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Support fusing 3D Conv with Add/Mul. #23

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Merged
Weixing Zhang (weixingzhang) merged 5 commits into from
Dec 3, 2018
Merged

Support fusing 3D Conv with Add/Mul. #23

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4a52868
Support fusing 3D Conv with Add/Mul.
weixingzhang Nov 25, 2018
639d035
Updated it based on feedback.
weixingzhang Nov 28, 2018
7f5cff3
Updated it based on review feedback.
weixingzhang Nov 30, 2018
a01f92e
Change the implementation of scale_by_axis rather than Mul.
weixingzhang Nov 30, 2018
5f87427
Refactor the code to make the compiler optimize it easily.
weixingzhang Nov 30, 2018
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21 changes: 18 additions & 3 deletions onnxruntime/core/graph/conv_add_fusion.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -36,27 +36,42 @@ Status ConvAddFusion::Apply(onnxruntime::Graph& graph, bool& modified) const {

// Currently, fusion is only supported for float or double data type.
if (!Initializer::IsSupportedDataType(add_B_tensor_proto) ||
conv_W_tensor_proto->dims_size() != 4 ||
add_B_tensor_proto->dims_size() != 3 ||
conv_W_tensor_proto->dims_size() < 4 ||
add_B_tensor_proto->dims_size() != conv_W_tensor_proto->dims_size() - 1 ||
conv_W_tensor_proto->dims(0) != add_B_tensor_proto->dims(0)) {
continue;
}

// The dimensions of add_B should be equal to 1 except first dimension.
bool flag = false;
for (int i = 1; i < add_B_tensor_proto->dims_size(); i++) {
if (add_B_tensor_proto->dims(i) != 1) {
flag = true;
break;
}
}

if (flag) {
continue;
}

const ONNX_NAMESPACE::TensorProto* conv_B_tensor_proto = nullptr;
if (conv_inputs.size() == 3) {
graph.GetInitializedTensor(conv_inputs[2]->Name(), conv_B_tensor_proto);

if (!Initializer::IsSupportedDataType(conv_B_tensor_proto) ||
conv_B_tensor_proto->data_type() != add_B_tensor_proto->data_type() ||
conv_B_tensor_proto->dims_size() != 1 ||
add_B_tensor_proto->dims_size() != 3 ||
conv_B_tensor_proto->dims(0) != add_B_tensor_proto->dims(0)) {
continue;
}

auto conv_B = std::make_unique<Initializer>(conv_B_tensor_proto);
auto add_B = std::make_unique<Initializer>(add_B_tensor_proto);

if (conv_B->size() != add_B->size()) {
continue;
}
// Calculate new value of initializers of conv node
conv_B->add(*add_B);

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35 changes: 26 additions & 9 deletions onnxruntime/core/graph/conv_mul_fusion.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -37,17 +37,29 @@ Status ConvMulFusion::Apply(onnxruntime::Graph& graph, bool& modified) const {
if (!Initializer::IsSupportedDataType(conv_W_tensor_proto) ||
!Initializer::IsSupportedDataType(mul_B_tensor_proto) ||
conv_W_tensor_proto->data_type() != mul_B_tensor_proto->data_type() ||
!(conv_W_tensor_proto->dims_size() > 2 && conv_W_tensor_proto->dims(0) == mul_B_tensor_proto->dims(0))) {
conv_W_tensor_proto->dims_size() < 4 ||
!(mul_B_tensor_proto->dims_size() == 0 ||
(mul_B_tensor_proto->dims_size() == conv_W_tensor_proto->dims_size() - 1 &&
conv_W_tensor_proto->dims(0) == mul_B_tensor_proto->dims(0)))) {

@tracysh Tracy Sharpe (tracysh) Nov 29, 2018

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Does this have the same problem as the conv_add_fusion where the other dims of the multiplier should be 1?

