o
    Zhv                     @   s\  d Z ddlZddlZddlZddlZddlZddlZddlZddlZddl	Z	ddl
Z
ddlZddlZddlZddlZddlmZ ddlmZ ddlmZmZmZmZ ddlZddlmZ ddlZddlmZ ddlmZ ddlm Z  dd	l!m"Z" dd
l#m$Z$m%Z% ddl&m'Z' ddlm(Z(m)Z) e*e+Z,edZ-edZ.e./ Z0e0rddl1Z2g Z3dZ4e0rg dZ3e2j5j6j78 9ddZ:d;dd e3D Z4g dZ<G dd dZ=dd Z>dZ?G dd dZ@eAddd  ZBd!d"d#d$ZCd!d"d%d&ZDd'd( ZEd)d* ZFG d+d, d,eGZHd-d. ZIdWd/d0ZJ	!dXd!d!d1d2d3ZKd4d5 ZLd6d7 ZMd8d9 ZN	!dXd!d!d1d:d;ZOd<ed= d>d?d@d=fdAdBZPdCe-d@eee- ge-f fdDdEZQeQejRZSeQeTdFZUeQdZVeQd!ZWeQd!ZXG dGdH dHZYG dIdJ dJZZG dKdL dLZ[	M		dYdNee\e ge\e f dOe]dPee^e]e_f  dQee_ d@e^e]ef f
dRdSZ`dTe]d@ee-ge-f fdUdVZadS )Za  
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
    N)Counter)import_module)AnyCallableOptionalTypeVar)Tensor)rand_strided)is_float_dtype)StorageWeakRef)ContentStoreReaderContentStoreWriter   )config)clone_inputsget_debug_dirTztorch._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_opszfbcode://
c                 C      g | ]}d | dqS )ztorch.ops.load_library("z") .0xr   r   H/var/www/auris/lib/python3.10/site-packages/torch/_dynamo/debug_utils.py
<listcomp>K       r   )Zbuck2runz@mode/dev-nosanc                   @   s&   e Zd Zdd Zdd Zd	ddZdS )
BuckTargetWriterc                 C   s   t jt j|\| _| _| jdd| _| jdd d| j | _| j| jdd  | _| jdd  | _| j}||dd  dd  }d| d	| j | _	d S )
Nz.pyr   /.zfbcode.   zfbcode/r   :)
ospathsplitabspathsubdirpy_filereplacetargetfindcmd_line_path)selffilenametmpr   r   r   __init__R   s   zBuckTargetWriter.__init__c                 C   sD   d dd tD }td| j d| j dt d| d| j d	S )
Nr   c                 S   r   )z	        "z",r   r   r   r   r   r   a   r   z*BuckTargetWriter.build.<locals>.<listcomp>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_depstextwrapdedentr+   r)   
cur_targetr%   )r.   Zextra_cpp_depsr   r   r   build`   s   zBuckTargetWriter.buildTc                 C   sn   t j| jd}t|d}||   W d    n1 sw   Y  t| jg }|r5t	
dd| |S )NZTARGETSwzFFound an example that reproduces the error. Run this cmd to repro - %s )r$   r%   r2   r(   openwriter7   BUCK_CMD_PREFIXr-   logwarning)r.   Z	print_msgZtarget_filefdZ	cmd_splitr   r   r   r;   z   s   zBuckTargetWriter.writeN)T)__name__
__module____qualname__r1   r7   r;   r   r   r   r   r   Q   s    r   c                  C   sL   t jt d} | d u rt  dt  } t j| s$t j	| dd | S )NZminifierz
/minifier_T)exist_ok)
r$   r%   r2   r   tempfile
gettempdirgetpassgetuserexistsmakedirs)r%   r   r   r   minifier_dir   s   rJ      c                   @   s   e Zd Zejjejjejjejjejj	ejj
ejjejjejjejjejjejjejjejjejjejjejjejjejjejjejjgZedd Zedd ZdS )NNModuleToStringc                 C   sL   t  }|  D ]\}}t|tjvr|| qt|dkr$td| dS )Nr   z-We have not tested reprs of some modules - %sT)	setnamed_childrentyperL   
safe_reprsaddlenr=   r>   )gmZcant_convert_moduler   r   r   can_convert_to_string   s   
z&NNModuleToString.can_convert_to_stringc                 C   s  ddl m} d}td}|  D ]+\}}|  }t| d }|d ur-|jr-| d}||d  d| d| d	7 }q| j	
 D ]X\}}	|	d u rKqB|	 tkrcdd
lm}
 |
jtks^J t|	}n t|	rvdt|	j d|	j d}ndt|	j d|	j d}|	jr| d}||d  d| d| d7 }qB| j
 D ].\}}|d u rqd}|jrd}dt|j d|j | d}||d  d| d| d	7 }q||| jd d	7 }|S )Nr   )
_addindent    z
            from torch.nn import *
            class Repro(torch.nn.Module):
                def __init__(self) -> None:
                    super().__init__()
            z.cuda()   zself.z = r   )
PRINT_OPTSztorch.randn(z, dtype=)ztorch.randint(1, size=zself.register_buffer('z', z)
r   z, device="cuda"ztorch.nn.Parameter(torch.randn(z))rK   )Ztorch.nn.modules.modulerW   r4   r5   rN   __repr__next
parametersZis_cuda_buffersitemsZnumelMAX_CONSTANT_NUMEL_INLINEZtorch._tensor_strrZ   	thresholdreprtorchis_floating_pointlistshapedtype_parameterscode)rS   rW   tab	model_strmodule_namerU   Z
module_strZexample_paramZbuffer_namebufferrZ   Z
tensor_str
param_nameparammaybe_devicer   r   r   convert   sJ   	

