipIfNoSciPy.." sJr:z'test require SciPy, but SciPy not foundrrs&r; skipIfNoSciPyr! s .0Y Z[] ^^r:c(\RR4!V4#)c(R\P9#)PYTEST_CURRENT_TEST)rr r1r:r;r skip_if_pytest..& s%3r:zdoes not work under pytestrrs&r;skip_if_pytestr$ s 35Q   r:c(\RR4!V4#)c\'*#rrr1r:r;rskipIfNoXPU..* sH r:z)test required PyTorched compiled with XPUrrs&r; skipIfNoXPUr) s ,.Y Z[] ^^r:c(\RR4!V4#)aMSkip if the CUDA/HIP caching allocator is not active. Covers both the runtime toggle (``torch.cuda.caching_allocator_enable(False)``) and the env-var bypass (``PYTORCH_NO_CUDA_MEMORY_CACHING`` / ``PYTORCH_NO_HIP_MEMORY_CACHING``). The CPU-only case (no CUDA built) is treated as "allocator irrelevant", so the test is allowed to run.c\PP4;'d$\PP 4'*#r)rwrArxr%_cuda_cudaCachingAllocator_is_enabledr1r:r;r0skipIfCachingAllocatorDisabled..3 s6 '')AA>>@ @Ar:zGrequires the CUDA/HIP caching allocator (current allocator is uncached)rrs&r;skipIfCachingAllocatorDisabledr, s$  AQ    r:cNa\S4V3Rl4pRVPR&V#)c\<\'g\P!R4hS!V/VBR#).wrapper9 s'~##$bc c   r:T slow_test)r __dict__rsf r;slowTestr 8 s0 2Y  %)G[! 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Calls the test object's `DeviceTypeTestBase.get_primary_device()` method at call time, so this only works on test classes that have had `instantiate_device_type_tests` applied to them. cWnR#r_dtype)rrs&&r;r8'set_default_dtype_if_supported.__init__ s r:cJaa\P!S4VV3Rl4pV#)c<VP4pRp\P!^VSPR7RpV'd6\P !4p\P !SP4S!V.VO5/VBVe\P !V4## \dRpLvi;i Te\P !T4ii;i)TrrFN)get_primary_devicerwemptyrIrZrArB) test_selfrrrdtype_supportedrCrrs&*, r;r8set_default_dtype_if_supported.__call__..wrapper s113F"O ( AfDKK@K#557 '' 4 9)5d5f5*++K8+ ("' (*++K8+s#B8 B2 B/.B/2Cr)rrrsff r;rb'set_default_dtype_if_supported.__call__ s%   9  9(r:rHN) r2r3r4r5rDr8rbr9rrs@r;rFrF sr:rFc#"\P!.4P4p\P!V4Rx\P!V4R# \P!T4i;i5ir)rwtensorrPset_default_tensor_type) tensor_typesaved_tensor_types& r;rVrV sQ R(--/ !!+.9  %%&78%%&78s;A5AA5A22A5cVP4^8Xd \^4#VP4^8Xd\VP^44#\RVP44!#)ruc38"TFp\V4xK R#5irr)rrs& r;riter_indices.. s5}!U1XX}s)dimrr5r )rUs&r; iter_indicesr] sQ zz|qQx zz|qV[[^$$ 5v{{}5 66r:cB\V4R# \dR#i;ir)rrZ)r7s&r; is_iterabler_ s# S  rc\V\P4'dR#\V4^8XdV#\ V4F&p\V\P4'dK%R# R# \ dR#i;i)zReturns True if iterable is an iterable of tensors and False o.w. 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Tr:r6c"]tRtRt]]3tRtRtR#) StringPairi stringr1N) r2r3r4r5rbytesr8r=r9r1r:r;rErE s ,CIr:rEc]tRtRt]tRtR#)SetPairi r1N)r2r3r4r5rar8r9r1r:r;rIrI s Cr:rIc]tRtRt]tRtR#)TypePairi r1N)r2r3r4r5rPr8r9r1r:r;rKrK s Cr:rKc]tRtRt]tRtR#) ObjectPairi r1N)r2r3r4r5rr8r9r1r:r;rMrM s Cr:rMc2aa]tRtRtoV3RltRtVtV;t#),AssertRaisesContextIgnoreNotImplementedErrori c<Ve5\V\4'dVPPRV 24\SV`WV4#)Nznot_implemented: ) issubclassrXrskipTestrr)rrurvtbrs&&&&r;r5AssertRaisesContextIgnoreNotImplementedError.__exit__ sB  Jx9L$M$M NN # #&7 {$C DwR88r:r1)r2r3r4r5rr9rrrs@@r;rOrO s99r:rOc$V^8dQhR\/#)rMnew_valr)rSs"r;rTrTs''T'r:c#"\P!4p\P!V4Rx\P!V4R# \P!T4i;i5ir)rwis_warn_always_enabledset_warn_always)rVold_vals& r;set_warn_always_contextr[sE**,G '"'  g&g&rDc]tRtRtRtRtR#)NoTestiFr1N)r2r3r4r5__test__r9r1r:r;r]r]sHr:r]caa]tRtRto^t^tRt^t]PPPt ] PtRtRt]V3RlRl4t]P(V3RlRl4t]V3R lR l4t]P(V3R lR l4tRtRtRtR\V3R lltR]RltRt]R4tRtRtRt Rt!R^V3Rllt"R^RRRR/Rlt#R_V3Rllt$R_V3Rllt%Rt&Rt'Rt(R t)R!t*R"t+R]V3R#llt,R]R$lt-R%t.R&t/]R'R/R(l4t0R)R`R*^/R+lt1R*^/R,lt2R*^/R-lt3R*^/R.lt4R*^/R/lt5R0t6RaR1lt7R2t8R3t9R_R4lt:V3R5lR6lt;V3R7lR8ltRR?RR@R/V3RAlRBlllt=R]R9RR:R/V3RClRDlllt>V3RElV3RFllt?V3RGlRHlt@V3RIlRJltAV3RKltBV3RLltCRMtDRNtERbROltF]GRbRPl4tHR]RQltIR]RRltJRcRSltKRbRTltLRdRUltM]R]RVl4tN]RW4tOR]V3RXlRYlltPV3RZltQR[tRVtSV;tT#)erRiFNc@\PP4'd"\PP4R#R# \dKp\ R\ PR7\ \T4\ PR7Rp?R#Rp?ii;i)zDTEST SUITE EARLY TERMINATION due to torch.cuda.synchronize() failureryNTF) rwrAis_initializedrnrorgr8rqr)rrtes& r;_should_stop_test_suite TestCase._should_stop_test_suite*sk :: $ $ & &  &&(    \cfcmcmnc#hSZZ0 sA B?BBc <V^8dQhRS[/#rr)rSrs"r;rTTestCase.__annotate__9s5r:cVP#r _precisionr;s&r; precisionTestCase.precision8s r:c$<V^8dQhRS[RR/#rMprecrONrg)rSrs"r;rTrh=ser:cWnR#rrjrrps&&r;rlrm<sr:c <V^8dQhRS[/#rrg)rSrs"r;rTrhAsr:cVP#r_rel_tolr;s&r;rel_tolTestCase.rel_tol@s }}r:c$<V^8dQhRS[RR/#rorg)rSrs"r;rTrhEsEdr:cWnR#rrurrs&&r;rwrxDs r:c<VR8wdTp\S V`V4\WR4pVEet\'d^V;P\VRR4,unVP'd(\ 'gVP WP4V;P\VRR4,unVP'd(\ 'gVP WP4VP'dVPVR4\'d{Rp\P!\V44pV!V4p\V4P pRV RV R V 2p\"P%4p W.p VPVV 3R l4R#R#R# \&d,p \(P+R \-T 4R 7Rp ? R#Rp ? ii;i) runTestNrTrc \\4#r)rrXr1r:r;r#TestCase.__init__..es ;NOb;cr:cF\V4Pp\V4FXwr#VR8XgKV^8d"\PP !VRV!MRp\PP WR7u# \PPV4^,#)rNrK)r;)r rrr1rrrhrelpathr{) abs_test_pathrrr-partbase_dirs& r;_get_rel_test_path-TestCase.__init__.._get_rel_test_pathis|!%] 3 9 9'0'7GA#v~GH1u277<<r+CRT')ww}'U U(8 "ww}}];A>>r:zpython rdrWc\V4#r)r)rs&r;rr~s 8N{8[r:zcould not print repro string)extra)rr8rXrrrHwrap_with_cuda_policyassertLeaksNoCudaTensorsrenforceNonDefaultStream_ignore_not_implemented_errorwrap_with_policyrrgetfilerPr2rrrrinfor) r method_name methodName test_methodrrr class_name test_run_cmdenv_var_prefixrrqrs &&& r;r8TestCase.__init__Os  "$K %d6  "''//7;Hdfj3kk/222::..{A((A-cV<Vf\P!R4p\SV` W4#Nrw)logging getLoggerr assertLogsrloggerlevelrs&&&r;rTestCase.assertLogss( >&&w/Fw!&00r:cV<Vf\P!R4p\SV` W4#r)rrr assertNoLogsrs&&&r;rTestCase.assertNoLogss( >&&w/Fw#F22r:c\W4p^RIHpVP4P 4pV'd.RV9gRV9d\ WVP W244R#R#R#)ru) TEST_CUDAgpurAN)rXr5rr rXrwrap_method_with_policy)rrpolicyrrfullnames&&& r;rTestCase.wrap_with_cuda_policysRd0 B779??