@weixingzhang Weixing Zhang (weixingzhang) Nov 29, 2018

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Yeah, it is better to check it.

continue;
}

auto conv_W = std::make_unique<Initializer>(conv_W_tensor_proto);
auto mul_B = std::make_unique<Initializer>(mul_B_tensor_proto);
// The dimensions of mul_B should be equal to 1 except first dimension.
if (mul_B_tensor_proto->dims_size() != 0) {
bool flag = false;
for (int i = 1; i < mul_B_tensor_proto->dims_size(); i++) {
if (mul_B_tensor_proto->dims(i) != 1) {
flag = true;
break;
}
}

if (conv_W->data_type() != mul_B->data_type() ||
!(conv_W->dims().size() > 2 && conv_W->dims()[0] == mul_B->dims()[0])) {
continue;
if (flag) {
continue;
}
}
auto conv_W = std::make_unique<Initializer>(conv_W_tensor_proto);
auto mul_B = std::make_unique<Initializer>(mul_B_tensor_proto);

const ONNX_NAMESPACE::TensorProto* conv_B_tensor_proto = nullptr;
std::unique_ptr<Initializer> conv_B = nullptr;
Expand All @@ -57,17 +69,22 @@ Status ConvMulFusion::Apply(onnxruntime::Graph& graph, bool& modified) const {

if (!Initializer::IsSupportedDataType(conv_B_tensor_proto) ||
conv_B_tensor_proto->data_type() != mul_B_tensor_proto->data_type() ||
conv_B_tensor_proto->dims_size() != 1 || mul_B_tensor_proto->dims_size() != 3 ||
conv_B_tensor_proto->dims(0) != mul_B_tensor_proto->dims(0)) {
conv_B_tensor_proto->dims_size() != 1 || (mul_B_tensor_proto->dims_size() != 0 &&
conv_B_tensor_proto->dims(0) != mul_B_tensor_proto->dims(0))) {
continue;
}
conv_B = std::make_unique<Initializer>(conv_B_tensor_proto);
}

// Calculate new value of initializers of conv node
conv_W->scale_by_axis(*mul_B, 1);

if (conv_inputs.size() == 3) {
conv_B->mul(*mul_B);
if (mul_B_tensor_proto->dims_size() != 0) {
conv_B->mul(*mul_B);
} else {
conv_B->scale_by_axis(*mul_B, 0);
}
}

// Create new initializers of conv
Expand Down
91 changes: 62 additions & 29 deletions onnxruntime/core/graph/initializer.h
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Original file line number Diff line number Diff line change
Expand Up @@ -190,19 +190,22 @@ class Initializer final {
return dims_;
}

size_t size() const { return size_; }
int64_t size() const { return size_; }

Initializer& add(float value) {
int64_t n = size();
switch (data_type_) {
case ONNX_NAMESPACE::TensorProto_DataType_FLOAT: {
for (int i = 0; i < size_; i++) {
data<float>()[i] += value;
float* dst = data<float>();
for (int i = 0; i < n; i++) {
dst[i] += value;
}
break;
}
case ONNX_NAMESPACE::TensorProto_DataType_DOUBLE: {
for (int i = 0; i < size_; i++) {
data<double>()[i] += value;
double* dst = data<double>();
for (int i = 0; i < n; i++) {
dst[i] += value;
}
break;
}
Expand All @@ -213,16 +216,21 @@ class Initializer final {
}

Initializer& add(const Initializer& other) {
int64_t n = size();
switch (data_type_) {
case ONNX_NAMESPACE::TensorProto_DataType_FLOAT: {
for (int i = 0; i < size_; i++) {
data<float>()[i] += other.data<float>()[i];
float* dst = data<float>();
const float* src = other.data<float>();
for (int i = 0; i < n; i++) {
dst[i] += src[i];
}
break;
}
case ONNX_NAMESPACE::TensorProto_DataType_DOUBLE: {
for (int i = 0; i < size_; i++) {
data<double>()[i] += other.data<double>()[i];
double* dst = data<double>();
const double* src = other.data<double>();
for (int i = 0; i < n; i++) {
dst[i] += src[i];
}
break;
}
Expand All @@ -232,16 +240,21 @@ class Initializer final {
return *this;
}
Initializer& sub(const Initializer& other) {
int64_t n = size();
switch (data_type_) {
case ONNX_NAMESPACE::TensorProto_DataType_FLOAT: {
for (int i = 0; i < size_; i++) {
data<float>()[i] -= other.data<float>()[i];
float* dst = data<float>();
const float* src = other.data<float>();
for (int i = 0; i < n; i++) {
dst[i] -= src[i];
}
break;
}
case ONNX_NAMESPACE::TensorProto_DataType_DOUBLE: {
for (int i = 0; i < size_; i++) {
data<double>()[i] -= other.data<double>()[i];
double* dst = data<double>();
const double* src = other.data<double>();
for (int i = 0; i < n; i++) {
dst[i] -= src[i];
}
break;
}
Expand All @@ -252,16 +265,21 @@ class Initializer final {
}