 


 	zNNModuleToString.convertN)r@   rA   rB   rd   nnZLinearZConv1dZConv2dZConv3dZBatchNorm1dZBatchNorm2dZBatchNorm3dZ	LayerNormZDropoutZSoftmaxZReLUZGELUZIdentityZ	MaxPool2dZ	EmbeddingZTanhZConvTranspose1dZGLUZLSTMZFlattenZAdaptiveAvgPool2drP   staticmethodrV   rr   r   r   r   r   rL      s6    
rL   c               	   C   s   t j sdS d} z!tddg}| d}ddd |D }| | d7 } W n ttj	fy:   | d	7 } Y nw t
d
d tt j D }| d7 } | D ]\}}| d| d| d7 } qQ| d7 } | S )Nz:# torch.cuda.is_available()==False, no GPU info collected
z# CUDA Info: 
Znvccz	--versionr   r   c                 S   s    g | ]}|d vrd| dqS ))r   #  
r   )r   sr   r   r   r     s     z-_cuda_system_info_comment.<locals>.<listcomp>z# nvcc not found
c                 s   s    | ]	}t j|V  qd S N)rd   cudaZget_device_name)r   ir   r   r   	<genexpr>  s    
z,_cuda_system_info_comment.<locals>.<genexpr>z# GPU Hardware Info: 
ru   z : rv   )rd   ry   Zis_available
subprocesscheck_outputdecoder&   r2   FileNotFoundErrorCalledProcessErrorr   rangeZdevice_countr`   )rl   Zcuda_version_outZcuda_version_linescommentZ	gpu_namesnamecountr   r   r   _cuda_system_info_comment   s&   
r   F)stable_outputc                    sT   | rdS g d g d fddfddt j D }d|}d	| d
S )zl
    Generate a string configuration for environment variables related to Dynamo, Inductor, and Triton.
    z+# env var omitted due to stable_output=True)ZTORCHZDYNAMOZINDUCTORZTRITON)ZTRITON_LIBDEVICE_PATHZTRITON_PTXAS_PATHZTRITON_LIBCUDA_PATHc                    s   t  fddD o vS )Nc                 3   s    | ]}| v V  qd S rx   r   )r   stringkeyr   r   r{     s    z;generate_env_vars_string.<locals>.filter.<locals>.<genexpr>)anyr   )
allow_list	skip_listr   r   filter  s   z(generate_env_vars_string.<locals>.filterc                    s*   g | ]\}} |rd | d| dqS )zos.environ['z'] = ''r   r   r   value)r   r   r   r      s    z,generate_env_vars_string.<locals>.<listcomp>r   z
import os
z
    )r$   environr`   r2   )r   Zconfig_linesZconfig_stringr   )r   r   r   r   generate_env_vars_string  s   

