^j4dZddlmZddlZddlZddlZddlZddlZddlZddl Z ddl Z ddl Z ddl Z ddl Z ddlZddlZddlZddlmZddlmZddlmZmZmZmZddlZddlmZddlZddlmZddl m!Z!dd l"m#Z#dd lm$Z$dd l%m&Z&dd l'm(Z(m)Z)d dl*m+Z+d dlm,Z,m-Z-m.Z.erddl/m0Z0m1Z1ddl2m3Z3ddl4m5Z5e jle7Z8edZ9edZ:e:jwZgZ?dZ@dZAejjjjjddZAdje?Dcgc]}d|d c}Z@gdZHGddZIdAdZJd ZKGd!d"eLZMGd#d$ZNejdAd%ZPd&d'dBd(ZQd&d'dBd)ZRdAd*ZSdCd+ZTGd,d-eUZVdDd.ZW dE dFd/ZX dGd&d&d0 dHd1ZYdId2ZZ dJd3Z[ dKd4Z\ dGd&d&d0 dLd5Z] dMd6Z^dNd7Z_e_ejZae_ejd8Zce_dZde_d&Zee_d&ZfGd9d:ZgGd;d<ZhGd=d>Zi dO dPd?ZjdQd@Zkycc}w)Ra Debug utilities for TorchDynamo compilation and execution. This module provides various debugging tools and utilities for TorchDynamo, including: - Minification support for reducing test cases while preserving bugs - Input/output handling via InputReader and InputWriter for reproducible testing - Accuracy checking between original and compiled models - Neural network module string conversion via NNModuleToString - Profiling tools and system information collection - Buck build system integration for Meta-internal testing Key classes: - InputReader/InputWriter: Handle serialization of model inputs/outputs - NNModuleToString: Converts nn.Modules to string representations - BuckTargetWriter: Manages Buck build system integration ) annotationsN)Counter) import_module)Anycast TYPE_CHECKINGTypeVar)Tensor) rand_strided)normalize_path_separator)is_float_dtype)StorageWeakRef)ContentStoreReaderContentStoreWriter)config) clone_inputs get_debug_dir warn_once)CallableSequence)tqdm)UntypedStorageTztorch._inductor.config)z1//caffe2/torch/fb/sparsenn:sparsenn_operators_gpuz-//caffe2/torch/fb/sparsenn:sparsenn_operatorsz///deeplearning/fbgemm/fbgemm_gpu:sparse_ops_cpuz+//deeplearning/fbgemm/fbgemm_gpu:sparse_opszfbcode:// ztorch.ops.load_library(""))buck2runz@mode/dev-nosanc&eZdZddZddZdddZy) BuckTargetWriterc.tjjtjj|\|_|_|j j dd|_|jj ddd|j|_|j|jjdd|_|jdd|_|j}||jdddd}d|d |j|_ y) Nz.pyr/.zfbcode.zfbcode/r:) ospathsplitabspathsubdirpy_filereplacetargetfind cmd_line_path)selffilenametmps d/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/torch/_dynamo/debug_utils.py__init__zBuckTargetWriter.__init__[s$&GGMM"''//(2K$L! T\ll**5"5 {{**345Qt{{mD IIdiinnY79: IIabM kk#((9%'(,!#a }5c djtDcgc]}d|d c}}tjd|jd|j dt d|d|jd Scc}w) Nrz "z",za load("@fbcode_macros//build_defs:python_binary.bzl", "python_binary") python_binary( name="z", srcs = ["z"], compile = False, deps = [ "//caffe2:torch", "//caffe2:libtorch", "//caffe2/functorch:functorch", "//triton:triton", "z", ], cpp_deps = [ z ], main_module = "z", par_style = "xar", ) )join extra_depstextwrapdedentr/r- cur_targetr))r2xextra_cpp_depss r5buildzBuckTargetWriter.buildisz#J!is"$5#JK  ;;-ll^  II;#   $Ks A/cPtjj|jd}t |d5}|j |j dddt|jgz}|r%tjddj||S#1swYFxYw)NTARGETSwzFFound an example that reproduces the error. 