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Add binary comparators to the OneDNN (dnnl) execution provider (#11641)

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* Added Bool output support by using u8 datatype

Signed-off-by: George Nash <george.nash@intel.com>

* Add Equal, Greater, GreaterOrEqual, Less, and LessOrEqual Operators

Signed-off-by: George Nash <george.nash@intel.com>

Co-authored-by: Erick Munoz Alvarado <erick.munoz.alvarado@intel.com>
This commit is contained in:
George Nash 2022-06-06 09:15:42 -07:00 committed by GitHub
parent 4296968f20
commit 981d45d8d5
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GPG key ID: 4AEE18F83AFDEB23
4 changed files with 14 additions and 1 deletions
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5
onnxruntime/core/providers/dnnl/dnnl_op_manager.cc
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@ -17,6 +17,7 @@ DnnlOpManager::DnnlOpManager() {
dnnl_ops_map_.emplace(std::make_pair("Div", std::unique_ptr<DnnlNodeCapability>(new DnnlBinaryNodeCapability())));
dnnl_ops_map_.emplace(std::make_pair("DynamicQuantizeLinear", std::unique_ptr<DnnlNodeCapability>(new DnnlDynamicQuantizeLinearNodeCapability())));
dnnl_ops_map_.emplace(std::make_pair("Elu", std::unique_ptr<DnnlNodeCapability>(new DnnlElementwiseCapability())));
dnnl_ops_map_.emplace(std::make_pair("Equal", std::unique_ptr<DnnlNodeCapability>(new DnnlBinaryNodeCapability())));
dnnl_ops_map_.emplace(std::make_pair("Erf", std::unique_ptr<DnnlNodeCapability>(new DnnlErfNodeCapability())));
dnnl_ops_map_.emplace(std::make_pair("Exp", std::unique_ptr<DnnlNodeCapability>(new DnnlElementwiseCapability())));
dnnl_ops_map_.emplace(std::make_pair("FastGelu", std::unique_ptr<DnnlNodeCapability>(new DnnlDefaultNodeCapability())));
@ -25,8 +26,12 @@ DnnlOpManager::DnnlOpManager() {
dnnl_ops_map_.emplace(std::make_pair("Gemm", std::unique_ptr<DnnlNodeCapability>(new DnnlGemmNodeCapability())));
dnnl_ops_map_.emplace(std::make_pair("GlobalAveragePool", std::unique_ptr<DnnlNodeCapability>(new DnnlPoolNodeCapability())));
dnnl_ops_map_.emplace(std::make_pair("GlobalMaxPool", std::unique_ptr<DnnlNodeCapability>(new DnnlPoolNodeCapability())));
dnnl_ops_map_.emplace(std::make_pair("Greater", std::unique_ptr<DnnlNodeCapability>(new DnnlBinaryNodeCapability())));
dnnl_ops_map_.emplace(std::make_pair("GreaterOrEqual", std::unique_ptr<DnnlNodeCapability>(new DnnlBinaryNodeCapability())));
dnnl_ops_map_.emplace(std::make_pair("LayerNormalization", std::unique_ptr<DnnlNodeCapability>(new DnnlLayerNormalizationNodeCapability())));
dnnl_ops_map_.emplace(std::make_pair("LeakyRelu", std::unique_ptr<DnnlNodeCapability>(new DnnlElementwiseCapability())));
dnnl_ops_map_.emplace(std::make_pair("Less", std::unique_ptr<DnnlNodeCapability>(new DnnlBinaryNodeCapability())));
dnnl_ops_map_.emplace(std::make_pair("LessOrEqual", std::unique_ptr<DnnlNodeCapability>(new DnnlBinaryNodeCapability())));
dnnl_ops_map_.emplace(std::make_pair("Log", std::unique_ptr<DnnlNodeCapability>(new DnnlElementwiseCapability())));
dnnl_ops_map_.emplace(std::make_pair("LRN", std::unique_ptr<DnnlNodeCapability>(new DnnlDefaultNodeCapability())));
dnnl_ops_map_.emplace(std::make_pair("MatMul", std::unique_ptr<DnnlNodeCapability>(new DnnlMatMulNodeCapability())));

3
onnxruntime/core/providers/dnnl/subgraph/dnnl_subgraph.cc
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@ -104,6 +104,9 @@ dnnl::memory::data_type DnnlTensor::Type() const {
return dnnl::memory::data_type::s8;
case ONNX_NAMESPACE::TensorProto_DataType_UINT8:
return dnnl::memory::data_type::u8;
// Same here, we use u8 as the handler for bool
case ONNX_NAMESPACE::TensorProto_DataType_BOOL:
return dnnl::memory::data_type::u8;
default:
ORT_THROW("Unsupported data type: ", data_type);
}

2
onnxruntime/core/providers/dnnl/subgraph/dnnl_subgraph_primitive.cc
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@ -163,7 +163,7 @@ int Product(dnnl::memory::dims d) {
}
void DnnlSubgraphPrimitive::AddKernels() {
std::unordered_set<std::string> binary_ops = {"Add", "Div", "Mul", "Sub"};
std::unordered_set<std::string> binary_ops = {"Add", "Div", "Equal", "Greater", "GreaterOrEqual", "Less", "LessOrEqual", "Mul", "Sub"};
std::unordered_set<std::string> elementwise_ops = {"Abs", "Elu", "Exp", "LeakyRelu", "Log", "Relu", "Round", "Sigmoid", "Softplus", "Sqrt", "Tanh"};
std::unordered_set<std::string> pool_ops = {"AveragePool", "GlobalAveragePool", "GlobalMaxPool", "MaxPool"};
std::unordered_set<std::string> reduce_ops = {"ReduceL1", "ReduceL2", "ReduceLogSum", "ReduceLogSumExp", "ReduceMax", "ReduceMean", "ReduceMin", "ReduceProd", "ReduceSum", "ReduceSumSquare"};

5
onnxruntime/core/providers/dnnl/subgraph/dnnl_util.cc
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@ -21,10 +21,15 @@ dnnl::algorithm OrtOperatorToDnnlAlgorithm(std::string op) {
{"Abs", dnnl::algorithm::eltwise_abs},
{"BiasGelu", dnnl::algorithm::eltwise_gelu_erf},
{"Elu", dnnl::algorithm::eltwise_elu}, // algorithm requires alpha value
{"Equal", dnnl::algorithm::binary_eq},
{"Exp", dnnl::algorithm::eltwise_exp},
{"FastGelu", dnnl::algorithm::eltwise_gelu_tanh},
{"Gelu", dnnl::algorithm::eltwise_gelu_erf},
{"Greater", dnnl::algorithm::binary_gt},
{"GreaterOrEqual", dnnl::algorithm::binary_ge},
{"LeakyRelu", dnnl::algorithm::eltwise_relu}, // algorithm requires alpha value
{"Less", dnnl::algorithm::binary_lt},
{"LessOrEqual", dnnl::algorithm::binary_le},
{"Log", dnnl::algorithm::eltwise_log},
{"Relu", dnnl::algorithm::eltwise_relu},
{"Round", dnnl::algorithm::eltwise_round},