# Parameters for low-rank SVD q = 512 # Rank for approximation # Try disabling power iterations niter = 0 # Perform low-rank SVD on the dense matrix U1, S1, V1 = torch.svd_lowrank(sparse_matrix, q=q, niter=niter) seeder() U2, S2, V2 = torch.svd_lowrank(sparse_matrix, q=q,...
支持稀疏张量的常规torch函数 cat()dstack()empty()empty_like()hstack()index_select()is_complex()is_floating_point()is_nonzero()is_same_size()is_signed()is_tensor()lobpcg()mm()native_norm()pca_lowrank()select()stack()svd_lowrank()unsqueeze()vstack()zeros()zeros_like() 支持稀疏张量的...
addbmm() addmm() addmv() addr() baddbmm() bmm() chain_matmul() dot() eig() inner() inverse() det() logdet() slogdet()matmul() matrix_power() matrix_rank() matrix_exp() mm() mv() outer() pinverse() svd() svd_lowrank() pca_lowrank() vdot() 8. Utilities result_type()...
7rank = int(os.environ['RANK']) 8world_size = int(os.environ['WORLD_SIZE']) 9 10dist.init_process_group(backend='nccl', init_method='env://', rank=rank, world_size=world_size) 11 12model = MyModel() 13model = DDP(model, device_ids=[rank], output_device=rank) 1. 2. 3....
svd svd_lowrank swapaxes swapdims symeig sys t take tan tan_ tanh tanh_ tensor tensor_split tensordot testing textwrap th_dll_path threshold threshold_ tile topk torch trace transpose trapz triangular_solve tril tril_indices triplet_margin_loss triu triu_indices true_divide trunc trunc_ typename ...
For large and very sparse matrices, you have the classic SVD approximation described in https://arxiv.org/abs/0909.4061, that I believe would work well for your case, as you can give a bound on the rank of your matrix. You even have a post from meta describing what's what, and ...
其全称叫做 Low Rank Adaption,也就是低秩适配方法,由微软2021年提出的。 LoRA的思想非常简单,有些像SVD奇异值分解的想法。 保持预训练好的参数矩阵不变,在它的旁边学习两个小的低秩矩阵,用它们的乘积来作为大的参数矩阵需要改变的增量。 用公式表示如下: W=W0+ΔW=W0+BAW=W0+ΔW=W0+BA shape(W0)=(m,...
torch.matrix_rank(input, tol=None, bool symmetric=False) → Tensor torch.mm(input, mat2, out=None) → Tensor torch.mv(input, vec, out=None) → Tensor torch.orgqr(input, input2) → Tensor torch.ormqr(input, input2, input3, left=True, transpose=False) → Tensor torch.pinverse(input...
pca_lowrank() select() stack() svd_lowrank() unsqueeze() vstack() zeros() zeros_like() torch.Storage torch.Storage 是一个单个数据类型的,连续的一维数组。 每个张量都有一个对应的相同数据类型的 storage。 CLASS torch.FloatStorage() bool() #char() ...
torch.matrix_rank(input, tol=None, bool symmetric=False) → Tensor (input, mat2, out=None) → Tensor (input, vec, out=None) → Tensor torch.orgqr(input, input2) → Tensor torch.ormqr(input, input2, input3, left=True, transpose=False) → Tensor ...