qiE*ddlmZddlmZmZddlmZmZmZm Z ddl Z ddl Z ddl m Z ddlmZddl mZmZmZer dd lmZddlZGd d eZGd d eZy)) annotations)ABCabstractmethod)AnyOptional TYPE_CHECKINGUnionN)ir)V) FixedLayoutFlexibleLayoutLayout)SequenceceZdZdZ d ddZdddZeddZeddZddZ ddZ dd Z dd Z dd Z dd Zdd ZdddZeddZd dZed!d"dZy)# 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. Ncn||_d|_||ni|_||_t |dkDsJdy)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 Nrz Expected at least one input node) _input_nodes _device_name_scalars _out_dtypelen)self input_nodesscalars out_dtypes c/home/ubuntu/crypto_trading_bot/.venv/lib/python3.12/site-packages/torch/_inductor/kernel_inputs.py__init__zKernelInputs.__init__sB(+/#*#6B #;!#G%GG#c| |jSt|jt|k(s)Jdt|jdt||Dcgc]}|j|c}Scc}w)a7 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)rreorderis rnodeszKernelInputs.nodes.sz ?$$ $4$$%W5 'D,=,=(>'?tCL> R 5/66!!!$666sA6c,t|jS)zh Get the number of input nodes. Returns: The number of input nodes )rrrs rcountzKernelInputs.count@s4$$%%rcFtj|jdS)z| Get the device type of the first node. Returns: The device type (e.g., 'cuda', 'cpu') r)r get_device_typerr&s r device_typezKernelInputs.device_typeJs !!$"3"3A"677rc<|jdjS)zn Get the device of the first node. Returns: The device of the first node r)r get_devicer&s rdevicezKernelInputs.deviceUs  #..00rc|jO|j}|jdk(r0tjj |}|j |_|jS)zu Get the device name information. Returns: A tuple of (gpu_name, vendor, model) cuda)rr-r*torchr/get_device_properties gcnArchName)rr-device_propertiess r device_namezKernelInputs.device_name^sX    $[[]F6)$)JJ$D$DV$L!$5$A$A!   rc:td|jDS)z Get the symbolic shapes of all input nodes. Returns: A tuple of shape tuples for each input node c3<K|]}|jywN)get_size.0nodes r z/KernelInputs.shapes_symbolic..ssCT]]_Ctuplerr&s rshapes_symboliczKernelInputs.shapes_symboliclsC1B1BCCCrc:td|jDS)z Get the size hints for shapes of all input nodes. Returns: A tuple of shape tuples with integer hints for each input node c3K|]_}tjjj|j t j jjayw)fallbackN) r graphsizevars size_hintsr8r0 _inductorconfigunbacked_symint_fallbackr9s rr<z-KernelInputs.shapes_hinted..|sP  GG   ' ' //HH (  A%A'r>r&s r shapes_hintedzKernelInputs.shapes_hintedu& ))    rc:td|jDS)z Get the symbolic strides of all input nodes. Returns: A tuple of stride tuples for each input node c3<K|]}|jywr7) get_strider9s rr<z0KernelInputs.strides_symbolic..sE4T__&Er=r>r&s rstrides_symboliczKernelInputs.strides_symbolicsE43D3DEEErc:td|jDS)z Get the size hints for strides of all input nodes. Returns: A tuple of stride tuples with integer hints for each input node c3K|]_}tjjj|j t j jjaywrC) r rErFrGrPr0rHrIrJr9s rr<z.KernelInputs.strides_hinted..sQ  GG   ' '!