tvm
Classes | Namespaces | Functions
conv2d.h File Reference
#include <tvm/runtime/c_runtime_api.h>
#include <tvm/runtime/device_api.h>
#include <cassert>
Include dependency graph for conv2d.h:

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Classes

class  tvm::runtime::hexagon::SDLTensor< NDIM >
 

Namespaces

 tvm
 runtime implementation for LibTorch/TorchScript.
 
 tvm::runtime
 
 tvm::runtime::hexagon
 

Functions

void * tvm::runtime::hexagon::to_ptr (uintptr_t v)
 
uintptr_t tvm::runtime::hexagon::to_uint (void *ptr)
 
constexpr int tvm::runtime::hexagon::xyc_to_sm_16b (int y, int x, int c)
 
constexpr int tvm::runtime::hexagon::hwio_to_sm_16b (int width, int y, int x, int i, int o)
 
constexpr int tvm::runtime::hexagon::round_up (int v, int p2)
 
uintptr_t tvm::runtime::hexagon::nhwc_at (const DLTensor &a, int n, int y, int x, int c)
 
uintptr_t tvm::runtime::hexagon::hwio_at (const DLTensor &f, int y, int x, int i, int o)
 
void tvm::runtime::hexagon::blockize_hwc_16b (void *out, void *inp_flat, int height, int width, int depth)
 Function to "blockize" the flat input data The term "blockize" is used to mention that the data is stored in non-contiguous blocks. More...
 
void tvm::runtime::hexagon::deblockize_hwc_16b (void *out_flat, void *inp, int height, int width, int depth)
 Convert back from non-contguous layout to a flat layout. More...
 
void tvm::runtime::hexagon::chunkify_hwio_16b (void **out_ptr, int out_ptr_size, void *out, void *inp, int height, int width, int idepth, int odepth)
 Convert the layout of weights from flat to "chunked". The term chunked is explained below: More...
 
SDLTensor< 4 > tvm::runtime::hexagon::prepare_nhwc (tvm::runtime::DeviceAPI *device_api, const DLTensor *nhwc_flat, bool copy_data)
 
int tvm::runtime::hexagon::calculate_num_weight_chunks (int64_t *shape_hwio)
 
SDLTensor< 4 > tvm::runtime::hexagon::prepare_hwio (tvm::runtime::DeviceAPI *device_api, const DLTensor *hwio_flat, int num_chunks, void **ptr_table)
 
template<size_t N>
void tvm::runtime::hexagon::release (tvm::runtime::DeviceAPI *device_api, const SDLTensor< N > &tensor)