mkldnn activations.relu

onnxruntime/core/providers/cpu/activation/activations.h

@@ -90,7 +90,7 @@ class ParametricSoftplus final : public OpKernel {
 };
 
 template <typename T>
-class Relu final : public OpKernel {
+class Relu : public OpKernel {
  public:
   Relu(const OpKernelInfo& info) : OpKernel(info) {}
 

onnxruntime/core/providers/mkldnn/activation/activations.cc

@@ -0,0 +1,238 @@
+/* Copyright(C) 2018 Intel Corporation
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http ://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions
+and limitations under the License.
+==============================================================================*/
+
+#ifdef _WIN32
+#pragma warning(disable : 4244)
+#endif
+
+#include "core/providers/mkldnn/mkldnn_common.h"
+#include "core/providers/mkldnn/activation/activations.h"
+#include "core/providers/mkldnn/mkldnn_fwd.h"
+
+namespace onnxruntime {
+namespace mkl_dnn {
+
+namespace {
+// Struct which encapsulates parameters for MKLDNN Pool primitive.
+struct ReluParams {
+  mkldnn::memory::dims& src_dims;
+  mkldnn::memory::dims& dst_dims;
+  size_t num_dimensions;
+
+  ReluParams(mkldnn::memory::dims& src_dims, mkldnn::memory::dims& dst_dims, 
+		size_t dimensions = 0)
+      : src_dims(src_dims),
+        dst_dims(dst_dims),
+        num_dimensions(dimensions) {}
+
+  // Used as the key for Pool Primitive Reuse Pool.
+  std::string ToString() const {
+    std::string key;
+    key.reserve(128);
+    key.append("Relu_");
+    AddDimsToKey(key, src_dims);
+    AddDimsToKey(key, dst_dims);
+    return key;
+  }
+};
+
+template <typename T>
+class ReluPrimitive final : public PrimitiveBase {
+ public:
+  explicit ReluPrimitive(const ReluParams& params)
+      : cpu_engine_(GetEngine()) {
+    context_.stream.reset(new mkldnn::stream(mkldnn::stream::kind::eager));
+    if (context_.relu_fwd == nullptr) {
+      Initialize(params);
+    }
+  }
+
+  ~ReluPrimitive() = default;
+
+  void Compute(const T* src_data, const T* dst_data) {
+	  context_.src_mem->set_data_handle(
+		  static_cast<void*>(const_cast<T*>(src_data)));
+	  context_.dst_mem->set_data_handle(
+		  static_cast<void*>(const_cast<T*>(dst_data)));
+	  context_.stream->submit(context_.net);
+
+	  context_.src_mem->set_data_handle(nullptr);
+	  context_.dst_mem->set_data_handle(nullptr);
+	  return;
+  }
+
+  mkldnn::memory::format GetSrcMemoryFormat() const { return context_.src_fmt; }
+  mkldnn::memory::format GetDstMemoryFormat() const { return context_.dst_fmt; }
+
+  std::unique_ptr<mkldnn::memory::desc> 
+	GetDstMemoryDesc() const { return context_.dst_md; }
+
+  std::unique_ptr<mkldnn::eltwise_forward::primitive_desc> 
+	GetPrimitiveDesc() const {
+		return context_.relu_fwd_pd;
+  }
+
+ private:
+  struct ReluContext {
+    mkldnn::memory::format src_fmt;
+    mkldnn::memory::format dst_fmt;
+
+    std::unique_ptr<mkldnn::memory> src_mem;
+    std::unique_ptr<mkldnn::memory> dst_mem;
+
+    std::unique_ptr<mkldnn::eltwise_forward::desc> fwd_desc;
+    std::unique_ptr<mkldnn::eltwise_forward::primitive_desc> relu_fwd_pd;
+    std::unique_ptr<mkldnn::primitive> relu_fwd;
+
+    std::unique_ptr<mkldnn::memory::desc> src_md;
+    std::unique_ptr<mkldnn::memory::desc> dst_md;
+
+    std::unique_ptr<mkldnn::stream> stream;
+    std::vector<mkldnn::primitive> net;
+
+    ReluContext()
+    : src_fmt(mkldnn::memory::format::any),
+      dst_fmt(mkldnn::memory::format::any),
+      src_mem(nullptr),
+      dst_mem(nullptr),
+      fwd_desc(nullptr),
+      relu_fwd_pd(nullptr),
+      relu_fwd(nullptr),
+      src_md(nullptr),
+      dst_md(nullptr),
+      stream(nullptr) {}
+  };
+
+  void Initialize(const ReluParams& params) {
+
+    mkldnn::memory::format fmt = mkldnn::memory::format::any;
+    switch (params.num_dimensions) {
+    case 1: { fmt = mkldnn::memory::format::x; break; }
+    case 2: { fmt = mkldnn::memory::format::nc; break; }
