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add dropout_infer op, PE and tests and EfficientNet model #242

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Sep 29, 2020
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add dropout_infer op, PE and tests and EfficientNet model #242

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de23d1e
add dropout_infer op, PE and tests and Efficient model
wenming2014 Sep 28, 2020
22c7b3b
fix code styles
wenming2014 Sep 29, 2020
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2 changes: 2 additions & 0 deletions build.sh
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Original file line number Diff line number Diff line change
Expand Up @@ -70,6 +70,8 @@ function prepare_model {
tar -xvf ResNet18.tar
wget https://paddle-inference-dist.bj.bcebos.com/CINN/MobileNetV2.tar
tar -xvf MobileNetV2.tar
wget https://paddle-inference-dist.bj.bcebos.com/CINN/EfficientNet.tar
tar -xvf EfficientNet.tar
python $workspace/python/tests/fake_model/naive_mul.py
python $workspace/python/tests/fake_model/naive_multi_fc.py
python $workspace/python/tests/fake_model/resnet_model.py
Expand Down
141 changes: 110 additions & 31 deletions cinn/frontend/paddle_model_to_program.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -21,23 +21,23 @@ void PaddleModelToProgram::AddOpMapper_feed() {

void PaddleModelToProgram::AddOpMapper_fetch() {
op_mappers_["fetch"] = [&](const paddle::cpp::OpDesc& op_desc) {
CHECK(!op_desc.Input("X").empty());
CHECK_EQ(op_desc.Input("X").size(), 1UL);
auto output_name = op_desc.Input("X").front();
LOG(INFO) << "detect model output: [" << output_name << "]";
};
}

void PaddleModelToProgram::AddOpMapper_scale() {
op_mappers_["scale"] = [&](const paddle::cpp::OpDesc& op_desc) {
CHECK(!op_desc.Input("X").empty());
CHECK_EQ(op_desc.Input("X").size(), 1UL);
auto x_name = op_desc.Input("X").front();
auto x = GetVar(utils::TransValidVarName(x_name));
float scale{};
if (op_desc.HasAttr("scale")) { // the old model format
scale = op_desc.GetAttr<float>("scale");
} else { // the newly refactored format
// load scale tensor
CHECK(!op_desc.Input("ScaleTensor").empty());
CHECK_EQ(op_desc.Input("ScaleTensor").size(), 1UL);
auto* scale_tensor_var = scope_->FindVar(op_desc.Input("ScaleTensor").front());
CHECK(scale_tensor_var) << "No scale tensor found in the scope";
auto& scale_tensor = std::get<hlir::framework::Tensor>(*scale_tensor_var);
Expand All @@ -46,7 +46,7 @@ void PaddleModelToProgram::AddOpMapper_scale() {
std::unordered_map<std::string, hlir::framework::NodeAttr::attr_t> attrs;
attrs["scale"] = scale;
auto out = program_->scale(x, attrs);
CHECK(!op_desc.Output("Out").empty());
CHECK_EQ(op_desc.Output("Out").size(), 1UL);
auto out_name = op_desc.Output("Out").front();
AddVar(utils::TransValidVarName(out_name), out);
var_model_to_program_map_[out_name] = out->id;
Expand All @@ -55,9 +55,9 @@ void PaddleModelToProgram::AddOpMapper_scale() {

void PaddleModelToProgram::AddOpMapper_mul() {
op_mappers_["mul"] = [&](const paddle::cpp::OpDesc& op_desc) {
CHECK(!op_desc.Input("X").empty());
CHECK_EQ(op_desc.Input("X").size(), 1UL);
auto x_name = op_desc.Input("X").front();
CHECK(!op_desc.Input("Y").empty());
CHECK_EQ(op_desc.Input("Y").size(), 1UL);
auto y_name = op_desc.Input("Y").front();
auto x = GetVar(utils::TransValidVarName(x_name));
auto y = GetVar(utils::TransValidVarName(y_name));
Expand All @@ -68,7 +68,7 @@ void PaddleModelToProgram::AddOpMapper_mul() {
VLOG(4) << "x shape: " << utils::Join(x->shape, ",");
VLOG(4) << "y shape: " << utils::Join(y->shape, ",");
auto out = program_->mul(x, y, x_num_col_dims, y_num_col_dims);
CHECK(!op_desc.Output("Out").empty());
CHECK_EQ(op_desc.Output("Out").size(), 1UL);
auto out_name = op_desc.Output("Out").front();
AddVar(utils::TransValidVarName(out_name), out);
var_model_to_program_map_[out_name] = out->id;
Expand All @@ -77,9 +77,9 @@ void PaddleModelToProgram::AddOpMapper_mul() {

