Pytorch export onnx: RuntimeError Exporting the operator view_as_complex to ONNX opset version 9 is not supported. Please open a bug to request ONNX export support for the missing operator also :onnx/onnx#3173 cc@houseroad@spandantiwari@lara-hdr@BowenBao@neginraoof ...
view_as_complex(xq_) xk_ = torch.view_as_complex(xk_) # 应用旋转操作,然后将结果转回实数域 # xq_out.shape = [batch_size, seq_len, dim] xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(2) xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(2) return xq_out.type_...
23 - auto new_strides = computeStrideForComplex(self.strides()); 24 + auto new_strides = computeStrideForViewAsReal(self.strides()); 24 25 auto new_storage_offset = 2 * self.storage_offset(); 25 26 const auto float_type = c10::toValueType(self.scalar_type()); 26 27 return...
., d_k/2) q_rope=torch.view_as_complex(q.float().reshape(B, n_heads, T, -1, 2)) k_rope=torch.view_as_complex(k.float().reshape(B, n_heads, T, -1, 2)) # 调整 freqs_cis 形状以广播: (T, d_k/2) -> (1, 1, T, d_k/2) freqs_cis_broadcast=freqs_cis.unsqueeze(...
view_as(self, other) vsplit(self, split_size_or_sections) where(self, condition, y) xlogy(self, other) xlogy_(self, other) xpu(self, device=None, non_blocking=False, memory_format=None) zero_(self) _coalesced_(self, *args, **kwargs) ...
reshape与view的区别如下: view只能改变连续(.contiguous())的tensor,如果已经对tensor进行了permute、transpose等操作,tensor在内存中会变得不连续,此时调用view会报错。且view方法与原来的tensor共享内存。 reshape再调用时自动检测原tensor是否连续,如果是,则等价于view;如果不是,先调用.contiguous(),再调用view,此时返...
view_as_complex 否 addbmm 是 addmm 是 addmv 是 addr 是 baddbmm 是 bmm 是 chain_matmul 是 cholesky 否 cholesky_inverse 否 cholesky_solve 否 dot 是 eig 否 geqrf 否 ger 是 inner 是 inverse 是 det 否 logdet 否 slogdet 是 lstsq 否 lu 否 lu_solve 否 lu_unpack 否 matmul ...
reshape再调用时自动检测原tensor是否连续,如果是,则等价于view;如果不是,先调用.contiguous(),再调用view,此时返回值与原来tensor不共享内存。 defreshape(self, shape:Sequence[Union[_int, SymInt]]) -> Tensor: ... 1.3.8 数学运算 defmean(self, dim=None, keepdim=False, *args, **kwargs):# real...
importtorchimportnumpyasnp#只能创建指定数据的张量# 1. 定义一个常量tensordata=torch.tensor(10)print(data)#2. numpy转tensordata=np.random.randn(2,3)data=torch.tensor(data)print(data)#3.列表,下面代码使用默认类型float32data=[[10,20,30],[40,50,60]]data=torch.tensor(data)print(data) ...
'So she was considering in her own mind (as well as she could, for thehot day made her feel very sleepy and stupid), whether the pleasureof making a daisy-chain would be worth the trouble of getting up andpicking th...