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review.mlplatform.org / ml / ComputeLibrary / ce3c48c7af02555f81c0f5e7ef2677916cecef34 / . / compute_kernel_writer / prototype / src / Prototype.h
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/*
* Copyright (c) 2023 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#ifndef CKW_PROTOTYPE_SRC_PROTOTYPE_H
#define CKW_PROTOTYPE_SRC_PROTOTYPE_H
#include <vector>
#include <map>
#include <string>
#include <cstdint> // int32_t
#include <iostream> // cout (to be removed)
#include <cassert> // assert (to be removed)
#include <unordered_map>
#include <chrono>
#include <cmath>
#include <memory>
#include <algorithm>
#include <array>
#include <stdexcept>
#include "ckw/Types.h"
#include "ckw/TensorInfo.h"
#include "ckw/Error.h"
namespace ckw
{
namespace prototype {
// Dummy data structure for Size2D
using Size2D = std::vector<int32_t>;
// Dummy Status
using Status = void;
enum class ComponentType : int32_t
{
Complex = 0,
Simple = 1,
Unfusable = 2
};
enum class GpuCompilationSpeed
{
Fast = 0x00, // fast compilation may increase the latency of the network
Slow = 0x01 // slow compilation may decrease the latency of the network
};
enum class GpuExtensions
{
Fp16,
Dot8,
Mmul,
FastMath
};
struct TensorInfo
{
TensorShape shape { {0} };
DataType data_type { DataType::Unknown };
TensorDataLayout data_layout { TensorDataLayout::Nhwc };
int32_t id { -1 };
};
struct ComponentAttribute
{
GpuCompilationSpeed compilation_speed {GpuCompilationSpeed::Fast};
bool overwrite_tile { true };
};
inline std::string data_type_to_cl_type(DataType dt)
{
switch(dt)
{
case DataType::Fp32:
return "float";
case DataType::Fp16:
return "half";
case DataType::Int8:
return "char";
case DataType::Uint8:
return "uchar";
case DataType::Uint16:
return "ushort";
case DataType::Int16:
return "short";
case DataType::Uint32:
return "uint";
case DataType::Int32:
return "int";
case DataType::Bool:
return "bool";
default:
assert(false);
return "";
}
}
inline int32_t width_to_cl_vector_size(int32_t width)
{
switch(width)
{
case 1:
return 1;
case 2:
return 2;
case 3:
return 3;
case 4:
return 4;
case 5:
case 6:
case 7:
case 8:
return 8;
case 9:
case 10:
case 11:
case 12:
case 13:
case 14:
case 15:
case 16:
return 16;
default:
assert(false);
return 0;
}
}
inline std::string get_cl_data_type(DataType dt, int32_t width)
{
std::string data_type;
int32_t w = width_to_cl_vector_size(width);
data_type += data_type_to_cl_type(dt);
if(w != 1)
{
data_type += std::to_string(w);
}
return data_type;
}
inline std::string to_opencl_store(int32_t vector_length)
{
if(vector_length != 1)
{
return "vstore" + std::to_string(vector_length) + "(";
}
else
{
return "*(";
}
}
struct TileInfo
{
TileInfo() {}
TileInfo(DataType dt) : dt(dt), w(1), h(1) {}
TileInfo(DataType dt, int32_t width) : dt(dt), w(width), h(1) {}
TileInfo(DataType dt, int32_t width, int32_t height) : dt(dt), w(width), h(height) {}
DataType dt{ DataType::Unknown }; // Data type of the tile
int32_t w{ 0 }; // Width (i.e. c0 - portion of the channels)
int32_t h{ 0 }; // Height (i.e. s0 - portion of the spatial dimensions)
};
inline std::ostream& operator << (std::ostream& o, const TileInfo& a)
{
o << a.w << " x " << a.h;
return o;
}
struct DataTypeAsString
{
std::string str { "" };
DataType dt { DataType::Unknown };
int32_t size { 1 };
};
struct ValueAsString
{
std::string str { "" };
DataTypeAsString type { };
};
// https://stackoverflow.com/questions/51515378/storing-and-accessing-tile-properties-in-c
// A Tile is a collection of variables used to express a 2D data.
class IScalarTile
{
public:
virtual ~IScalarTile() = default;
/** Method to get the scalar variable from a tile
* @param[in] x X coordinate on the width of the tile. If out-of-bound, the coordinate is clamped to the nearest valid edge
* @param[in] y Y coordinate on the height of the tile. If out-of-bound, the coordinate is clamped to the nearest valid edge
*
* @return the scalar variable as a string
*/
virtual ValueAsString scalar(int32_t x, int32_t y) const = 0;
/** Method to get the list of underlying variable names used by the tile
*
* @return the list of variable names
*/
virtual std::vector<ValueAsString> underlying_source_variables() const = 0;
/** Method to get the name of the tile.
*
* @return the name of the tile
*/
std::string name() const
{
return _basename;
}
/** Method to get the tile format
*
* @return the format
*/
TileInfo format() const
{
return _format;
}
/** Method to know whether the tile is assignable or not (constant)
*
* @return true if the tile is assignable
*/
virtual bool is_assignable() const = 0;
/** Method to know whether the tile needs to be declared
*
* @return true if the tile needs to be declared in the code before being used
*/
virtual bool need_declaration() const = 0;
protected:
TileInfo _format { }; // Tile format
std::string _basename { "" }; // Tile name
};
// A tile is a collection of variables used to express a 2D data. The variables are vectors in the GPU context.
// The vector size is given by the width of the tile. The number of vectors height by depth defines the number of vectors
class IVectorTile : public IScalarTile
{
public:
virtual ~IVectorTile() = default;
/** Method to get the vector variable from a tile. A vector is an ordered homogeneous collection of two or more scalars.
* The user can query the list of supported width for the vectors through preferred_vector_sizes().
*
* @param[in] y Y coordinate on the height of the tile. If out-of-bound, the coordinate is clamped to the nearest valid edge
*
* @return the vector variable as a string
*/
virtual ValueAsString vector(int32_t y) const = 0;
/** Method to get a vector variable from a tile. A vector is an ordered homogeneous collection of two or more scalars.
*
* @return the vector variable as a string
*/
virtual ValueAsString vector(int32_t x_start, int32_t width, int32_t y) const = 0;
/** Method to get the preferred vector sizes.
*
* @return a vector with the preferred vector sizes
*/
//virtual std::vector<int32_t> preferred_vector_sizes() const = 0;
};
class ClTile : public IVectorTile
{
public:
ClTile(const std::string& name, TileInfo format)
{
_format = format;
_basename = name;
}
ValueAsString scalar(int32_t x, int32_t y) const override
{
x = std::max(std::min(x, _format.w - 1), static_cast<int32_t>(0));
y = std::max(std::min(y, _format.h - 1), static_cast<int32_t>(0));
ValueAsString t;
t.str = build_variable_name(y);
t.type.str = get_cl_data_type(_format.dt, 1);
t.type.dt = _format.dt;
t.type.size = 1;
// Check required because if the width has only one element, we cannot use .s0
if(_format.w != 1)
{
// Automatic broadcasting
t.str += ".s" + std::to_string(x);
}
return t;
}
ValueAsString vector(int32_t y) const override
{
y = std::max(std::min(y, _format.h - 1), static_cast<int32_t>(0));
ValueAsString t;
t.str = build_variable_name(y);
t.type.str = get_cl_data_type(_format.dt, _format.w);
t.type.dt = _format.dt;
t.type.size = _format.w;
return t;
}
ValueAsString vector(int32_t x_start, int32_t width, int32_t y) const override
{
y = std::max(std::min(y, _format.h - 1), static_cast<int32_t>(0));
ValueAsString t;
t.str = build_variable_name(y);
t.type.str = get_cl_data_type(_format.dt, width);
t.type.dt = _format.dt;
t.type.size = width;
if(_format.w != 1)
{
t.str += ".s";
for(int i = 0; i < width; ++i)
{
t.str += to_scalar_hex(x_start + i);
}
}
return t;
}
std::vector<ValueAsString> underlying_source_variables() const override
{
std::vector<ValueAsString> vars;
for(int32_t y = 0; y < _format.h; ++y)
{
ValueAsString t;
t.str = build_variable_name(y);
t.type.str = get_cl_data_type(_format.dt, _format.w);
t.type.dt = _format.dt;
t.type.size = _format.w;
vars.push_back(t);
}
return vars;
}
bool is_assignable() const override
{
return true;
}
bool need_declaration() const override
{
return true;
}
private:
std::string build_variable_name(int32_t y) const
{
std::string var_name = _basename;
if(_format.h == 1)
{
return var_name;
}
else
{
var_name += "_";
var_name += std::to_string(y);
}
return var_name;
}
std::string to_scalar_hex(int32_t x) const
{
switch(x)
{
case 0:
case 1:
case 2:
case 3:
case 4:
case 5:
case 6:
case 7:
case 8:
case 9:
return std::to_string(x);
case 10:
return "A";
case 11:
return "B";
case 12:
return "C";
case 13:
return "D";
case 14:
return "E";
case 15:
return "F";
default:
std::cout << "Unsupported hexadecimal value" << std::endl;
assert(false);
return "";
}
}
};
// Unique features: It contains values in the form of string. The name used for this object is misleading since the variables can change the value over time.
