+ j,^RIHt^RIHtHt^RIHtHt^RIt^RI t^RI H t ^RI H t ^RI HtHtHt]'d ^RIHt^RIt!R R ]4t!R R ]4tR#) ) annotations)ABCabstractmethod)Any TYPE_CHECKINGN)ir)V) FixedLayoutFlexibleLayoutLayout)Sequencec]tRt^tRtR"RRlltR#RRllt]RRl4t]R R l4t R R lt R Rlt RRlt RRlt RRltRRltRRltR$RRllt]RRl4tRRlt]R%RR ll4tR!tR#)& KernelInputsz Class to store and provide access to input nodes for kernels. This class takes in a tuple of input nodes and provides methods to access information about these nodes, such as their device type and device. Nc$V^8dQhRRRRRR/#) input_nodes list[Any]scalarsdict[str, float | int] | None out_dtypetorch.dtype | None)formats"^/home/luca/screen-checking/.venv/lib/python3.14/site-packages/torch/_inductor/kernel_inputs.py __annotate__KernelInputs.__annotate__s-EEE/E& Ec |WnRVnVeTM/VnW0n\ V4^8:d \ R4hR#)z Initialize with a tuple of input nodes. Args: input_nodes: A tuple of input nodes to store out_dtype: Optional output dtype to store Nz Expected at least one input node) _input_nodes _device_name_scalars _out_dtypelenAssertionError)selfrrrs&&&&r__init__KernelInputs.__init__sC((,#*#6B # { q  !CD D !rc V^8dQhRRRR/#)rreorderzSequence[int] | Nonereturnrr)rs"rrr/s7717Y7rc Vf VP#\VP4\V48wd.\R\VP4 R\V4 24hVUu.uFq PV,NK up#uupi)z Return the stored input nodes, optionally reordered. Args: reorder: Optional sequence of indices to reorder the nodes. For example, (2, 0, 1) would return nodes in that order. Returns: The tuple of input nodes, optionally reordered zReorder length mismatch:  vs )rr#r$)r%r)is&& rnodesKernelInputs.nodes/s ?$$ $ t  !S\ 1 +C0A0A,B+C4G ~V /66g!!!$$g666s%BcV^8dQhRR/#rr*intr)rs"rrrCs&&s&rc ,\VP4#)zH Get the number of input nodes. Returns: The number of input nodes )r#rr%s&rcountKernelInputs.countBs4$$%%rcV^8dQhRR/#rr*z str | Noner)rs"rrrMs88Z8rc P\P!VP^,4#)z\ Get the device type of the first node. Returns: The device type (e.g., 'cuda', 'cpu') )rget_device_typerr4s&r device_typeKernelInputs.device_typeLs !!$"3"3A"677rcV^8dQhRR/#)rr*z torch.devicer)rs"rrrWs11 1rc DVP^,P4#)zN Get the device of the first node. Returns: The device of the first node )r get_devicer4s&rdeviceKernelInputs.deviceWs  #..00rcV^8dQhRR/#r8r)rs"rrr`s ! !Z !rc VPfRVP4pVPR8Xd1\PP V4pVP VnVP#)zU Get the device name information. Returns: A tuple of (gpu_name, vendor, model) cuda)r r@r;torchrDget_device_properties gcnArchName)r%r@device_propertiess& r device_nameKernelInputs.device_name`sX    $[[]F6)$)JJ$D$DV$L!$5$A$A!   rcV^8dQhRR/#)rr*ztuple[tuple[Any, ...], ...]r)rs"rrrnsDD!