r   c              	   C   s\   dd l }dd l}| rdS |jjj }d|jj  d|jj  d|j	j  d| d	S )Nr   z*# config omitted due to stable_output=Truez~import torch._dynamo.config
import torch._inductor.config
import torch._functorch.config
import torch.fx.experimental._config
r   )
Ztorch._functorch.configZtorch._inductor.configZfxZexperimental_configZcodegen_config_dynamor   Z	_inductorZ
_functorch)r   rd   Zexperimental_configr   r   r   generate_config_string,  s   


r   c                   C   s   t jt dS )Nzminifier_launcher.py)r$   r%   r2   rJ   r   r   r   r   get_minifier_repro_path@  s   r   c              
   C   s   t  }td| trt|  zt|d}||  W d    W d S 1 s)w   Y  W d S  tyF } z
td t	d|d }~ww )NzWriting minified repro to:
%sr8   r   z(Could not write to {minified_repro_path})
r   r=   r>   use_buckr   r;   r:   OSError	exceptionNotImplementedError)contentsZminified_repro_pathr?   er   r   r   helper_for_dump_minifyD  s   &

r   c                   @   s   e Zd ZdS )AccuracyErrorN)r@   rA   rB   r   r   r   r   r   S  s    r   c                 C   sB   t | }tt| D ]}t|| tjr|| | | j q
|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.
    )r   r   rR   
isinstancerd   r   Zrequires_grad_requires_grad)example_inputsZcloned_inputsidxr   r   r   clone_inputs_retaining_gradnessW  s   r   c           	      C   s   ddl m}m}m} t| } |st|}t| dr| d t| dr(| |n| | }|r0|S ||r<||}|	  || |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_resultsreduce_to_scalar_lossrequires_bwd_pass	zero_gradTZ_boxed_callN)
testingr   r   r   copydeepcopyr   hasattrr   Zbackward)	rS   argsonly_fwdZdisable_cloner   r   r   outZlossr   r   r   run_fwd_maybe_bwdd  s   


r   require_fp64ignore_non_fpc                C   s   ddl m} t| ||}d}tjr:ztt| t|\}	}
t|	|
|}W n t	y9   |r2t
dtd Y nw zt|||}W n t	yQ   td Y dS w ||||tjd|d}|S )	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 = FalsezCould 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)ZtolZ	equal_nanr   )utilsr   r   r   Zsame_two_models_use_fp64cast_to_fp64r   r   r   	ExceptionRuntimeErrorr=   r>   r   Zrepro_tolerance)rS   Zopt_gmr   r   r   r   r   refZfp64_refZ
fp64_modelZfp64_examplesresZpassingr   r   r   same_two_models  sB   
r   c                 C   s   | j jD ]Q}|jdkr5|jtjjjjkr5t	|j
dksJ t|j
d r5|j
d tjkr5|j
d tjf|_
|jdkrU|jd}|d urUt|rUt|j}tj|d< ||_q| j   |   | S )NZcall_functionrY   r   r   rh   )graphnodesopr+   rd   opsZprimsZconvert_element_typedefaultrR   r   r
   float64kwargsgetdictZlintZ	recompile)modelnoderh   Z
new_kwargsr   r   r   cast_dtype_args_to_fp64  s    




