-1, 2)).to(device) #xq_:[bsz, seq_len, n_heads, head_dim/2]xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)).to(device) #xk_:[bsz, seq_len, n_heads, head_dim/2]
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) ...
# 其次:将xq和xk转换为复数,因为旋转矩阵只适用于复数xq_=torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1,2)).to(device)#xq_:[bsz, seq_len, n_heads, head_dim/2]xk_=torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1,2)).to(device)#xk_:[bsz, seq...
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...
@TOC前言本文只是对于pytorch深度学习框架的使用方法的介绍,如果涉及算法中复杂的数学原理,本文将不予阐述,敬请读者自行阅读相关论文或者文献。1.tensor基...
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 ...
inline std::vector<int64_t> computeStrideForComplex(IntArrayRef oldstride) { inline std::vector<int64_t> computeStrideForViewAsReal(IntArrayRef oldstride) { auto res = oldstride.vec(); for(size_t i = 0; i < res.size(); i++) { res[i] = res[i] * 2; @@ -13,17 +13,52 ...
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 ...
q = q.view(B, T_current, n_heads, d_k).transpose(1, 2) k = k.view(B, T_current, n_heads, d_k).transpose(1, 2) v = v.view(B, T_current, n_heads, d_k).transpose(1, 2) # Apply RoPE q_rope = torch.view_as_complex(q.float().reshape(B, n_heads, T_current, -...
import numpy as np a = np.array((1, 2)) a array([1, 2]) 然后,使用torch.tensor()将NumPy数组转换为张量: t1 = torch.tensor(a) t1 tensor([1, 2], dtype=torch.int32) 在这里,我们可以看到,张量不仅包含数据值,还包括数据类型(dtype)。从输出中可以看出,t1张量的类型是torch.int32,这意味着...