class ClConstantTile : public IVectorTile
{
public:
ClConstantTile(const std::vector<std::vector<std::string>> &in, DataType dt)
{
_format.w = in[0].size();
_format.h = in.size();
_format.dt = dt;
_data = std::vector<std::vector<std::string>>(_format.h, std::vector<std::string>(_format.w));
for(int32_t y = 0; y < _format.h; ++y)
{
for(int32_t x = 0; x < _format.w; ++x)
{
_data[y][x] = in[y][x];
}
}
}
ValueAsString scalar(int32_t x, int32_t y) const override
{
x = std::max(std::min(x, _format.w - 1), static_cast<int32_t>(0));
y = std::max(std::min(y, _format.h - 1), static_cast<int32_t>(0));
ValueAsString t;
t.str = _data[y][x];
t.type.str = get_cl_data_type(_format.dt, 1);
t.type.dt = _format.dt;
t.type.size = 1;
return t;
}
ValueAsString vector(int32_t y) const override
{
y = std::max(std::min(y, _format.h - 1), static_cast<int32_t>(0));
return vector(0, _format.w, y);
}
ValueAsString vector(int32_t x_start, int32_t width, int32_t y) const override
{
y = std::max(std::min(y, _format.h - 1), static_cast<int32_t>(0));
ValueAsString t;
t.str = "";
t.type.str = get_cl_data_type(_format.dt, width);
t.type.dt = _format.dt;
t.type.size = width;
if(width > 1)
{
t.str += "((" + get_cl_data_type(_format.dt, width) + ")(";
}
int32_t x = x_start;
for(; x < width - 1; ++x)
{
t.str += scalar(x, y).str;
t.str += ", ";
}
t.str += scalar(x, y).str;
if(width > 1)
{
t.str += "))";
}
return t;
}
std::vector<ValueAsString> underlying_source_variables() const override
{
std::vector<ValueAsString> vars;
for(int32_t y = 0; y < _format.h; ++y)
{
for(int32_t x = 0; x < _format.w; ++x)
{
ValueAsString t;
t.str = _data[y][x];
t.type.str = get_cl_data_type(_format.dt, 1);
t.type.dt = _format.dt;
t.type.size = 1;
vars.push_back(t);
}
}
return vars;
}
bool is_assignable() const override
{
return false;
}
bool need_declaration() const override
{
return false;
}
private:
std::vector<std::vector<std::string>> _data{};
};
enum class TensorComponentIndex : int32_t
{
IndexMask = 0x0000000f,
};
enum class TensorComponentType : int32_t
{
OffsetFirstElement = 0x00000100,
Stride = 0x00001000,
Dimension = 0x00010000,
FoldedDimension = 0x00100000,
Constant = 0x01000000
};
enum class TensorComponent : int32_t
{
Unknown = 0x00000000,
OffsetFirstElement = 0x00000100,
Stride1 = 0x00001001,
Stride2 = 0x00001002,
Stride3 = 0x00001003,
Stride4 = 0x00001004,
Dim0 = 0x00010000,
Dim1 = 0x00010001,
Dim2 = 0x00010002,
Dim3 = 0x00010003,
Dim4 = 0x00010004,
C = 0x00010000, // Dim0
W = 0x00010001, // Dim1
H = 0x00010002, // Dim2
D = 0x00010003,
N = 0x00010004,
Dim1xDim2 = 0x00100021,
Dim1xDim2xDim3 = 0x00100321,
WxH = 0x00100021,
WxHxD = 0x00100321
};
inline std::string to_string(TensorComponent x)
{
switch(x)
{
case TensorComponent::Unknown:
return "Unknown";
case TensorComponent::OffsetFirstElement:
return "OffsetFirstElement";
case TensorComponent::Stride1:
return "Stride1";
case TensorComponent::Stride2:
return "Stride2";
case TensorComponent::Stride3:
return "Stride3";
case TensorComponent::Stride4:
return "Stride4";
case TensorComponent::Dim0:
return "Dim0";
case TensorComponent::Dim1:
return "Dim1";
case TensorComponent::Dim2:
return "Dim2";
case TensorComponent::Dim3:
return "Dim3";
case TensorComponent::Dim4:
return "Dim4";
case TensorComponent::Dim1xDim2:
return "Dim1xDim2";
case TensorComponent::Dim1xDim2xDim3:
return "Dim1xDim2xDim3";
default:
assert(false);
}
}
class ITensorArgument
{
public:
virtual ~ITensorArgument() = default;
/** Method to get the tensor component as a string
*
* @param[in] x tensor component to query
*
* @return the tensor component as a string
*/
virtual std::string component(TensorComponent x) = 0;
/** Method to get the tensor component type declaration as a string
*
* @return the tensor component type declaration as a string
*/
virtual std::string component_type_declaration() const = 0;
/** Method to get the tensor component data type
*
* @return the tensor component data type
*/
virtual DataType component_data_type() const = 0;
/** Method to get the tensor component declarations
*
* @return a vector containing the tensor component declarations
*/
virtual std::vector<TensorComponent> component_declarations() const = 0;
/** Method to get the name of the tensor argument.