r   c                    sB   ddl m} | } tjkrt|}| fdd|}||fS )Nr   )tree_mapc                    s"   t | tjr|  r|  S | S rx   )r   rd   r   re   tor   rh   r   r   <lambda>  s
   
zcast_to.<locals>.<lambda>)Ztorch.utils._pytreer   r   rd   r   r   )rh   r   inputsr   r   r   r   cast_to  s   


r   c                 C   s   t tj| |S rx   )r   rd   r   )r   r   r   r   r   r     s   r   c                C   sL   z|t | t|}t| |||||d W S  ty%   td Y dS w )Nr   zWhile minifying the program in accuracy minification mode, ran into a runtime exception which is likely an unrelated issue. Skipping this graphF)r   r   r   r   r   r=   r   )rS   r   Zcompiler_fnr   r   r   Zcompiled_gmr   r   r   backend_accuracy_fails  s$   	
r   strideztorch._prims_common.StrideTyperg   ztorch._prims_common.ShapeTypereturnc                C   s   | d ur| S t |S rx   )r   Zmake_contiguous_strides_for)r   rg   r   r   r   _stride_or_default  s   r   dc                    s    fddS )Nc                    s   | d ur| S  S rx   r   r   r   r   r   r     s    z_mk_defaulter.<locals>.<lambda>r   r   r   r   r   _mk_defaulter  s   r   cpuc                   @   s6   e Zd ZdddZdddddZdd	 Zd
d ZdS )NopInputReaderr   Nc                 C   s
   d| _ d S )Nr   total)r.   r   r   r   r1   !  s   
zNopInputReader.__init__device
dtype_hintc                C   s   |  j d7  _ d S )Nr   r   )r.   storage_hashnbytesr   r   r   r   r   storage$  s   zNopInputReader.storagec                 O      d S rx   r   r.   r   r   r   r   r   tensor'     zNopInputReader.tensorc                 O   r   rx   r   r   r   r   r   symint*  r   zNopInputReader.symintr   Nr@   rA   rB   r1   r   r   r   r   r   r   r   r      s
    
r   c                   @   sL   e Zd ZdddddZdddddZ	ddddddd	d
Zdd ZdS )InputReaderN)pbarc                C   s8   |d u r	t d |d urt|nd | _g | _|| _d S )Nz0no save_dir specified, will generate random data)r=   r>   r   storer   r   )r.   save_dirr   r   r   r   r1   1  s
   

zInputReader.__init__r   c                C   s   | j d ur| j d t|}t|}| jd ur=|d ur=z| j|}W n	 ty-   Y nw ||jkr;t	d||j |S t	d| ||j
 f}td |d}t|||| S )Nr   zdevice mismatch: %s != %sz1could not load %s, generating random data insteadrg   )r   update_device_or_default_dtype_or_defaultr   Zread_storager   r   r=   r>   itemsizer   r	   untyped_storage)r.   r   r   r   r   r   rg   r   r   r   r   r   <  s"   

zInputReader.storage)storage_offsetrh   r   is_leafc          
      K   s  t ||d}t|}t|}t|}t|}tjg ||j|d}	t  |		|||| W d    n1 s7w   Y  |sut
  |	jtjd}	W d    n1 sTw   Y  t  |		|||| W d    n1 spw   Y  tjj|	|ksJ tj|	| | j|	 |	S )Nr   )rh   r   r   )Zmemory_format)r   _storage_offset_or_defaultr   _is_leaf_or_default_requires_grad_or_defaultrd   r   r   Zno_gradset_Zenable_gradcloneZpreserve_format_subclasses
meta_utilssafe_is_leaf_utilsZset_tensor_metadatar   append)
r.   r   rg   r   r   rh   r   r   metadatatr   r   r   r   R  s,   



