o Zh@sddlZddlmZmZmZddlZddlmZmZe edfZ eee e fge edffZ gdZdedefdd Zd ejjdeeeffd d Zd eedeeeffddZdedefddZdS)N)AnyCallableUnion)tree_flatten_with_pathtree_map.)normalize_source_namemodule_to_nested_dicttrack_dynamism_across_examplesclone_and_convert_to_metanamereturncCstdd|S)Nz\.([a-zA-Z_][a-zA-Z0-9_]*)z['\1'])resub)r rN/var/www/auris/lib/python3.10/site-packages/torch/fx/experimental/_dynamism.pyrsrmodulecCsi}i|d<i|d<t|D]-}|ds;tt||s;t||}t|tjjs;t|tt tj fr;t |t ur;|||<q|j ddD] \}}||d|<qB|jddD] \}}||d|<qS|D] \}}t||d|<qb|S)ziRecursively converts an nn.Module into a nested dictionary with explicit 'parameters' and 'modules' keys. _parametersZ_modules_F)recurse)dir startswithcallablegetattr isinstancetorchnnModuleintfloatTensortypeboolZnamed_parametersZ named_buffersZnamed_childrenr)rZ self_dictZ attr_nameZ attr_valuer parambuffer submodulerrrrs(    rexample_inputscCsi}|D]}d|vrt|dtjjrt|d|d<t|\}}|D]j\}}t|tttjfs1q#t|tjr?t |j }d}n|f}d}||vrXddt t |D|f||<n!||\} } | |krc t | t |kry| tt | t |kskt|D]\} } ||d| | q}q#qi} |D].\}\} }t dd| D}d d d d|D}|dj}|| vri| |<|| ||<q| S) a This function analyzes a list of example inputs to determine the dynamism of their shapes. It tracks whether the dimensions of tensors or non-tensor values change across different examples. The function returns a dictionary where each key represents a path to a value in the input examples, and the corresponding value is a tuple indicating which dimensions are dynamic (i.e., change across examples). This helps in understanding how the structure of data varies across different instances. selfTFcSsg|]}tqSr)set).0rrrr Osz2track_dynamism_across_examples..rcss|] }t|dkVqdS)N)len)r(srrr [sz1track_dynamism_across_examples..Lcss|]}t|VqdS)N)str)r(krrrr-\s)rrrrrrrrrtupleshaperanger+appendr' enumerateadditemsjoinkey)r%ZtrackingexZleaves_with_pathsrZkey_pathvaluer3Z is_tensorZdim_setsflagidimoutputZ _is_tensorZ final_dynZkey_strr:rrrr 4sD         r example_inputcCsdtdtfdd}t||S)z This function takes a list of example inputs and for each tensor, clones it and converts it to device=meta. For non-tensor values, it keeps the reference. It uses pytree to handle nested structures recursively. r<r cSs t|tjr|jddS|S)Nmeta)Zdevice)rrrcloneto)r<rrr transform_fnjs z/clone_and_convert_to_meta..transform_fn)rr)rArErrrr ds r )r typingrrrrZtorch.utils._pytreerrr2ZKeyPathrrZNonTensorShapeFn__all__r0rrrdictrlistr r rrrrs   0