+    case 3: { fmt = mkldnn::memory::format::ntc; break; }
+    case 4: { fmt = mkldnn::memory::format::nchw; break; }
+    case 5: { fmt = mkldnn::memory::format::ncdhw; break; }
+    default: {  fmt = mkldnn::memory::format::any; break; }
+    }
+
+    context_.src_md.reset(new mkldnn::memory::desc(
+      { params.src_dims }, MklDnnType<T>(), fmt));
+    context_.dst_md.reset(new mkldnn::memory::desc(
+      { params.dst_dims }, MklDnnType<T>(), fmt));
+
+    context_.fwd_desc.reset(new mkldnn::eltwise_forward::desc(
+      mkldnn::prop_kind::forward_inference, mkldnn::algorithm::eltwise_relu, 
+      *context_.src_md, 0, 0));
+
+    context_.relu_fwd_pd.reset(
+      new mkldnn::eltwise_forward::primitive_desc(*context_.fwd_desc,
+      cpu_engine_));
+
+    context_.src_fmt = static_cast<mkldnn::memory::format>(
+      context_.relu_fwd_pd.get()->dst_primitive_desc().desc().data.format);
+
+    context_.dst_fmt = static_cast<mkldnn::memory::format>(
+      context_.relu_fwd_pd.get()->dst_primitive_desc().desc().data.format);
+
+    context_.src_mem.reset(
+     new mkldnn::memory(context_.relu_fwd_pd.get()->dst_primitive_desc(), 
+			  nullptr));
+    context_.dst_mem.reset(
+      new mkldnn::memory(context_.relu_fwd_pd.get()->dst_primitive_desc(), 
+			nullptr));
+    context_.relu_fwd.reset(
+      new mkldnn::eltwise_forward(*context_.relu_fwd_pd, *context_.src_mem, 
+			*context_.dst_mem));
+    context_.net.push_back(*context_.relu_fwd);
+  }
+
+  ReluContext context_;
+  mkldnn::engine& cpu_engine_;
+};
+
+// Pool which allows for reuse of MKLDNN Conv2d primitives which are expensive 
+// to instantiate. To address thread safety, the primitives are stored in a map 
+// on thread local storage.
+template <typename T>
+class ReluPrimitivePool : public PrimitivePool<T> {
+ public:
+  static ReluPrimitive<T>* Get(const ReluParams& params) {
+    ReluPrimitive<T>* primitive = dynamic_cast<ReluPrimitive<T>*>(
+        ReluPrimitivePool<T>::GetInstance().GetPrimitive(params.ToString()));
+
+    if (primitive == nullptr) {
+      auto relu_primitive = std::make_unique<ReluPrimitive<T>>(params);
+      primitive = relu_primitive.get();
+      ReluPrimitivePool<T>::GetInstance().SetPrimitive(params.ToString(), 
+		std::move(relu_primitive));
+    }
+    return primitive;
+  }
+
+ private:
+  ReluPrimitivePool() = default;
+  ~ReluPrimitivePool() = default;
+
+  static ReluPrimitivePool& GetInstance() {
+    static ReluPrimitivePool pool;
+    return pool;
+  }
+};
+}	// namespace
+
+template <typename T>
+Status Relu<T>::Compute(OpKernelContext* context) const {
+  const Tensor* X = context->Input<Tensor>(0);
+  Tensor* Y = context->Output(0, X->Shape());
+
+  const TensorShape& x_shape = X->Shape();
+  const auto& x_dims = x_shape.GetDims();
+
+  if (X->Shape().NumDimensions() > 5 ) {
+    return onnxruntime::Relu<T>::Compute(context);
+  }
+
+  const TensorShape& y_shape = Y->Shape();
+  auto& y_dims = y_shape.GetDims();
+
+  const T* src_data = X->template Data<T>();
+  T* dst_data = Y->template MutableData<T>();
+
+  mkldnn::memory::dims src_dims_mkl(x_dims.begin(), x_dims.end());
+  mkldnn::memory::dims dst_dims_mkl(y_dims.begin(), y_dims.end());
+
+  try {
+    ReluParams pool_params(src_dims_mkl, dst_dims_mkl, x_shape.NumDimensions());
+    ReluPrimitive<T>* relulPrimitive = ReluPrimitivePool<T>::Get(pool_params);
+
+    relulPrimitive->Compute(src_data, dst_data);
+  } catch (mkldnn::error& e) {
+    return ONNXRUNTIME_MAKE_STATUS(ONNXRUNTIME, FAIL, "Status: ", e.status, 
+		", message: ", e.message.c_str());
+  }
+
+  return Status::OK();
+}
+
+ONNX_OPERATOR_KERNEL_EX(
+    Relu,
+    kOnnxDomain,
+    6,
+    kMklDnnExecutionProvider,
+    KernelDefBuilder().TypeConstraint("T", DataTypeImpl::GetTensorType<float>()),
+    Relu<float>);
+
+}  // namespace mkl_dnn
+}  // namespace onnxruntime

