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Merged
ke1337 merged 2 commits into from
Sep 26, 2019
Merged

Changed ConvBase into a class member variable #1927

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ec0be28
Changed ConvBase into a class member variable
yangchen-MS Sep 25, 2019
b3577e9
fixed cuda build issue
yangchen-MS Sep 26, 2019
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14 changes: 7 additions & 7 deletions onnxruntime/contrib_ops/cpu/nchwc_ops.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -98,7 +98,7 @@ Status NchwcConv::Compute(OpKernelContext* context) const {
const auto* B = context->Input<Tensor>(2);
const auto* Sum = context->Input<Tensor>(3);

ORT_RETURN_IF_ERROR(ConvBase::ValidateInputShape(X, W));
ORT_RETURN_IF_ERROR(conv_attrs_.ValidateInputShape(X, W));

const auto& X_shape = X->Shape();
const auto& W_shape = W->Shape();
Expand All @@ -108,28 +108,28 @@ Status NchwcConv::Compute(OpKernelContext* context) const {
ORT_ENFORCE((static_cast<size_t>(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0));

std::vector<int64_t> kernel_shape;
ORT_RETURN_IF_ERROR(ConvBase::ComputeKernelShape(W_shape, kernel_shape));
ORT_RETURN_IF_ERROR(conv_attrs_.ComputeKernelShape(W_shape, kernel_shape));
if (kernel_shape.size() != 2) {
return Status(common::ONNXRUNTIME, common::INVALID_ARGUMENT, "Unsupported convolution size.");
}

std::vector<int64_t> pads(ConvBase::pads_);
std::vector<int64_t> pads(conv_attrs_.pads);
if (pads.empty()) {
pads.resize(kernel_shape.size() * 2, 0);
}
std::vector<int64_t> dilations(ConvBase::dilations_);
std::vector<int64_t> dilations(conv_attrs_.dilations);
if (dilations.empty()) {
dilations.resize(kernel_shape.size(), 1);
}
std::vector<int64_t> strides(ConvBase::strides_);
std::vector<int64_t> strides(conv_attrs_.strides);
if (strides.empty()) {
strides.resize(kernel_shape.size(), 1);
}

std::vector<int64_t> Y_dims;
Y_dims.insert(Y_dims.begin(), {X_shape[0], W_shape[0]});
TensorShape input_shape = X->Shape().Slice(2);
ORT_RETURN_IF_ERROR(ConvBase::InferOutputShape(input_shape, kernel_shape, strides, dilations, &pads, &Y_dims));
ORT_RETURN_IF_ERROR(conv_attrs_.InferOutputShape(input_shape, kernel_shape, strides, dilations, &pads, &Y_dims));
auto* Y = context->Output(0, Y_dims);
auto* y_data = Y->template MutableData<float>();

Expand All @@ -151,7 +151,7 @@ Status NchwcConv::Compute(OpKernelContext* context) const {
pads.data(),
strides.data(),
Y_dims.data(),
static_cast<size_t>(ConvBase::group_),
static_cast<size_t>(conv_attrs_.group),
X->template Data<float>(),
W->template Data<float>(),
B != nullptr ? B->template Data<float>() : nullptr,
Expand Down
8 changes: 5 additions & 3 deletions onnxruntime/contrib_ops/cpu/nchwc_ops.h
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@

#include "core/common/common.h"
#include "core/framework/op_kernel.h"
#include "core/providers/cpu/nn/conv_base.h"
#include "core/providers/cpu/nn/conv_attributes.h"
#include "core/providers/cpu/nn/pool.h"
#include "contrib_ops/cpu/fused_activation.h"

Expand Down Expand Up @@ -35,15 +35,17 @@ class ReorderOutput : public OpKernel {
int64_t channels_;
};

class NchwcConv : public OpKernel, public ConvBase {
class NchwcConv : public OpKernel {
public:
NchwcConv(const OpKernelInfo& info) : OpKernel(info), ConvBase(info) {
NchwcConv(const OpKernelInfo& info) : OpKernel(info), conv_attrs_(info) {
ORT_ENFORCE(GetFusedActivationAttr(info, activation_).IsOK());
}

Status Compute(OpKernelContext* context) const override;

private:
ConvAttributes conv_attrs_;

MLAS_ACTIVATION activation_;
};

