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_len, n_heads, head_dim/2] # 旋转矩阵(freqs_cis)...
# 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_...
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_len, n_heads, head_dim/2] # 旋转...
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 ...
istft(t, n_fft=128, length=1024) RuntimeError: istft requires a complex-valued input tensor matching the output from stft with return_complex=True. >>> t_complex = torch.view_as_complex(t) >>> _ = torch.istft(t_complex, n_fft=128, length=1024) Change default behavior of sparse ...
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) ...
q_per_token_as_complex_numbers_rotated.shape torch.Size([17, 64]) q_per_token_split_into_pairs_rotated = torch.view_as_real(q_per_token_as_complex_numbers_rotated) q_per_token_split_into_pairs_rotated.shape torch.Size([17, 64, 2]) ...
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...
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,此时返回值与原来tensor不共享内存。def reshape(self, shape: Sequence[Union[_int, SymInt]]) -> Tensor: ... ...
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,此时返回值与原来tensor不共享内存...