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?";;FC ? ## $ $$ %6--0 1fmm& 'v~~( )* +fmm& 'FKKt+, -F//2 3  VT4 ( > KK266*% / !1!1 45  Ga $022r7c&tj|}|tjvrytj|}|yt |j t |j d|jdu|g}|ddj|dS)Nrrrr) rhrqat_linear_modulesrr in_features out_featuresrr9)rrrrs r5_qat_linear_constructorz(NNModuleToString._qat_linear_constructor6s&88@ .AA A";;FC ? ## $ $$ %FKKt+, -    a $022r7c|t|tjvry td|ddy#t$rYywxYw)NFzmod = z execT)rrh safe_reprscompile SyntaxError)r module_strs r5_can_emit_module_constructorz-NNModuleToString._can_emit_module_constructorGsI </:: :  fZL)?F C  s / ;;Fallow_unsafe_reprct|}tj||r|Stj|xs,tj |xstj |}||S|r|SyN)rrhrrrr)rrrconstructor_strs r5r|z+NNModuleToString._module_constructor_stringRsx&\  8 8 L   4 4V < @55f= @77?   &" "  r7c0ddlm}d}tjd}|j D]\}}t j ||}|td|t|jd}||nt|jd} | +| jjdk7r|d| jd }||d zd |d |d z }|jjD]\} } |  | jt krEddlm} | j&t kr t)d| j&dt dt+| } nbt-j.| r'dt1| j2d| j4d} n&dt1| j2d| j4d} | jjdk7r| d| jd } ||d zd| d| dz }|j6jD]l\}}| d}|jjdk7rd|jd}dt1|j2d|j4|d} ||d zd |d | d z }n|||j8dd z }|S)Nr) _addindent z from torch.nn import * class Repro(torch.nn.Module): def __init__(self) -> None: super().__init__() rz!Cannot convert module to string: cpuz.to("rrzself.z = r) PRINT_OPTSzPRINT_OPTS.threshold (z() must be >= MAX_CONSTANT_NUMEL_INLINE (rz torch.randn(z, dtype=ztorch.randint(1, size=zself.register_buffer('z', z) rz , device=""ztorch.nn.Parameter(torch.randn(z))rb)torch.nn.modules.modulerr;r<r{rhr|rdnext parametersbuffersdevicer_buffersitemsnumelMAX_CONSTANT_NUMEL_INLINEtorch._tensor_strr thresholdrrris_floating_pointlistshaper _parameterscode)rrrtab model_str module_namerr example_paramexample_tensor buffer_namebufferr tensor_str param_nameparam maybe_devices r5convertzNNModuleToString.convertds6OO  $#4#6 I K)DD*;EJ!.7zB !!2!2!4d;M!,&..*D1  )n.C.C.H.HE.Q *|51F1F0GrJ C!G9E+c*RH HI% I($&;;#4#4#6  K~||~!::8''*CC(01E1E0FG66O5PPQS"&\ ((0+D,>+?x ~UVW -T&,,-?,@VWX}}!!U* *|5rB 7)1+c*SQ I+ 2"$!5!5!7 H J}L||  E)!+ELL>; :4 ;L:MXV[VaVaUbcobpprsJ C!G9E*S BG GI H  277A./r22 r7N)rtorch.fx.GraphModulerOrR)rtorch.nn.ModulerOrN)rz type[object]rOrN)rrrO str | None)rrrrNrOrR)rrrrRrOr)rrrrRrOrN)*rTrUrVrrrrrnnLinearConv1dConv2dConv3d BatchNorm1d BatchNorm2d BatchNorm3d LayerNormDropoutSoftmaxReLUGELUIdentity MaxPool2d EmbeddingTanhConvTranspose1dGLULSTMFlattenAdaptiveAvgPool2dr staticmethodrrrrrrrrr|rrWr7r5rhrhs#:K  0B$J                             ""+J0  DD66332  33>33 >C7; "GLLLr7rhcttjjsyd} tjjht j ddg}|jjd}dj|Dcgc]}|dk7s d|dc}}||dz }n|d z }td ttjjD}|d