Caffe

Network Configuration

TensorOp

TensorOp represents a BMNET IR, which is a bridge between front end and back end. it provides lots of member method to set information to or get from it. Below is the prototype:

namespace bmnet { 
class TensorOp {
public:
  int input_shape_size();
  int output_shape_size();
  const TensorShape& input_shape(int index);
  const TensorShape& output_shape(int index);
  TensorShape* add_output_shape();
  u64 global_input(int index);
  u64 global_output(int index);
  TGCustomizedParameter* mutable_tg_customized_param(); 
  const TGCustomizedParameter& tg_customized_param();
};
}

TensorOp::input_shape_size

void TensorOp::input_shape_size()

Return the number of inputs.

TensorOp::output_shape_size

void TensorOp::output_shape_size()

Return the number of outputs.

TensorOp::input_shape

const TensorShape& TensorOp::input_shape( int index)

Return shape of input by index.

Parameter	Type	Description
index	int	[Required] index of input that to be returned.

TensorOp::output_shape

const TensorShape& TensorOp::output_shape(int index)

Return shape of output by index.


Parameter	Type	Description
index	int	[Required] index of output that to be returned.

TensorOp::add_output_shape

TensorShape* TensorOp::add_output_shape()

Return a mutable pointer to a new added TensorShape of outputs. The returned TensorShape could be modified latter.

TensorOp::global_input

u64 TensorOp::global_input(int index) 

Return offset of input tensor by index, while it was stored in device memory.

Parameter	Type	Description
index	int	[Required] index of input that to be returned.

TensorOp::global_output

u64 TensorOp::global_output(int index) 

Return offset of output tensor by index, while it was stored in device memory.

Parameter	Type	Description
index	int	[Required] index of output that to be returned.

TensorOp::mutable_tg_customized_param

TGCustomizedParameter* TensorOp::mutable_tg_customized_param() 

Return a mutable pointer to parameters of customized BMNET IR.

TensorOp::tg_customized_param

const TGCustomizedParameter& TensorOp::tg_customized_param() 

Return reference of customized BMNET IR’s paramters.

CustomizedCaffeLayer

CustomizedCaffeLayer is abstract class, which is used to implement a Layer to convert CAFFE Layer into BMNet IR(please refer to Chapter 5 for details about BMNet IR). If you want to introduce a customized CAFFE layer into BMNet, please inherit this class and implement all pure virtual functions of it. The CustomizedCaffeLayer inherits from CaffeLayer/Layer class. Below are the prototypes of them:

namespace bmnet {
class Layer {
 public:
   Layer();
   virtual  ~Layer(void);
   virtual  std::string layer_name() = 0;
   virtual  void dump () = 0;
   virtual  void codegen(TensorOp *op) = 0;

protected:
   void add_output_offset(int offset);
};
} 

namespace bmnet {

class CaffeLayer : public Layer { 
public:
   CaffeLayer(){}
   virtual ~CaffeLayer(void); 
protected:
   caffe::LayerParameter &layer_; 
};
} 

namespace bmnet {

class CustomizedCaffeLayer : public CaffeLayer { 
public:
   CustomizedCaffeLayer(); 
   ~CustomizedCaffeLayer();
   void setup(TensorOp* op) override {
 ... 
 ...
     TGCustomizedParameter* param = op->mutable_tg_customized_param ();
     param->set_sub_type(layer_name()); 
    }
};
}

CustomizedCaffeLayer::layer_name

std::string CustomizedCaffelayer::layer_name() 

Pure virtual function, return type of new added CAFFE layer.

CustomizedCaffeLayer::dump

Pure virtual function, is used to print information of CAFFE Layer.

CustomizedCaffeLayer:: setup

void CustomizedCaffelayer::setup() 

Option. It is used to set sub type of Customized Layer only. Implement by default. If child class will override it, this parent class setup function must be call first.

CustomizedCaffeLayer::codegen

Pure virtual function, is used to setup BMNET IR according to LayerParameter of CAFFE Layer. In this function, you should setup output shape and fill parameters to TensorOp.

TBD

Parameter	Type	Description
op	TensorOp*	[Required] pointer to a instance of BMNET IR

CustomizedCaffeLayer::add_output_offset

void CustomizedCaffelayer::add_output_offset (int offset)

Protected member method, should be called when setup output offset of Layer’s top.

Parameter	Type	Description
offset	int	[Required] offset of output, should be 0.