*
* @return the name of the tensor argument
*/
std::string name() const
{
return _basename;
}
/** Method to get the tensor format
*
* @return the format
*/
TensorInfo format() const
{
return _format;
}
protected:
TensorInfo _format { };
std::string _basename {};
};
enum class GpuTensorStorage : int32_t
{
Unknown = 0x0000,
BufferUint8Ptr = 0x0012,
Image2dReadOnly = 0x0020,
Image2dWriteOnly = 0x0021,
Image3dReadOnly = 0x0030,
Image3dWriteOnly = 0x0031
};
class IGpuTensorArgument : public ITensorArgument
{
public:
virtual ~IGpuTensorArgument() = default;
/** Method to get the tensor storage, which is the underlying storage used to keep the data memory
*
* @param[in] x tensor storage to query
*
* @return the tensor storage as a string
*/
virtual std::string storage(GpuTensorStorage x) = 0;
/** Method to get the tensor storage type declaration as a string
*
* @param[in] x tensor component to query
*
* @return the tensor storage type declaration as a string
*/
virtual std::string storage_type_declaration(GpuTensorStorage x) const = 0;
/** Method to get the tensor storage declarations
*
* @return a vector containing the tensor storage declarations
*/
virtual std::vector<GpuTensorStorage> storage_declarations() const = 0;
};
class ClTensorArgument : public IGpuTensorArgument
{
public:
ClTensorArgument(const std::string& name, const TensorInfo& x, bool return_by_value_when_possible)
{
_basename = name;
_format = x;
_return_by_value_when_possible = return_by_value_when_possible;
}
// Methods to override
std::string component(TensorComponent x) override
{
if((static_cast<int32_t>(x) & static_cast<int32_t>(TensorComponentType::Constant)))
{
int32_t idx = static_cast<int32_t>(x) & static_cast<int32_t>(TensorComponentIndex::IndexMask);
return std::to_string(idx - 1);
}
if(_return_by_value_when_possible)
{
if((static_cast<int32_t>(x) & static_cast<int32_t>(TensorComponentType::Dimension)))
{
int32_t idx = static_cast<int32_t>(x) & static_cast<int32_t>(TensorComponentIndex::IndexMask);
return std::to_string(_format.shape[idx]);
}
if((static_cast<int32_t>(x) & static_cast<int32_t>(TensorComponentType::FoldedDimension)))
{
switch(x)
{
case TensorComponent::Dim1xDim2:
return std::to_string(_format.shape[1] * _format.shape[2]);
case TensorComponent::Dim1xDim2xDim3:
return std::to_string(_format.shape[1] * _format.shape[2] * _format.shape[2]);
default:
std::cout << "Unsupported folded dimension" << std::endl;
assert(false);
}
}
}
if(std::find(_components_required.begin(), _components_required.end(), x) == _components_required.end())
{
_components_required.push_back(x);
}
return build_component_name(x);
}
std::string component_type_declaration() const override
{
return "int";
};
DataType component_data_type() const override
{
return DataType::Int32;
}
std::string storage(GpuTensorStorage x) override
{
if(std::find(_storage_required.begin(), _storage_required.end(), x) == _storage_required.end())
{
_storage_required.push_back(x);
}
return build_storage_name(x);
}
std::string storage_type_declaration(GpuTensorStorage x) const override
{
switch(x)
{
case GpuTensorStorage::BufferUint8Ptr:
return "__global uchar*";
case GpuTensorStorage::Image2dReadOnly:
return "__read_only image2d_t";
case GpuTensorStorage::Image2dWriteOnly:
return "__write_only image2d_t";
case GpuTensorStorage::Image3dReadOnly:
return "__read_only image3d_t ";
case GpuTensorStorage::Image3dWriteOnly:
return "__write_only image3d_t ";
default:
std::cout << "Unsupported storage" << std::endl;
assert(false);
return "";
}
};
std::vector<GpuTensorStorage> storage_declarations() const override
{
return _storage_required;
}
std::vector<TensorComponent> component_declarations() const override
{
return _components_required;
}
private:
std::string build_storage_name(GpuTensorStorage x) const
{
std::string var_name = _basename;
switch(x)
{
case GpuTensorStorage::BufferUint8Ptr:
return var_name + "_ptr";
case GpuTensorStorage::Image2dReadOnly:
case GpuTensorStorage::Image2dWriteOnly:
return var_name + "_img2d";
case GpuTensorStorage::Image3dReadOnly:
case GpuTensorStorage::Image3dWriteOnly:
return var_name + "_img3d";
default:
std::cout << "Unsupported storage" << std::endl;
assert(false);
}
return var_name;
}
std::string build_component_name(TensorComponent x) const
{
std::string var_name = _basename;
switch(x)
{
case TensorComponent::OffsetFirstElement:
return var_name + "_offset_first_element";
case TensorComponent::Stride1:
return var_name + "_stride1";
case TensorComponent::Stride2:
return var_name + "_stride2";
case TensorComponent::Stride3:
return var_name + "_stride3";
case TensorComponent::Dim0:
return var_name + "_dim0";
case TensorComponent::Dim1:
return var_name + "_dim1";
case TensorComponent::Dim2:
return var_name + "_dim2";
case TensorComponent::Dim3:
return var_name + "_dim3";
case TensorComponent::Dim1xDim2:
return var_name + "_dim1xdim2";
case TensorComponent::Dim1xDim2xDim3:
return var_name + "_dim1xdim2xdim3";
default:
std::cout << "Unsupported component" << std::endl;
assert(false);
}
return var_name;
}
bool _return_by_value_when_possible { false };
std::vector<GpuTensorStorage> _storage_required {};
std::vector<TensorComponent> _components_required {};
};
/**
* @brief Data structure that contains the declared tiles by the components.
* The registry is a linear data structure that follows the similar principle of the stack. The user can use the @p increment_registry_level() method to
* increase the level of the stack (0 when it starts). When the user uses the @p decrement_registry_level() method, the registry decreases the level of the stack
* and remove (pop) all the tiles from the level above.
* When a tile is declared on the level 0, it is a global tile. A global tile is visible in all parts of the code.
* Since different components may use the same name to define a tile, the registry adopts the IdSpace concept, an @p id to prevent name collisions
* when declaring tiles among different components.
*
*/
class GpuTileRegistry
{
public:
enum class RegistryTileType
{
Tile,
Link
};
using RegistryIdSpace = int32_t;
using RegistryLevel = int32_t;
using RegistryTileName = std::string;
struct RegistryTileTableEntry
{
RegistryLevel registry_level { 0 };
std::unique_ptr<IVectorTile> tile_object { nullptr };
};
struct RegistryTileTypeTableEntry
{
RegistryTileType tile_type { RegistryTileType::Tile };
RegistryTileName tile_name {};
RegistryIdSpace registry_idspace { 0 };
RegistryLevel registry_level { 0 };
};
using RegistryTileTable = std::map<RegistryIdSpace, std::map<RegistryTileName, RegistryTileTableEntry>>;
using RegistryTileTypeTable = std::map<RegistryIdSpace, std::map<RegistryTileName, RegistryTileTypeTableEntry>>;
/**
* @brief Construct a new Gpu Tile Registry object
*
*/
GpuTileRegistry()
{
_language = GpuTargetLanguage::Unknown;
}
/**
* @brief Construct a new Gpu Tile Registry object providing the Gpu programming language
*
* @param[in] language Gpu programming language to use
*/
GpuTileRegistry(GpuTargetLanguage language)
{
_language = language;
}
/**
* @brief Default destructor. Destroy the Gpu Tile Registry object
*
*/
~GpuTileRegistry() = default;
/**
* @brief Set the working IdSpace for the tile registry. IdSpace is used to prevent name collisions when declaring tiles.
* Therefore, the IdSpace should be set before declaring any tiles.
*
* @param[in] id The IdSpace id
*/
void set_IdSpace(int32_t id)
{
_IdSpace = id;
}
/**
* @brief Get the current working IdSpace for the tile registry. IdSpace is used to prevent name collisions when declaring tiles
*
* @return The IdSpace id
*/
int32_t IdSpace() const
{
return _IdSpace;
}
/**
* @brief Gets all the IdSpace declarations defined in the tile registry.
*
* @return all the IdSpace declarations defined in the tile registry as std::vector<int32_t>. It returns an empty vector if there are no IdSpace declarations.