zInputReader.tensorc                 C   s   | j | |S rx   )r   r   )r.   valr   r   r   r   s  s   zInputReader.symintrx   r   r   r   r   r   r   0  s    !r   c                   @   s^   e Zd ZddddZdd Zdddd	efd
dZdddZdd ZdddZ	dddZ
dS )InputWriterFstable_hashc                C   s:   g | _ t | _|| _|d urt||dnd | _i | _d S )Nr   )_lines	itertoolsr   storage_counterr   r   r   seen_storages)r.   r   r  r   r   r   r1     s   

zInputWriter.__init__c                 C   s*   dg}| dd | jD  |d |S )Nzdef load_args(reader):c                 s   s    | ]}d | V  qdS )rX   Nr   )r   lr   r   r   r{     s    z$InputWriter.lines.<locals>.<genexpr>zload_args._version = 0)extendr  r   )r.   rr   r   r   lines  s
   
zInputWriter.linesNr   device_hintr   c             
   C   s   t |}| j|}|d ur|S dt| j }d}td t|kr'd|}d}|j}|jdkr9|d us7J |}td |krDd|}|	 }	d }
| j
d ur[|jjdkr[| j
|}
| j| d|
d|	| | d || j|< |S )	Nbufr   z, dtype_hint=metaz	, device=z = reader.storage(, r[   )r   r  r   r]   r  r   r   rO   r   r   r   Zwrite_storager  r   )r.   r   r   r  wsvZmaybe_dtype_hintrq   r   r   r   r   r   r   r     s0   



zInputWriter.storagec           	   	   C   sN  ddl m}m} | j| |j|jd}g }||td |jd|	 s/|
tt|	  td |jkr?|
d|j |td | ksS|
d|  tj|}|rg|dd | D  td |jkrw|
d	|j tjj|}td |kr|
d
| | j
dd|tt|jg| d|   d S )Nr   )statically_known_truesym_eqr
  r   zdtype=zstorage_offset=c                 s   s"    | ]\}}| d |V  qdS )=Nr   )r   kr  r   r   r   r{     s     z%InputWriter.tensor.<locals>.<genexpr>zrequires_grad=zis_leaf=zreader.tensor(r  )  # )Z%torch.fx.experimental.symbolic_shapesr  r  r   r   rh   r   r   rg   r   r   strtupler   r   r   rd   r   Zget_tensor_metadatar  r`   r   r   r   r   r   r   r  r2   )	r.   r   r   r  r  r   r   Ztensor_metadatar   r   r   r   r     s<   zInputWriter.tensorc                 C   s   | j d| dt|  t|ttfrV| j d t|D ].\}}| d| d}t|tjr8| 	|| qt|t
tjfrG| || q| || q| j d d S d S )Nru   z# was unsupported type for dumping: z"""[])r  r   rO   r   rf   r  	enumeraterd   r   r   intSymIntr   unsupported)r.   r   argrz   aZname_ir   r   r   r    s   zInputWriter.unsupportedc                 C   s   | j d|d| d d S )Nzreader.const(r  z!, filtered out during compilation)r  r   )r.   r   r   r   r   const  s   zInputWriter.constc                 C   s0   t |tjr
|jj}| jd|d|  d S )Nzreader.symint(r  )r   rd   r  r   hintr  r   )r.   r   r   r   r   r   r     s   zInputWriter.symintr   )r@   rA   rB   r1   r	  r  r   r   r  r   r   r   r   r   r   r     s    
 
