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Fix inefficiencies in the mkldnn kernels. Some of these were (unfortunately) getting replicated in the new kernels. (#241)

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Pranav Sharma 2018-12-21 15:12:23 -08:00 committed by GitHub
parent 7af1887b33
commit 3875511f9e
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GPG key ID: 4AEE18F83AFDEB23
6 changed files with 16 additions and 17 deletions
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6
onnxruntime/core/providers/mkldnn/nn/conv.cc
View file

@ -68,7 +68,7 @@ class ConvPrimitive : public PrimitiveBase {
~ConvPrimitive() = default;
void Compute(const T* src_data, const T* filter_data,
const T* dst_data, const T* bias_data = nullptr) {
T* dst_data, const T* bias_data = nullptr) {
context_.src_mem->set_data_handle(
static_cast<void*>(const_cast<T*>(src_data)));
context_.filter_mem->set_data_handle(
@ -78,7 +78,7 @@ class ConvPrimitive : public PrimitiveBase {
static_cast<void*>(const_cast<T*>(bias_data)));
}
context_.dst_mem->set_data_handle(
static_cast<void*>(const_cast<T*>(dst_data)));
static_cast<void*>(dst_data));
context_.stream->submit(context_.net);
context_.src_mem->set_data_handle(nullptr);
@ -437,7 +437,7 @@ Status Conv<T>::Compute(OpKernelContext* context) const {
DoReorder<T>(params);
}
} catch (mkldnn::error& e) {
} catch (const mkldnn::error& e) {
return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, "Status: ", e.status, ", message: ", e.message.c_str());
}

2
onnxruntime/core/providers/mkldnn/nn/conv.h
View file

@ -10,7 +10,7 @@ namespace mkl_dnn {
template <typename T>
class Conv final : public onnxruntime::Conv<T> {
public:
Conv(const OpKernelInfo& info) : onnxruntime::Conv<T>(info) {
explicit Conv(const OpKernelInfo& info) : onnxruntime::Conv<T>(info) {
}
Status Compute(OpKernelContext* context) const override;

11
onnxruntime/core/providers/mkldnn/nn/lrn.cc
View file

@ -1,4 +1,3 @@
// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.
@ -25,13 +24,13 @@ ONNX_OPERATOR_TYPED_KERNEL_EX(
namespace {
// Struct which encapsulates parameters for MKLDNN LRN primitive.
struct LRNParams {
mkldnn::memory::dims& dims_;
const mkldnn::memory::dims& dims_;
float alpha_;
float beta_;
float bias_;
int size_;
LRNParams(mkldnn::memory::dims& dims, float alpha, float beta, float bias, int size)
LRNParams(const mkldnn::memory::dims& dims, float alpha, float beta, float bias, int size)
: dims_(dims), alpha_(alpha), beta_(beta), bias_(bias), size_(size) {}
// Used as the key for LRN Primitive Reuse LRN.
@ -62,9 +61,9 @@ class LRNPrimitive : public PrimitiveBase {
~LRNPrimitive() = default;
void Compute(const T* src_data, const T* dst_data) {
void Compute(const T* src_data, 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_.dst_mem->set_data_handle(static_cast<void*>(dst_data));
context_.stream->submit(context_.net);
context_.src_mem->set_data_handle(nullptr);
@ -231,7 +230,7 @@ Status LRN<T>::Compute(OpKernelContext* context) const {
MemoryReorderParams params(src, dst);
DoReorder<T>(params);
}
} catch (mkldnn::error& e) {
} catch (const mkldnn::error& e) {
return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, "Status: ", e.status, ", message: ", e.message.c_str());
}

4
onnxruntime/core/providers/mkldnn/nn/lrn.h
View file

@ -11,10 +11,10 @@ namespace mkl_dnn {
template <typename T>
class LRN final : public onnxruntime::LRN<T> {
public:
LRN(const OpKernelInfo& info) : onnxruntime::LRN<T>(info) {}
explicit LRN(const OpKernelInfo& info) : onnxruntime::LRN<T>(info) {}
Status Compute(OpKernelContext* p_op_kernel_context) const override;
};
}
}
}

8
onnxruntime/core/providers/mkldnn/nn/pool.cc
View file

@ -45,7 +45,7 @@ struct PoolParams {
mkldnn::memory::dims& padding_right;
bool count_include_pad;
PoolParams(std::string op_name, std::string version,
PoolParams(const std::string& op_name, const std::string& version,
mkldnn::memory::dims& src_dims, mkldnn::memory::dims& dst_dims,
mkldnn::memory::dims& kernel, mkldnn::memory::dims& strides,
mkldnn::memory::dims& padding_left, mkldnn::memory::dims& padding_right,
@ -90,9 +90,9 @@ class PoolPrimitive : public PrimitiveBase {
~PoolPrimitive() = default;
void Compute(const T* src_data, const T* dst_data) {
void Compute(const T* src_data, 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_.dst_mem->set_data_handle(static_cast<void*>(dst_data));
context_.stream->submit(context_.net);
context_.src_mem->set_data_handle(nullptr);
@ -326,7 +326,7 @@ Status Pool<T, PoolType>::Compute(OpKernelContext* context) const {
MemoryReorderParams params(src, dst);
DoReorder<T>(params);
}
} catch (mkldnn::error& e) {
} catch (const mkldnn::error& e) {
return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, "Status: ", e.status, ", message: ", e.message.c_str());
}

2
onnxruntime/core/providers/mkldnn/nn/pool.h
View file

@ -11,7 +11,7 @@ namespace mkl_dnn {
template <typename T, typename PoolType>
class Pool final : public onnxruntime::Pool<T, PoolType> {
public:
Pool(const OpKernelInfo& info) : onnxruntime::Pool<T, PoolType>(info) {
explicit Pool(const OpKernelInfo& info) : onnxruntime::Pool<T, PoolType>(info) {
// Since there are multiple versions of Pooling kernels, we need to use
// the opset version as part of the key for caching Pooling Primitives.
int start, end;