//HH (  rKr>r&s rstrides_hintedzKernelInputs.strides_hintedrMrc:td|jDS)zx Get the dtypes of all input nodes. Returns: A tuple of dtypes for each input node c3<K|]}|jywr7) get_dtyper9s rr<z&KernelInputs.dtypes..sD$T^^%Dr=r>r&s rdtypeszKernelInputs.dtypessD$2C2CDDDrc<|j|jS)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 )rrW)ridxs rdtypezKernelInputs.dtypes  %//11rcy) Get the output dtype, whether passed in or inferred from the nodes Returns: The output dtype Nr&s rrzKernelInputs.out_dtypercR||jvs Jd|d|j|S)z 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)rnames r get_scalarzKernelInputs.get_scalars4t}}$Nv5M&NN$}}T""rcy)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^)rflexibles r output_layoutzKernelInputs.output_layoutr_r)NN)r list[Any]r&Optional[dict[str, Union[float, int]]]rOptional[torch.dtype]r7)r"zOptional[Sequence[int]]returnrf)riint)riz Optional[str])riz torch.device)riztuple[tuple[Any, ...], ...])riztuple[tuple[int, ...], ...])riz%tuple[tuple[sympy.Integer, ...], ...])riztuple[torch.dtype, ...])r)rZrjri torch.dtyperirk)rastrrizUnion[float, int]Trdboolrir)__name__ __module__ __qualname____doc__rr$propertyr'r*r-r4r@rLrQrTrXr[rrrbrer^rrrrs;?+/ HH8H) H&7$&&881 !D  F  E 2   #  rrcneZdZdZ d d fd Z d dZd dZd d dZddZddZ xZ S)MMKernelInputsz Specialized KernelInputs for matrix multiplication operations. Provides additional methods to access M, N, K dimensions. cVt||||t|jdk\sJd||}}|dkr|t|z }|dkr|t|z }d|cxkrt|ks nJd|d|cxkrt|ks nJd|||_||_y)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 nodesrzInvalid mat1_idx: zInvalid mat2_idx: N)superrrr _mat1_idx _mat2_idx) rrrrmat1_idxmat2_idxm1_idxm2_idx __class__s rrzMMKernelInputs.__init__s gy94$$%*M,MM*"8 a< c+& &F a< c+& &FF-S--N1CH:/NN-F-S--N1CH:/NN-!!rcl|j|j}|j|j}|jd}|jd}|jd}|jd}tj j j|||||fS)a 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$r{r|r8r rErF check_equals)rmat1mat2mknk0s r mnk_symboliczMMKernelInputs.mnk_symbolicszz|DNN+zz|DNN+ MMOB  MMOB  MMOB ]]_R  %%a,1ayrct|j |jS|jdjS)r]r)rmat1mat2rWr&s rrzMMKernelInputs.out_dtypes2 ?? &?? "}}q!++--rc|j\}}|j}|j^}}}|j^}} } t||D cgc]/\} } tj j j| | 1c} } } g| || } |rt|j|| St|j|| Scc} } w)z Handle output layout generation for matrix multiplication. Args: out_dtype: Optional output dtype. If not provided, infer from inputs flexible: If True, return FlexibleLayout, otherwise FixedLayout ) rrr8zipr rErFcheck_equals_and_simplifyrr-r )rrdrrrb1rk1b2k2rabsizes rrezMMKernelInputs.output_layouts]]_ dNN$ ]]_ Q]]_ RJMbRT+ V$!QQWW   7 71 = VzAzqz !$++-DA At{{}i> > Ws4Cc^|j}||j||jfS)zj Get the mat1 and mat2 nodes. Returns: A tuple of (mat1, mat2) nodes )r$r{r|)rr$s rrzMMKernelInputs.mat1mat22s, T^^$eDNN&;;;rc|j}||j}||j}|d}|d}|d}|d}||k(s Jd|d||||fS)a  Get the hinted M, N, K dimensions for matrix multiplication. Handles both 2D (MM) and 3D (BMM) tensors. Uses shapes_hinted from the base class to get integer hints for dimensions. Returns: A tuple of (M, N, K) dimensions as integers rrzK dimensions don't match: r!)rLr{r|)r hinted_shapes mat1_shape mat2_shaperrrk_checks r mnk_hintedzMMKernelInputs.mnk_hinted<s**, "4>>2 "4>>2 rN rN rNR.G|J9!D JJ|1ayr)NNrr) rrfrrgrrhr}rjr~rj)riz2tuple[sympy.Integer, sympy.Integer, sympy.Integer]rlrnro)riztuple[Any, Any])riztuple[int, int, int]) rqrrrsrtrrrrerr __classcell__)rs@rrwrwsm;?+/ ""8") "  "  "B ;4 .?(<rrw) __future__rabcrrtypingrrrr r0torch._inductor.configtorch._inductorr torch._inductor.virtualizedr r rrcollections.abcrsympyrrwr^rrrsK"#66 )33({ 3{ |@\@r