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#include <cub/block/block_reduce.cuh>
#include "caffe2/sgd/adagrad_op.h"
#include "caffe2/core/common_gpu.h"
#include "caffe2/core/context_gpu.h"
namespace caffe2 {
__global__ void AdagradUpdate(
int N,
const float* w,
const float* g,
const float* h,
float* nw,
float* nh,
float epsilon,
float decay,
const float* lr) {
CUDA_1D_KERNEL_LOOP(i, N) {
float gi = g[i];
float hi = nh[i] = decay * h[i] + gi * gi;
nw[i] = w[i] + lr[0] * gi / (sqrtf(hi) + epsilon);
}
}
template <>
void adagrad_update<CUDAContext>(
int N,
const float* w,
const float* g,
const float* h,
float* nw,
float* nh,
float epsilon,
float decay,
const float* lr,
CUDAContext* context) {
AdagradUpdate<<<
CAFFE_GET_BLOCKS(N),
CAFFE_CUDA_NUM_THREADS,
0,
context->cuda_stream()>>>(N, w, g, h, nw, nh, epsilon, decay, lr);
}
template <typename SIndex, typename THalf>
__global__ void SparseAdagradKernel(
const size_t N,
const size_t grad_slice_sz,
const float epsilon,
THalf* param,
THalf* param_mom,
const SIndex* indices,
const float* grad,
const float* lr) {
const float LR = lr[0];
CUDA_1D_KERNEL_LOOP(i, N)
{
const size_t gradIdx = i;
const SIndex index = indices[i / grad_slice_sz];
const size_t paramIdx = index * grad_slice_sz + (i % grad_slice_sz);
float mom_new = grad[gradIdx] * grad[gradIdx] + param_mom[paramIdx];
param_mom[paramIdx] = mom_new;
float param_new =
LR * grad[gradIdx] / (sqrtf(mom_new) + epsilon) + param[paramIdx];
param[paramIdx] = param_new;
}
}
/**
* Calculate RowwiseSparseAdagrad
* M: gradients.dims[0]
* N: gradients.size_from_dim(1)
* grad: pointer to the gradients
* param: pointer to weights
* param_mom: pointer to the momentum
* indices: keys
*/
template <typename SIndex>
__global__ void RowWiseSparseAdagradKernel(
const int M,
const int N,
const float epsilon,
float* param,
float* param_mom,
const SIndex* indices,
const float* grad,
const float* lr) {
typedef cub::BlockReduce<float, CAFFE_CUDA_NUM_THREADS> BlockReduce;
__shared__ BlockReduce::TempStorage temp_storage;
// in case gridDim is smaller than M
for (int i = blockIdx.x; i < M; i += gridDim.x) {
const SIndex index = indices[i];
float sum_squares = 0.0;
__shared__ float row_sum_squares_avg;
// in case N is bigger than block size which is 512 by default
for (int j = threadIdx.x; j < N; j += blockDim.x) {
const float x_ij = grad[i * N + j];
sum_squares += x_ij * x_ij;
}
float reduce_result = BlockReduce(temp_storage).Sum(sum_squares);
if (threadIdx.x == 0) {
row_sum_squares_avg = reduce_result / (float)N;
param_mom[index] += row_sum_squares_avg;
}
__syncthreads();
// update param
float step = lr[0] / (sqrtf(param_mom[index]) + epsilon);
for (int j = threadIdx.x; j < N; j += blockDim.x) {
param[index * N + j] = param[index * N + j] + grad[i * N + j] * step;
}
}
}
template <typename T, class Context>
class CUDASparseAdagradOp final : public Operator<Context> {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
CUDASparseAdagradOp(const OperatorDef& operator_def, Workspace* ws)
: Operator<Context>(operator_def, ws),