Expand Down
64 changes: 32 additions & 32 deletions onnxruntime/core/providers/cpu/nn/conv.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -34,39 +34,39 @@ Status Conv<T>::Compute(OpKernelContext* context) const {
const int64_t N = X->Shape()[0];
const int64_t C = X->Shape()[1];
const int64_t M = W->Shape()[0];
ORT_RETURN_IF_ERROR(ValidateInputShape(X, W));
ORT_RETURN_IF_ERROR(conv_attrs_.ValidateInputShape(X, W));

std::vector<int64_t> kernel_shape;
ORT_RETURN_IF_ERROR(ComputeKernelShape(W->Shape(), kernel_shape));
ORT_RETURN_IF_ERROR(conv_attrs_.ComputeKernelShape(W->Shape(), kernel_shape));

bool Is2DKernel = kernel_shape.size() == 2;
std::vector<int64_t> pads(pads_);
std::vector<int64_t> pads(conv_attrs_.pads);
if (pads.empty()) {
pads.resize(kernel_shape.size() * 2, 0);
}
std::vector<int64_t> dilations(dilations_);
std::vector<int64_t> dilations(conv_attrs_.dilations);
if (dilations.empty()) {
dilations.resize(kernel_shape.size(), 1);
}
std::vector<int64_t> strides(strides_);
std::vector<int64_t> strides(conv_attrs_.strides);
if (strides.empty()) {
strides.resize(kernel_shape.size(), 1);
}

std::vector<int64_t> Y_dims;
Y_dims.insert(Y_dims.begin(), {N, M});
TensorShape input_shape = X->Shape().Slice(2);
ORT_RETURN_IF_ERROR(InferOutputShape(input_shape, kernel_shape, strides, dilations, &pads, &Y_dims));
ORT_RETURN_IF_ERROR(conv_attrs_.InferOutputShape(input_shape, kernel_shape, strides, dilations, &pads, &Y_dims));
Tensor* Y = context->Output(0, TensorShape(Y_dims));
TensorShape output_shape = Y->Shape().Slice(2);

const int64_t input_image_size = input_shape.Size();
const int64_t output_image_size = output_shape.Size();
const int64_t kernel_size = TensorShape(kernel_shape).Size();
const int64_t X_offset = C / group_ * input_image_size;
const int64_t Y_offset = Y->Shape().Size() / Y->Shape()[0] / group_;
const int64_t W_offset = W->Shape().Size() / group_;
const int64_t kernel_dim = C / group_ * kernel_size;
const int64_t X_offset = C / conv_attrs_.group * input_image_size;
const int64_t Y_offset = Y->Shape().Size() / Y->Shape()[0] / conv_attrs_.group;
const int64_t W_offset = W->Shape().Size() / conv_attrs_.group;
const int64_t kernel_dim = C / conv_attrs_.group * kernel_size;
const int64_t col_buffer_size = kernel_dim * output_image_size;

AllocatorPtr alloc;
Expand All @@ -85,11 +85,11 @@ Status Conv<T>::Compute(OpKernelContext* context) const {
output_shape.GetDims().end());

for (int image_id = 0; image_id < N; ++image_id) {
for (int group_id = 0; group_id < group_; ++group_id) {
for (int group_id = 0; group_id < conv_attrs_.group; ++group_id) {
if (Is2DKernel) {
math::Im2col<T, CPUMathUtil, StorageOrder::NCHW>(
Xdata + group_id * X_offset,
C / group_,
C / conv_attrs_.group,
input_shape[0],
input_shape[1],
kernel_shape[0],
Expand Down Expand Up @@ -122,7 +122,7 @@ Status Conv<T>::Compute(OpKernelContext* context) const {
math::Gemm<T>(
CblasNoTrans,
CblasNoTrans,
M / group_,
M / conv_attrs_.group,
output_image_size,
kernel_dim,
1,
Expand All @@ -139,8 +139,8 @@ Status Conv<T>::Compute(OpKernelContext* context) const {
Ymatrix.rowwise() += Bvec.transpose();
}

Xdata += X_offset * group_;
Ydata += Y_offset * group_;
Xdata += X_offset * conv_attrs_.group;
Ydata += Y_offset * conv_attrs_.group;
}

return Status::OK();
Expand All @@ -157,28 +157,28 @@ Status Conv<float>::Compute(OpKernelContext* context) const {
const int64_t N = X->Shape()[0];
const int64_t C = X->Shape()[1];
const int64_t M = W->Shape()[0];
ORT_RETURN_IF_ERROR(ValidateInputShape(X, W));
ORT_RETURN_IF_ERROR(conv_attrs_.ValidateInputShape(X, W));

std::vector<int64_t> kernel_shape;
ORT_RETURN_IF_ERROR(ComputeKernelShape(W->Shape(), kernel_shape));
ORT_RETURN_IF_ERROR(conv_attrs_.ComputeKernelShape(W->Shape(), kernel_shape));

std::vector<int64_t> pads(pads_);
std::vector<int64_t> pads(conv_attrs_.pads);
if (pads.empty()) {
pads.resize(kernel_shape.size() * 2, 0);
}
std::vector<int64_t> dilations(dilations_);
std::vector<int64_t> dilations(conv_attrs_.dilations);
if (dilations.empty()) {
dilations.resize(kernel_shape.size(), 1);
}
std::vector<int64_t> strides(strides_);
std::vector<int64_t> strides(conv_attrs_.strides);
if (strides.empty()) {
strides.resize(kernel_shape.size(), 1);
}

std::vector<int64_t> Y_dims;
Y_dims.insert(Y_dims.begin(), {N, M});
TensorShape input_shape = X->Shape().Slice(2);
ORT_RETURN_IF_ERROR(InferOutputShape(input_shape, kernel_shape, strides, dilations, &pads, &Y_dims));
ORT_RETURN_IF_ERROR(conv_attrs_.InferOutputShape(input_shape, kernel_shape, strides, dilations, &pads, &Y_dims));
Tensor* Y = context->Output(0, TensorShape(Y_dims));
TensorShape output_shape = Y->Shape().Slice(2);