*/
std::vector<int32_t> IdSpace_declarations() const
{
std::vector<int32_t> x;
auto it = _frags.begin();
while (it != _frags.end())
{
x.push_back(it->first);
it++;
}
return x;
}
/**
* @brief Declare a tile from a previously created tile
*/
void insert(const std::string& name, const IVectorTile *frag)
{
assert(_language == GpuTargetLanguage::OpenCL);
const int32_t key_IdSpace = _IdSpace;
const std::string key_var_name = name;
const std::string var_name = frag->name();
TileInfo format = frag->format();
// First check whether a tile with the same name exists
IVectorTile *result = (*this)[key_var_name];
assert(result == nullptr);
if(result == nullptr)
{
std::unique_ptr<ClTile> tile = std::make_unique<ClTile>(var_name, format);
_frags[key_IdSpace][key_var_name].tile_object = std::move(tile);
_frags[key_IdSpace][key_var_name].registry_level = _registry_level;
_frag_types[key_IdSpace][key_var_name].tile_type = RegistryTileType::Link;
_frag_types[key_IdSpace][key_var_name].tile_name = key_var_name;
_frag_types[key_IdSpace][key_var_name].registry_idspace = _IdSpace;
_frag_types[key_IdSpace][key_var_name].registry_level = _registry_level;
}
}
/**
* @brief Declare a tile with TileInfo. The tile will be stored in the IdSpace set with @p set_IdSpace()
*
* @note The reference name used for declaring the tile should not be previously used in the IdSpace
*
* @param[in] name Reference name for the tile. The reference name can be used to retrieve the tile stored in the registry.
* @param[in] format Tile format use to use
*/
void insert(const std::string& name, const TileInfo& format)
{
assert(_language == GpuTargetLanguage::OpenCL);
const int32_t key_IdSpace = _IdSpace;
const std::string key_var_name = name;
const std::string var_name = generate_tile_name(name);
// First check whether a tile with the same name exists
IVectorTile *result = (*this)[key_var_name];
assert(result == nullptr);
if(result == nullptr)
{
std::unique_ptr<ClTile> tile = std::make_unique<ClTile>(var_name, format);
_frags[key_IdSpace][key_var_name].tile_object = std::move(tile);
_frags[key_IdSpace][key_var_name].registry_level = _registry_level;
_frag_types[key_IdSpace][key_var_name].tile_type = RegistryTileType::Tile;
_frag_types[key_IdSpace][key_var_name].tile_name = key_var_name;
_frag_types[key_IdSpace][key_var_name].registry_idspace = _IdSpace;
_frag_types[key_IdSpace][key_var_name].registry_level = _registry_level;
}
}
/**
* @brief Declare a constant tile. The content of the tile is passed as a vector of std::string
*
* @note The reference name used for declaring the tile should not be previously used in the IdSpace
*
* @param[in] name Reference name for the tile. The reference name can be used to retrieve the tile stored in the registry.
* @param[in] in A 3D std::vector of std::string. From the 3D std::vector we can know the dimensions for the tile
* @param[in] dt The data type for the elements stored in the 3D std::vector as std::string. It is user's responsibilty to ensure
* that the data type is aligned with the content of the std::string.
*/
void insert(const std::string& name, const std::vector<std::vector<std::string>>& in, DataType dt)
{
assert(_language == GpuTargetLanguage::OpenCL);
const int32_t key_IdSpace = _IdSpace;
const std::string key_var_name = name;
// First check whether a tile with the same name exists
IVectorTile *result = (*this)[key_var_name];
assert(result == nullptr);
if(result == nullptr)
{
std::unique_ptr<ClConstantTile> tile = std::make_unique<ClConstantTile>(in, dt);
_frags[key_IdSpace][key_var_name].tile_object = std::move(tile);
_frags[key_IdSpace][key_var_name].registry_level = _registry_level;
_frag_types[key_IdSpace][key_var_name].tile_type = RegistryTileType::Tile;
_frag_types[key_IdSpace][key_var_name].tile_name = key_var_name;
_frag_types[key_IdSpace][key_var_name].registry_idspace = _IdSpace;
_frag_types[key_IdSpace][key_var_name].registry_level = _registry_level;
}
}
/**
* @brief Declare an anonymous constant tile. The content of the tile is passed as a vector of std::string
*
* @note This method can be used to declare temporary tiles that need to be accessed only once.
*
* @param[in] in A 3D std::vector of std::string. From the 3D std::vector we can know the dimensions for the tile
* @param[in] dt The data type for the elements stored in the 3D std::vector as std::string. It is user responsibilty to ensure
* that the data type is aligned with what passed with the std::string.
*
* @return IVectorTile* the anonymous constant tile
*/
IVectorTile* insert(const std::vector<std::vector<std::string>>& in, DataType dt)
{
assert(_language == GpuTargetLanguage::OpenCL);
const int32_t key_IdSpace = _IdSpace;
const std::string key_var_name = "_" + std::to_string(_anonymous_frag_count++);
// First check whether a tile with the same name exists
IVectorTile *result = (*this)[key_var_name];
assert(result == nullptr);
if(result == nullptr)
{
std::unique_ptr<ClConstantTile> tile = std::make_unique<ClConstantTile>(in, dt);
_frags[key_IdSpace][key_var_name].tile_object = std::move(tile);
_frags[key_IdSpace][key_var_name].registry_level = _registry_level;
_frag_types[key_IdSpace][key_var_name].tile_type = RegistryTileType::Tile;
_frag_types[key_IdSpace][key_var_name].tile_name = key_var_name;
_frag_types[key_IdSpace][key_var_name].registry_idspace = _IdSpace;
_frag_types[key_IdSpace][key_var_name].registry_level = _registry_level;
}
return (*this)[key_var_name];
}
/**
* @brief Get the tile from the registry. This method searches the tile in the IdSpace provided by the user
*
* @param[in] name The name of the tile to retrieve
* @param[in] IdSpace The IdSpace id where to search the tile
*
* @return IVectorTile* The tile
*/
IVectorTile* get(const std::string& name, int32_t IdSpace)
{
const int32_t key_IdSpace = IdSpace;
const std::string key_var_name = name;
IVectorTile* result = nullptr;
auto search_IdSpace = _frags.find(key_IdSpace);
if(search_IdSpace != _frags.end())
{
auto search_tile = _frags[key_IdSpace].find(key_var_name);
if(search_tile != _frags[key_IdSpace].end())
{
result = search_tile->second.tile_object.get();
assert(result != nullptr);
}
}
return result;
}
/**
* @brief Get the tile from the registry. This method searches the tile in the IdSpace set with @p set_IdSpace()
*
* @param[in] name The name of the tile to retrieve
*
* @return IVectorTile* The tile
*/
IVectorTile* operator[](const std::string& name)
{
return get(name, _IdSpace);
}
/**
* @brief Check whether the tile in the in the IdSpace provided by the user exists
*
* @param[in] name Name of the tile to search for
* @param[in] IdSpace The IdSpace id where to search the tile
*
* @return true if the tile exists
* @return false if the tile does not exist
*/
bool has_tile(const std::string& name, int32_t IdSpace) const
{
const int32_t key_IdSpace = IdSpace;
const std::string key_var_name = name;
// IVectorTile* result = nullptr;
auto search_IdSpace = _frags.find(key_IdSpace);
return search_IdSpace != _frags.end();
}
/**
* @brief Check whether the tile within the current IdSpace exists
*
* @param[in] name Name of the tile to search for
*
* @return true if the tile exists
* @return false if the tile does not exist
*/
bool has_tile(const std::string& name) const
{
return has_tile(name, _IdSpace);
}
/**
* @brief Get all the tiles declared within the IdSpace provided by the user
*
* @param[in] IdSpace IdSpace where to retrieve all the declared tiles
*
* @return std::vector<IVectorTile*> A vector with all the declared tiles in the IdSpace provided by the user
*/
std::vector<IVectorTile*> tile_declarations(int32_t IdSpace)
{
std::vector<IVectorTile*> tiles;
std::map<RegistryTileName, RegistryTileTypeTableEntry>::iterator it = _frag_types[IdSpace].begin();
while (it != _frag_types[IdSpace].end())
{
// The following line should be enabled. However, we cannot at this stage
// because it used to retrieve the output tile produced by each component.
// However, this method should NOT be used to retrieve the output tile
//if(it->second.tile_type == RegistryTileType::Tile)
{
tiles.push_back(get(it->second.tile_name, it->second.registry_idspace));
}
it++;
}
return tiles;
}
/**
* @brief Increase the level of stack.