$ %8+v/A Dt'C'CK'X Y0B9r:cR\W4p\WVPW244R#r)rXrr)rrrrs&&& r;rTestCase.wrap_with_policys!d0 4#?#? #TUr:c\aa\S4VV3Rl4p\P!W04#)cp<S!4;_uu_4S!V/VBRRR4R# +'giR#;irr1)rrrrbrs&*,r;r1TestCase.wrap_method_with_policy..wrappers#''s $ 5 )r r MethodType)rrbrrs&ff r;r TestCase.wrap_method_with_policys. v (  (..r:c8VPWP4#r)rr)rrbs&&r;wrap_with_cuda_memory_check$TestCase.wrap_with_cuda_memory_checks++F4Q4QRRr:cLVPP RVP 2#)rW)rr2rr;s&r;_dynamo_test_keyTestCase._dynamo_test_keys%..))*!D,@,@+ABBr:cN\PPW#R7!V4#))r)rwroptimize)rrr5rs&&&&r; compile_fnTestCase.compile_fns}}%%g%A"EEr:c 4 <a\V\P4p\SS`pVP p\ ;'g\;'g\pRpRpRpV'd\P!\V44p \PPV 4p \P !RV 4p V e6V P#^4p \'d^RIHp W9pRpV R8XdRpMRpR\P,9d\P,R,R8XdRpRpV'dW\/SSP04p\3VR4'dVP4pM!\3VR4'dVP4pMTp\/VR R4;'dTpV'g V'd \6P8P;4MSP=4\6P>PAR 4V'd V'*pM$\6P8PBPDpV'd+\6PFPBPIRR 7M\JPL!4p\PNPIR V4;_uu_4T;_uu_4\'dSPQVR V4pEM\ 'g\'Ed\'dSPQVRV4pMSPQVRV4pSPS4pV3Rlp\'d Rp^RIH*pMRp^RIH+pVV9dU\/SSP04p\PPYVV4p\[SSP0V!VV44V3Rlp\'d Rp^RIH.pMRp^RIH/pVV9dU\/SSP04p\PPYVV4p\[SSP0V!VV44^RIH0p\6P8PBPb'dVV9d\e4!V4pV!VR7RRR4RRR4V'g V'd \6P8P;4Mc\6P8PBPb'd*\6P8PbP;4MSP=4V'dSPg4'dVPi4'd}\k4p\le/^RI7H8pV!VV4pVPrVn:RVn;RVn<VPzP}VR34VPi4RJd \R 4hVP4R#R#R# \(\*3dELi;i +'giEL;i +'giEL;i)!Fz.*/test/(.*).pyN)FIXME_inductor_non_strictTtest_opsSTRICT_DEFAULTrrrr) size_assertsraot_eager_decomp_partitionreager_noexceptc8<aa\S4VVV3Rl4pV#)c<S!V/VB\RS R24h \dpSPT4Rp?L0Rp?ii;i)Nz#Unexpected success, please remove ``) BaseExceptionrRro)rrrqrM file_namers*, r;r=TestCase._run_custom..expect_failure..wrapper`sP-t.v.+-PQZP[[\+]^^ -- MM!,,-s A;ArrMrrrsff r;expect_failure,TestCase._run_custom..expect_failure_s"1X__ #Nr:ztest/inductor_expected_failures)inductor_expected_failuresztest/dynamo_expected_failures)dynamo_expected_failuresc8<aa\S4VVV3Rl4pV#)c<S!V/VB\SSP4p\VRR4'dSPR4R#SPRS R24R# \dpSPT4Rp?LtRp?ii;i)N__unittest_expecting_failure__Fzunexpected successz'This test passed, maybe we can remove `r)rrRrXr)rrrqrbrMrrs*, r;r=TestCase._run_custom..ignore_failure..wrappervs-t.v.")t/C/C!D"6+KUSS MM*>? MM,ST]S^^_*`a -- MM!,,-sA B)A??Brrsff r;ignore_failure,TestCase._run_custom..ignore_failureus"1X b b#Nr:ztest/inductor_skips)inductor_skipsztest/dynamo_skips) dynamo_skips)compiled_autograd_skipsr,) _TestInforTestSuiteEarlyFailurez%TestSuite execution was aborted earlyz@expected result.wasSuccessful() to be False after adding failure)ArYrQ TestResultrrun__self__r&r$r"rrrPrrabspathrmatchrdynamo_test_failuresrOSErrorrZr rXrr&rrwrrrcompiler set_stancersuppress_errors _inductorr contextlib nullcontextrrrrrrhrrrrrrrdr-rRrQr*rERRORr elapsed_timetest_descriptionfailuresrfrstop)rr,using_unittest super_runtest_clscompiledstrict_defaultshould_reset_dynamoshould_disable_size_assertsr full_pathrrr strict_moderrr maybe_disable_size_assertskeyrsubdirexpected_failuresrbrrskipsrrrrsf& r; _run_customTestCase._run_customs#FH,?,?@GK %%)ZZ,?ZZCZ#',#  tH~6GGOOD1 !3Y?$${{1~H..S)1)R.2+#z1:>7)- 2::-::./36%)N !$(<(<=K{O44)77 ?33&44 , 8%=uERR( - MM   !  * * , !!), "-oO#mm22BBO+ OO " " ( (e ( <'') # ]] !G Y Y[u[u"" OOI7SU]^ &&*A*A** $ :x PI!% ;KX VI++-#+*>Fe\ : oY'  V\v[u[u Y Y Ysj$A5WW0W X(-W2  W2 # W2 /AW2 8A=W2 6BW2  W2 2XW/.W/2 X = X X c\P!4;_uu_4p\'dVP\ 44VP VR7RRR4R# +'giR#;i)rN)r ExitStackr enter_contextrr#)rr,rs&& r;r TestCase.runsN  ! ! # #u!!##LN3     $ # # #s 8A  A1 cn\V4\4\PPP 4Vn\PPP4VP'dJ\P!4\P8wd"\R\P!4 24h\P!4Vn \PP4Vn\PP#4Vn\'4VnR#).expected default dtype to be torch.float, got N)rrIrwsparsecheck_sparse_tensor_invariants is_enabled_check_invariantsenable_default_dtype_check_enabledrArris_grad_enabled_prev_grad_stater_len_torch_function_stack#_prev_torch_function_mode_stack_len_get_torch_function_state_prev_torch_function_state_snapshot_fp32_precision_prev_fp32_precisionr;s&r;setUpTestCase.setUps"'!L!L!W!W!Y  33::<  , , ,&&(EKK7$DUE\E\E^D_` !& 5 5 738883U3U3W0*/((*L*L*N'$<$>!r:c \VR4'ddVP'd*\PPP 4M(\PPP 4VP'dJ\P!4\P8wd"\R\P!4 24h\VR4'd!\P!VP4\PP4pWP8wd\\!WP, 4F!p\PP#4K# \RVP RV 24h\PP%4pW0P&8wdE\PP)VP&4\RVP& RV 24h\VR4'd\*P,P/R4R8wd\14pW@P28wd\5VP24\74p\9WPP2V4UUUUu.uFwwpr'pWx8wgKR V R V: R V: 2NK! p pppp\R R P;V 4,4hR#R#R#uuppppi)r.r*r2z:torch function mode stack was leaked: length changed from z to z.torch function state was leaked: changed from r8)PYTORCH_DISABLE_FP32_PRECISION_LEAK_CHECKrz z: was z , became z#fp32 precision flag leak detected: rN)r&r.rwr+r,r/disabler0rArrset_grad_enabledr2rr3r4r_pop_torch_function_stackr5r6_set_torch_function_staterr rr7r8_restore_fp32_precision_fp32_precision_flag_specsrrh) rafterrtf_statecurrentspecsr}prevcur mismatchess & r;tearDownTestCase.tearDownsb 4, - -%%% ;;BBD ;;CCE  , , ,&&(EKK7$DUE\E\E^D_` 4+ , ,  " "4#8#8 9224 << <5#K#KKL224M ''+'O'O&PPTUZT[]  88557 66 6 HH . .t/N/N O  $ ? ?@XJP  D0 1 1 JKsR.0G333'(A(AB2458?X?XZa4b4b0 1S{?bvdXYsg>4b %:TYYz=RR4S 2s K K r?c  Naa^Tu;8:dSS,8:gM\RV RS RS 24hVV3Rlp\P!S^,V\P!R4R7p^;rV!W4p V 'dV\ V S48dTp S^, p V!W4p W, ^8dEW,^,pV!W4pV^8XgW.S,, \ VS48dYrKMTp KQYrV'g \R4hWx)RP S4V 'dW(S,, \ V SV, 48dT pS^, pV!VV4p VV, ^8deVV,^,pV!VV4pV^8Xg;W(S,, VSV, ,, \ VSV, 48dTTp pKnTpKrTT rV 'g \R 4hV^SV, ^,P V 4V 'Ed{\ W(S,, V SV, ,, SV , SV , ^,,^,4wpp^VSV , ^,,,p\P!