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#include "caffe2/sgd/momentum_sgd_op.h"
#include "caffe2/core/common_gpu.h"
#include "caffe2/core/context_gpu.h"
namespace caffe2 {
inline int CaffeGetBlocksSGD(const int N) {
return std::max(
(N + CAFFE_CUDA_NUM_THREADS - 1) / CAFFE_CUDA_NUM_THREADS,
// Use at least 1 block, since CUDA does not allow empty block
1);
}
template <bool nesterov>
__global__ void MomentumSGDKernel(
const int N,
const float* g,
const float* m,
float* ng,
float* nm,
const float* lr,
const float momentum,
float* param);
template <>
__global__ void MomentumSGDKernel<true>(
const int N,
const float* g,
const float* m,
float* ng,
float* nm,
const float* lr,
const float momentum,
float* param) {
const float LR = lr[0];
CUDA_1D_KERNEL_LOOP(i, N) {
const float mi = m[i];
const float mi_new = momentum * mi + LR * g[i];
nm[i] = mi_new;
ng[i] = fmaf(momentum, mi_new - mi, mi_new);
if (param != nullptr) {
param[i] -= ng[i];
}
}
}
template <>
__global__ void MomentumSGDKernel<false>(
const int N,
const float* g,
const float* m,
float* ng,
float* nm,
const float* lr,
const float momentum,
float* param) {
const float LR = lr[0];
CUDA_1D_KERNEL_LOOP(i, N) {
const float adjusted_gradient = LR * g[i] + momentum * m[i];
nm[i] = adjusted_gradient;
ng[i] = adjusted_gradient;
if (param != nullptr) {
param[i] -= adjusted_gradient;
}
}
}
template <>
void momentum_sgd_update<CUDAContext>(
const int N,
const float* g,
const float* m,
float* ng,
float* nm,
const float* lr,
const float momentum,
const bool nesterov,
float* param,
CUDAContext* context) {
if (nesterov) {
MomentumSGDKernel<true>
<<<CaffeGetBlocksSGD(N),
CAFFE_CUDA_NUM_THREADS,
0,
context->cuda_stream()>>>(N, g, m, ng, nm, lr, momentum, param);
C10_CUDA_KERNEL_LAUNCH_CHECK();
} else {
MomentumSGDKernel<false>
<<<CaffeGetBlocksSGD(N),
CAFFE_CUDA_NUM_THREADS,
0,
context->cuda_stream()>>>(N, g, m, ng, nm, lr, momentum, param);
C10_CUDA_KERNEL_LAUNCH_CHECK();
}
}
template <typename SIndex>
__global__ void SparseMomentumSGDKernel(
const size_t N,
const size_t sz,
const float momentum,
const bool nesterov,
float *param,
float *param_mom,
const SIndex *indices,
const float *gradIn,
float *gradOut,
const float *lr)
{
const float LR = lr[0];
CUDA_1D_KERNEL_LOOP(i, N)
{
const size_t gradIdx = i;
const SIndex index = indices[i / sz];
const size_t paramIdx = index * sz + (i % sz);
if (!nesterov)
{
const float adjusted_gradient = LR * gradIn[gradIdx] +
momentum * param_mom[paramIdx];
gradOut[gradIdx] = adjusted_gradient;
param_mom[paramIdx] = adjusted_gradient;
param[paramIdx] -= adjusted_gradient;
} else {
const float mom_old = param_mom[paramIdx];
const float mom_new = LR * gradIn[gradIdx] + momentum * mom_old;
param_mom[paramIdx] = mom_new;
const float adjusted_gradient = (1 + momentum) * mom_new -
momentum * mom_old;
gradOut[gradIdx] = adjusted_gradient;
param[paramIdx] -= adjusted_gradient;
}
}
}
// Specialization of DoRunWithType for CUDA
template <>
template <typename SIndex>
bool SparseMomentumSGDUpdateOp<float, CUDAContext>::DoRunWithType() {
auto N = Input(GRAD).size();
auto grad_slice_sz = Input(GRAD).size_from_dim(Input(INDICES).ndim());
SparseMomentumSGDKernel<SIndex><<<
CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS, 0,
context_.cuda_stream()>>>(
N, grad_slice_sz,
momentum_, nesterov_,
Output(OUTPUT_PARAM)->template mutable_data<float>(),
Output(OUTPUT_MOMENTUM)->template mutable_data<float>(),
Input(INDICES).template data<SIndex>(),
Input(GRAD).template data<float>(),
Output(OUTPUT_GRAD)->template mutable_data<float>(),
Input(LR).template data<float>());
C10_CUDA_KERNEL_LAUNCH_CHECK();
return true;
}
REGISTER_CUDA_OPERATOR(MomentumSGD, MomentumSGDOp<float, CUDAContext>);
REGISTER_CUDA_OPERATOR(MomentumSGDUpdate, MomentumSGDUpdateOp<float, CUDAContext>);
REGISTER_CUDA_OPERATOR(SparseMomentumSGDUpdate, SparseMomentumSGDUpdateOp<float, CUDAContext>);
}
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