*
*/
void increment_registry_level()
{
_registry_level++;
}
/**
* @brief Remove all the tiles declared at the current stack level and decrease the level of the stack.
*
*/
void decrement_registry_level()
{
assert(_registry_level >= 0);
// Remove all variables in the local scope
std::map<RegistryTileName, RegistryTileTableEntry>::iterator it = _frags[_IdSpace].begin();
while (it != _frags[_IdSpace].end())
{
if (it->second.registry_level == _registry_level)
{
it = _frags[_IdSpace].erase(it);
}
else
{
it++;
}
}
std::map<RegistryTileName, RegistryTileTypeTableEntry>::iterator it_type = _frag_types[_IdSpace].begin();
while (it_type != _frag_types[_IdSpace].end())
{
if (it_type->second.registry_level == _registry_level)
{
it_type = _frag_types[_IdSpace].erase(it_type);
}
else
{
it_type++;
}
}
_registry_level--;
}
/**
* @brief Get the level of the stack
*
*/
int32_t level() const
{
return _registry_level;
}
private:
// This method ensures that the key is unique among different components
std::string generate_tile_name(const std::string& name)
{
assert(_IdSpace >= 0 );
if(_registry_level == 0)
{
return "_G" + std::to_string(_IdSpace) + "_" + name;
}
else
{
return name;
}
}
RegistryTileTable _frags {};
RegistryTileTypeTable _frag_types {};
RegistryLevel _registry_level { 0 };
RegistryIdSpace _IdSpace { -1 };
int32_t _anonymous_frag_count { 0 }; // Counter used to create the anonymous tiles
GpuTargetLanguage _language { GpuTargetLanguage::Unknown }; // Gpu programming language
};
using TensorEntry = std::unique_ptr<IGpuTensorArgument>;
/**
* @brief Data structure that contains the tensors consumed by the components.
* Since different components may use the same name as reference for a tensor, the registry adopts the IdSpace concept, an @p id to prevent name collisions
* when declaring tensors among different components.
*
*/
class GpuTensorArgumentRegistry
{
public:
/**
* @brief Construct a new Gpu Tensor Registry object
*
*/
GpuTensorArgumentRegistry()
{
_language = GpuTargetLanguage::Unknown;
}
/**
* @brief Construct a new Gpu Tensor Registry object
*
* @param[in] language Gpu programming language to use
*/
GpuTensorArgumentRegistry(GpuTargetLanguage language)
{
_language = language;
}
/**
* @brief Default destructor. Destroy the Gpu Tensor Registry object
*
*/
~GpuTensorArgumentRegistry() = default;
/**
* @brief Set the working IdSpace for the tensor registry. IdSpace is used to prevent name collisions when declaring tensors.
* Therefore, the IdSpace should be set before declaring any tensors.
*
* @param[in] id The IdSpace id
*/
void set_IdSpace(int32_t id)
{
_IdSpace = id;
}
/**
* @brief Get the current working IdSpace for the tensor registry. IdSpace is used to prevent name collisions when declaring tensors
*
* @return The IdSpace id
*/
int32_t IdSpace() const
{
return _IdSpace;
}
/**
* @brief Gets all the IdSpace declarations defined in the tensor registry.
*
* @return all the IdSpace declarations defined in the tensor registry as std::vector<int32_t>. It returns an empty vector if there are no IdSpace declarations.
*/
std::vector<int32_t> IdSpace_declarations() const
{
std::vector<int32_t> x;
auto it = _refs.begin();
while (it != _refs.end())
{
x.push_back(it->first);
it++;
}
return x;
}
/**
* @brief Declare a tensor with TensorInfo. The tensor will be stored in the IdSpace set with @p set_IdSpace()
*
* @note The reference name used for declaring the tensor should not be previously used in the IdSpace
*
* @param[in] name Reference name for the tensor. The reference name can be used to retrieve the tensor stored in the registry.
* @param[in] x Pair of tensor info and tensor id
* @param[in] return_by_value_when_possible True if we want the value stored in the tensor components
*/
void insert(const std::string& name, const TensorInfo& x, bool return_by_value_when_possible)
{
assert(_language == GpuTargetLanguage::OpenCL);
const int32_t key_IdSpace = _IdSpace;
const int32_t tensor_id = x.id;
const std::string key_var_name = name;
const std::string var_name = generate_tensor_name(name, tensor_id);
// First, check whether the tensor has already a reference. If so, trigger an assert
assert(!has_tensor_argument(name));
// Check whether a tensor with that tensorID exists
auto result = _tensor_arguments.find(tensor_id);
if(result == _tensor_arguments.end())
{
// It means that we haven't added a tensor with that tensor_id yet. Create a IGpuTensorArgument before creating the reference
std::unique_ptr<ClTensorArgument> arg = std::make_unique<ClTensorArgument>(var_name, x, return_by_value_when_possible);
_tensor_arguments[tensor_id] = std::move(arg);
}
_refs[key_IdSpace][key_var_name] = tensor_id;
}
/**
* @brief Get the tensor from the registry. This method searches the tensor in the IdSpace set with @p set_IdSpace()
*
* @param[in] name The name of the tensor to retrieve
*
* @return IGpuTensor* The tensor
*/
IGpuTensorArgument* operator[](const std::string& name)
{
const int32_t key_IdSpace = _IdSpace;
const std::string key_var_name = name;
IGpuTensorArgument* result = nullptr;
auto search_IdSpace = _refs.find(key_IdSpace);
if(search_IdSpace != _refs.end())
{
auto search_tensor_id = _refs[key_IdSpace].find(key_var_name);
if(search_tensor_id != _refs[key_IdSpace].end())
{
const int32_t tensor_id = search_tensor_id->second;
auto search_tensor_argument = _tensor_arguments.find(tensor_id);
if(search_tensor_argument != _tensor_arguments.end())
{
result = search_tensor_argument->second.get();
}
assert(result != nullptr);
}
}
return result;
}
/**
* @brief Get all the tensors declared in the IdSpace provided by the user
*
* @return std::vector<IGpuTensorArgument*> A vector with all the declared tensors
*/
std::vector<IGpuTensorArgument*> tensor_argument_declarations()
{
std::vector<IGpuTensorArgument*> args;
auto it = _tensor_arguments.begin();
while (it != _tensor_arguments.end())
{
args.push_back(it->second.get());
it++;
}
return args;
}
/**
* @brief Check whether the tensor argument in the IdSpace set with @p set_IdSpace() exists
*
* @param[in] name Name of the tensor argument to search for
*
* @return true if the tensor argument exists
* @return false if the tensor argument does not exist
*/
bool has_tensor_argument(const std::string& name)
{
const int32_t key_IdSpace = _IdSpace;
const std::string key_var_name = name;
auto search_IdSpace = _refs.find(key_IdSpace);
if(search_IdSpace != _refs.end())
{
auto search_tensor_id = _refs[key_IdSpace].find(key_var_name);
return search_tensor_id != _refs[key_IdSpace].end();
}
else
{
return false;
}
}
/**
* @brief Check whether the tensor argument is in the the IdSpace provided by the user
*
* @param[in] name Name of the tensor argument to search for
* @param[in] IdSpace The IdSpace id where to search the tensor argument
*
* @return true if the tile exists
* @return false if the tile does not exist
*/
bool has_tensor_argument(const std::string& name, int32_t IdSpace)
{
const int32_t key_IdSpace = IdSpace;
const std::string key_var_name = name;
auto search_IdSpace = _refs.find(key_IdSpace);
if(search_IdSpace != _refs.end())