^V,4pVV^,,^V,8d V^,pVVV^,,^,, pV^8dV ^8d\R V R V 24h\P!V^, WGPR7SV , ^,,V^V%VVVV,^,;;;\P!V^,WGPR7, uuu%M&^pW(S,, V SV, ,, pVV;;,V, uu&V'd6\P!SVPR 7pVR ,V,VR &TpVP^R7VPVR 7#)axReturn crow_indices of a CSR tensor with size (n_rows, n_cols) and the number of specified elements nnz. If random is True, the column counts of rows are in random order. Otherwise, the column counts of rows are defined by the used sampling method. Sampling method --------------- The used sampling method was introduced in https://pearu.github.io/csr_sampling.html, and here we give only an overall description of the method. Notice that crow_indices can be defined as cumsum(counts) where counts is a sequence of non-negative integers satisfying the following conditions: len(counts) == n_rows + 1 counts.max() <= n_cols while counts[i + 1] is interpreted as the number of specified elements in the i-th row. The used sampling method aims at increasing the diversity of CSR samples, that is, a CSR sample should contain (i) rows that are all filled, (ii) rows with no elements at all, and (iii) rows that are partially filled. At the same time and for the given total number of specified elements (nnz), there should be minimal preference to rows with a given number of elements. To achieve this, the sampling method is built-up on using a sawteeth model for counts. In the simplest case, we would have counts = arange(n_rows + 1) % (n_cols + 1) that has equal number of all possible column counts per row. This formula can be used only for specific input values of n_rows, n_cols, and nnz. To generalize this model to any combinations of inputs, the counts model above is extended with an incomplete sawtooth, and the right and lower rectangular parts that will guarantee that counts.sum() == nnz for any combination of n_rows, n_cols, and nnz. Basically, we'll find a maximal window in (n_rows + 1, n_cols + 1)-grid that is able to hold a sequence of sawteeth and so-called final correction, while the external part of the window is filled with counts to meet the nnz constraint exactly. zAnnz out of bounds: expected 0 <= nnz <= n_rows * n_cols, got nnz=z , n_rows=z , n_cols=c <SV, SV, ^,,^,pSV, SV, ^,,pVSV, SV, ^,,,W3^, ,^,,#rr1)nmMKn_colsn_rowss&& r;sawteeth-TestCase._make_crow_indices..sawteethNsb ! Q/14A! Q/A!!a89AQK1S$S'/W%Fq$%9:: 23K  f % 6z!SFQJ%77FqjGq'*GF"Q&"W,2#Ax0q=Cf*$4x6A:7N$NQTU]_ehi_iQj$j'/WGW%FGq$%9:: 1VaZ!^ $ * *1 - 1#F *Q&1*-==!A:&1*q.9Q>@DAqA!a((A 1q5!AAE{QU"QqAE{a''DAAE$NqcQUVWUXY ,,q1uE--PTZ]^T^abTbcF1QK 1QUQY 5<<AU==#Y Y AV#a6A:&66D q T >>&?DD)F2J #f--r: blocksize dense_dimsc :aaaaaaa^RIHp ^RIHp ^p \;QJd1V3Rl\ \ S444F 'dK RM& RM"!V3Rl\ \ S4444'gV^8Xg\RS RV 24h\ S4V 8d\RV R \ S4 24hS'd\ S4^8wd\R S R S 24hSRV, ,S^,,^8wd\R S R S 24hSRV, ,S^,,^8wd\R S R S 24hSwrM^;r\S4oS\ S4V, RoVVVVVV3RlpSRRV, pV !W^4pV\P\P09d0SRV, ,V ,SRV, ,V ,ppM.SRV, ,V ,SRV, ,V ,ppW,V ,,p\ V4Uu.uF pV!VVV4NK pp\\\VRR/4p\P!\!V44P"!.VOVNSOSO5!p\P!\!V44P"!.VORN5!p\P!\!V44P"!.VORN5!p\P$!VVVSSVSR7#uupi)ru)rm)reducec3<<"TFpSV,^8xK R#5irr1rrZr5s& r;r5TestCase.genSparseCompressedTensor..s:)9ADGaK)9r-FTinvalid arguments: size=, nnz=zexpected len(size) >= , got z'expected len(blocksize) == 2, got size= , blocksize=z>size[-2 - dense_dims] must be divisible by blocksize[0]: size=z>size[-1 - dense_dims] must be divisible by blocksize[1]: size=Nc <SPWVS SR7p\P!VSS R7p\V4FhpW5^,,W5,, p\P!\P !VSS R7RV4wWCV,W5^,,%pKj S\P 8wdRM^pS\P 8wd^M^p \V3S ,S ,S SWR7p WV3#)rMr)Nrrlowhighr!)rmrwrVrsortr[r,r#)n_compressed_dims n_plain_dimsr]compressed_indices plain_indicesr-countrr|r}rro dense_sizerr index_dtypers&&& r;random_sparse_compressedDTestCase.genSparseCompressedTensor..random_sparse_compresseds!%!8!89JZ]flt!8"A !KK;vNM,-*q514F4IITYT^T^NN<{6RSYTYZU\Q 34F1u4MNPQ.,"!C,1!D #)!3j!@W\beqF}< &G$&GHT"345==n{nHnW`ncmn"[[.?)@AII[;[XZ[ D):$;<DDVkVSUV --.@-.44uU[djl l ws3Lc LVPW\PVWERVR7#)rrrrrorpr1)rrwrrr5r]rrrrps&&&$$$$r;genSparseCSRTensorTestCase.genSparseCSRTensors2--d@P@PY_49^`mw.y yr:c LVPW\PVWER^R7#)rurr1)rrw sparse_cscrs&&&$$$$r;genSparseCSCTensorTestCase.genSparseCSCTensors2--d@P@PY_49^`mn.p pr:c \V4^8wd\R\V4 24hVPW\PVWVW'R7#rMz"expected len(blocksize) == 2, got r)rrrrwrrr5ror]rrrrps&&&&$$$$r;genSparseBSRTensorTestCase.genSparseBSRTensorS y>Q  #Ec)nEU!VW W--d@P@PY_49^g.@ @r:c \V4^8wd\R\V4 24hVPW\PVWVW'R7#r)rrrrw sparse_bscrs&&&&$$$$r;genSparseBSCTensorTestCase.genSparseBSCTensorrr:c"a\;QJd(V3Rl\V44F 'dK RM RM!V3Rl\V444'gV^8Xg\RS RV RV 24hV.\SVR4,p\ WuVR ^R7p\ P !W#VR7p V P\ P!SRV4P^4PV 44V P\ P4p V'dJV R RV^,1R 3,p V R RV^,^,1R 3,p \ P!W.^4p \ P!W\ P!S4WeR 7p V'gV P4p M-V P!4P#4P%R4p WP'4P#4V P)4P#43#) c3<<"TFpSV,^8xK R#5irr1rts& r;r+TestCase.genSparseTensor..s;):ADGaK):r-FTrvz , sparse_dim=rwNr{rg:NNN.r)r!)rrrrgr#rwrandmul_rU unsqueezer0longcatsparse_coo_tensorSizecoalescedetachclone _coalesced__indices_values) rr5rr]is_uncoalescedrrv_sizerr-i1i2r,s &f&&&&& r;genSparseTensorTestCase.genSparseTensors;z):;;z):;;;sax #;D6zlZ`ad`e!fg gd:;/00 U K JJzv 6 u||D*-.88;>>qAB DD  1kqk3&'B1'qQ',-B 2(A&A  # #A%**T*:% W A   "..u5A**,$$& (9(9(;;;r:c# raa'a(a)a*a+"Vf\PpV\P\P\P\P 09pV 'EdvSP WW4VWxV V V V RR7 EFTwppV'd-\;QJd.RV4FNK 5M !RV44pV\PJd\V4^8wd\R\V4 24hVPRR4pVf \R4hV'd+V^,PV4P4xKV^,PV4xKV\PJd\P!V/VBxEKV'd.VP!VR7\P"!V/VBxEKI\R V 24h R#R o)R o*V)V*3R lo+V(V+3R lo(V'3Rlo'Vf.RO.RO.RR..RO3.RO.RO..RO.RO..RO.RO...RO.RO..RO.RO..RO.RO...RR .RR.3.R!O.R"O.R#O.R$O.R%O.R&O.R'O.R(O.R .RR.3^^.^^..^^.