<Drc \;QJd.RVP4FNK 5#!RVP44#)zg Get the symbolic shapes of all input nodes. Returns: A tuple of shape tuples for each input node c3@"TFqP4xK R#5iN)get_size.0nodes& r /KernelInputs.shapes_symbolic..usC1B]]__1Btuplerr4s&rshapes_symbolicKernelInputs.shapes_symbolicns2uC1B1BCuCuC1B1BCCCrcV^8dQhRR/#rr*ztuple[tuple[int, ...], ...]r)rs"rrrws   :  rc \;QJd.RVP4FNK 5#!RVP44#)z Get the size hints for shapes of all input nodes. Returns: A tuple of shape tuples with integer hints for each input node c3"TF<p\PPPVP 44xK> R#5irN)r graphsizevarsoptimization_hintsrOrPs& rrS-KernelInputs.shapes_hinted..~s5 ) GG   / /  @ @)AArVr4s&r shapes_hintedKernelInputs.shapes_hintedwBu )) u u ))   rcV^8dQhRR/#)rr*z%tuple[tuple[sympy.Integer, ...], ...]r)rs"rrrsFF"GFrc \;QJd.RVP4FNK 5#!RVP44#)zi Get the symbolic strides of all input nodes. Returns: A tuple of stride tuples for each input node c3@"TFqP4xK R#5irN) get_striderPs& rrS0KernelInputs.strides_symbolic..sE3D4__&&3DrUrVr4s&rstrides_symbolicKernelInputs.strides_symbolics2uE43D3DEuEuE43D3DEEErcV^8dQhRR/#r[r)rs"rrrs   ;  rc \;QJd.RVP4FNK 5#!RVP44#)z Get the size hints for strides of all input nodes. Returns: A tuple of stride tuples with integer hints for each input node c3"TF<p\PPPVP 44xK> R#5irN)r r^r_r`rirPs& rrS.KernelInputs.strides_hinted..s6 ) GG   / /0A B B)rbrVr4s&rstrides_hintedKernelInputs.strides_hintedrercV^8dQhRR/#)rr*ztuple[torch.dtype, ...]r)rs"rrrsEE/Erc \;QJd.RVP4FNK 5#!RVP44#)zX Get the dtypes of all input nodes. Returns: A tuple of dtypes for each input node c3@"TFqP4xK R#5irN) get_dtyperPs& rrS&KernelInputs.dtypes..sD2C$^^%%2CrUrVr4s&rdtypesKernelInputs.dtypess2uD$2C2CDuDuD$2C2CDDDrc V^8dQhRRRR/#)ridxr2r* torch.dtyper)rs"rrrs 2 2 2[ 2rc DVPV,P4#)z Get the dtype of a specific input node. Args: idx: Index of the node to get the dtype from (default: 0) Returns: The dtype of the specified input node )rrv)r%r{s&&rdtypeKernelInputs.dtypes  %//11rcV^8dQhRR/#rr*r|r)rs"rrrs  ; rc R#)c Get the output dtype, whether passed in or inferred from the nodes Returns: The output dtype Nrr4s&rrKernelInputs.out_dtyperc V^8dQhRRRR/#)rnamestrr*z float | intr)rs"rrrs # #s #{ #rc fWP9d\RV R24hVPV,#)zr Get the scalar value for a given name. Args: name: Name of the scalar to get Returns: The scalar value zScalar z not found, but required)r!r$)r%rs&&r get_scalarKernelInputs.get_scalars2 }} $ 74&0H!IJ J}}T""rc V^8dQhRRRR/#rflexibleboolr*r r)rs"rrrs  d f rc R#)z Abstract method to handle output layout generation. Args: out_dtype: Optional output dtype. If not provided, infer from inputs flexible: If True, return FlexibleLayout, otherwise FixedLayout Nr)r%rs&&r output_layoutKernelInputs.output_layoutrr)r rr"r!)NNrN)rT)__name__ __module__ __qualname____firstlineno____doc__r&r.propertyr5r;r@rIrXrcrkrqrxr~rrrr__static_attributes__rrrrrs E(7&&&881 !D  F  E 2   #  rrca]tRt^tRtRRV3RllltRRltRRltRRR lltR R lt R R lt RRlt Rt V;t #)MMKernelInputszz Specialized KernelInputs for matrix multiplication operations. Provides additional methods to access M, N, K dimensions. c ,V^8dQhRRRRRRRRR R/#) rrrrrrrmat1_idxr2mat2_idxr)rs"rrMMKernelInputs.