73 lines
3.3 KiB
C++
73 lines
3.3 KiB
C++
|
#include <torch/torch.h>
|
||
|
|
#include <torch/script.h>
|
||
|
|
#include "ATen/cuda/CUDAContext.h"
|
||
|
|
#include "triton/driver/stream.h"
|
||
|
|
#include "triton/dnn/batchnorm.h"
|
||
|
|
#include "triton/tools/bench.hpp"
|
||
|
|
|
||
|
|
std::vector<torch::Tensor>
|
||
|
|
batchnorm_ymv(const torch::Tensor fw_x,
|
||
|
|
const torch::Tensor fw_g,
|
||
|
|
const torch::Tensor fw_b,
|
||
|
|
float eps) {
|
||
|
|
// Wrap CUDA handles
|
||
|
|
c10::DeviceIndex device = fw_x.storage().device().index();
|
||
|
|
CUstream custream = (CUstream)at::cuda::getCurrentCUDAStream(device).stream();
|
||
|
|
triton::driver::cu_stream stream(custream, false);
|
||
|
|
triton::driver::context* ctx = stream.context();
|
||
|
|
// get sizes
|
||
|
|
int C = fw_x.size(0);
|
||
|
|
int H = fw_x.size(1);
|
||
|
|
int W = fw_x.size(2);
|
||
|
|
int B = fw_x.size(3);
|
||
|
|
// allocate outputs
|
||
|
|
torch::Tensor fw_y = torch::empty(fw_x.sizes()).cuda();
|
||
|
|
torch::Tensor fw_m = torch::empty(fw_g.sizes()).cuda();
|
||
|
|
torch::Tensor fw_v = torch::empty(fw_g.sizes()).cuda();
|
||
|
|
triton::driver::cu_buffer x(ctx, (CUdeviceptr)fw_x.storage().data(), false);
|
||
|
|
triton::driver::cu_buffer g(ctx, (CUdeviceptr)fw_g.storage().data(), false);
|
||
|
|
triton::driver::cu_buffer b(ctx, (CUdeviceptr)fw_b.storage().data(), false);
|
||
|
|
triton::driver::cu_buffer y(ctx, (CUdeviceptr)fw_y.storage().data(), false);
|
||
|
|
triton::driver::cu_buffer m(ctx, (CUdeviceptr)fw_m.storage().data(), false);
|
||
|
|
triton::driver::cu_buffer v(ctx, (CUdeviceptr)fw_v.storage().data(), false);
|
||
|
|
// create template
|
||
|
|
triton::dnn::batchnorm_forward batchnorm(C, 1, H, W, B, "fp32", eps);
|
||
|
|
batchnorm.enqueue(&stream, {&y, &m, &v, &x, &g, &b});
|
||
|
|
return {fw_y, fw_m, fw_v};
|
||
|
|
}
|
||
|
|
|
||
|
|
std::vector<torch::Tensor>
|
||
|
|
batchnorm_dxdgdb(const torch::Tensor fw_dy,
|
||
|
|
const torch::Tensor fw_x,
|
||
|
|
const torch::Tensor fw_g,
|
||
|
|
const torch::Tensor fw_m,
|
||
|
|
const torch::Tensor fw_v,
|
||
|
|
float eps) {
|
||
|
|
// Wrap CUDA handles
|
||
|
|
c10::DeviceIndex device = fw_x.storage().device().index();
|
||
|
|
CUstream custream = (CUstream)at::cuda::getCurrentCUDAStream(device).stream();
|
||
|
|
triton::driver::cu_stream stream(custream, false);
|
||
|
|
triton::driver::context* ctx = stream.context();
|
||
|
|
// get sizes
|
||
|
|
int C = fw_x.size(0);
|
||
|
|
int H = fw_x.size(1);
|
||
|
|
int W = fw_x.size(2);
|
||
|
|
int B = fw_x.size(3);
|
||
|
|
// allocate outputs
|
||
|
|
torch::Tensor fw_dx = torch::empty(fw_x.sizes()).cuda();
|
||
|
|
torch::Tensor fw_dg = torch::empty(fw_g.sizes()).cuda();
|
||
|
|
torch::Tensor fw_db = torch::empty(fw_g.sizes()).cuda();
|
||
|
|
// triton handles
|
||
|
|
triton::driver::cu_buffer dy(ctx, (CUdeviceptr)fw_dy.storage().data(), false);
|
||
|
|
triton::driver::cu_buffer x(ctx, (CUdeviceptr) fw_x.storage().data(), false);
|
||
|
|
triton::driver::cu_buffer g(ctx, (CUdeviceptr) fw_g.storage().data(), false);
|
||
|
|
triton::driver::cu_buffer m(ctx, (CUdeviceptr) fw_m.storage().data(), false);
|
||
|
|
triton::driver::cu_buffer v(ctx, (CUdeviceptr) fw_v.storage().data(), false);
|
||
|
|
triton::driver::cu_buffer dx(ctx, (CUdeviceptr)fw_dx.storage().data(), false);
|
||
|
|
triton::driver::cu_buffer dg(ctx, (CUdeviceptr)fw_dg.storage().data(), false);
|
||
|
|
triton::driver::cu_buffer db(ctx, (CUdeviceptr)fw_db.storage().data(), false);
|
||
|
|
// create config
|
||
|
|
triton::dnn::batchnorm_backward batchnorm(C, 1, H, W, B, "fp32", eps);
|
||
|
|
batchnorm.enqueue(&stream, {&dx, &dg, &db, &dy, &x, &g, &m, &v});
|
||
|
|
}
|