^^...R).V 'dR.M.3.pR+V3RllpVEFwpppV'gVUu.uFpV'dK VNK ppV'd V'gK=\P$!V\PR7pV'gVP&^8dK~VEFepS(!VV4V,pVEFJpVRR*Uu.uFpVP)W$R7NK ppS'!VR*,V4P)W#R7p\+W#VP,V,R7pVeVP!VR7.VOVN5VP/43xV 'dVP&^8dnR,FgwppVUu.uFpV!VVVR7NK p p.V OVN5VP/43xV 'gKCV!VR*^R7p!.V OV!N5VP/43xKi V 'gEK&V!VR*^R7p!.VOV!N5VP/43xEKM EKh EK V 'Ed*R-R..V'd.R/OMR.3R0.R1OR.3R2R..R.33EFwp"ppVEFpVEFpV\P8Xd$Rp\P0!V"V,W#R7pEMvV\P8XdB\P0!\V"4^W$R73p\P0!^.VO5W#R7pEM V\P8Xdi\P$!^.V"^,^,,W$R7p#\P0!^W$R7p$V#V$3p\P0!^.VO5W#R7pEMV\P8Xdi\P$!^.V"^,^,,W$R7p%\P0!^W$R7p&V%V&3p\P0!^.VO5W#R7pEM&V\P8Xdx\P$!^.V"^,V^,,^,,W$R7p#\P0!^W$R7p$V#V$3p\P0!^.VOVO5W#R7pMV\P 8Xdx\P$!^.V"^,V^,,^,,W$R7p%\P0!^W$R7p&V%V&3p\P0!^.VOVO5W#R7pM\R V 24h\+W#V"V,R7pVeVP!VR7.VOVN5V3xEK EK EK R#R#uupiuupiuupi5i)3avGenerator of simple inputs for tensor constructors of the given layout. The generated tensor inputs have the following properties: - tensor shapes are minimal but not trivial - tensor values are sorted sequences for COO and CSR formats, e.g. [1, 2, 3, 4] - the generated tensors represent the same mathematical tensor for all layouts - the generated tensors include regular, zero-sized, and optionally, batched or/and hybrid tensors. - the generated tensors include contiguous or non-contiguous tensors both in indices and values If output_tensor is True, yield tensors with the given layout. Otherwise, yield inputs to the corresponding tensor constructors: - sparse compressed input is defined as (compressed_indices, plain_indices, values), dict(size=expected_size_from_shape_inference, device=device, dtype=dtype, pin_memory=pin_memory) - sparse COO input is defined as (indices, values), dict(size=expected_size_from_shape_inference, device=device, dtype=dtype, pin_memory=pin_memory) - strided input is defined as (values,), dict(device=device, dtype=dtype) NF) rrr pin_memory enable_batch enable_hybridenable_zero_sizedenable_non_contiguous_indicesenable_non_contiguous_valuesenable_batch_variable_nse output_tensorc3@"TFqP4xK R#5irr)rrs& r;r2TestCase.generate_simple_inputs..Fs >Asz0expected len(args) == 1 for strided layout, got r5z(size must not be None for strided layout)rzunreachable: unexpected layout c:VPpV^,V^,,^8wd\RV RV 24hV^,V^,,^8wd\RV RV 24hVPRV^,V^,V^,,V^,4PRR4P R4P R4p\ P !^VP4^,4PVP4pV^8gV,#)ruz8basesize[0] must be divisible by blocksize[0]: basesize=ryz8basesize[1] must be divisible by blocksize[1]: basesize=r!r)shaperr transposerrwrZr)rrobasesize blockpattern block_idss&& r;get_blockpattern9TestCase.generate_simple_inputs..get_blockpatternZs}}H{Yq\)Q.$NxjXdendop{Yq\)Q.$NxjXdendop#??2+4Q<+3A;)A,+F+4Q<9:C2r9J33r7SVSVWYSZ  Q (:(:(.get_sparse_dataks}}H8}!$'UV^U_%`aakk'Q,/G++g.K ;;x{QekkJL$||KN,C,ChWXk,C,Z\]^L %a.K"[[EN \\!QWQZ%8 LF&,7 # G$5$5a$;q$@AI!KK 2M ;;x{QekkJL$||M!,<,E,EPXYZP[,E,\^_`L '*K'11!Q7 BJ$${&;$$|&&I$$|*&MMMN#46 6r:c <S !V4pS !W4pS !V4pV\P,^,pV\P,^,p\P!VP ^^4UUu.uFXwrxWWV^,,V^,V^,,1W^,,V^,V^,,13,NKZ upp4p W\P ,^,^, ,p \P .V\P,R,OV N5\P.V\P ,R,OV N5/VC#uuppi)rur) rwrrrrrrrr) rro nonblock_datar block_datar block_indicesbibj bsr_values bsc_valuesrrs && r;get_sparse_data_with_blockCTestCase.generate_simple_inputs..get_sparse_data_with_blocksV+G4M+G?L(6J*5==9!.get_batch_sparse_data..s$?i[hVW!q@Q@Q[hs%()rrr1r^rrwrrfullrset_rrr) rror5 batch_datar-rmrr,ext_coo_indices1rZrZget_batch_sparse_datars && @r;r>TestCase.generate_simple_inputs..get_batch_sparse_datas==D4yA~1'EEJ$W-!6t!G!M!M!OIFA'^^F3F!1!11,199ejj!S1YQRZ_ZeZe6fhijkhl5m+n(!>;KQqT:RRF%7"1INN599fQiAQ5RTU+VW"1INN599fQi15F+GH!>:?%?i[`adefag[h?i%%?i[`adefag[h?i:iiF%7%*3q6] & uyy&)QqT^^TUEV9W/X Y&3!"P.& r:c |<V'gV#\V\4'g>VP^8d-\P!VUu.uF pS!W!4NK up4#V^8Xd'\P !V\P R7#\P!V^,\P R7p\VR,4FWwrE\P!V\P R7^ V^,,,pVR,VR,,pKY VPV4V#uupi)a)Generates a tensor of shape densesize with values equal to base + i_1 * 10^0 + ... + i_d * 10^{d - 1} at indices i_1, ..., i_d (with 0 <= i_j < densesize[j] for any 1 <= j <= len(densesize)) This mapping produces unique values as long as densesize[i] < 10 for all i in range(len(densesize)). rr).N)N.) rYrndimrwrrVrrZr1add_)base densesizebrr-rZygenerate_valuess&& r;r 8TestCase.generate_simple_inputs..generate_valuess dC((TYY]{{4#P4aOA$A4#PQQqy{{9EKK@@ Yq\=A!)B-0LL%++6"Q-HiL1Y</1 FF4LH$QsD9c<SPVP44V^8dWP,pV^8dWP8g\RVP RV 24h\ ^V^,4p\ P !.VPRVOVPV,V,NVPV^,RO5VPVPR7p.\R43V,O\VRV4N5pWE,PV4pSPVP44SPW4V#)ruz&dim out of range: expected 0 <= dim < z , got dim=Nr)) assertTrue is_contiguousrrrrwrVrrrslicer6 assertFalser;)r8r\offsetsteptmp dim_slicesrrs&&& r;non_contiguous_copy.non_contiguous_copys OOAOO- .QwFFl1Hvv$'MaffXU_`c_d%effq&1*%D++W Wqwws|d/BWQWWSSTWXEVW_`_f_fopowowxCMU4[NS0M5t3LMJ%%a(A   Q__. /   Q "Hr:rrM)rrr5r)r\r )rrMru)rrur8rMr)rr8r1rMrA)r1r r )rrMr8)rruru)rurMrurMr8)rurrurMrurM)rurrururMru)r8r8r8rururu)rurururururu)rur rur r )r rur r r r )rururururTrT)r r rurTrT)rrr!)r!ru>rurrru)rMrurrM)r1r r )rurM)r r )r8rM)ruru)rwrrrrrgenerate_simple_inputsrrrrrrrrrupdaterrUrr0rrr'rO),rrrrrrmembers_pin_memoryrrrrrrrpatternsis_compressed_sparse_layoutrrr5r r blocksizes densesizesrrodatar rrrr\r indices_copy values_copyrrlrrrr rrrrs,f&&&&&&&&&&&&&& @@@@@r;r TestCase.generate_simple_inputssUL  ++K&,1A1A5CSCSUZUeUeglgwgw0x&x# = $ ; ;FY^GQIUN_ZwYuVoJO!<!Q f& 5 > >55 > >>DU]]*4yA~,/_`cdh`i_j-kll!::fd3D|,-WXX!"1good3>>@@"1good33u///114B6BB0MMM088$I&II(+J6()STT5!Q6  3"# 6J %( 8 2  4%v.0BD    !$*6"2RJ @%$$$$$$$&)/x"f?q6q6q6q6%X0IrT9UHB "08 +GZ )3=A1aa =:ll7%++>GGLL1$4' ,Wi@H!+IOSTWUWyYy!qtt6tEyGY,T"XyADDFD`F!7==S\C\]F!-  <,G,V,fkkm;;49I->MS&dk+ldk_`,?sSY,ZdkL+l"9L"9&"96;;="HH;;.A&bYZ.