void PaddleModelToProgram::AddOpMapper_relu() {
op_mappers_["relu"] = [&](const paddle::cpp::OpDesc& op_desc) {
CHECK(!op_desc.Input("X").empty());
CHECK_EQ(op_desc.Input("X").size(), 1UL);
auto x_name = op_desc.Input("X").front();
CHECK(!op_desc.Output("Out").empty());
CHECK_EQ(op_desc.Output("Out").size(), 1UL);
auto out_name = op_desc.Output("Out").front();
auto x = GetVar(TransValidVarName(x_name));
auto out = program_->relu(x);
Expand All @@ -91,16 +91,16 @@ void PaddleModelToProgram::AddOpMapper_relu() {

void PaddleModelToProgram::AddOpMapper_softmax() {
op_mappers_["softmax"] = [&](const paddle::cpp::OpDesc& op_desc) {
CHECK(!op_desc.Input("X").empty());
CHECK_EQ(op_desc.Input("X").size(), 1UL);
auto x_name = op_desc.Input("X").front();
CHECK(!op_desc.Output("Out").empty());
CHECK_EQ(op_desc.Output("Out").size(), 1UL);
auto out_name = op_desc.Output("Out").front();

std::unordered_map<std::string, hlir::framework::NodeAttr::attr_t> attrs;
if (op_desc.HasAttr("axis")) {
attrs["axis"] = op_desc.GetAttr<int>("axis");
} else {
attrs["axis"] = int(-1);
attrs["axis"] = static_cast<int>(-1);
}
auto x = GetVar(TransValidVarName(x_name));
auto out = program_->softmax(x, attrs);
Expand All @@ -111,11 +111,11 @@ void PaddleModelToProgram::AddOpMapper_softmax() {

void PaddleModelToProgram::AddOpMapper_elementwise_add() {
op_mappers_["elementwise_add"] = [&](const paddle::cpp::OpDesc& op_desc) {
CHECK(!op_desc.Input("X").empty());
CHECK_EQ(op_desc.Input("X").size(), 1UL);
auto x_name = op_desc.Input("X").front();
CHECK(!op_desc.Input("Y").empty());
CHECK_EQ(op_desc.Input("Y").size(), 1UL);
auto y_name = op_desc.Input("Y").front();
CHECK(!op_desc.Output("Out").empty());
CHECK_EQ(op_desc.Output("Out").size(), 1UL);
auto out_name = op_desc.Output("Out").front();
int axis = op_desc.GetAttr<int>("axis");

Expand All @@ -128,11 +128,30 @@ void PaddleModelToProgram::AddOpMapper_elementwise_add() {
};
}

void PaddleModelToProgram::AddOpMapper_elementwise_mul() {
op_mappers_["elementwise_mul"] = [&](const paddle::cpp::OpDesc& op_desc) {
CHECK_EQ(op_desc.Input("X").size(), 1UL);
auto x_name = op_desc.Input("X").front();
CHECK_EQ(op_desc.Input("Y").size(), 1UL);
auto y_name = op_desc.Input("Y").front();
CHECK_EQ(op_desc.Output("Out").size(), 1UL);
auto out_name = op_desc.Output("Out").front();
int axis = op_desc.GetAttr<int>("axis");

auto x = GetVar(TransValidVarName(x_name));
auto y = GetVar(TransValidVarName(y_name));
auto out = program_->elementwise_mul(x, y, axis);