__annotate__s<""""""/""& ""  ""  ""rc <\SV`WV4\VP4^8d \ R4hYErvV^8dV\V4, pV^8dV\V4, p^Tu;8:d\V48gM\ RV 24h^Tu;8:d\V48gM\ RV 24hW@nWPnR#)z Initialize with a tuple of input nodes. By default, we assume the last 2 input nodes are mat1 and mat2, but the caller can adjust when necessary zExpected at least 2 input nodeszInvalid mat1_idx: zInvalid mat2_idx: N)superr&r#rr$ _mat1_idx _mat2_idx) r%rrrrrm1_idxm2_idx __class__s &&&&&& rr&MMKernelInputs.__init__s y9 t  !A % !BC C" a< c+& &F a< c+& &FV.c+.. #5hZ!@A AV.c+.. #5hZ!@A A!!rcV^8dQhRR/#)rr*z2tuple[sympy.Integer, sympy.Integer, sympy.Integer]r)rs"rrrs ;rc VP4VP,pVP4VP,pVP4R,pVP4R,pVP4R,pVP4R,p\P P PWF4W5V3#)aq Get the symbolic M, N, K dimensions for matrix multiplication. Handles both 2D (MM) and 3D (BMM) tensors. M is extracted from the second-to-last dimension of the first operand (mat1). N is extracted from the last dimension of the second operand (mat2). K is extracted from the last dimension of the first operand (mat1). Returns: A tuple of (M, N, K) dimensions )r.rrrOr r^r_ check_equals)r%mat1mat2mknk0s& r mnk_symbolicMMKernelInputs.mnk_symbolicszz|DNN+zz|DNN+ MMOB  MMOB  MMOB ]]_R  %%a,ayrcV^8dQhRR/#rr)rs"rrrs . .; .rc VPe VP#VP4^,P4#)r)r"mat1mat2rvr4s&rrMMKernelInputs.out_dtypes2 ?? &?? "}}q!++--rc V^8dQhRRRR/#rr)rs"rrrs??d?f?rc VP4wr#VP4pVP4EvrVpVP4Evrp \WX4U U u.uF.wr\P P PW4NK0 up p p .V OVNV Np V'd\VP4WM4#\VP4WM4#uup p i)z Handle output layout generation for matrix multiplication. Args: out_dtype: Optional output dtype. If not provided, infer from inputs flexible: If True, return FlexibleLayout, otherwise FixedLayout ) rrrOzipr r^r_check_equals_and_simplifyr r@r )r%rrrrb1rk1b2k2rabsizes&& rrMMKernelInputs.output_layouts]]_ NN$ ]]_ ]]_ JMb+ V+$!QWW   7 7 =+ VzAzqz !$++-A At{{}i> > Ws4CcV^8dQhRR/#)rr*ztuple[Any, Any]r)rs"rrr2s<>2 ">>2 rN rN rNR. < #=aSWI!NO OayrcV^8dQhRR/#r1r)rs"rrrUs  c rc |VP4pWP,p\V4^8d VR,#^#)z Get the hinted batch size for batched matrix multiplication. Returns 1 for non-batched (2D) operations. Returns: The batch size as an integer )rcrr#)r%rrs& r batch_hintedMMKernelInputs.batch_hintedUs7**, ">>2 z?a b> !r)rr)NNrrr)rrrrrr&rrrrrrr __classcell__)rs@rrrs6 """"H4 .?(<2  rr) __future__rabcrrtypingrrrEtorch._inductor.configtorch._inductorrtorch._inductor.virtualizedr r r r collections.abcr sympyrrrrrrsH"#% )33(x 3x vS\Sr