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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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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
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81 changes: 80 additions & 1 deletion cinn/frontend/paddle_model_to_program.cc
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Expand Up @@ -128,6 +128,25 @@ void PaddleModelToProgram::AddOpMapper_elementwise_add() {
};
}

void PaddleModelToProgram::AddOpMapper_elementwise_mul() {
op_mappers_["elementwise_mul"] = [&](const paddle::cpp::OpDesc& op_desc) {
CHECK(!op_desc.Input("X").empty());
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是不是 CHECK_EQ(op_desc.Input("X").size(), 1UL) 会更精确
一来，check 非空，二来，精确表明当size > 1的时候不支持会报错

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done，thanks~

auto x_name = op_desc.Input("X").front();
CHECK(!op_desc.Input("Y").empty());
auto y_name = op_desc.Input("Y").front();
CHECK(!op_desc.Output("Out").empty());
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());
Expand All @@ -141,7 +160,7 @@ 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;
Expand Down Expand Up @@ -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 Down Expand Up @@ -267,6 +289,63 @@ void PaddleModelToProgram::AddOpMapper_batchnorm() {
};
}

void PaddleModelToProgram::AddOpMapper_sigmoid() {
op_mappers_["sigmoid"] = [&](const paddle::cpp::OpDesc& op_desc) {
CHECK(!op_desc.Input("X").empty());
auto x_name = op_desc.Input("X").front();
CHECK(!op_desc.Output("Out").empty());
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(!op_desc.Input("Input").empty());
auto x_name = op_desc.Input("Input").front();
CHECK(!op_desc.Output("Out").empty());
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(!op_desc.Input("X").empty());
auto x_name = op_desc.Input("X").front();
CHECK(!op_desc.Output("Out").empty());
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);
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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);
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16 changes: 14 additions & 2 deletions cinn/frontend/syntax.h
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Expand Up @@ -174,7 +174,12 @@ struct Program {
/**
* Add two tensors element-wise.
*/
Variable elementwise_add(const Variable& a, const Variable& b, int axis = 0);
Variable elementwise_add(const Variable& a, const Variable& b, int axis = -1);

/**
* Multiply two tensors element-wise.
*/
Variable elementwise_mul(const Variable& a, const Variable& b, int axis = -1);

/**
* Apply Rectified Linear Unit on input Variable.
Expand All @@ -184,7 +189,7 @@ struct Program {
* @return The result.
*/
Variable relu(const Variable& a);
Variable relu6(const Variable& a, const std::unordered_map<std::string, attr_t>& attr_store);
Variable relu6(const Variable& a);

/**
* The convolution2D layer calculates the output based on the input, filter
Expand Down Expand Up @@ -220,6 +225,13 @@ struct Program {
Variable scale(const Variable& a, const std::unordered_map<std::string, attr_t>& attr_store);

Variable softmax(const Variable& a, const std::unordered_map<std::string, attr_t>& attr_store);

Variable sigmoid(const Variable& a);

Variable slice(const Variable& a, const std::unordered_map<std::string, attr_t>& attr_store);

Variable dropout_infer(const Variable& a, const std::unordered_map<std::string, attr_t>& attr_store);

/**
* Get \p i-th instruction.
*/
Expand Down
1 change: 1 addition & 0 deletions cinn/hlir/op/broadcast.cc
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Expand Up @@ -83,6 +83,7 @@ std::shared_ptr<OpStrategy> StrategyForElementwiseMul(const framework::NodeAttr
if (iter != attr_store.end()) {
axis = Expr(std::get<int>(iter->second));
}
LOG(INFO) << "StrategyForElementwiseMul" << A->shape << " " << B->shape;

auto out = pe::Multiply(A, B, UniqName("C"), axis);

Expand Down
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