AddVar(TransValidVarName(out_name), out);
var_model_to_program_map_[out_name] = out->id;
};
}

void PaddleModelToProgram::AddOpMapper_relu6() {
op_mappers_["relu6"] = [&](const paddle::cpp::OpDesc& op_desc) {
CHECK(!op_desc.Input("X").empty());
CHECK_EQ(op_desc.Input("X").size(), 1UL);
auto x_name = op_desc.Input("X").front();
CHECK(!op_desc.Output("Out").empty());
CHECK_EQ(op_desc.Output("Out").size(), 1UL);
auto out_name = op_desc.Output("Out").front();

std::unordered_map<std::string, hlir::framework::NodeAttr::attr_t> attrs;
Expand All @@ -141,19 +160,19 @@ void PaddleModelToProgram::AddOpMapper_relu6() {
attrs["threshold"] = op_desc.GetAttr<float>("threshold");

auto x = GetVar(TransValidVarName(x_name));
auto out = program_->relu6(x, attrs);
auto out = program_->relu6(x);

AddVar(TransValidVarName(out_name), out);
var_model_to_program_map_[out_name] = out->id;
};
}
void PaddleModelToProgram::AddOpMapper_depthwise_conv2d() {
op_mappers_["depthwise_conv2d"] = [&](const paddle::cpp::OpDesc& op_desc) {
CHECK(!op_desc.Input("Input").empty());
CHECK_EQ(op_desc.Input("Input").size(), 1UL);
auto x_name = op_desc.Input("Input").front();
CHECK(!op_desc.Input("Filter").empty());
CHECK_EQ(op_desc.Input("Filter").size(), 1UL);
auto y_name = op_desc.Input("Filter").front();
CHECK(!op_desc.Output("Output").empty());
CHECK_EQ(op_desc.Output("Output").size(), 1UL);
auto out_name = op_desc.Output("Output").front();

std::unordered_map<std::string, hlir::framework::NodeAttr::attr_t> attrs;
Expand All @@ -176,11 +195,11 @@ void PaddleModelToProgram::AddOpMapper_depthwise_conv2d() {

void PaddleModelToProgram::AddOpMapper_conv2d() {
op_mappers_["conv2d"] = [&](const paddle::cpp::OpDesc& op_desc) {
CHECK(!op_desc.Input("Input").empty());
CHECK_EQ(op_desc.Input("Input").size(), 1UL);
auto x_name = op_desc.Input("Input").front();
CHECK(!op_desc.Input("Filter").empty());
CHECK_EQ(op_desc.Input("Filter").size(), 1UL);
auto y_name = op_desc.Input("Filter").front();
CHECK(!op_desc.Output("Output").empty());
CHECK_EQ(op_desc.Output("Output").size(), 1UL);
auto out_name = op_desc.Output("Output").front();

std::unordered_map<std::string, hlir::framework::NodeAttr::attr_t> attrs;
Expand All @@ -203,9 +222,9 @@ void PaddleModelToProgram::AddOpMapper_conv2d() {

void PaddleModelToProgram::AddOpMapper_pool2d() {
op_mappers_["pool2d"] = [&](const paddle::cpp::OpDesc& op_desc) {
CHECK(!op_desc.Input("X").empty());
CHECK_EQ(op_desc.Input("X").size(), 1UL);
auto x_name = op_desc.Input("X").front();
CHECK(!op_desc.Output("Out").empty());
CHECK_EQ(op_desc.Output("Out").size(), 1UL);
auto out_name = op_desc.Output("Out").front();

std::unordered_map<std::string, hlir::framework::NodeAttr::attr_t> attrs;
Expand All @@ -217,6 +236,7 @@ void PaddleModelToProgram::AddOpMapper_pool2d() {
attrs["stride_size"] = op_desc.GetAttr<std::vector<int>>("strides");
CHECK(op_desc.HasAttr("paddings"));
auto padding_size = op_desc.GetAttr<std::vector<int>>("paddings");

if (padding_size.size() == 2) {
padding_size.insert(padding_size.begin(), padding_size.front());
padding_size.push_back(padding_size.back());
Expand All @@ -228,6 +248,8 @@ void PaddleModelToProgram::AddOpMapper_pool2d() {
attrs["exclusive"] = op_desc.GetAttr<bool>("exclusive");
CHECK(op_desc.HasAttr("data_format"));
attrs["data_format"] = op_desc.GetAttr<std::string>("data_format");
CHECK(op_desc.HasAttr("global_pooling"));
attrs["global_pooling"] = op_desc.GetAttr<bool>("global_pooling");