r   ry   funcr   
sym_shapesdefault_sym_shapec                    s~  ddl m} dd | D }d| }t| }d| d}d| d	}	d
}
G dd d}i }p5i  fdddtffdd}| j}| D ]:\}}|dkrWqNt	
|	|}|rw| \}}t|d}|| }|||||< t	
|
|}|r|d||< qNdt| jv r| }||d< t	||D ]}| \}}}}t|d}|| }t||||| q|S )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_abbrsc                 S   s   i | ]\}}||qS r   r   r   r   r   r   
<dictcomp>  s    z*aot_graph_input_parser.<locals>.<dictcomp>|z(_tensor_constant\d+): \"(z0)\[\s*(.*?)\s*\]\" = self\.(_tensor_constant\d+)(z)\[\s*(.*?)\s*\]zSym\((s\d+)\)c                   @   s   e Zd ZdZdS )z/aot_graph_input_parser.<locals>.TensorContainerz#Container for tensors as attributesN)r@   rA   rB   __doc__r   r   r   r   TensorContainer  s    r*  c                    s,   t  v p	d u fdd  S )Nc                      s
     dS )Nz; not in symbolic_shapes and default sym shape not passed inr   r   r   r   r   r   '  s   
 z=aot_graph_input_parser.<locals>.get_sym_int.<locals>.<lambda>)rd   _checkr   r+  )r$  r#  r+  r   get_sym_int$  s
   
z+aot_graph_input_parser.<locals>.get_sym_intr   c           
         s   g }g }t | D ]$\}}| }d|v r#|}|| || q|r,|t| q|jr3tjntj}||| d}|D ]	}	tj	||	 q?|S )Nrw   )rh   r   )
r  stripr   r  re   rd   ZrandnZzerosr   Zmark_dynamic)
rg   rh   Zresolved_shapeZdynamic_dimsrz   dimrw   constructorr   r   )r   r-  r   r   
gen_tensor+  s    
z*aot_graph_input_parser.<locals>.gen_tensor,r   r.   )Ztorch.fx.graphr%  r`   r2   valuesinspect	getsourcer   __annotations__researchgroupsr  r&   group	signaturer^   finditersetattr)r"  r   r#  r$  r%  Z	dtype_mapZdtype_patternsourceZtensor_assignment_regexZtensor_regexZsym_shape_regexr*  r   r1  annotationsrp   
annotationmatchZ	data_typeZ	shape_strrg   rh   	containerZ	attr_namerT   r   )r$  r   r-  r#  r   aot_graph_input_parser  sF   
rC  r/   c                    sD   t  tjtj  fdd} fdd}t| |S )z
    Decorator to cProfile a given function and save the result to disk on process exit.

    Args:
        filename: filename to save profile to
    c                    s   t   fdd}|S )Nc                     s,      z | i |W   S   w rx   )enabledisable)r   r   )fnprofr   r   wrapperh  s   z3profile_to_file.<locals>.decorator.<locals>.wrapper)	functoolswraps)rF  rH  )rG  )rF  r   	decoratorg  s   z"profile_to_file.<locals>.decoratorc                	      s.      tjtd  d  d d S )Nz!                Wrote profile to z+, view with:

                    snakeviz z

                )Z
dump_statssysstderrr;   r4   r5   r   r/   rG  r   r   save_itr  s   
z profile_to_file.<locals>.save_it)cProfileZProfiler$   r%   r'   
expanduseratexitregister)r/   rK  rO  r   rN  r   profile_to_file]  s   
rT  )FF)F)ry   NN)br)  rR  r   rP  rI  rF   r4  r  loggingr$   r7  r|   rL  rD   r4   collectionsr   	importlibr   typingr   r   r   r   rd   Ztorch._prims_commonZ_prims_commonr   Ztorch._subclasses.meta_utilsr   Ztorch._dynamo.testingr	   r
   Z torch.multiprocessing.reductionsr   Ztorch.utils._content_storer   r   r   r   r   r   	getLoggerr@   r=   r   Zinductor_configZ	is_fbcoder   Zlibfb.py.build_infoZlibfbr3   Zextra_importspyZ
build_infoZ	BuildInfoZget_build_ruler*   r6   r2   r<   r   rJ   ra   rL   	lru_cacher   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   Zfloat32r   r   r   r   r   r   r   r   r   rf   r  r   r  rC  rT  r   r   r   r   <module>   s   
7	f

:)
 
Tx

 c