{
auto search_tensor_id = _refs[key_IdSpace].find(key_var_name);
return search_tensor_id != _refs[key_IdSpace].end();
}
else
{
return false;
}
}
private:
// This method ensures that the key is unique among different components
std::string generate_tensor_name(const std::string& name, int32_t tensor_id)
{
assert(tensor_id >= 0 );
return name + std::to_string(tensor_id);
}
std::map<int32_t, TensorEntry> _tensor_arguments {};
std::map<int32_t, std::map<std::string, int32_t>> _refs {};
int32_t _IdSpace { -1 };
GpuTargetLanguage _language { GpuTargetLanguage::Unknown }; // Gpu programming language
};
enum class OpType : int32_t
{
Elementwise = 0x0000,
Relational = 0x1000,
Algebra = 0x2000
};
inline std::string to_string(AssignmentOp op)
{
switch(op)
{
case AssignmentOp::Decrement:
return "-=";
case AssignmentOp::Increment:
return "+=";
default:
assert(false);
return "";
}
}
inline std::string to_string(BinaryOp op)
{
switch(op)
{
case BinaryOp::Add:
return "+";
case BinaryOp::Sub:
return "-";
case BinaryOp::Mul:
return "*";
case BinaryOp::Div:
return "/";
case BinaryOp::Mod:
return "%";
case BinaryOp::Equal:
return "==";
case BinaryOp::Less:
return "<";
case BinaryOp::LessEqual:
return "<=";
case BinaryOp::Greater:
return ">";
case BinaryOp::GreaterEqual:
return ">=";
case BinaryOp::LogicalAnd:
return "&&";
case BinaryOp::LogicalOr:
return "||";
case BinaryOp::LogicalNot:
return "!";
default:
assert(false);
return "";
}
}
inline std::string binary_op_string(BinaryOp op)
{
switch(op)
{
case BinaryOp::Add:
return "add";
case BinaryOp::Sub:
return "sub";
case BinaryOp::Mul:
return "mul";
case BinaryOp::Div:
return "div";
case BinaryOp::Mod:
return "mod";
case BinaryOp::Equal:
return "eq";
case BinaryOp::Less:
return "gt";
case BinaryOp::LessEqual:
return "gteq";
case BinaryOp::Greater:
return "lt";
case BinaryOp::GreaterEqual:
return "lte";
default:
assert(false);
return "";
}
}
enum class OperandType : int32_t
{
Unknown = 0x00000000,
ScalarFp32 = 0x00001011, // Immediate scalar tile
ScalarFp16 = 0x00001012, // Immediate scalar tile
ScalarInt32 = 0x00001021, // Immediate scalar tile
ScalarInt16 = 0x00001022, // Immediate scalar tile
ScalarInt8 = 0x00001024, // Immediate scalar tile
ScalarUInt32 = 0x00001031, // Immediate scalar tile
ScalarUInt16 = 0x00001032, // Immediate scalar tile
ScalarUInt8 = 0x00001034, // Immediate scalar tile
ScalarBool = 0x00001041, // Immediate scalar tile
ScalarTile = 0x00001050, // Scalar from a tile
Tile = 0x00010000, // Tile
TensorStride1 = 0x00100001, // Tensor component
TensorStride2 = 0x00100002, // Tensor component
TensorStride3 = 0x00100003, // Tensor component
TensorStride4 = 0x00100004, // Tensor component
TensorDim0 = 0x00100010, // Tensor component
TensorDim1 = 0x00100020, // Tensor component
TensorDim2 = 0x00100030, // Tensor component
TensorDim3 = 0x00100040, // Tensor component
TensorDim4 = 0x00100050, // Tensor component
TensorC = 0x00100010, // Tensor component
TensorW = 0x00100020, // Tensor component
TensorH = 0x00100030, // Tensor component
TensorD = 0x00100040, // Tensor component
TensorN = 0x00100050, // Tensor component
TensorDim1xDim2 = 0x00100100, // Tensor component
TensorDim1xDim2xDim3 = 0x00100200, // Tensor component
TensorWxH = 0x00100300, // Tensor component
TensorWxHxD = 0x00100400, // Tensor component
TensorDataOffset = 0x00100500, // Tensor component
};
struct ScalarTileCoord
{
ScalarTileCoord() {}
ScalarTileCoord(int32_t x0, int32_t y0) : x(x0), y(y0) {}
int32_t x { -1 };
int32_t y { -1 };
};
/**
* @brief Operand class. This object is used to pass the operands to the operations performed by the writer.
* Operand can be of three types:
* -# Scalar immediate: constant expression
* -# Tile: A tile
* -# Tensor component: A component (scalar) of a tensor
*
*/
class Operand
{
public:
Operand(const std::string &val)
{
_str = val;
_type = OperandType::Tile;
}
Operand(const std::string &val, const ScalarTileCoord& coord)
{
_str = val;
_type = OperandType::ScalarTile;
_coord = coord;
}
Operand(const std::string &val, OperandType type)
{
_str = val;
_type = type;
}
Operand(const Operand& t)
{
_str = t.value();
_type = t.type();
}
Operand& operator=(const Operand& t)
{
_str = t.value();
_type = t.type();
_coord = t.scalar_tile_coordinate();
return *this;
}
std::string value() const
{
return _str;
}
OperandType type() const
{
return _type;
}
ScalarTileCoord scalar_tile_coordinate() const
{
return _coord;
}
private:
std::string _str {};
OperandType _type { OperandType::Unknown };
ScalarTileCoord _coord {};
};
enum class GpuSamplerTensorStorage : int32_t
{
Unknown = static_cast<int32_t>(GpuTensorStorage::Unknown),
BufferUint8Ptr = static_cast<int32_t>(GpuTensorStorage::BufferUint8Ptr),
Image2dReadOnly = static_cast<int32_t>(GpuTensorStorage::Image2dReadOnly),
Image2dWriteOnly = static_cast<int32_t>(GpuTensorStorage::Image2dWriteOnly),
Image3dReadOnly = static_cast<int32_t>(GpuTensorStorage::Image3dReadOnly),
Image3dWriteOnly = static_cast<int32_t>(GpuTensorStorage::Image2dWriteOnly),
};
struct GpuSampler
{
GpuSampler() = default;
TensorSamplerFormat format { TensorSamplerFormat::Unknown };
GpuSamplerTensorStorage storage { GpuSamplerTensorStorage::Unknown };
TensorSamplerAddressModeX address_mode_x { TensorSamplerAddressModeX::Unknown };
TensorSamplerAddressModeY address_mode_y { TensorSamplerAddressModeY::Unknown };
TensorSamplerAddressModeZ address_mode_z { TensorSamplerAddressModeZ::Unknown };
};
inline GpuSampler create_simple_sampler(const TensorInfo* tensor_info_id, GpuSampler sampler, int32_t step_x, int32_t step_y, int32_t step_z)
{
CKW_UNUSED(step_x, step_y, step_z);
auto tensor = tensor_info_id->shape;
GpuSampler dst_sampler;
dst_sampler.format = sampler.format;
dst_sampler.storage = GpuSamplerTensorStorage::BufferUint8Ptr;
dst_sampler.address_mode_x = sampler.address_mode_x;
dst_sampler.address_mode_y = sampler.address_mode_y;
dst_sampler.address_mode_z = sampler.address_mode_z;
int32_t dim_x = 0;
int32_t dim_y = 0;
int32_t dim_z = 0;
switch(sampler.format)
{
case TensorSamplerFormat::C_W_H:
dim_x = tensor[0];
dim_y = tensor[1];
dim_z = tensor[2];
break;
case TensorSamplerFormat::C_WH_1:
dim_x = tensor[0];
dim_y = tensor[1] * tensor[2];
dim_z = 1;
break;
default:
std::cout << "Unsupported tensor format" << std::endl;
assert(false);
break;
}
if(dim_x == 1)
{
assert(step_x == 1);
dst_sampler.address_mode_x = TensorSamplerAddressModeX::None;
}
if(dim_y == 1)
{
assert(step_y == 1);
dst_sampler.address_mode_y = TensorSamplerAddressModeY::None;
}
if(dim_z == 1)
{
assert(step_z == 1);
dst_sampler.address_mode_z = TensorSamplerAddressModeZ::None;
}
return dst_sampler;
}
class GpuOutputSampler
{
public:
GpuOutputSampler() = default;
/**
* @brief Method used to initialize the GpuOutputSampler. The GpuOutputSampler can be initialized only once
* by the root component. Once initialized, all simpler components will need to used this sampler
* or a broadcasted version of it
*
* @param[in] sampler GpuSampler
* @param[in] step_x Increment step in the X direction. Not necessarily it is the same of n0 of tile!
