TaoLv commented on a change in pull request #16735: Use single-bit for mask in
dropout operator
URL: https://github.com/apache/incubator-mxnet/pull/16735#discussion_r379713840
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File path: src/operator/nn/dropout-inl.h
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@@ -152,15 +181,20 @@ class DropoutOp {
const std::vector<TBlob> &out_grad) {
Stream<xpu> *s = ctx.get_stream<xpu>();
Tensor<xpu, 2, DType> grad = out_grad[dropout::kOut].FlatTo2D<xpu,
DType>(s);
- Tensor<xpu, 2, DType> mask = out_data[dropout::kMask].FlatTo2D<xpu,
DType>(s);
+ Tensor<xpu, 1, uint8_t> mask = out_data[dropout::kMask].FlatTo1D<xpu,
uint8_t>(s);
Tensor<xpu, 2, DType> gdata = in_grad[dropout::kData].FlatTo2D<xpu,
DType>(s);
DType *ingradptr = gdata.dptr_;
const DType *outgradptr = grad.dptr_;
- const DType *maskptr = mask.dptr_;
- const int count = mask.shape_[0] * mask.shape_[1];
-#pragma omp parallel for
num_threads(engine::OpenMP::Get()->GetRecommendedOMPThreadCount())
- for (int i = 0; i < count; ++i) {
- ingradptr[i] = outgradptr[i] * maskptr[i];
+ const uint8_t *maskptr = mask.dptr_;
+ const index_t count = grad.shape_[0] * grad.shape_[1];
+ const float pk_1 = 1.0f / this->pkeep_;
+ const int nthr = engine::OpenMP::Get()->GetRecommendedOMPThreadCount();
+#pragma omp parallel for num_threads(nthr) schedule(static, 8)
+ for (index_t i = 0; i < count; ++i) {
+ auto mask_idx = i >> 3; // div 8;
+ uint8_t mask_offset = i & 7; // mod 8
+ bool mask_val = maskptr[mask_idx] & (1U << mask_offset);
+ ingradptr[i] = outgradptr[i] * mask_val * pk_1;
Review comment:
We're writing to `ingradptr`. We also hope the elements in one cache line
will be handled by one openmp thread. With the original parallelization, one
cache line is loaded and only one element in it is handled by the current
thread. For the next thread, it need load the same cache line, and handle the
next element.
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