[ &B &Bk&BFKKM&Q Q .?43&9&bQR&S 555v{{}DD)",(08B  fX]'9QSPTU5t<fXt,50*j ",I%/ !U]]2&(G%*[[(Y2FPV%dF#u'7'77',{{3x=!F'f&hG%*[[!i%]F#u'7'77+0<<x{Q8OX^+rL*/++a*ZK'3[&AG%*[[!i%]F#u'7'77+0<<x{Q8OX^+rL*/++a*ZK'3[&AG%*[[!i%]F#u'7'77+0<<x{iXYl?Z]^?^8_hn,CL*/++a*ZK'3[&AG%*[[!1Li1L)1LU[%iF#u'7'77+0<<x{iXYl?Z]^?^8_hn,CL*/++a*ZK'3[&AG%*[[!1Li1L)1LU[%iF"03RSYRZ1["\\!%VxR[G[!\%1"MMZM@000&88A&0", 5 ?>Z,mswA^7'3^7B^7 A<^7C<^7 ^((^(. ^78^7/^706^7&^- A6^78^7^2 ) ^7-^7;^7=L:^7cVP\P8XdVP4pVP 4#r)rrwrrto_dense)rr8s&&r; safeToDenseTestCase.safeToDensems+ 88u'' ' Azz|r:c  VP4pVPVPVPrpVPVPVPrp V!V .V O5/V Bp V!V.VO5/VBp VP!W3RR/VBR#) exact_deviceFN)rrrrr;)rtorch_fnref_fn sample_inputr numpy_samplen_inpn_argsn_kwargst_inpt_argst_kwargsrrs&&&&, r;compare_with_referenceTestCase.compare_with_referenceus#))+ "."4"4l6G6GI\I\x"."4"4l6G6GI\I\x%5&5H5%5&5H5 HHHr:c |\'g \R4h\V\P4'd~Ve \R4hVe \R4hVP 4P 4pVP\PJdVP4pVP4pTp Mq\P!\4p \PV \P&\P!W:V,R7p\P !W4VR7p V!V4p V!V 4P 4p \V \P"4'd\P$!V 4p V P\PJd\V P\PJd>V P\PJd V P)\P4p VP*!W3/VBR# \&d(\P$!T P44p Li;i)z1TEST_NUMPY must be True to use compare_with_numpyNz0device must be None when tensor_like is a Tensorz/dtype must be None when tensor_like is a TensorrrM)rrrYrwrrr@rbfloat16rrr'torch_to_numpy_dtype_dictr,rrrUrrrr0r;) rr+ np_fn tensor_likerrrt_cpurr8rZ np_result torch_results &&&&&&, r;compare_with_numpyTestCase.compare_with_numpysz !TU U k5<< 0 0!$%WXX $%VWW&&(,,.E{{enn,  AA 34A " Aenn eH5A [uEA!H {(  i , , ?!,,Y7 ww%..(\-?-?5>>-QV_VeVeinititVt+u{{;  ;F; ?",,Y^^-=>  ?s%H /H;:H;c<V^8dQhRR/#rr1)rSrs"r;rTrhsDDDr:c*VP!VRR/VB#) exact_dtypeF)r;)rrrs&*,r;assertEqualIgnoreTypeTestCase.assertEqualIgnoreTypesC5CFCCr:c<V^8dQhRR/#rr1)rSrs"r;rTrhs 7 7 7r:c 6\V\4'g\P!V4V,p\V\P4'g2\P!V4\P !V4,pVP !W.VO5/VB#)zHTests if tensor x equals to y, if y to be broadcast to x.shape. )rYrrw ones_likerrUr;)rr,r rrs&&&*,r;assertEqualBroadcasting TestCase.assertEqualBroadcastingsk!X&&"Q&A!U\\**"U\\!_4A6t6v66r:rr equal_nanrC r* exact_layout exact_strideexact_is_coalescedc~<V^8dQhRS[S[S[.S[3,,R,RS[R,RS[R,/#)rMrNrr)rrr)rSrs"r;rTrhsM[[xs ++d2 [ $, [$,[r:caRp \;QJdRW34F 'gK RM RM!RW344'dRp V !V4pV !V4pM\V\P4'dD\V\4'd.\P !W!P VPR7pMb\V\4'dM\V\P4'd-\P !WP VPR7p\V\P4'dBVP'd0VP\P8XdVP4p\V\P4'dBVP'd0VP\P8XdVP4p\W4wr\VV\\\ \"\$\&\(\*\,3 \\.\0\2\4\P6P8P:P<3\>\@\B\D3VVPFVVPHVVVV V V R7pV'd\V.pVPK4^,PM\S\N4'dVPP'd V3Rl4hS4hR#)Tc3"TFAp\V\P4;'d\VP4'*xKC R#5ir)rYr,rrr)rrs& r;r'TestCase.assertEqual..s; jpafJubjj ) \ \2OPUP[P[2\.\ \jps %A #A Fc\V\P\P34'dVP 4#\ V4#r)rYrwrr,rtolistrgrs&r;to_list%TestCase.assertEqual..to_lists3)3EELL"**;U)V)Vu||~g\`af\ggr:r)) pair_typessequence_types mapping_typesrrrrrK check_devicer check_layout check_stridecheck_is_coalescedc<V RS 2#)rr1) generated_msgrs&r;r&TestCase.assertEqual..s-3%'@r:N))rrYrwrr as_tensorrr is_nestedrrunbindr$r'r&rrrr#rErIrKrMrrrrrr! datasetSubsetrrrrrwrlrto_errorr longMessage)rr,r rrrrK rC r* rL rM rN __tracebackhide__rV error_metass&&&f$$$$$$$$ r;r;TestCase.assertEquals4"! 3 kljp 333 kljp    h A A5<< ( (Z8-D-DBA 8 $ $Au||)D)DBA a & &1;;;188u}};T A a & &1;;;188u}};T A-a3+ "!!     ((// #J zJ,,..%#%%1A! F &-K//#A&//FPPSUXEYEY^b^n^n^n@  ux   r:cZ<V^8dQhRS[R,RS[R,RS[R,RR/#)rMrNrrrOrr)rSrs"r;rTrhs>HHd H"T\H8= HZ^Hr:c VP\VR7;_uu_4VP!WV3RVRV/VBRRR4R# +'giR#;i)rgrrN) assertRaisesrr;)rr,r rrrrs&&&&$$,r;assertNotEqualTestCase.assertNotEqualsF   ~3  7 7   Q3 GT G G G8 7 7 7s A A c <V^8dQhRS[/#rr)rSrs"r;rTrhs88#8r:cT<\V4'd V!V4#\SV` W4#r)rr_formatMessage)rr standardMsgrs&&&r;rt TestCase._formatMessages' C=={# #w%c77r:c<V^8dQhRR/#rr1)rSrs"r;rTrh!s33T3r:cVPVPVP4VPVPVP4VPVPVP4R#r)r;rr is_sparse)rr,r s&&&r;assertEqualTypeStringTestCase.assertEqualTypeString!sJ 188, !''* akk2r:c:<V^8dQhRS[RS[S[,RR/#)rMr7rarON)rr)rSrs"r;rTrh's&==#=#=4=r:cbVFp\V4\V48XgKR# \R4h)Nzobject not found in iterable)r r)rr7raelems&&& r;assertObjectInTestCase.assertObjectIn's-D#w"T(";<>"5tD D7,-?aRVaZ`a ar:caRoV3Rlp\P!4p\P!4\PP RV4;_uu_4V!V/VBRRR4V'd\P !4TPS4R# +'giL@;i T'd\P !4ii;i)Nc <VoR#rr1) unraisableraiseds&r;record_unraisable6TestCase.assertNoUnraisable..record_unraisableWsFr:zsys.unraisablehook)r isenabledr=rQrrr/ assertIsNone)rrrrr rGr s&&*, @r;assertNoUnraisableTestCase.assertNoUnraisableTs ||~  $$%9;LMM$)&)N  &! NM s#(B1 B&B1 B. )B11 CcRpRV9d VR,pVRV!V/VBTPRR7R# Td'pTP\T4T4Rp?R#Rp?ii;i)Nsubnamerrg)assertExpectedrr)rrurrrr rqs&&&*, r;assertExpectedRaisesTestCase.assertExpectedRaisesksm  Y'Gy!  d %f % 6 7     A 0  s3A AA c  R\P!RR7;_uu_4p\P!R4\R4;_uu_4V!4RRR4VP \ V4^8HV4RRR4R# +'giL8;i +'giR#;i)z4 Test if :attr:`callable` does not raise a warning. TrecordalwaysN)r$r"r#r[r r)rrrwss&&& r; assertNotWarnTestCase.assertNotWarnxsk $ $D 1 1R  ! !( +(.. / OOCGqL# . 2 1..2 1 1s#*B B &B B B B& c # 0aa"\P!V4o\P!RR7;_uu_4p\P!R4\ R4;_uu_4RxRRR4\ V4^8XdVPR4TP\;QJdV3RlV4F 'gK RM RM!V3RlV444TP\;QJdV3RlV4F 'gK RM RM!V3RlV44S R VUu.uF*p\VP4SJgKVPNK, up 24RRR4R# +'giEL ;iuupi +'giR#;i5i) zContext manager for code that *must always* warn This filters expected warnings from the test and fails if the expected warning is not caught. It uses set_warn_always() to force TORCH_WARN_ONCE to behave like TORCH_WARN Tr r Nzno warning caughtc3R<"TFp\VP4SJxK R#5ir)rPr)rwcategorys& r;r0TestCase.assertWarnsOnceRegex..sHRQYY8 ;Rs$'Fc3x<"TF/p\P!S\VP44xK1 R#5ir)rrrr)rr rs& r;rr s'Br!BHHWc!))n55rs7:r) rrr$r"r#r[rrr rrPr)rr regexr r rs&f& @r;assertWarnsOnceRegexTestCase.assertWarnsOnceRegexs **U#  $ $D 1 1R  ! !( +(../2w!