auto x = GetVar(TransValidVarName(x_name));
auto out = program_->pool2d(x, attrs);
Expand All @@ -239,15 +261,15 @@ void PaddleModelToProgram::AddOpMapper_pool2d() {

void PaddleModelToProgram::AddOpMapper_batchnorm() {
op_mappers_["batch_norm"] = [&](const paddle::cpp::OpDesc& op_desc) {
CHECK(!op_desc.Input("X").empty());
CHECK_EQ(op_desc.Input("X").size(), 1UL);
auto x_name = op_desc.Input("X").front();
CHECK(!op_desc.Input("Scale").empty());
CHECK_EQ(op_desc.Input("Scale").size(), 1UL);
auto scale_name = op_desc.Input("Scale").front();
CHECK(!op_desc.Input("Bias").empty());
CHECK_EQ(op_desc.Input("Bias").size(), 1UL);
auto bias_name = op_desc.Input("Bias").front();
CHECK(!op_desc.Input("Mean").empty());
CHECK_EQ(op_desc.Input("Mean").size(), 1UL);
auto mean_name = op_desc.Input("Mean").front();
CHECK(!op_desc.Input("Variance").empty());
CHECK_EQ(op_desc.Input("Variance").size(), 1UL);
auto variance_name = op_desc.Input("Variance").front();
CHECK(!op_desc.Output("Y").empty());
auto out_name = op_desc.Output("Y").front();
Expand All @@ -267,6 +289,63 @@ void PaddleModelToProgram::AddOpMapper_batchnorm() {
};
}

void PaddleModelToProgram::AddOpMapper_sigmoid() {
op_mappers_["sigmoid"] = [&](const paddle::cpp::OpDesc& op_desc) {
CHECK_EQ(op_desc.Input("X").size(), 1UL);
auto x_name = op_desc.Input("X").front();
CHECK_EQ(op_desc.Output("Out").size(), 1UL);
auto out_name = op_desc.Output("Out").front();

auto x = GetVar(TransValidVarName(x_name));
auto out = program_->sigmoid(x);

AddVar(TransValidVarName(out_name), out);
var_model_to_program_map_[out_name] = out->id;
};
}

void PaddleModelToProgram::AddOpMapper_slice() {
op_mappers_["slice"] = [&](const paddle::cpp::OpDesc& op_desc) {
CHECK_EQ(op_desc.Input("Input").size(), 1UL);
auto x_name = op_desc.Input("Input").front();
CHECK_EQ(op_desc.Output("Out").size(), 1UL);
auto out_name = op_desc.Output("Out").front();

std::unordered_map<std::string, hlir::framework::NodeAttr::attr_t> attrs;
CHECK(op_desc.HasAttr("starts"));
attrs["starts"] = op_desc.GetAttr<std::vector<int>>("starts");
CHECK(op_desc.HasAttr("ends"));
attrs["ends"] = op_desc.GetAttr<std::vector<int>>("ends");
CHECK(op_desc.HasAttr("axes"));
attrs["axes"] = op_desc.GetAttr<std::vector<int>>("axes");
auto x = GetVar(TransValidVarName(x_name));
auto out = program_->slice(x, attrs);

AddVar(TransValidVarName(out_name), out);
var_model_to_program_map_[out_name] = out->id;
};
}

void PaddleModelToProgram::AddOpMapper_dropout_infer() {
op_mappers_["dropout"] = [&](const paddle::cpp::OpDesc& op_desc) {
CHECK_EQ(op_desc.Input("X").size(), 1UL);
auto x_name = op_desc.Input("X").front();
CHECK_EQ(op_desc.Output("Out").size(), 1UL);
auto out_name = op_desc.Output("Out").front();

std::unordered_map<std::string, hlir::framework::NodeAttr::attr_t> attrs;
CHECK(op_desc.HasAttr("dropout_prob"));
attrs["dropout_prob"] = op_desc.GetAttr<float>("dropout_prob");
CHECK(op_desc.HasAttr("dropout_implementation"));
attrs["dropout_implementation"] = op_desc.GetAttr<std::string>("dropout_implementation");
auto x = GetVar(TransValidVarName(x_name));
auto out = program_->dropout_infer(x, attrs);