CustomizedCaffeLayer::layer_

caffe::LayerParameter CustomizedCaffelayer::&layer_ 

Protected member variable, which is reference of customized CAFFE layer’s LayerParameter.

CustomizedTensorFixedInst

CustomizedTensorFixedInst is abstract class, which is used to implement a Layer to convert BMNET IR into instructions by BMKernel APIs. Please inherit this class and implement all pure virtual functions of it. The CustomizedTensorFixedInst inherits from TensorFixedInst/ TensorInst class. Below are the prototypes of them:

namespace bmnet {
class TensorFixedInst: public TensorInst { 
public:
  TensorFixedInst() : TensorInst() {} 
  TensorFixedInst(TensorOp &op) : TensorInst(op) {} 
  virtual ~ TensorFixedInst (void);
  void SetCalibrationParameter(
     const LayerCalibrationParameter &calibration_parameter) { 
	m_calibrationParameter = calibration_parameter;
}
  void AddInputCalibrationParameter(
     const LayerCalibrationParameter &calibration_parameter){  
	m_inputCalibrationParameter.push_back(calibration_parameter);
} 
protected:
  LayerCalibrationParameter m_calibrationParameter; 
  std::vector <LayerCalibrationParameter >
     m_inputCalibrationParameter;
};
}

namespace bmnet {
class TensorInst {
public:
  TensorInst();
  virtual ~TensorInst(void);
  virtual std::string inst_name() = 0;
  virtual void dump () = 0;
  virtual void encode () = 0;

protected:
  TensorOp &op_; 
};
}

namespace bmnet {

class CustomizedTensorFixedInst : public TensorFixedInst { 
public:
  CustomizedTensorFixedInst ();
  ~CustomizedTensorFixedInst (); 
protected:
  u64 get_global_neuron_base();
  u64 get_global_weight_base();
}; 
}

CustomizedTensorFixedInst::inst_name

std::string CustomizedTensorFixedInst::inst_name() 

Pure virtual function, return type of customized BMNET IR.

CustomizedTensorFixedInst::dump

void CustomizedTensorFixedInst::dump() 

Pure virtual function, is used to print information of BMNET IR.

CustomizedTensorFixedInst::encode

void CustomizedTensorFixedInst::encode()

Pure virtual function, is used to convert BMNET IR into instructions using BMKernel APIs.

CustomizedTensorFixedInst::get_global_neuron_base

u64 CustomizedTensorFixedInst::get_global_neuron_base() 

Protected member method, return the base address, where the neurons are stored in device memory.

CustomizedTensorFixedInst::get_global_weight_base

u64 CustomizedTensorFixedInst::get_global_weight_base() 

Protected member method, return the base address, where weight is stored in device memory.

CustomizedTensorFixedInst::op_

TensorOp CustomizedTensorFixedInst::&op_ 

Protected member variable, which is reference of BMNET IR.

TGCustomizedParamter

TGCustomizedParamter represents a customized BMNET IR’s parameters. It provides member methods to set parameters to or get from it. Below is the prototype:

namespace bmnet {

class TGCustomizedParameter {
public:
  int i32_param_size();
  int f32_param_size();
  int i32_param(int index);
  float f32_param(int index);
  void add_i32_param(int value);
  void add_f32_param(float value);
};
}

TGCustomizedParamter::i32_param_size

void TGCustomizedParamter::i32_param_size() 

Return the number of int parameters, which stored in TGCustomizedParamter.

TGCustomizedParamter::f32_param_size

void TGCustomizedParamter::f32_param_size() 

Return the number of float parameters, which stored in TGCustomizedParamter.

TGCustomizeParamter::i32_param

int TGCustomizedParamter::i32_param(int index) 

Return int parameter by index.

Parameter	Type	Description
index	index	[Required] index of int parameter that to be returned.

TGCustomizeParamter::f32_param

float TGCustomizedParamter::f32_param( int index) 	

Return int parameter by index.

Parameter	Type	Description
index	index	[Required] index of float parameter that to be returned.

TGCustomizeParamter::add_i32_param

void TGCustomizedParamter::add_i32_param(int value)

Append a new int parameter to TGCustomizedParamter.

Parameter	Type	Description
value	int	[Required] int parameter.

TGCustomizeParamter::add_f32_param

void TGCustomizedParamter::add_f32_param(int value)

Append a new int parameter to TGCustomizedParamter.

Parameter	Type	Description
value	float	[Required] float parameter.