* @param[in] step_y Increment step in the Y direction. Not necessarily it is the same of m0 of tile!
* @param[in] step_z Increment step in the Z direction. Not necessarily it is the same of d0 of tile!
*/
void initialize(const TensorInfo *tensor_info_id, GpuSamplerTensorStorage tensor_storage, TensorSamplerFormat tensor_format, int32_t step_x, int32_t step_y, int32_t step_z)
{
assert(_is_initialized == false);
_step_x = step_x;
_step_y = step_y;
_step_z = step_z;
_tensor_info_id = tensor_info_id;
_sampler = create_sampler(tensor_storage, tensor_format);
_is_initialized = true;
};
GpuSampler sampler() const
{
return _sampler;
};
int32_t step_x() const
{
return _step_x;
};
int32_t step_y() const
{
return _step_y;
};
int32_t step_z() const
{
return _step_z;
};
private:
GpuSampler create_sampler(GpuSamplerTensorStorage tensor_storage, TensorSamplerFormat tensor_format)
{
// Output can only be in output mode
assert(tensor_storage != GpuSamplerTensorStorage::Image2dReadOnly);
assert(tensor_storage != GpuSamplerTensorStorage::Image3dReadOnly);
auto tensor = _tensor_info_id->shape;
GpuSampler sampler;
sampler.format = tensor_format;
sampler.storage = tensor_storage;
sampler.address_mode_x = TensorSamplerAddressModeX::None;
sampler.address_mode_y = TensorSamplerAddressModeY::None;
sampler.address_mode_z = TensorSamplerAddressModeZ::None;
// In the case of texture, we do not need any special checks at the border
if(tensor_storage == GpuSamplerTensorStorage::BufferUint8Ptr)
{
int32_t dim_x = 0;
int32_t dim_y = 0;
int32_t dim_z = 0;
switch(tensor_format)
{
case TensorSamplerFormat::C_W_H:
dim_x = tensor[0];
dim_y = tensor[1];
dim_z = tensor[2];
break;
case TensorSamplerFormat::C_WH_1:
dim_x = tensor[0];
dim_y = tensor[1] * tensor[2];
dim_z = 1;
break;
default:
std::cout << "Unsupported tensor format" << std::endl;
assert(false);
break;
}
if((dim_x % _step_x) != 0 && dim_x != 1)
{
sampler.address_mode_x = TensorSamplerAddressModeX::OverlappingMin;
}
if((dim_y % _step_y) != 0 && dim_y != 1)
{
sampler.address_mode_y = TensorSamplerAddressModeY::ClampToMaxEdgeOnly;
}
if((dim_z % _step_z) != 0 && dim_z != 1)
{
sampler.address_mode_z = TensorSamplerAddressModeZ::ClampToMaxEdgeOnly;
}
}
return sampler;
}
GpuSampler _sampler { }; // GpuSampler
int32_t _step_x { 1 };
int32_t _step_y { 1 };
int32_t _step_z { 1 };
const TensorInfo* _tensor_info_id { nullptr };
bool _is_initialized { false };
};
/**
* @brief Tensor operand class. This object is used to pass the operands as tensor to the operations performed by the writer.
*/
class TensorOperand
{
public:
TensorOperand(const std::string &val, GpuSampler sampler) : _str(val), _sampler(sampler)
{
}
TensorOperand& operator=(const TensorOperand& t)
{
_str = t.value();
_sampler = t.sampler();
return *this;
}
std::string value() const
{
return _str;
}
GpuSampler sampler() const
{
return _sampler;
}
private:
std::string _str {};
GpuSampler _sampler {};
};
/**
* @brief Data structure that contains all the necessary information to write the Gpu kernel with the Gpu kernel Writer
* This data structure must be initialized before being passed to the Gpu Kernel Writer
*
*/
class GpuKernelWriterDataHolder
{
public:
/**
* @brief Construct a new Gpu Kernel Data object. In this phase, we should also store
* the GPU target and target specific capabilities (extensions). For now, we just initialize the
* programming language
*
* @param[in] language Gpu programming language to use
*/
GpuKernelWriterDataHolder(GpuTargetLanguage language) : tiles(language), arguments(language), code(""), _language(language)
{
}
/**
* @brief Get the Gpu programming language used
*
* @return GpuTargetLanguage the Gpu programming language
*/
GpuTargetLanguage programming_language() const
{
return _language;
}
/**
* @brief @ref GpuTileRegistry
*
*/
GpuTileRegistry tiles{};
/**
* @brief @ref GpuTensorArgumentRegistry
*
*/
GpuTensorArgumentRegistry arguments{};
/**
* @brief @ref GpuOutputSampler.
*
*/
GpuOutputSampler output_sampler{};
/**
* @brief Source code
*
*/
std::string code{};
// GpuExtensionRegistry extensions{};
private:
GpuTargetLanguage _language;
};
struct LWS
{
int32_t x {1};
int32_t y {1};
int32_t z {1};
};
/**
* @brief Utility class used to get the tile from the operand. If the operand is not a tile, @ref OperandUnpacker
* declare an anonymous tile in the tile registry.
*/
class OperandUnpacker
{
public:
OperandUnpacker(GpuTileRegistry& tiles, GpuTensorArgumentRegistry& arguments) : _tiles(tiles), _arguments(arguments)
{
// Increase the level of the stack to allocate possible temporary tiles
_tiles.increment_registry_level();
};
~OperandUnpacker()
{
// Decrease the level of the stack to deallocate any temporary tiles
_tiles.decrement_registry_level();
}
IVectorTile* unpack(const Operand& src)
{
// Get the tile
if(src.type() == OperandType::Tile)
{
assert(_tiles.has_tile(src.value()));
return _tiles[src.value()];
}
// Create an anonymous tile with a constant
else if(static_cast<int32_t>(src.type()) & 0x00001000)
{
if(src.type() == OperandType::ScalarTile)
{
ScalarTileCoord coord = src.scalar_tile_coordinate();
assert(_tiles.has_tile(src.value()));
assert(coord.x >= 0);
assert(coord.y >= 0);
auto val = _tiles[src.value()]->scalar(coord.x, coord.y);
return _tiles.insert({{{val.str}}}, val.type.dt);
}
else
{
return _tiles.insert({{{src.value()}}}, to_tile_data_type(src.type()));
}
}
// Create an anonymous tile with the tensor component
else
{
assert(_arguments.has_tensor_argument(src.value()));
auto x = _arguments[src.value()];
const std::string val = x->component(to_tensor_component(src.type()));
const DataType dt = x->component_data_type();
return _tiles.insert({{{val}}}, dt);
}
}
private:
DataType to_tile_data_type(OperandType x)
{
return static_cast<DataType>(static_cast<int32_t>(x) & 0x00ff);
}
TensorComponent to_tensor_component(OperandType x)
{
switch(x)
{
case OperandType::TensorDim0:
return TensorComponent::Dim0;
case OperandType::TensorDim1:
return TensorComponent::Dim1;
case OperandType::TensorDim2:
return TensorComponent::Dim2;
case OperandType::TensorDim3:
return TensorComponent::Dim3;
case OperandType::TensorDim4:
return TensorComponent::Dim4;
case OperandType::TensorStride1:
return TensorComponent::Stride1;
case OperandType::TensorStride2:
return TensorComponent::Stride2;
case OperandType::TensorStride3:
return TensorComponent::Stride3;
case OperandType::TensorStride4:
return TensorComponent::Stride4;
case OperandType::TensorDim1xDim2:
return TensorComponent::Dim1xDim2;
case OperandType::TensorDim1xDim2xDim3:
return TensorComponent::Dim1xDim2xDim3;
case OperandType::TensorDataOffset:
return TensorComponent::OffsetFirstElement;
default:
assert(false);
return TensorComponent::Unknown;
}
}
GpuTileRegistry& _tiles;
GpuTensorArgumentRegistry& _arguments;
};
/**
* @brief Utility class used to get the tensor argument from the operand. If the operand is not a tile, @ref OperandUnpacker
* declare an anonymous tile in the tile registry.