z }|jD]\}}|d|d |dz }|dz }|Scc}w#tt jf$r|d z }YwxYw)Nz:# torch.cuda.is_available()==False, no GPU info collected z# CUDA Info: nvccz --versionrrz# z z'# Not searching for nvcc on ROCM setup z# nvcc not found c3ZK|]#}tjj|%ywr)rcudaget_device_name).0is r5 z,_cuda_system_info_comment..s$*+ ""1%s)+z# GPU Hardware Info: z : )rr is_availableversionhip subprocess check_outputdecoder*r9FileNotFoundErrorCalledProcessErrorrrange device_countr)rcuda_version_outcuda_version_linesscomment gpu_namesnamecounts r5_cuda_system_info_commentr&sJ :: " " $L!I * ==   $)66 7LM !1!8!8!:!@!@!F gg4FRq!r'A3c{RSG G9B 'I C CI/4UZZ5L5L5N/OI**I (. er$s5'-- . I S z<< =*)) *s*AD D DDDD76D7F) stable_outputc P|rygdgdddgdfd }tjjDcgc]&\}}||rd|d|jd d d (}}}|j d Dd j |}t d |dScc}}w)zl Generate a string configuration for environment variables related to Dynamo, Inductor, and Triton. z+# env var omitted due to stable_output=True)TORCHDYNAMOINDUCTORTRITON)TRITON_LIBDEVICE_PATHTRITON_PTXAS_PATHTRITON_LIBCUDA_PATHTORCHDYNAMO_REPRO_AFTERTORCHDYNAMO_REPRO_LEVELcFtfdDxr vxrvS)Nc3&K|]}|v ywrrW)rstringkeys r5rz;generate_env_vars_string..filter..s7&# 7s)any)r5 allow_listrepro_env_vars skip_lists`r5filterz(generate_env_vars_string..filters0 7J7 7 *9$ *>) r7z os.environ['z'] = ''rc3(K|] }d|d yw)zos.environ.pop('z', None)NrW)rr5s r5rz+generate_env_vars_string..sSS*3%x8Ssrz import os z )r5rNrOrR)r(environrr.rr9r ) r'r:r5value config_lines config_stringr7r8r9s @@@r5generate_env_vars_stringrAs<:JUI/1JKN ****, C #; VEMM#s$;# ??r7cRt}tjd|trt |j  t |d5}|j |dddy#1swYyxYw#t$r)}tjdtd||d}~wwxYw)NzWriting minified repro to: %srCrzCould not write to ) rOrHrIuse_buckr"rFrEOSError exceptionNotImplementedError)contentsminified_repro_pathrLes r5helper_for_dump_minifyrXs13KK02EF,-335V %s + r HHX     V b!$78K7L"MNTUUVs6 A4 A(A4(A1-A41A44 B&=$B!!B&c eZdZy) AccuracyErrorNrerWr7r5rZrZrfr7rZct|}tt|D]A}t||tj s!||j ||jC|S)z This clone inputs is different from utils clone_input. In case of minifier, all the tensors are leaf tensors while creating a new graph. So, we set the requires_grad field w/o checking the leafness of the tensor. )rrr~ isinstancerr requires_grad_ requires_grad)example_inputs cloned_inputsidxs r5clone_inputs_retaining_gradnessrbsb !0MS()Q mC(%,, 7 #  - -nS.A.O.O PQ r7c&ddlm}m}m}t j |}|s t |}t|dr|jdt|ddr||n||}|r|S||r||}|j|||d|S)z Runs a forward and possibly backward iteration for a given mod and args. When disable_clone is True, we will use args as-is without cloning. This is higher fidelity but we may destroy the args in the process. r)collect_resultsreduce_to_scalar_lossrequires_bwd_pass zero_gradT _boxed_callFN) testingrdrerfcopydeepcopyrbrrgrbackward) rronly_fwd