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2 changes: 1 addition & 1 deletion paddle/gserver/layers/MKLDNNLayer.h
Original file line number Diff line number Diff line change
Expand Up @@ -36,7 +36,7 @@ class MKLDNNLayer : public Layer {
protected:
// batch size
int bs_;
// they sizes are always from the first input layer
// their sizes are always from the first input layer
// input image channel, height and width
int ic_, ih_, iw_;
// output image channel, height and width
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2 changes: 1 addition & 1 deletion paddle/gserver/tests/test_MKLDNN.cpp
Original file line number Diff line number Diff line change
Expand Up @@ -315,7 +315,7 @@ TEST(MKLDNNLayer, AddtoLayer) {

static void getMKLDNNConcatConfig(TestConfig& cfg,
const std::vector<testImageDesc>& inputs) {
CHECK_GE(inputs.size(), 2) << "at least two inputs";
CHECK_GE(inputs.size(), 2UL) << "at least two inputs";
int oc = inputs[0].ic;
for (size_t i = 1; i < inputs.size(); ++i) {
CHECK_EQ(inputs[i].bs, inputs[0].bs);
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2 changes: 1 addition & 1 deletion paddle/operators/beam_search_op.cc
Original file line number Diff line number Diff line change
Expand Up @@ -139,7 +139,7 @@ bool BeamSearch::NextItemSet(std::vector<BeamSearch::Item> *items) {
items->reserve(framework::product(ids.dims()));
for (size_t offset = abs_lod[lod_level_][sent_offset_];
offset < abs_lod[lod_level_][sent_offset_ + 1]; offset++) {
for (int d = 0; d < instance_dim; d++) {
for (size_t d = 0; d < instance_dim; d++) {
const size_t dim_offset = offset * instance_dim + d;
items->emplace_back(offset, ids_data[dim_offset],
scores_data[dim_offset]);
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2 changes: 1 addition & 1 deletion paddle/trainer/Trainer.cpp
Original file line number Diff line number Diff line change
Expand Up @@ -138,7 +138,7 @@ void Trainer::init(const std::shared_ptr<TrainerConfigHelper>& config,
}

if (FLAGS_use_mkldnn) {
CHECK_EQ(FLAGS_trainer_count, 1UL) << "MKLDNN only need 1 trainer";
CHECK_EQ(FLAGS_trainer_count, 1) << "MKLDNN only need 1 trainer";
}

if (testing) {
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