Initializer& mul(const Initializer& other) {
int64_t n = size();
switch (data_type_) {
case ONNX_NAMESPACE::TensorProto_DataType_FLOAT: {
for (int i = 0; i < size_; i++) {
data<float>()[i] *= other.data<float>()[i];
float* dst = data<float>();
const float* src = other.data<float>();
for (int i = 0; i < n; i++) {
dst[i] *= src[i];
}
break;
}
case ONNX_NAMESPACE::TensorProto_DataType_DOUBLE: {
for (int i = 0; i < size_; i++) {
data<double>()[i] *= other.data<double>()[i];
double* dst = data<double>();
const double* src = other.data<double>();
for (int i = 0; i < n; i++) {
dst[i] *= src[i];
}
break;
}
Expand All @@ -271,16 +289,21 @@ class Initializer final {
return *this;
}
Initializer& div(const Initializer& other) {
int64_t n = size();
switch (data_type_) {
case ONNX_NAMESPACE::TensorProto_DataType_FLOAT: {
for (int i = 0; i < size_; i++) {
data<float>()[i] /= other.data<float>()[i];
float* dst = data<float>();
const float* src = other.data<float>();
for (int i = 0; i < n; i++) {
dst[i] /= src[i];
}
break;
}
case ONNX_NAMESPACE::TensorProto_DataType_DOUBLE: {
for (int i = 0; i < size_; i++) {
data<double>()[i] /= other.data<double>()[i];
double* dst = data<double>();
const double* src = other.data<double>();
for (int i = 0; i < n; i++) {
dst[i] /= src[i];
}
break;
}
Expand All @@ -291,16 +314,19 @@ class Initializer final {
}

Initializer& sqrt() {
int64_t n = size();
switch (data_type_) {
case ONNX_NAMESPACE::TensorProto_DataType_FLOAT: {
for (int i = 0; i < size_; i++) {
data<float>()[i] = std::sqrt(data<float>()[i]);
float* dst = data<float>();
for (int i = 0; i < n; i++) {
dst[i] = std::sqrt(dst[i]);
}
break;
}
case ONNX_NAMESPACE::TensorProto_DataType_DOUBLE: {
for (int i = 0; i < size_; i++) {
data<double>()[i] = std::sqrt(data<double>()[i]);
double* dst = data<double>();
for (int i = 0; i < n; i++) {
dst[i] = std::sqrt(dst[i]);
}
break;
}
Expand All @@ -316,19 +342,26 @@ class Initializer final {
num *= dims_[k];
}

int64_t n = size()/num;
switch (data_type_) {
case ONNX_NAMESPACE::TensorProto_DataType_FLOAT: {
for (int64_t i = 0; i < dims_[0]; i++) {
float* dst = data<float>();
const float* src = other.data<float>();
for (int i = 0; i < n; i++) {
int index = other.size() == 1 ? 0 : i;
for (int64_t j = 0; j < num; j++) {
data<float>()[i * num + j] *= other.data<float>()[i];
dst[i * num + j] *= src[index];
}
}
break;
}
case ONNX_NAMESPACE::TensorProto_DataType_DOUBLE: {
for (int64_t i = 0; i < dims_[0]; i++) {
double* dst = data<double>();
const double* src = other.data<double>();
for (int i = 0; i < n; i++) {
int index = other.size() == 1 ? 0 : i;
for (int64_t j = 0; j < num; j++) {
data<double>()[i * num + j] *= other.data<double>()[i];
dst[i * num + j] *= src[index];
}
}
break;
Expand Down
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