| -. OOCCHRHCCCHRHH I OOBrBBrBB)2"T"QQYY88Syqyy"TUV X2 1...U2 1 1s^6F*F$E) )>F(F?1F1FF&E=E= F F) E: 4F F Fc  .aa a a \S\4'g \R4hRpVPPpV!VP 4VR,4o \ PP\PV,P4p\ PP\ PPV4RS 4o Ro V'dS RV,, o RV R2o S R , o R pV V VV 3R lp\S 4;_uu_4pVP4pR R R 4\.'d1\0P2!RRT4p\0P2!RRS4oTP5RR\6P8P: R24p\&P('dTS8wd T!R4#R #\=TR4'dTP?TS4R #TPAST4R # +'giL;i \ drp T P"\"P$8wdh\&P('dT!R 4uR p ? #\+R S S RS R\,P RS R2 4R hR p ? ii;i)al Test that a string matches the recorded contents of a file derived from the name of this test and subname. This file is placed in the 'expect' directory in the same directory as the test script. You can automatically update the recorded test output using --accept. If you call this multiple times in a single function, you must give a unique subname each time. zassertExpected is strings onlycPVPV4'dV\V4R#V#r)r r)textprefixs&&r; remove_prefix.TestCase.assertExpected..remove_prefixs'v&&CKL))Kr:rWrrKrrzrxz.expectNc <\RV RS S RS 24\SR4;_uu_4p\P!RRS4pVP V4RRR4R# +'giR#;i)z Accepting r: r z(producer_version): "[0-9.]*"z\1: "CURRENT_VERSION"N)rgrrrr) update_typerMs_tag expected_file munged_idrsubname_outputs& r; accept_output.TestCase.assertExpected..accept_outputsd J{m5 N;K5QRPST UmS))Q?7< *)))s *A  A1 rzI got this output for r zC No expect file exists; to accept the current output, run: python rdz --acceptzCppOp\[(.+?)\]zCppOp[]z#producer_version: "CURRENT_VERSION"zproducer_version: "rzupdated outputassertMultiLineEqual)!rYrrZrr3r rrrealpathr8rrrhdirnamerreadr errnoENOENTrWr0ror1rHrrrrwonnxproducer_versionr&r r;) rrr r module_id test_filerr rMrqr r r s &f& @@@r;r TestCase.assertExpecteds3!S!!<= = NN-- !$'')Y_= GG$$S[[%;%D%DE  RWW__Y%?%-%.0   S7] *M!'!_N"    Tm$$668% :vv/HEH()Q7A## 1!%**"="=!>a @     1}$%566t344))(A6  H-A%$ Tww%,,&"""$X..".yk.9IqcR ( 1 12!I;iIJPTT TsHH!H2H H HH J#5JJ J&)JJcX\P!RRV4pVPW4R#)z__torch__[^ ]+rKN)rrr )rrr s&&&r;assertExpectedStripMangled#TestCase.assertExpectedStripMangleds# FF$b! , A'r:c VeVe \R4hW8dR#W!, pVeWe8:dR#V RV RV R2pM#Vf^p\Wc4^8XdR#V RV RV R2pVPWG4pVPV4h)zAssert that ``first`` is greater than or almost equal to ``second``. The equality of ``first`` and ``second`` is determined in a similar way to the ``assertAlmostEqual`` function of the standard library. Nz specify delta or places not bothz not greater than or equal to z within z deltaz places)rZroundrt r<)rfirstsecondplacesrdeltadiffru s&&&&&& r;assertGreaterAlmostEqual!TestCase.assertGreaterAlmostEquals  !3>? ? ? ~  }"G#A&RWQXX^_K~T"a'"G#A&RXQYY`aK!!#3##C((r:cVPPUu.uF+pVPR8XgKVPR8XgK)VNK- ppVP V4VFXpVP Uu/uF'qwP VPP4bK) ppVPR4V8XgKXM VPXR,V4VPVPRR4V4R#uupiuupi)ATenzorg.pytorch.atenre overload_namerKN) graphnodeop_typedomainr attributererdecoderr;) r onnx_modelrer riall_aten_nodesopattrattrss &&&& r; assertAtenOpTestCase.assertAtenOps%/%5%5%:%:U%:YY&056XXAS5S!%:U ' B<>LLILDYY /LEIyy$0! z*H5 ?B7GU JsC8C8C80-C=c  VR,p\R4;_uu_4V'd2VP\VRR7;_uu_4V!4RRR4MV!4RRR4\RRR7;_uu_4V'd0VP \ V4;_uu_4V!4RRR4M\P!RR 7;_uu_4p\P!R 4V!4VFLp\V\ 4'gKVP\P!V\V44RJ4KN RRR4RRR4R# +'giEL;i \d?pR\T49d)TP R \T4,R,4hRp?ii;i +'giEL_;i +'giL;i +'giL;i +'giR#;i) a}Checks that an operation produces a nondeterministic alert when expected while `torch.use_deterministic_algorithms(True)` is set. Args: fn (callable): Function to check for a nondeterministic alert caller_name (str): Name of the operation that produces the nondeterministic alert. This name is expected to appear in the error/warning message. should_alert (bool, optional): If True, then the check will only pass if calling `fn` produces a nondeterministic error/warning with the expected message. If False, then the check will only pass if calling `fn` does not produce an error. Default: `True`. z: does not have a deterministic implementation, but you setTz9expected a non-deterministic error, but it was not raisedrgNz,does not have a deterministic implementationrrr r zEdid not expect non-deterministic error message, but got one anyway: ")rvr rorrassertWarnsRegexrGr$r"r#rYr rr)rr caller_name should_alert alert_messagerqr rs&&&& r;check_nondeterministic_alert%TestCase.check_nondeterministic_alerts"$&bb   % %++$%W,YYD YYD&,  5 5**#%''D'' ,,D99Q))(3D#$%g{;; OOBIImS\,RVZ,Z[$%:6 5)YYY$EQO 68;A?ADEF & % %0'' :96 5 5s)F5E  F5E)<'G/#G +(G/6G 5G G/ E& F5) F249F--F22F55 G G G/ G, 'G// H c^RIpVP!\PRV.VPVPVR7;_uu_4pVP 4wrEWE3uuRRR4# +'giR#;i)ruN-c)rprqro)rNrsr8r(PIPE communicate)coderorNrirprqs&& r;run_process_no_exception!TestCase.run_process_no_exceptionNse    ^^T4 (????    }} V#    s A(( A9 c\PP4pRVR&VPRR4VPRR4\P WR7wr#VP R4#)rPYTORCH_API_USAGE_STDERRrNTEST_SHOWLOCALS)roascii)rr r'rrRr r )r ro_stdoutrqs& r;runWithPytorchAPIUsageStderr%TestCase.runWithPytorchAPIUsageStderr\s`jjoo*- &' d !4(";;D;J}}W%%r:cR<V^8dQhRS[PRS[RS[R,RR/#)rMry import_stringexpected_failure_messageNrO)pathlibr r)rSrs"r;rTrhgsAGGllGG#&* G  Gr:c  hRV RV R2pV'd+VP\\P!V44M\P !4pT;_uu_4\ P!\PRV.\PP\PP\44\ PR7RRR4R# \ P d+p\TP"P%R44RhRp?ii;i +'giR#;i)a Attempts weights_only `torch.load` in a subprocess. This is used to test that weights_only `torch.load` works as expected without global imports. Args: file (pathlib.Path): The path to the checkpoint to load. import_string (str): import string to add to the script expected_failure_message (str, optional): The expected failure message if the checkpoint fails to load. If None, the test will pass z import torch;z torch.load(r'z', weights_only=True)r )rnrqr@N)r rorescaperrrN check_outputr8r(rrr r rSTDOUTCalledProcessErrorrr )rryr r scriptcmrqs&&&& r;_attempt_load_from_subprocess&TestCase._attempt_load_from_subprocessgs !