Instructions to use replicate/paged-attention with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Kernels
How to use replicate/paged-attention 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/paged-attention", version=1) - Notebooks
- Google Colab
- Kaggle
File size: 1,412 Bytes
4c6b316 | 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 39 40 41 | #pragma once
#include <stdio.h>
#if defined(__HIPCC__)
#define HOST_DEVICE_INLINE __host__ __device__
#define DEVICE_INLINE __device__
#define HOST_INLINE __host__
#elif defined(__CUDACC__) || defined(_NVHPC_CUDA)
#define HOST_DEVICE_INLINE __host__ __device__ __forceinline__
#define DEVICE_INLINE __device__ __forceinline__
#define HOST_INLINE __host__ __forceinline__
#else
#define HOST_DEVICE_INLINE inline
#define DEVICE_INLINE inline
#define HOST_INLINE inline
#endif
#define CUDA_CHECK(cmd) \
do { \
cudaError_t e = cmd; \
if (e != cudaSuccess) { \
printf("Failed: Cuda error %s:%d '%s'\n", __FILE__, __LINE__, \
cudaGetErrorString(e)); \
exit(EXIT_FAILURE); \
} \
} while (0)
int64_t get_device_attribute(int64_t attribute, int64_t device_id);
int64_t get_max_shared_memory_per_block_device_attribute(int64_t device_id);
namespace cuda_utils {
template <typename T>
HOST_DEVICE_INLINE constexpr std::enable_if_t<std::is_integral_v<T>, T>
ceil_div(T a, T b) {
return (a + b - 1) / b;
}
}; // namespace cuda_utils |