PawelGlomski-Intel commented on a change in pull request #20981:
URL: https://github.com/apache/incubator-mxnet/pull/20981#discussion_r837304928
##########
File path: src/operator/tensor/elemwise_unary_op.h
##########
@@ -457,30 +457,67 @@ class UnaryOp : public OpBase {
};
#if MXNET_USE_ONEDNN == 1
-inline bool TanhStorageType(const nnvm::NodeAttrs& attrs,
- const int dev_mask,
- DispatchMode* dispatch_mode,
- std::vector<int>* in_attrs,
- std::vector<int>* out_attrs) {
+inline bool EltwiseStorageType(const nnvm::NodeAttrs& attrs,
+ const int dev_mask,
+ DispatchMode* dispatch_mode,
+ std::vector<int>* in_attrs,
+ std::vector<int>* out_attrs) {
CHECK_EQ(in_attrs->size(), 1);
CHECK_EQ(out_attrs->size(), 1);
return DNNLStorageType(attrs, dev_mask, true, dispatch_mode, in_attrs,
out_attrs);
}
-inline void TanhComputeExCPU(const nnvm::NodeAttrs& attrs,
- const OpContext& ctx,
- const std::vector<mxnet::NDArray>& inputs,
- const std::vector<OpReqType>& req,
- const std::vector<mxnet::NDArray>& outputs) {
- if (SupportDNNLTanh(inputs[0], outputs[0])) {
+template <typename OP>
+struct DNNLAlgorithm {};
+template <>
+struct DNNLAlgorithm<op::mshadow_op::plus> {
+ static const dnnl::algorithm value = dnnl::algorithm::binary_add;
+};
+template <>
+struct DNNLAlgorithm<op::mshadow_op::minus> {
+ static const dnnl::algorithm value = dnnl::algorithm::binary_sub;
+};
+template <>
+struct DNNLAlgorithm<op::mshadow_op::mul> {
+ static const dnnl::algorithm value = dnnl::algorithm::binary_mul;
+};
+template <>
+struct DNNLAlgorithm<op::mshadow_op::div> {
+ static const dnnl::algorithm value = dnnl::algorithm::binary_div;
+};
+template <>
+struct DNNLAlgorithm<op::mshadow_op::tanh> {
+ static const dnnl::algorithm value = dnnl::algorithm::eltwise_tanh;
+};
+template <>
+struct DNNLAlgorithm<op::mshadow_op::exp> {
+ static const dnnl::algorithm value = dnnl::algorithm::eltwise_exp;
+};
+template <>
+struct DNNLAlgorithm<op::mshadow_op::square> {
+ static const dnnl::algorithm value = dnnl::algorithm::eltwise_square;
+};
+template <>
+struct DNNLAlgorithm<op::mshadow_op::square_root> {
+ static const dnnl::algorithm value = dnnl::algorithm::eltwise_sqrt;
+};
Review comment:
I believe we can turn `DNNLAlgorithm` into a constexpr function which
would be more readable imo.