AddVar(TransValidVarName(out_name), out);
var_model_to_program_map_[out_name] = out->id;
};
}

void PaddleModelToProgram::AddOp(const paddle::cpp::OpDesc& op_desc) {
const auto& op_type = op_desc.Type();
auto it = op_mappers_.find(op_type);
Expand Down
8 changes: 8 additions & 0 deletions cinn/frontend/paddle_model_to_program.h
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Expand Up @@ -32,12 +32,16 @@ class PaddleModelToProgram {
AddOpMapper_scale();
AddOpMapper_relu();
AddOpMapper_elementwise_add();
AddOpMapper_elementwise_mul();
AddOpMapper_conv2d();
AddOpMapper_batchnorm();
AddOpMapper_pool2d();
AddOpMapper_softmax();
AddOpMapper_relu6();
AddOpMapper_depthwise_conv2d();
AddOpMapper_sigmoid();
AddOpMapper_slice();
AddOpMapper_dropout_infer();
}

std::unique_ptr<Program> operator()(const std::string& model_dir, bool is_combined);
Expand All @@ -52,12 +56,16 @@ class PaddleModelToProgram {
void AddOpMapper_mul();
void AddOpMapper_relu();
void AddOpMapper_elementwise_add();
void AddOpMapper_elementwise_mul();
void AddOpMapper_conv2d();
void AddOpMapper_batchnorm();
void AddOpMapper_pool2d();
void AddOpMapper_softmax();
void AddOpMapper_relu6();
void AddOpMapper_depthwise_conv2d();
void AddOpMapper_sigmoid();
void AddOpMapper_slice();
void AddOpMapper_dropout_infer();
// @}

const std::unordered_map<std::string, Variable>& var_map() const { return var_map_; }
Expand Down
33 changes: 32 additions & 1 deletion cinn/frontend/syntax.cc
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Expand Up @@ -103,6 +103,30 @@ Variable Program::softmax(const Variable& a, const std::unordered_map<std::strin
return instr.GetOutput(1);
}

Variable Program::sigmoid(const Variable& a) {
Instruction instr("sigmoid", {a});
AppendInstruction(instr);
return instr.GetOutput(0);
}

Variable Program::slice(const Variable& a, const std::unordered_map<std::string, attr_t>& attr_store) {
Instruction instr("slice", {a});
for (auto& iter : attr_store) {
instr.SetAttr(iter.first, iter.second);
}
AppendInstruction(instr);
return instr.GetOutput(0);
}

Variable Program::dropout_infer(const Variable& a, const std::unordered_map<std::string, attr_t>& attr_store) {
Instruction instr("dropout_infer", {a});
for (auto& iter : attr_store) {
instr.SetAttr(iter.first, iter.second);
}
AppendInstruction(instr);
return instr.GetOutput(0);
}

Instruction& Program::operator[](size_t i) {
CHECK_LT(i, instrs_.size());
return instrs_[i];
Expand Down Expand Up @@ -159,13 +183,20 @@ Variable Program::elementwise_add(const Variable& a, const Variable& b, int axis
return instr.GetOutput(0);
}

Variable Program::elementwise_mul(const Variable& a, const Variable& b, int axis) {
Instruction instr("elementwise_mul", {a, b});
instr.SetAttr("axis", axis);
AppendInstruction(instr);
return instr.GetOutput(0);
}

Variable Program::relu(const Variable& a) {
Instruction instr("relu", {a});
AppendInstruction(instr);
return instr.GetOutput(0);
}

Variable Program::relu6(const Variable& a, const std::unordered_map<std::string, attr_t>& attr_store) {
Variable Program::relu6(const Variable& a) {
Instruction instr("relu6", {a});
AppendInstruction(instr);
return instr.GetOutput(0);
Expand Down
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