disable_clonerdrerfoutlosss r5run_fwd_maybe_bwdrq(sSR r B .t4r; Tb-7"T(RYC $S)  2sD$ //r7 require_fp64 ignore_non_fpcddlm}t|||}d}tjr9 t t j|t|\} } t| | |} t|||} ||| |tjd|} | S#t$r%|r tdtjdYYwxYw#t$rtjdYywxYw) aa Check two models have same accuracy. require_fp64: if True, raise an error if we unable to calculate the fp64 reference ignore_non_fp: if True, do not compare outputs which are not floating point. This is mostly useful for the minifier (which wants to avoid quantizing floating point error into integer/boolean error) r)sameNzfCould not generate fp64 outputs, workaround with torch._dynamo.config.same_two_models_use_fp64 = FalsezCould not generate fp64 outputszWhile minifying the program in accuracy minification mode, ran into a runtime exception which is likely an unrelated issue. Skipping this graph.T)tol equal_nanrt)utilsrvrqrsame_two_models_use_fp64 cast_to_fp64rjrkrb Exception RuntimeErrorrHrIrSrepro_tolerance) ropt_gmr_rmrsrtrvreffp64_ref fp64_model fp64_examplesrespassings r5same_two_modelsrHs" B 9CH && ;(4 b!#B>#R) %J )]HMH A    " "# G N7 ;"| KK9 :  ;   $ s#8B B< +B98B9<CCc&|jjD]L}|jdk(r|jtj j jjurt|jdk7r!tdt|jt|jdrD|jdtjk7r$|jdtjf|_ |jdk(s|jjd}|t|st!|j}tj|d<||_O|jj#|j%|S)N call_functionrz"Expected node to have 2 args, got rrr)graphnodesopr/ropsprimsconvert_element_typedefaultr~rrr float64kwargsgetdictlint recompile)modelnoder new_kwargss r5cast_dtype_args_to_fp64rs# !!) GG & uyyCCKKK499~"$8TYY8HIdiil+ !  0M!YYq\5==9 77o %KKOOG,E ^E%:!$++. &+mm 7#( !)$ KK OO Lr7cddlm}|j}tjk(r t |}|fd|}||fS)Nr)tree_mapc~t|tjr!|jr|j S|Sr)r\rr rto)r>rs r5zcast_to..s3 a &1+>+>+@$$u+ r7)torch.utils._pytreerrrrr)rrinputsrs` r5cast_torsM- HHUOE  (.    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T "&7 &C C!.zn=   '' ;;& "$P!F d #v -&vj1L '') :: !o&<&<&A&AV&K::33ODL c#L#32fZ ~N^M__` a "#2r7c tddlm}m}|j|j |j |j }g}||td|j|js1|jtt|jtd|j k7r|jd|j |td|jk(s"|jd|jt j"j%|}|r&|j'd|j)Dt+d|j,k7r|jd|j,t j.j0j3|}t5d|k7r|jd ||j6jd d j9|tt|jg|zd |zy) Nr)statically_known_truesym_eq)rrrrzstorage_offset=c30K|]\}}|d|yw)=NrW)rkrs r5rz%InputWriter.tensor..sIA1#Qqe Iszrequires_grad=zis_leaf=zreader.tensor(r) # )%torch.fx.experimental.symbolic_shapesrrrrrrrrrrrNtuplerrrrrget_tensor_metadatarrrr^rrrrr r9) r2r$rrrrrtensor_metadatars r5rzInputWriter.tensorsW,,   AGG $ %d!'':AHHJ G  KKE!((*-. / T "agg - KK& , -$ &t ,0@0@0B B  KK/!*:*:*<)?@ A,,::1=  KKI1F1F1HI I $T *aoo = KK.(;< =##..;;A> t $ / KK(7+. /  ii#eAGGn"5==> ?dVn  r7c |jjd|dt|t|tt fr|jjdt |D]\}}|d|d}t|tjr|j||;t|ttjfr|j||n|j|||jjdyy)Nzreader.unsupported(z#) # unsupported type for dumping: z"""[])r rrr\rr# enumeraterr rrSymIntrr)r2r$argraname_is r5rzInputWriter.unsupporteds !