onnxruntime/core/providers/mkldnn/activation/activations.h

@@ -0,0 +1,32 @@
+/* Copyright(C) 2018 Intel Corporation
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http ://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions
+and limitations under the License.
+==============================================================================*/
+
+#pragma once
+#include "core/framework/op_kernel.h"
+#include "core/providers/cpu/activation/activations.h"
+
+namespace onnxruntime {
+namespace mkl_dnn {
+
+template <typename T>
+class Relu : public onnxruntime::Relu<T> {
+ public:
+  Relu(const OpKernelInfo& info) : onnxruntime::Relu<T>(info) {}
+
+  Status Compute(OpKernelContext* context) const override;
+};
+
+}  // namespace mkl_dnn
+}  // namespace onnxruntime

onnxruntime/core/providers/mkldnn/mkldnn_execution_provider.cc

@@ -65,6 +65,7 @@ class ONNX_OPERATOR_KERNEL_CLASS_NAME(kMklDnnExecutionProvider, kOnnxDomain, 1,
 class ONNX_OPERATOR_KERNEL_CLASS_NAME(kMklDnnExecutionProvider, kOnnxDomain, 7, Gemm);
 class ONNX_OPERATOR_KERNEL_CLASS_NAME(kMklDnnExecutionProvider, kOnnxDomain, 1, MemcpyFromHost);
 class ONNX_OPERATOR_KERNEL_CLASS_NAME(kMklDnnExecutionProvider, kOnnxDomain, 1, MemcpyToHost);
+class ONNX_OPERATOR_KERNEL_CLASS_NAME(kMklDnnExecutionProvider, kOnnxDomain, 6, Relu);
 class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kMklDnnExecutionProvider, kOnnxDomain, 7, 8, float, AveragePool);
 class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kMklDnnExecutionProvider, kOnnxDomain, 1, 8, float, GlobalAveragePool);
 class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kMklDnnExecutionProvider, kOnnxDomain, 1, 7, float, MaxPool);
@@ -77,6 +78,7 @@ void RegisterMKLDNNKernels(std::function<void(KernelCreateInfo&&)> fn) {
   fn(BuildKernel<ONNX_OPERATOR_KERNEL_CLASS_NAME(kMklDnnExecutionProvider, kOnnxDomain, 7, Gemm)>());
   fn(BuildKernel<ONNX_OPERATOR_KERNEL_CLASS_NAME(kMklDnnExecutionProvider, kOnnxDomain, 1, MemcpyFromHost)>());
   fn(BuildKernel<ONNX_OPERATOR_KERNEL_CLASS_NAME(kMklDnnExecutionProvider, kOnnxDomain, 1, MemcpyToHost)>());
+  fn(BuildKernel<ONNX_OPERATOR_KERNEL_CLASS_NAME(kMklDnnExecutionProvider, kOnnxDomain, 6, Relu)>()); 
   fn(BuildKernel<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kMklDnnExecutionProvider, kOnnxDomain, 7, 8, float, AveragePool)>());
   fn(BuildKernel<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kMklDnnExecutionProvider, kOnnxDomain, 1, 8, float, GlobalAveragePool)>());
   fn(BuildKernel<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kMklDnnExecutionProvider, kOnnxDomain, 1, 7, float, MaxPool)>());