}TFBWX(  " "<;S1T U-7-C-C-E R G''^^T62(8(8(BC%,, R00 G"188??7#;<$F GRRs+D A6CD3%DDD  D1 cJ<V^8dQh/S[;R&S[;R&S[;R&S[;R&#)rMrkrvr0r4)rrr)rSrs"r;rTrhs9  #'.*-0r:)r.rkr8r2r4r6rv)r|r|r)ruFFrr1) NNNNNTTTTTFTN)rK)NNNr)Ur2r3r4r5rkrvr0r4rwr_TorchFunctionStateENABLEDr6r8maxsize_diffThresholdmaxDiffrdpropertyrlsetterrwrrrr8rrrrrrrrrrrrrrrrrrr#rr9rJrmrrrrrrr r' r5 r? rD rI r;rp rt rz r ro r r r r rr r r r r r r r r rr9rrrs@@r;rRrRs. JH*/ /0'!&!=!=!E!E[[NG  ^^"'"'%*!3Kj/)& *) ) r 823 8EI 8VZ 81 3 ZV /SCFl^?8<|S.48S.S.j5ldf5lst5lnyVWypVWp@ab@ @ab@ <:Z9x I#>,.se15 c>a8$!+,sN E 'E "D9 'E / E 9 E E  E E E F01:F++F0cp\\P!\P\P44;_uu_4pVP \P \P ^4VPR4VP4wrVuuRRR4# +'giR#;i)aj Finds an available port and returns that port number. NOTE: If this function is being used to allocate a port to Store (or indirectly via init_process_group or init_rpc), it should be used in conjunction with the `retry_on_connect_failures` decorator as there is a potential race condition where the allocated port may become unavailable before it can be used N) localhostru) rsocketAF_INET SOCK_STREAM setsockopt SOL_SOCKET SO_REUSEADDRbind getsockname)sockrports r;find_free_portr' sr v~~v/A/AB C Ct ))6+>+>B "#""$ D C C Cs AB$$ B5 zaddress already in usezconnect() timed out.c^aaSf\\SR7#\S4VV3Rl4pV#)zqReruns a test if the test returns a RuntimeError and the exception contains one of the strings in connect_errors.)connect_errorsc ~<a^ pTpS!V/VB# \do\;QJdT3RlS4F 'gK RM RM!T3RlS44'd[T^,pT^8Xd\RT R\S4 24Sh\P!\ P !44Ro>KhRo>ii;i) Tc3<"TF6q\S49;'gV\S4P49xK8 R#5ir)rrX)rcers& r;r=retry_on_connect_failures..wrapper..s5]nSZ'CC2U1A1A1C+CCns A%AFzFailing after z retries with error: N)rorrtimesleepr?)rr n_retriestries_remainingrr) rs*, @r;r*retry_on_connect_failures..wrappers # T,V,, 3]n]333]n]]]#q(O&!+*^I;F[\_`e\f[g+hiottJJv}}/ s& B<B7B7AB76B77B<)r retry_on_connect_failuresr )rr) rsff r;r4 r4 s5 |0PP 4[   Nr:c aaaaVVVV3RlpV#)c:<a\S4VVVVV3Rl4pV#)c J<S Sr2V^8d S!V/VB#S!V/VB# Sd?pT RTR R2p\T4\P!T4T^,pRp?KYRp?ii;i Sd6p\P!RS R\ T4 24S 'dThThRp?ii;i)rz, Retrying in rZz seconds...NzSkipping after z consecutive )rgr/ r0 rQr r) rrmtriesmdelayrqrExceptionToCheckdelayrMskip_after_retriestriess *, r;f_retry*retry..deco_retry..f_retrys"EF1* d-f--  |$)&)) ( C~fQZ{CC#JJJv&aKF  $ |''/% cRSfX(VWbt]^{z{{ |s+A&A#3AA#&B"-0BB"r)rMr> r: r; r< r= sf r; deco_retryretry..deco_retrys( q | |  |r:r1)r: r= r; r< r@ sffff r;retryrB s" r:cdW8d\RV RV 24h\P!WW#R7p\PP VRR7wrVp\ V4F"pW8d^Wh&KWh,^8XgK^Wh&K$ WVP V4PR4,V,#)zrank must be <= l, got rank=z, l=r)F full_matricesr)rrwrandnlinalgsvdrr0r) lrankrrAurvhr-s &&&& r;random_square_matrix_of_rankrN s x;D6aSIJJ A5A||7HA" 1X 9AD TQYAD  U %%b) )R //r:r?sigmagMbP?c\P\P\P\P\P\P\P\P/p\P !W@VR7pVP R 4pVP R 4p\PPVRR7wrp \P!VRR \Wx43,RWP,RV/V,V,PR RR7PPV4p WPR 4,V ,#) z Returns a random rectangular matrix (batch of matrices) with singular values sampled from a Gaussian with mean `mean` and standard deviation `sigma`. The smaller the `sigma`, the better conditioned the output matrix is. r)FrD NrrT) descendingrr!)rwrdoublecfloatcdoublerr5rG rH rF minr~rr0r) rrrrP rprimitive_dtyper,rOrNrL rrM rs $$$$* r;random_well_conditioned_matrixrX s  U[[ ell ekk u|| O  5f5A r A r A||7HA" uSbzSYL0 _9O _X^ _bg gjn n bT "66""U) B 2 %%r:cVP4'gV#VPP'gVPP'd\P pM$VP\ P8XdRpM^ pVPVPR,4pWR&VP4VR&VR,pVPVP4V#)Tr ).ru).r) r ris_floating_point is_complexrXnanrwr new_emptyrrrequires_grad_r1)r8rr,s& r;noncontiguous_liker_ s ??   ww   AGG$6$6$6 EJJ  [[4 (F6NXXZF6N F^F !//* Mr:cVPR\P4pVPRR4p\P!WV3,RVRV/pWUP,P ^4pV#rrr@)rrwrS rF mTdiv_rI batchesrrrrK s&*, r;random_symmetric_matrixrf &^ JJw -E ZZ% (F gA&DuDVDA TTA Hr:cVR,VR,8wd \RVR, RVR, 24h\W VR7pW3P,P^4pV#)r&expected square matrix, got shape[-1]= != shape[-2]=rMr!r)rr#rb rc )rrrr8s$$* r;make_symmetric_matricesrk /s] RyE"IEeBi[P^_deg_h^ijkkE6A TTA Hr:cVPR\P4pVPRR4p\P!WV3,RVRV/pWUP,P ^4pV#ra )rrwrS rF mHrc rd s&*, r;random_hermitian_matrixrn 6rg r:cVPR\P4pVPRR4p\P!WV3,RVRV/pWUP,#)aC Returns a batch of random symmetric positive-semi-definite matrices. The shape of the result is batch_dims + (matrix_size, matrix_size) The following example creates a tensor of size 2 x 4 x 3 x 3 >>> # xdoctest: +SKIP("undefined variables") >>> matrices = random_symmetric_psd_matrix(3, 2, 4, dtype=dtype, device=device) rrr@)rrwrS rF rb rd s&*, r;random_symmetric_psd_matrixrp >sR JJw -E ZZ% (F gA&DuDVDA tt8Or:rrcf\P!W0V3,RVRV/pWDP,#)aC Returns a batch of random Hermitian positive-semi-definite matrices. The shape of the result is batch_dims + (matrix_size, matrix_size) The following example creates a tensor of size 2 x 4 x 3 x 3 >>> # xdoctest: +SKIP("undefined variables") >>> matrices = random_hermitian_psd_matrix(3, 2, 4, dtype=dtype, device=device) rr)rwrF rm  matrix_sizerr batch_dimsrK s&$$* r;random_hermitian_psd_matrixru Ls0  j#==[e[TZ[A tt8Or:c.VPR\P4pVPRR4p\P!WV3,RVRV/p\P!WUP 4\P !WVR7R,,#)rrr@r)h㈵>)rrwrS rF matmulrb eye)rs rt rrrrK s&*, r;random_symmetric_pd_matrixrz Ys} JJw -E ZZ% (F j#== 0 0(. 0A <<44 ))KV >> # xdoctest: +SKIP("undefined variables") >>> matrices = random_hermitian_pd_matrix(3, 2, 4, dtype=dtype, device=device) rrr))rwrF rm ry rr s&$$* r;random_hermitian_pd_matrixr~ ksH  j#== 0 0(. 