epsilon_(this->template GetSingleArgument<float>("epsilon", 1e-5f)) {
const T decay = this->template GetSingleArgument<T>("decay", 1.0f);
CAFFE_ENFORCE_EQ(decay, 1.0, "Decay is not supported for SparseAdagradOp");
}
bool RunOnDevice() override {
// Enforce shapes
CAFFE_ENFORCE_EQ(Input(PARAM).size(), Input(MOMENT_1).size());
CAFFE_ENFORCE_EQ(Input(LR).size(), 1);
CAFFE_ENFORCE_EQ(
Input(PARAM).size_from_dim(1),
Input(GRAD).size_from_dim(Input(INDICES).ndim()));
return DispatchHelper<TensorTypes<int32_t, int64_t>>::call(
this, Input(INDICES));
}
template <typename IndexType>
bool DoRunWithType() {
auto n = Input(INDICES).size();
if (n == 0) {
return true;
}
return DispatchHelper<TensorTypes2<float, at::Half>, IndexType>::call(
this, Input(PARAM));
}
template <typename IndexType, typename THalf>
bool DoRunWithType2() {
const auto* lr = Input(LR).template data<T>();
const auto* indices = Input(INDICES).template data<IndexType>();
const auto* gradIn = Input(GRAD).template data<T>();
const auto* paramIn = Input(PARAM).template data<THalf>();
const auto* momentIn = Input(MOMENT_1).template data<THalf>();
auto* paramOut = Output(OUTPUT_PARAM)->template mutable_data<THalf>();
auto* momentOut = Output(OUTPUT_MOMENT_1)->template mutable_data<THalf>();
auto N = Input(GRAD).size();
auto grad_slice_sz = Input(GRAD).size_from_dim(Input(INDICES).ndim());
if (N == 0) {
// empty grad, nothing to do here, not even launching the kernel
return true;
}
SparseAdagradKernel<IndexType, THalf>
<<<CAFFE_GET_BLOCKS(N),
CAFFE_CUDA_NUM_THREADS,
0,
context_.cuda_stream()>>>(
N,
grad_slice_sz,
epsilon_,
Output(OUTPUT_PARAM)->template mutable_data<THalf>(),
Output(OUTPUT_MOMENT_1)->template mutable_data<THalf>(),
Input(INDICES).template data<IndexType>(),
Input(GRAD).template data<float>(),
Input(LR).template data<float>());
return true;
}
protected:
T epsilon_;
INPUT_TAGS(PARAM, MOMENT_1, INDICES, GRAD, LR);
OUTPUT_TAGS(OUTPUT_PARAM, OUTPUT_MOMENT_1);
};
template <>
template <typename SIndex>
bool RowWiseSparseAdagradOp<float, CUDAContext>::DoRunWithType() {
auto N = Input(GRAD).size();
if (N == 0) {
// empty grad, nothing to do here, not even launching the kernel
return true;
}
// size of the 1st dimension of the input gradient
auto GRAD_M = Input(GRAD).dim32(0);
auto GRAD_N = N / GRAD_M;
// each thread block will handle multiple rows of the input and output
RowWiseSparseAdagradKernel<<<
min(GRAD_M, CAFFE_MAXIMUM_NUM_BLOCKS),
CAFFE_CUDA_NUM_THREADS,
0,
context_.cuda_stream()>>>(
GRAD_M,
GRAD_N,
epsilon_,
Output(OUTPUT_PARAM)->template mutable_data<float>(),
Output(OUTPUT_MOMENT_1)->template mutable_data<float>(),
Input(INDICES).template data<SIndex>(),
Input(GRAD).template data<float>(),
Input(LR).template data<float>());
return true;
}
REGISTER_CUDA_OPERATOR(Adagrad, AdagradOp<float, CUDAContext>);
REGISTER_CUDA_OPERATOR(SparseAdagrad, CUDASparseAdagradOp<float, CUDAContext>);
REGISTER_CUDA_OPERATOR(
RowWiseSparseAdagrad,
RowWiseSparseAdagradOp<float, CUDAContext>);
}
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