$)LTRUYK X cD%= ) KK  u %!# 01 61#Qa.KK*C#67KK*$$VQ/ 0 KK  u % *r7cH|jjd|d|dy)Nz reader.const(r!z!, filtered out during compilationr rrs r5rzInputWriter.consts' D85.O P r7c.t|tjrZt|jj }|jj }|jjd|d|d|y|jjd|d|y)Nzreader.symint(z, expr=r!) r\rr*rNrrhintr r)r2r$rexpr_strr1s r5rzInputWriter.symintst c5<< (388==)H88==D KK  xwxl%PTvV W KK  weD6B Cr7c|j}|jjd|jd|jd|y)Nzreader.generator(rr!)rr rrindex)r2r$r+rs r5rzInputWriter.generator s<  b0@dV L r7cF|jjd|d|y)Nzreader.opaque(r!r/)r2r$rs r5rzInputWriter.opaques# ^,=+@dVLMr7)rrrrRrOrP)rOrS)rrrrrrrOrN)r$rNrrrOrP)r$rNr+rrOrPr)r$rNrrrOrP)r$rNr+ztorch._C.GeneratorrOrP)r$rNrrNrOrP) rTrUrVr6rrrrrrrrrWr7r5rrsfDI ;&BF)- #'#? # ' # #J @&( D Nr7rc ddlm}|jDcic]\}}|| }}}dj|j }t j |} d|d} d|d} d} Gd d } i}|xsidfd dfd }|j}|jD]\}}|d k(r tj| |}|r>|j\}}t|jd}||}|||||<tj| |}|s{|jd||<dt j|jvro| }||d<tj | | D]J}|j\}}}}t|jd}||}t#|||||L|Scc}}w)a Takes in a function which has been printed with print_readable() and constructs kwargs to run it. Handles Tensor inputs, Symints, and a graph module which might have tensor constants. Consider a function `forward` defined as follows: def forward(self, primals_1: "f32[1001, 6]", primals_2: "f32[s0]", primals_3: "Sym(s0)",): _tensor_constant0: "i64[4190]" = self._tensor_constant0 # Further implementation kwargs = aot_graph_input_parser(forward) forward(**kwargs) r) dtype_abbrs|z(_tensor_constant\d+): \"(z0)\[\s*(.*?)\s*\]\" = self\.(_tensor_constant\d+)rz)\[\s*(.*?)\s*\]z Sym\((s\d+)\)ceZdZdZy)/aot_graph_input_parser..TensorContainerz#Container for tensors as attributesN)rTrUrV__doc__rWr7r5TensorContainerr:8s-r7r<rOcltjvxsdufdjS)NcdS)Nz; not in symbolic_shapes and default sym shape not passed inrW)rsr5rz=aot_graph_input_parser..get_sym_int..CsvhYZr7)r_checkr)rdefault_sym_shapesym_shapes_dicts`r5 get_sym_intz+aot_graph_input_parser..get_sym_int@s; o % F):$)F Z ""6+<==r7cg}g}t|D]a\}}|j}d|vr+ |}|j||j|E|sH|jt|c|jrt j nt j}||| }|D]"} t jj|| $|S)Nr!)rr) r)striprrrrrandnzerosrI mark_dynamic) rrresolved_shape dynamic_dimsrdimr! constructorrorrrBs r5 gen_tensorz*aot_graph_input_parser..gen_tensorGs & 4FAs))+Ccz$%%a(##A&"))#c(3 4&+%<% dtype_map dtype_patternsourcetensor_assignment_regex tensor_regexsym_shape_regexr<rrLrr annotationmatch data_type shape_strrr container attr_namerrBrAs ` ` @@r5aot_graph_input_parserrhs*5&1%6%6%8)!sEs )I)+"4"4"67M  t $F"*&&K.[..08z H   , 3 #(<<> Iy)//#./Ei(E&ue4F5M /:6 ' A7F5M8"""4(333#% "v[[!8&A DE16 .Iy)Q)//#./Ei(E Iy*UE*B C  D M[)s Gctjtjj tjj dfd }dfd }t j||S)z Decorator to cProfile a given function and save the result to disk on process exit. 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