0A tt8eii H HHr:r1c \P!4;_uu_4\W0VR7p\PP VRR7wrVpVP P 'dVPP M VP p\VR,VR,4p \P!^V ^,WR7p V R;;,R ,uu&V P4PR4WZPVP 4,V,p RRR4X PV4V # +'giL#;i) rMFrD r):rNrMrO Nr!rg)rwr/r#rG rH rr[ realrV rZ reciprocal_rr0r^ ) rrr1rr8rL rrM real_dtyperirr,s $$$* r;4make_fullrank_matrices_with_distinct_singular_valuesr ys  E :<<##AU#;b%&WW%7%7%7QVV\\QWW b 59 % LLAE C $3 R agg " $! "]# H% s DD?? E cVPR\P4pVPRR4pVPRR4pVPRR4pV'd9\PP'g\P !WWER7#\P !W V3,WER7pVP4^8XdV#\PPVRR7wrp \W4p \P!^V ^,, ^WVR7p V'd^W^, &V ^8d^V ^&WPR 4,V ,#) zReturn rectangular matrix or batches of rectangular matrices. Parameters: dtype - the data type device - the device kind singular - when True, the output will be singular rrr@silentFsingularr)rD r) rrwrS rronesrF rrG rH rV linspacer)rowscolumnsrt rrrr r rK rL rrM rirs&&*, r; random_matrixr s  JJw -E ZZ% (F ZZ% (Fzz*e,H ehh)))zz$uDD J0MAwwyA~||7HA" DA qAE{AqfEAa% q5AaD B 2 %%r:c\VPR\P4pVPRR4pVPRR4pVPRR4p.VOVNVN5p\P!W,4p \P !VRVRV/V , p V P V4V #)aReturn rectangular matrix or batches of rectangular matrices with entries being iid and sampled from N(0, sigma^2) such that the variance of (A @ A.T)[..., i, j] is 1 if reduction_dim=-1, or the variance of (A.T @ A)[..., i, j] is 1 if reduction_dim=-2. Parameters: dtype - the data type device - the device kind requires_grad - whether output requires grad reduction_dim - the row/column dimension to re-scale. Expected to be either -1 (columns) or -2 (rows). rrr@r1F reduction_dimr!)rrwrS rXsqrtrF r^ ) r r rt rrrr1r r red_scaler9s &&*, r;'random_matrix_with_scaled_reduction_dimr s JJw -E ZZ% (FJJ6MJJ3M (j ($ ( (E %./I ++u 9E 9& 9I EC}% Jr:c`\W.VO5/VBp\W.VO5/VBpVPV4#)zNReturn rectangular matrix or batches of rectangular matrices with given rank. )r rx )rJ r r rt rBCs&&&*, r;random_lowrank_matrixr s: d8:88Ad;j;F;A 88A;r:c\V^8dQhR\R\R\R\P/#)rMr cols num_indicesrO)rrwr)rSs"r;rTrTs/ / /C /s / /QVQ]Q] /r:c a.p\P!W , 4p\\V44p\V4F3oVP V3Rl\ P !WTR744K5 \P!VRV4#)z\Generate indices for a row x cols matrix, preferring at least one index per row if possible.c3,<"TF pSV3xK R#5irr1)rrrs& r;r4_generate_indices_prefer_all_rows..sP'O!1v'Or)riN) rXceilrgrrr?r%rwrU)r r r r n_per_rowrrs&&& @r;!_generate_indices_prefer_all_rowsr shG +,-IuT{#K 4[Pv~~k'OPP << - ..r:{Gz?c 2VPR\P4pVPRR4p\\ W4\ W,V,44p\ WV4p\P!WdVR7pT\P!VU U u.uFwr\W, 4^,)NK up p WER7P4,p\P!VP4WV3VR7p V P4#uup p i)a Return rectangular random sparse matrix within given density. The density of the result approaches to given density as the size of the matrix is increased and a relatively small value of density is specified but higher than min(rows, columns)/(rows * columns) for non-singular matrices. rrr@r)rg)rrwrS rrV rr rF rUrexprr8r) r r densityrrrnonzero_elementsrrr-rZrK s &&&, r;random_sparse_matrixr s JJw -E ZZ% (F3t-s4>G3K/LM/?OPG [[)v FF ellAU15\1_,A^bbddF  VG_VTA ::<Bs$D c ^RIpVPR\4R,4pVPRVP4pVPRR4p\ V4Uu/uFpWw3\ V^,4V, bK ppR Rlp W,V,p \ V4V 8d\P!^V^, 4p\P!^V^, 4p W{8wgKT\P!^^VP,4p VP!V 4p VP!V 4pV !WW{WRR7V !WW{WR R7K...ppp\VP44F;wwr{pVPV4VPV 4VPV4K= VP !VV.4pVP"!VVW3WVR 7#uupi) aReturn random sparse positive-definite matrix with given density. The eigenvalues of the matrix are defined as:: arange(1, matrix_size+1)/matrix_size Algorithm: A = diag(arange(1, matrix_size+1)/matrix_size) while : R = A = R^T A R Nrwrrr@Tcj\V4FpV'dWr3Ws3rMW'3W73rVPV^4VPV ^4rWJ,W[,,V)V ,WK,,rV 'dWV&MVPVR4V 'dWV &KVPV R4K R#r)rrr) r! r_r-rZcssnleftriikjkaikajks &&&&&&& r;multiply)random_sparse_pd_matrix..multiplysqA!B!BxxAQx"(*RC#I,@RT"RT"r:)r Fr)r)rXrrrS rrrr?randintuniformpicossinsortedr^rfrUr)rs r rrXrwrrr-r! r target_nnzrZthetar r icoordsjcoordsrrindices_tensors&&, r;random_sparse_pd_matrixr s JJw ' 2 3E JJw -E ZZ% (F;' )'FE!a%L;. .'  )#"&4J d)j NN1kAo . NN1kAo . 6NN1a$''k2E%B%B Tb4 @ Tb5 A!2rfWGDJJL) qq a*\\7G"45N  " ">6K;U]b rrK )s!$GcVFpV\P8wgK\P!RWBVR7pVPWEP4VPW%P 4VP W5P4K R#)rM)rrrNr )rwfloat16rVassertIsrrr;r)rdtypesrrrouts&&&& r;do_test_dtypesr *s\ EMM !++fEPC MM% + MM&** -   VZZ 0 r:c aa \P!^^.4o VV 3RlpRp\P!4pV!\P!S 4V\PR RR4V!\P !S R 4V\PR RR4VEF>pVP R0EF'pV!V4p \P!S WsW(R7p V!WW#RV4V P4p V!\P!S WW(R7WrVRV4V!V PS 4WrVRR4V!V PS WRR7WVRR4V!\P!V 4WrVRR4V!\P!WW#RR7WVRR4V\PJgKV\P8wgEK ^p \P !S WW#VR7p V!WW#W4V!V PS V ^,4WrW<^,R4V P4p V!\P !S V ^,WW(R7WrW<^,V4V!V PS V ^,WRR7WW<^,R4V!\P!W^,4WrW<^,R4V!\P!W^,WVRR7WW<^,R4EK* EKA R#) rMc<SPSVP4SPWP4SPW P4SPVP V4VP 'd VeSPW0P4Ve4VPS4PV4pSPW4R#R#r) r;rr rrr1is_cudarr3rW) rUrrrrr1fillrrs &&&&&& r; check_value'do_test_empty_full..check_value6s  - e\\* fmm, --}= >>>f0   V]] 3  ::e$**51D   V * r:cRP\V4PR4^R4pV'g\P#\ P !V4!\4P#)rWr!)rhrr{rwrre attrgetter)rrs& r;get_int64_dtype+do_test_empty_full..get_int64_dtypeAsO#e***3/"56;; ""6*51777r:NF)rrrr1)r rrr1)rrr1)rrrr1r!g)rwrrArOrrrZ r3r] empty_liker rnew_full full_like)rr rrr r default_dtyperrg int64_dtyperr fvrsf&&& @r;do_test_empty_fullr 3st JJ1v E +8 ++-M E"M5=="dER 5#& u}}b$PUV**E2B)%0K Ef_A &$ ;%%'C  Es&cvtR 9  E*E64 O  E[` a#VT5 B ((+UFD% P ((fkpq#VT5 BEMM)f8H8H.HJJubfcefAfb=AJJub1f5ufSTfV[\eegEJJub1f#U[n!662?AJJub1fKfkJl'aHEOOAAv6vTUvW\]EOOAAv2=U[kpr'aH33r:c\PP\PP\P ^,44pVP R4'dVsR#R# \dR#i;i)rurN) rrrr r8r(endswithrunning_script_pathr) running_files r;set_running_script_pathr js^ wwrww'7'7 'DE   ' '".  (    sA'A0*A00 A?>A?c 2\fR#\PP\PP \ P !VP444pV\8wd(\RVP4 R\ RV R24hR#)NzClass of loaded TestCase "z(" is not defined in the running script "z ", but in "zE". 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