2j)dZddlmZddlZddlZej5ddlZddlmZmZm Z dddn #1swxYwYdZ dZ ddZ d Z d Zd Zd Zed kr edSdS)a3Atheris fuzz harness for onnx.shape_inference. Two input paths are exercised per iteration, selected by a fuzzer-controlled toggle byte read from the *tail* of the input (so the head remains a valid candidate for the raw-bytes path): * Raw bytes -> onnx.load_model_from_string -> infer_shapes Catches protobuf parser bugs and bugs reachable only through crafted serialized models the structured builder will not produce. * Structured -> helper.make_model from FuzzedDataProvider -> infer_shapes Constructs ModelProto objects whose graphs include subgraph-bearing ops (If / Loop / Scan) so the recursive visitor inside shape_inference is reached on most iterations rather than only when the parser happens to accept a random byte string. Both strict_mode values and both check_type values are sampled. ) annotationsN) TensorProtohelpershape_inference) ReluSigmoidTanhAbsNegExpLogSqrtIdentityFloorCeil)IfLoopScanTcxtj|tjg|g}tjdg|g|S)NConstantvalue)r make_tensorrBOOL make_node)namertensors OC:\Projects\Historical\venv\Lib\site-packages\onnx/fuzz/fuzz_shape_inference.py _const_boolr7s9  k&6UG D DF  JTF& A A AAc g}d|}tj|tjdgdg}|tjdg|g|||kr|rt|dttdz }d|}t||dz|}|dkrjd |} |t| t||dz|} |tjd| g|g|| n|d krd |} tj| tj gdg} |tjdg| g| d |} |t| |tjd | | g|g| n.|tjd|g|g|d|} n|} |dd}t|D]m}t|dttdz }d|d|}|tj|| g|g|} ntj|d|gtj| tjdgS)a)Build a self-contained subgraph. Self-contained means the subgraph produces its own starting tensor via a Constant node, so the branch does not depend on outer-scope captures we did not declare. With probability the branch nests one of If/Loop/Scan, which is what drives the recursion inside shape_inference. Loop and Scan body subgraphs are deliberately not signature-conformant; the recursive visitor descends before signature checks run, so the recursion path is still exercised even when inference ultimately fails. s_grrrsub_rc_) then_branch else_branchrM_)bodyr)r)num_scan_inputsv__branch_N)inputsoutputs)rrrFLOATappendr ConsumeBool _SUBGRAPH_OPSConsumeIntInRangelen _build_branchrINT64range_UNARY make_graphmake_tensor_value_info)fdpdepth max_depthnodesstart start_tensorsub_opoutr)cond else_bodytriptrip_tlastn_opsiopnxts rr7r7<s" E LLE%e[->cUKKL LL!*b5'NNNOOO yS__..s44QM8J8JQ8NOOPUnnS%!)Y77 T>><r?) opset_imports)r5r7r make_model make_opsetid)r=r?graphopsets r _build_modelrXsr%%a,,I #Q) < < =B>ctjtjtjt dtjdS)NT)enable_python_coverage)r_instrument_allSetupsysargvrjFuzzr rmainrss9  M#(LFFFF LNNNNNr __main__)T)__doc__ __future__rror_instrument_importsrarrrr:r4rr7rXrjrs__name__rrr rrysM&#""""" W!!::KKK9999999999::::::::::::::: "' BBBB HHHV   D  zDFFFFFs ;??