##########
File path: src/operator/tensor/elemwise_unary_op.h
##########
@@ -457,30 +457,67 @@ class UnaryOp : public OpBase {
};
#if MXNET_USE_ONEDNN == 1
-inline bool TanhStorageType(const nnvm::NodeAttrs& attrs,
- const int dev_mask,
- DispatchMode* dispatch_mode,
- std::vector<int>* in_attrs,
- std::vector<int>* out_attrs) {
+inline bool EltwiseStorageType(const nnvm::NodeAttrs& attrs,
+ const int dev_mask,
+ DispatchMode* dispatch_mode,
+ std::vector<int>* in_attrs,
+ std::vector<int>* out_attrs) {
CHECK_EQ(in_attrs->size(), 1);
CHECK_EQ(out_attrs->size(), 1);
return DNNLStorageType(attrs, dev_mask, true, dispatch_mode, in_attrs,
out_attrs);
}
-inline void TanhComputeExCPU(const nnvm::NodeAttrs& attrs,
- const OpContext& ctx,
- const std::vector<mxnet::NDArray>& inputs,
- const std::vector<OpReqType>& req,
- const std::vector<mxnet::NDArray>& outputs) {
- if (SupportDNNLTanh(inputs[0], outputs[0])) {
+template <typename OP>
+struct DNNLAlgorithm {};
+template <>
+struct DNNLAlgorithm<op::mshadow_op::plus> {
+ static const dnnl::algorithm value = dnnl::algorithm::binary_add;
+};
+template <>
+struct DNNLAlgorithm<op::mshadow_op::minus> {
+ static const dnnl::algorithm value = dnnl::algorithm::binary_sub;
+};
+template <>
+struct DNNLAlgorithm<op::mshadow_op::mul> {
+ static const dnnl::algorithm value = dnnl::algorithm::binary_mul;
+};
+template <>
+struct DNNLAlgorithm<op::mshadow_op::div> {
+ static const dnnl::algorithm value = dnnl::algorithm::binary_div;
+};
+template <>
+struct DNNLAlgorithm<op::mshadow_op::tanh> {
+ static const dnnl::algorithm value = dnnl::algorithm::eltwise_tanh;
+};
+template <>
+struct DNNLAlgorithm<op::mshadow_op::exp> {
+ static const dnnl::algorithm value = dnnl::algorithm::eltwise_exp;
+};
+template <>
+struct DNNLAlgorithm<op::mshadow_op::square> {
+ static const dnnl::algorithm value = dnnl::algorithm::eltwise_square;
+};
+template <>
+struct DNNLAlgorithm<op::mshadow_op::square_root> {
+ static const dnnl::algorithm value = dnnl::algorithm::eltwise_sqrt;
+};
+
+template <typename OP, bool computeMixed = true>
+inline void EltwiseComputeExCPU(const nnvm::NodeAttrs& attrs,
+ const OpContext& ctx,
+ const std::vector<mxnet::NDArray>& inputs,
+ const std::vector<OpReqType>& req,
+ const std::vector<mxnet::NDArray>& outputs) {
+ auto fallBackFunction =
+ computeMixed ? UnaryOp::ComputeMixedType<cpu, OP> :
UnaryOp::Compute<cpu, OP>;
+ if (SupportDNNLEltwise(inputs[0], outputs[0])) {
DNNL_OPCHECK_INIT(false, outputs.size(), inputs, outputs);
- DNNLRun(DNNLTanhForward, attrs, ctx, inputs[0], req[0], outputs[0]);
- DNNL_OPCHECK_RUN(
- (UnaryOp::ComputeMixedType<cpu, mshadow_op::tanh>), attrs, ctx,
inputs, req, outputs);
+ DNNLRun(
+ DNNLEltwiseForward<DNNLAlgorithm<OP>::value>, attrs, ctx, inputs[0],
req[0], outputs[0]);
+ DNNL_OPCHECK_RUN(fallBackFunction, attrs, ctx, inputs, req, outputs);
} else {
- FallBackCompute(
- UnaryOp::ComputeMixedType<cpu, mshadow_op::tanh>, attrs, ctx, inputs,
req, outputs);
+ FallBackCompute(fallBackFunction, attrs, ctx, inputs, req, outputs);
Review comment:
```suggestion
if constexpr (computeMixed) {
FallBackCompute(UnaryOp::ComputeMixedType<cpu, OP>, attrs, ctx,
inputs, req, outputs);
} else {
FallBackCompute(UnaryOp::Compute<cpu, OP>, attrs, ctx, inputs, req,
outputs);
}
```
##########
File path: src/operator/nn/dnnl/dnnl_eltwise-inl.h
##########
@@ -50,17 +52,18 @@ class DNNLTanhFwd {
std::shared_ptr<eltwise_fwd_pd_t> fwd_pd;
};
-inline void DNNLTanhForward(const nnvm::NodeAttrs& attrs,
- const OpContext& ctx,
- const NDArray& input,
- const OpReqType& req,
- const NDArray& output) {
- DNNLTanhFwd& fwd = DNNLTanhFwd::GetTanhForward(input, output);
+template <dnnl::algorithm algorithm>
+inline void DNNLEltwiseForward(const nnvm::NodeAttrs& attrs,
Review comment:
Any reason why this is a template?
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