TensorShape

TensorShape represents a shape of tensor. Below is the prototype:

namespace bmnet {

class TensorShape { 
public:
  void CopyFrom(const TensorShape& from); 
  int dim_size() const;
  int dim(int index);
  void add_dim(int value);
};
}

TensorShape::dim_size

int TensorShape::dim_size()

Return the number of dims.

TensorShape::dim

int TensorShape::dim(int index) 

Return one dim by index.

Parameter	Type	Description
Index	int	[Required] index of dim that to be returned.

TensorShape::add_dim

void TensorShape::add_dim(int value)

Append a dim to TensorShape.

Parameter	Type	Description
value	int	[Required] new dim to be appended.

TensorShape::CopyFrom

void TensorShape::CopyFrom(const TensorShape& from)

Copy from another TensorShape instance.

Parameter	Type	Description
value	const TensorShape&	[Required] source TensorShape instance.

CaffeBuilder

CaffeBuilder is a class, which provides a uniform interface to combine front end/optimizer/back end core code into one, to compile CAFFE neuron network graph into bmodel file. The CaffeBuilder inherits from Builder class, which is a base compiler class. Below are the prototypes of them:

namespace bmnet { 

class Builder {
public:
  Builder(CHIP_VER ver);
  virtual ~Builder();
  void addCustomizedTensorInst(TensorInst *inst);
  void build(int n, int c, int h, int w, int opt);
  void store_prototxt(const char *dst);
  void store_model(const char *net_name, const char *dst);
};
}

namespace bmnet { 

class CaffeBuilder : public Builder { 
public:
  CaffeBuilder(CHIP_VER ver, const char *modified_proto , const char *caffemodel , const char *weight_bin , const char *in_ctable , const char *out_ctable);
  ~CaffeBuilder();
  void addCustomizedLayer(Layer *layer);
};

CaffeBuilder::CaffeBuilder

CaffeBuilder::CaffeBuilder(
CHIP_VER ver ,
const char *modified_proto ,
const char *caffemodel ,
const char *weight_bin ,
const char *in_ctable ,
const char *out_ctable)

Constructor function of CaffeBuilder class.

Parameter	Type	Description
ver	CHIP_VER	[Required] The target chip version. Currently only BM_CHIP_BM1880 is available.
modified_proto	const char*	[Optional] The modified prototxt file, please refer Chapter 4 to get more detail.
caffemodel	const char*	[Required] The specified caffemode file of network
weight_bin	const char*	[Optional] The specified weight file of network
in_ctable	const char*	[Required] The specified calibration table file of network
out_ctable	const char*	[Required] The specified weight file of network

modified_proto are optional parameters, that means you no need to fill all of this parameters. Below combination are valid: 1) caffemodel only; 2) caffemodel, as well as modified_protos

CaffeBuilder::Builder

Core member function of CaffeBuilder class, used to compile the network by specifying input shape and optimization level.

Parameter	Type	Description
n,c,h,w	int	[Required] The input shape
opt	int	[Optional] The input optimization options. The default value is BM_OPT_LAYER_GROUP_WITH_WEIG

Below are the values for opt.

value	Description
OPT_NONE	No optimization
BM_OPT_LAYER_GROUP	Divides layers into clusters to optimize the bandwidth overhead.
BM_OPT_LAYER_GROUP_WITH_WEIG	Add additional optimization to reduce the device memory footprint and reshape weight.

CaffeBuilder::store_prototxt

store the optimized network graph as a file.

Parameter	Type	Description
dst	const char*	[Required] File to be stored

CaffeBuilder::store_model

void CaffeBuilder::store_model(
const char* net_name ,
const char* dst,
onst char* plugin_path=nullptr)

Store compiled instructions, weight and other information of the network as a bmodel file.

Parameter	Type	Description
net_name	const char*	[Required] the network name.
dst	const char*	[Required] File to store bmodel.
Plugin_path	const char*	[Required] cpu op plugins.

CaffeBuilder::addCustomizedLayer

void CaffeBuilder::addCustomizedLayer( Layer* layer)

Register a new added customized layer, which used to convert CAFFE layer into BMNet IR (Intermediate representation).

Parameter	Type	Description
Layer	Layer*	[Required] pointer to instance of Class Layer

CaffeBuilder::addCustomizedTensorInst

void CaffeBuilder::addCustomizedTensorInst(TensorInst* inst)

Register a new added customized TensorInst (Tensor Instruction), which used to convert BMNet IR into instructions.

Parameter	Type	Description
inst	TensorInst*	[Required] pointer to instance of Class Layer