eric-haibin-lin closed pull request #10312: [MXNET-72] improve sparse adagrad
on GPU
URL: https://github.com/apache/incubator-mxnet/pull/10312
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diff --git a/src/operator/optimizer_op-inl.h b/src/operator/optimizer_op-inl.h
index 104f20a61ee..c611a2b745d 100644
--- a/src/operator/optimizer_op-inl.h
+++ b/src/operator/optimizer_op-inl.h
@@ -1528,8 +1528,11 @@ inline bool AdagradStorageType(const nnvm::NodeAttrs&
attrs,
return dispatched;
}
+template<typename xpu>
+struct AdagradDnsRspDnsKernel;
-struct AdagradDnsRspDnsKernel {
+template<>
+struct AdagradDnsRspDnsKernel<cpu> {
template<typename DType, typename IType>
MSHADOW_XINLINE static void Map(int i, index_t row_length, DType* out_data,
DType* state_data, const DType* weight_data, const IType* grad_idx,
@@ -1555,6 +1558,30 @@ struct AdagradDnsRspDnsKernel {
}
};
+template<>
+struct AdagradDnsRspDnsKernel<gpu> {
+ template<typename DType, typename IType>
+ MSHADOW_XINLINE static void Map(int i, index_t row_length, DType* out_data,
+ DType* state_data, const DType* weight_data, const IType* grad_idx,
+ const DType* grad_data, const DType clip_gradient, const DType epsilon,
+ const DType lr, const DType rescale_grad) {
+ using nnvm::dim_t;
+ using namespace mshadow_op;
+ const dim_t row_id = i / row_length;
+ const dim_t col_id = i % row_length;
+ const dim_t data_i = grad_idx[row_id] * row_length + col_id;
+ DType grad_rescaled = grad_data[i] * rescale_grad;
+ if (clip_gradient >= 0.0f) {
+ grad_rescaled = clip::Map(grad_rescaled, clip_gradient);
+ }
+ const DType grad_squared = grad_rescaled * grad_rescaled;
+ state_data[data_i] += grad_squared;
+ const DType div = grad_rescaled / square_root::Map(state_data[data_i] +
epsilon);
+ // No need to use KERNEL_ASSIGN, as we already checked req is kWriteInplace
+ out_data[data_i] = weight_data[data_i] - div * lr;
+ }
+};
+
template<typename xpu>
void AdagradUpdateDnsRspDnsImpl(const AdagradParam& param,
const OpContext& ctx,
@@ -1582,7 +1609,11 @@ void AdagradUpdateDnsRspDnsImpl(const AdagradParam&
param,
DType* out_data = out->dptr<DType>();
const nnvm::dim_t nnr = grad.storage_shape()[0];
const auto row_length = weight.shape_.ProdShape(1, weight.ndim());
- Kernel<AdagradDnsRspDnsKernel, xpu>::Launch(s, nnr, row_length,
+ size_t num_threads = nnr;
+ if (std::is_same<xpu, gpu>::value) {
+ num_threads = nnr * row_length;
+ }
+ Kernel<AdagradDnsRspDnsKernel<xpu>, xpu>::Launch(s, num_threads,
row_length,
out_data, state_data, weight_data, grad_idx, grad_val,
static_cast<DType>(param.clip_gradient),
static_cast<DType>(param.epsilon),
static_cast<DType>(param.lr), static_cast<DType>(param.rescale_grad));
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