o Xi@sUdZddlmZgdZddlZddlmZmZddlmZ ddl Z ddl m Z er/ddl Z daded<dad ed <dddZdddZGddde jZdS)aCompatible adapters implementing the TensorProtocol interface for various framework tensor types. This module provides public classes that implement the :class:`onnx_ir.TensorProtocol` interface for various tensor types from popular deep learning frameworks. You can use these classes to create tensors and use them in the IR graph like any other tensor. Example:: import torch import onnx_ir as ir # Create a PyTorch tensor torch_tensor = torch.tensor([1, 2, 3]) # Wrap the PyTorch tensor in a TorchTensor object ir_tensor = ir.tensor_adapters.TorchTensor(torch_tensor) # Use the IR tensor in the graph attr = ir.AttrTensor("x", ir_tensor) print(attr) ) annotations)from_torch_dtypeto_torch_dtype TorchTensorN) TYPE_CHECKINGAny)_corez%dict[torch.dtype, ir.DataType] | None_TORCH_DTYPE_TO_ONNXz%dict[ir.DataType, torch.dtype] | None_ONNX_DTYPE_TO_TORCHdtype torch.dtypereturn ir.DataTypecCsltdurddl}i|jtjj|jtjj|jtjj |j tjj |j tjj |jtjj|jtjj|jtjj|jtjj|jtjj|jtjj|jtjj|jtjj|jtjj|j tjj!|j"tjj#|j$tjj%|j&tjj'|j(tjj)iat*|drtjj+t|j,<t*|drtjj-t|j.<t*|drtjj/t|j0<|tvrt1d|dt2t3t|S)z/Convert a PyTorch dtype to an ONNX IR DataType.Nrfloat8_e8m0fnuint2uint2zUnsupported PyTorch dtype 'z/'. Please use a supported dtype from the list: )4r torchbfloat16irDataTypeBFLOAT16boolBOOL complex128 COMPLEX128 complex64 COMPLEX64float16FLOAT16float32FLOATfloat64DOUBLE float8_e4m3fn FLOAT8E4M3FNfloat8_e4m3fnuzFLOAT8E4M3FNUZ float8_e5m2 FLOAT8E5M2float8_e5m2fnuzFLOAT8E5M2FNUZint16INT16int32INT32int64INT64int8INT8uint8UINT8uint16UINT16uint32UINT32uint64UINT64hasattr FLOAT8E8M0rINT2rUINT2r TypeErrorlistkeysr rrCK/home/ubuntu/.local/lib/python3.10/site-packages/onnx_ir/tensor_adapters.pyr5sj                       rcCstdurddl}itjj|jtjj|jtjj|j tjj |j tjj |j tjj|jtjj|jtjj|jtjj|jtjj|jtjj|jtjj|jtjj|jtjj|jtjj |j!tjj"|j#tjj$|j%tjj&|j'tjj(|j)iat*|dr|j+ttjj,<t*|dr|j-ttjj.<t*|dr|j/ttjj0<|tvr|tjj,krt1dt2d|dt3t4t|S) z/Convert an ONNX IR DataType to a PyTorch dtype.Nrrrrz}The requested DataType 'FLOAT8E8M0' is only supported in PyTorch 2.7+. Please upgrade your PyTorch version to use this dtype.z(Unsupported conversion from ONNX dtype 'z8' to torch. Please use a supported dtype from the list: )5r rrrrrrrrrrrrrr rr"r!r$r#r&r%r(r'r*r)r,r+r.r-r0r/r2r1r4r3r6r5r8r7r:r9r;rr<rr=rr> ValueErrorr?r@rArBrCrCrDrasr                        rcsTeZdZ ddfdd Zdd d ZddddZddZdddZd ddZZ S)!rNtensor torch.Tensorname str | None doc_stringcstj|t|j||ddS)N)r rHrJ)super__init__rr )selfrFrHrJ __class__rCrDrLs zTorchTensor.__init__r npt.NDArraycCsddl}||jtjjkr|j|jjdd|jS|jtjj tjj tjj tjj tjj hvrC|j|jjdd|jS|jtjjtjjhvr_|j|jjdd|jS|jjddS)NrT)force)rr rrrrawviewr5numpyr$r&r(r*r<r3r=r>)rMrrCrCrDrTs"""zTorchTensor.numpyr rcopy bool | NonecCs ~|dur |S||SN)rT __array__)rMr rUrCrCrDrXszTorchTensor.__array__cCsddl}|jj|j}Wdn1swYt||jjj r4t d|j d|t j |||fS)z4Get a ctypes byte array pointing to the tensor data.rNz.Cannot take content out from the FakeTensor ('z'). Please replace the tensor with a tensor backed by real data using ONNXProgram.apply_weights() or save the model without initializers by setting include_initializers=False.)torch._subclasses.fake_tensor _subclasses fake_tensorunset_fake_temporarilyrRdetachcpu contiguous isinstance FakeTensorr?rHctypesc_ubyte element_sizenumel from_addressdata_ptr)rMrrFrCrCrD _get_cbytess zTorchTensor._get_cbytesbytescCs|\}}t|SrW)rhri)rM_datarCrCrDtobytess zTorchTensor.tobytesNonecCs|\}}||SrW)rhwrite)rMfilerjrkrCrCrDtofiles  zTorchTensor.tofile)NN)rFrGrHrIrJrI)r rP)r rrUrVr rP)r ri)r rm) __name__ __module__ __qualname__rLrTrXrhrlrp __classcell__rCrCrNrDrs   r)r r r r)r rr r )__doc__ __future__r__all__rbtypingrr numpy.typingnptonnx_irrrrr __annotations__r rrTensorrrCrCrCrDs       ,2