Expand All @@ -197,15 +197,15 @@ Status Conv<float>::Compute(OpKernelContext* context) const {
MlasConvPrepare(&Parameters,
kernel_rank,
static_cast<size_t>(N),
static_cast<size_t>(group_),
static_cast<size_t>(C / group_),
static_cast<size_t>(conv_attrs_.group),
static_cast<size_t>(C / conv_attrs_.group),
input_shape.GetDims().data(),
kernel_shape.data(),
dilations.data(),
pads.data(),
strides.data(),
output_shape.GetDims().data(),
static_cast<size_t>(M / group_),
static_cast<size_t>(M / conv_attrs_.group),
&activation_,
&WorkingBufferSize,
tp);
Expand All @@ -224,10 +224,10 @@ Status Conv<float>::Compute(OpKernelContext* context) const {
const int64_t input_image_size = input_shape.Size();
const int64_t output_image_size = output_shape.Size();
const int64_t kernel_size = TensorShape(kernel_shape).Size();
const int64_t X_offset = C / group_ * input_image_size;
const int64_t Y_offset = Y->Shape().Size() / Y->Shape()[0] / group_;
const int64_t W_offset = W->Shape().Size() / group_;
const int64_t kernel_dim = C / group_ * kernel_size;
const int64_t X_offset = C / conv_attrs_.group * input_image_size;
const int64_t Y_offset = Y->Shape().Size() / Y->Shape()[0] / conv_attrs_.group;
const int64_t W_offset = W->Shape().Size() / conv_attrs_.group;
const int64_t kernel_dim = C / conv_attrs_.group * kernel_size;
const int64_t col_buffer_size = kernel_dim * output_image_size;

auto col_data = alloc->Alloc(sizeof(float) * col_buffer_size);
Expand All @@ -240,7 +240,7 @@ Status Conv<float>::Compute(OpKernelContext* context) const {
output_shape.GetDims().end());

for (int image_id = 0; image_id < N; ++image_id) {
for (int group_id = 0; group_id < group_; ++group_id) {
for (int group_id = 0; group_id < conv_attrs_.group; ++group_id) {
math::Im2colNd<float, CPUMathUtil, StorageOrder::NCHW>()(
Xdata + group_id * X_offset,
image_shape.GetDims().data(),
Expand All @@ -257,7 +257,7 @@ Status Conv<float>::Compute(OpKernelContext* context) const {
math::Gemm<float>(
CblasNoTrans,
CblasNoTrans,
M / group_,
M / conv_attrs_.group,
output_image_size,
kernel_dim,
1,
Expand All @@ -270,8 +270,8 @@ Status Conv<float>::Compute(OpKernelContext* context) const {

MlasActivation(&activation_, Ydata, Bdata, M, output_image_size, output_image_size);

Xdata += X_offset * group_;
Ydata += Y_offset * group_;
Xdata += X_offset * conv_attrs_.group;
Ydata += Y_offset * conv_attrs_.group;
}
}

Expand Down
16 changes: 11 additions & 5 deletions onnxruntime/core/providers/cpu/nn/conv.h
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Original file line number Diff line number Diff line change
Expand Up @@ -3,31 +3,37 @@

#pragma once

#include "core/providers/cpu/nn/conv_base.h"
#include "core/framework/op_kernel.h"
#include "core/providers/cpu/nn/conv_attributes.h"
#include "core/mlas/inc/mlas.h"

namespace onnxruntime {

template <typename T>
class Conv : public OpKernel, public ConvBase {
class Conv : public OpKernel {
public:
Conv(const OpKernelInfo& info) : OpKernel(info), ConvBase(info) {
Conv(const OpKernelInfo& info) : OpKernel(info), conv_attrs_(info) {
}

Status Compute(OpKernelContext* context) const override;

private:
ConvAttributes conv_attrs_;
};

template <>
class Conv<float> : public OpKernel, public ConvBase {
class Conv<float> : public OpKernel {
public:
Conv<float>(const OpKernelInfo& info) : OpKernel(info), ConvBase(info) {
Conv<float>(const OpKernelInfo& info) : OpKernel(info), conv_attrs_(info) {
activation_.ActivationKind = MlasIdentityActivation;
}

Status Compute(OpKernelContext* context) const override;

protected:
MLAS_ACTIVATION activation_;

ConvAttributes conv_attrs_;
};

} // namespace onnxruntime
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