Ph;ddlZddlmcmZddlmZddlmZm Z m Z ddl m Z ddl Z GddZeejj j"dd fd Zd Zd Zy) N)wrapper_set_seed)compiled_function#min_cut_rematerialization_partitionnop randomizeceZdZdZdZdZy)assert_raises_regexc ||_||_yN) exception_clsregex)selfrrs wC:\Users\daisl\Desktop\realtime-object-detection\venv\Lib\site-packages\torch/testing/_internal/optests/aot_autograd.py__init__zassert_raises_regex.__init__ s* cyr )rs r __enter__zassert_raises_regex.__enter__s rc||jk(rGt|}tj|j|st d|jd|y|t d|jd|t d)Nz*Expected exception to match regex. regex: z , exception: Tz Expected z% to be raised, instead got exception z,Expected exception to be raised but none was)rstrresearchrAssertionError)rexc_typeexc_val tracebackmsgs r__exit__zassert_raises_regex.__exit__s t)) )g,C99TZZ-$@ MZ]Y^_aa   D..//TU]T^_a aKLLrN)__name__ __module__ __qualname__rrr rrrr r s  Mrr TFc\tj||f\Dcgc]}t|tj} }Dcgc]}t|tjs|!}}fd} t | t t |t} t| |} |dk(rRtjtjd|} tjtjd| }| xr|}|st| |}||| tyt| | ||||ycc}wcc}w)aCompares func(*args, **kwargs) in eager-mode to under AOTAutograd. Compares outputs and (if check_gradients=True) gradients produced by AOTAutograd against eager-mode PyTorch. We assume that func(*args, **kwargs) succeeds in eager-mode PyTorch. cg}t|}D]H}t|tjr|j t |8|j |Jt j|\}}|i|Sr )iter isinstancetorchTensorappendnextpytreetree_unflatten)argsreconstructed_flat_argsvc_argsc_kwargs args_spec flat_argsfuncs rfunc_no_tensorsz+aot_autograd_check..func_no_tensors4st"$DzA!U\\*'..tDz:'..q1  "001H)TV(x((r)dynamic partition_fnautoc|jSr  requires_gradxs rz$aot_autograd_check..E PQP_P_rc|jSr r;r=s rr?z$aot_autograd_check..Fr@rrN) r, tree_flattenr'r(r)rrrr tree_any_only outputs_msg,_test_aot_autograd_forwards_backwards_helper)r5r.kwargsr7assert_raises_regex_fnassert_equals_fncheck_gradientstry_check_data_specializationargargs_is_tensorr6 compiled_foutany_tensor_requires_gradany_output_requires_grad compiled_outr3r4s` @@raot_autograd_checkrSs""..f~>Iy?HIyjell3yNI$ F9C 3 (EC9D F )#c7AdfJ ?D 1C& #)#7#7 F_ae#f #)#7#7 F_ad#e 2O7O ' D9 s <0T+ACS%'9J Fs!D$ D))D)aOutputs of the operator are different in eager-mode PyTorch vs AOTAutograd. This means the operator will have incorrect output underneath torch.compile. This could be because the operator's implementation not traceable or that there is a bug in AOTAutograd.clddfd }||d|rt|}||dyy)Nctj|}|Dcgc]+}t|tjs|j r|-}}t ||}tj|}d}|D]>}t|tjs||jjz }@t|tjsJ|tjj||dfScc}w)NrT) allow_unused) r,arg_tree_leavesr'r(r)r<r tree_leavessumabsautogradgrad) fr.r4rL diff_argsrOflat_outsmis rcall_forwards_backwardszM_test_aot_autograd_forwards_backwards_helper..call_forwards_backwards]s**D1 $-(ISC1N&&I (q$'%%c* A!U\\*aeegkkm#  "ell+++ENN''ID'III(s C/C/Fc  |\}}td|Dr td5 |dddyd} |\}}||t|||y#t$r|rYywxYw#1swYyxYw)Nc3$K|]}|du ywr r).0r>s r zN_test_aot_autograd_forwards_backwards_helper..check..ws,)QqDy)sz1does not require grad and does not have a grad_fnaGradients of the operator are different in eager-mode PyTorch vs AOTAutograd. This means the operator will have incorrect gradients underneath torch.compile. This could be because the operator's backward is incorrectly registered or not traceable or that there is a bug in AOTAutograd.rB) Exceptionall RuntimeErrorrE) r.ignore_failureorig_out orig_gradrrR compiled_gradrIrHrbrNr]s rcheckz;_test_aot_autograd_forwards_backwards_helper..checkns "9!T"B Hi ,), ,' 6ij' D9k  ' '>j$&O# mx[A s;+   k s A* A<* A97A9<B)rjT)Fr)r]rNr.rHrIrKrnrbs`` `` @rrFrFXs< J"<<4 $u% % d4(%r)r(torch.utils._pytreeutils_pytreer,2torch.testing._internal.common_methods_invocationsrfunctorch.compilerrrmake_fxr rr testing _comparison assert_closerSrErFrrrrxs] $$OYY MM4 322??&+0'fJ 8)r