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Disable GeluApproximation transformer by default (#3644)

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Disable GeluApproximation by default
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Tianlei Wu 2020-04-24 14:29:40 -07:00 committed by GitHub
parent ad8eb921d3
commit 63e6c257e4
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3 changed files with 32 additions and 2 deletions
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2
docs/ONNX_Runtime_Graph_Optimizations.md
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@ -55,6 +55,8 @@ These optimizations include complex node fusions. They are run after graph parti
To optimize inference performance of BERT model, approximation is used in GELU approximation and Attention fusion for cuda execution provider. There might be slight difference in result. The impact on accuracy could be neglected based on our evaluation: F1 score for a BERT model on SQuAD v1.1 is almost same (87.05 vs 87.03).
GELU approximation is disabled by default.
### Layout Optimizations
These optimizations change the data layout for applicable nodes to achieve higher performance improvements. They are run after graph partitioning and are only applied to nodes assigned to CPU execution provider. Available layout optimizations are as follows:

12
onnxruntime/core/optimizer/graph_transformer_utils.cc
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@ -136,8 +136,6 @@ std::vector<std::unique_ptr<GraphTransformer>> GenerateTransformers(TransformerL
transformers.emplace_back(onnxruntime::make_unique<BiasGelu>(cpu_cuda_execution_providers));
transformers.emplace_back(onnxruntime::make_unique<SkipLayerNormFusion>(cpu_cuda_execution_providers));
std::unordered_set<std::string> cuda_execution_providers = {onnxruntime::kCudaExecutionProvider};
transformers.emplace_back(onnxruntime::make_unique<GeluApproximation>(cuda_execution_providers));
transformers.emplace_back(onnxruntime::make_unique<FastGeluFusion>(cpu_cuda_execution_providers));
#endif
} break;
@ -164,6 +162,16 @@ std::vector<std::unique_ptr<GraphTransformer>> GenerateTransformers(TransformerL
}
return transformers;
}
// Some transformers have side-effect like result is not exactly same.
// These transformers could only be enabled by custom transformer list.
#ifndef DISABLE_CONTRIB_OPS
if (level == TransformerLevel::Level2) {
std::unordered_set<std::string> cuda_execution_providers = {onnxruntime::kCudaExecutionProvider};
transformers.emplace_back(onnxruntime::make_unique<GeluApproximation>(cuda_execution_providers));
}
#endif
std::vector<std::unique_ptr<GraphTransformer>> filtered_list;
// If the rule-based transformer is not empty, it should be included in the custom transformer list below.
if (rule_transformer != nullptr) {

20
onnxruntime/test/optimizer/graph_transform_utils_test.cc
View file

@ -60,5 +60,25 @@ TEST(GraphTransformerUtilsTests, TestGenerateGraphTransformers) {
#endif
}
TEST(GraphTransformerUtilsTests, TestCustomOnlyTransformers) {
// Transformers that are disabled by default. They can only be enabled by custom list.
std::string l2_transformer = "GeluApproximation";
std::vector<std::string> default_list = {};
auto default_transformers = optimizer_utils::GenerateTransformers(TransformerLevel::Level2, {}, default_list);
for (auto& transformer : default_transformers) {
ASSERT_TRUE(transformer->Name() != l2_transformer);
}
std::vector<std::string> custom_list = {l2_transformer};
auto custom_transformers = optimizer_utils::GenerateTransformers(TransformerLevel::Level2, {}, custom_list);
#ifndef DISABLE_CONTRIB_OPS
ASSERT_TRUE(custom_transformers.size() == 1);
ASSERT_TRUE(custom_transformers[0]->Name() == l2_transformer);
#else
ASSERT_TRUE(custom_transformers.size() == 0);
#endif
}
} // namespace test
} // namespace onnxruntime