Instructions to use replicate/yoso with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Kernels
How to use replicate/yoso with Kernels:
# !pip install kernels from kernels import get_kernel # a version (or an explicit revision) is required; see the "Files and versions" tab for the available ones kernel = get_kernel("replicate/yoso", version=1) - Notebooks
- Google Colab
- Kaggle
File size: 1,311 Bytes
6dbfa40 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | #include <torch/torch.h>
#include <ATen/ATen.h>
#include <vector>
at::Tensor lsh_cumulation(
at::Tensor query_mask, // [batch_size, num_query]
at::Tensor query_hash_code, // [batch_size, num_query, num_hash_f]
at::Tensor key_mask, // [batch_size, num_key]
at::Tensor key_hash_code, // [batch_size, num_key, num_hash_f]
at::Tensor value, // [batch_size, num_key, value_dim]
int hashtable_capacity,
bool use_cuda,
int version
);
std::vector<at::Tensor> fast_hash(
at::Tensor query_mask,
at::Tensor query_vector,
at::Tensor key_mask,
at::Tensor key_vector,
int num_hash_f,
int hash_code_len,
bool use_cuda,
int version
);
at::Tensor lsh_weighted_cumulation(
at::Tensor query_mask, // [batch_size, num_query]
at::Tensor query_hash_code, // [batch_size, num_query, num_hash_f]
at::Tensor query_weight, // [batch_size, num_query, weight_dim]
at::Tensor key_mask, // [batch_size, num_key]
at::Tensor key_hash_code, // [batch_size, num_key, num_hash_f]
at::Tensor key_weight, // [batch_size, num_key, weight_dim]
at::Tensor value, // [batch_size, num_key, value_dim]
int hashtable_capacity,
bool use_cuda,
int version
); |