* Tensor dimension reduction aims for reducing the tensor data dimension while keeping data's tensor structure.
*/
class TensorOperandUnpacker
{
public:
TensorOperandUnpacker(GpuTensorArgumentRegistry& arguments) : _arguments(arguments)
{
};
IGpuTensorArgument* unpack(const TensorOperand& src)
{
assert(_arguments.has_tensor_argument(src.value()));
return _arguments[src.value()];
}
private:
GpuTensorArgumentRegistry& _arguments;
};
/**
* @brief The GpuKernel will be used in three occasions (stages):
* #- Compilation stage
* #- Tuning stage
* #- Dispatch stage
*/
struct GpuKernel
{
// Compilation stage
std::string code {}; // Source code, required for the compilation stage
std::vector<GpuExtensions> list_extensions{}; // Extensions, required for the compilation stage
// Tuning stage
std::string config_id {}; // Unique id, required for the tuning stage
std::vector<LWS> list_lws{}; // LWS to test, required for the tuning stage
// Dispatch stage
GpuOutputSampler output_sampler{}; // GpuOutputSampler, required for the dispatch stage
std::vector<std::pair<int32_t, GpuTensorStorage>> list_tensor_storages; // List of tensor storages, required for the dispatch stage
std::vector<std::pair<int32_t, TensorComponent>> list_tensor_components;// List of tensor components (width, stride,..), required for the dispatch stage)
};
// This function should produce an object with the source
inline std::string generate_code(GpuKernelWriterDataHolder &in, const std::string& name)
{
std::string code;
code += "__kernel void ";
code += name;
code += "(\n";
auto IdSpaces = in.arguments.IdSpace_declarations();
std::vector<std::string> arg_str;
auto tensor_args = in.arguments.tensor_argument_declarations();
for(auto &i : tensor_args)
{
// For each tensor used, get the storage and tensor components
auto storages = i->storage_declarations();
auto components = i->component_declarations();
for(auto &y : storages)
{
std::string str;
str += i->storage_type_declaration(y);
str += " ";
str += i->storage(y);
arg_str.push_back(str);
}
for(auto &y : components)
{
std::string str;
str += i->component_type_declaration();
str += " ";
str += i->component(y);
arg_str.push_back(str);
}
}
for(size_t i = 0; i < arg_str.size(); ++i)
{
code += arg_str[i];
if(i + 1 < arg_str.size())
{
code += ",\n";
}
}
code += ")\n";
code += "{\n";
code += in.code;
code += "}\n";
return code;
}
/**
* @brief This class is responsible to map a N-Tensor to a 3d tensor. The mapper needs the GpuSampler to know
* how to reduce the dimensionality of a tensor
*
*/
class GpuTensor3dMapper
{
public:
GpuTensor3dMapper(IGpuTensorArgument* tensor, GpuSampler sampler) : _sampler(sampler), _tensor(tensor)
{
};
std::string tensor_component_x() const
{
const auto format = _sampler.format;
switch(format)
{
case TensorSamplerFormat::C_WH_1:
case TensorSamplerFormat::C_W_H:
return _tensor->component(TensorComponent::C);
default:
std::cout << "Unsupported tensor format" << std::endl;
assert(false);
return "";
}
}
std::string tensor_component_y() const
{
const auto format = _sampler.format;
switch(format)
{
case TensorSamplerFormat::C_WH_1:
return _tensor->component(TensorComponent::WxH);
case TensorSamplerFormat::C_W_H:
return _tensor->component(TensorComponent::W);
default:
std::cout << "Unsupported tensor format" << std::endl;
assert(false);
return "";
}
}
std::string tensor_component_z() const
{
const auto format = _sampler.format;
switch(format)
{
case TensorSamplerFormat::C_WH_1:
return "1";
case TensorSamplerFormat::C_W_H:
return _tensor->component(TensorComponent::H);
default:
std::cout << "Unsupported tensor format" << std::endl;
assert(false);
return "";
}
}
std::string tensor_component_stride_y() const
{
const auto format = _sampler.format;
switch(format)
{
case TensorSamplerFormat::C_WH_1:
case TensorSamplerFormat::C_W_H:
return _tensor->component(TensorComponent::Stride1);
default:
std::cout << "Unsupported tensor format" << std::endl;
assert(false);
return "";
}
}
std::string tensor_component_stride_z() const
{
const auto format = _sampler.format;
switch(format)
{
case TensorSamplerFormat::C_WH_1:
return "0";
case TensorSamplerFormat::C_W_H:
return _tensor->component(TensorComponent::Stride2);
default:
std::cout << "Unsupported tensor format" << std::endl;
assert(false);
return "";
}
}
std::string tensor_component_stride_batch() const
{
const auto format = _sampler.format;
switch(format)
{
case TensorSamplerFormat::C_WH_1:
case TensorSamplerFormat::C_W_H:
return _tensor->component(TensorComponent::Stride3);
default:
std::cout << "Unsupported tensor format" << std::endl;
assert(false);
return "";
}
}
bool is_one_component_x() const
{
auto t = _tensor->format();
const auto format = _sampler.format;
switch(format)
{
case TensorSamplerFormat::C_WH_1:
case TensorSamplerFormat::C_W_H:
return t.shape[0] == 1;
default:
std::cout << "Unsupported tensor format" << std::endl;
assert(false);
return "";
}
}
bool is_one_component_y() const
{
auto t = _tensor->format();
const auto format = _sampler.format;
switch(format)
{
case TensorSamplerFormat::C_WH_1:
return (t.shape[1] * t.shape[2]) == 1;
case TensorSamplerFormat::C_W_H:
return t.shape[1] == 1;
default:
std::cout << "Unsupported tensor format" << std::endl;
assert(false);
return "";
}
}
bool is_one_component_z() const
{
auto t = _tensor->format();
const auto format = _sampler.format;
switch(format)
{
case TensorSamplerFormat::C_WH_1:
return true;
case TensorSamplerFormat::C_W_H:
return t.shape[2] == 1;
default:
std::cout << "Unsupported tensor format" << std::endl;
assert(false);
return "";
}
}
bool is_one_component_batch() const
{
auto t = _tensor->format();
const auto format = _sampler.format;
switch(format)
{
case TensorSamplerFormat::C_WH_1:
case TensorSamplerFormat::C_W_H:
return t.shape[3] == 1;
default:
std::cout << "Unsupported tensor format" << std::endl;
assert(false);
return "";
}
}
GpuSampler gpu_sampler() const
{
return _sampler;
}
IGpuTensorArgument* tensor_argument() const
{
return _tensor;
}
private:
GpuSampler _sampler;
IGpuTensorArgument* _tensor;
};
struct GpuKernelWriterAttribute
{
bool return_tensor_component_by_value { false };
};
enum class ConvertPolicy
{
Wrap, /**< Wrap around */
Saturate /**< Saturate */
};
enum class RoundingMode
{
None,
Rte,
Rtz,
Rtp,
Rtn
};
// https://llvm.org/docs/tutorial/MyFirstLanguageFrontend/LangImpl05.html
class IGpuKernelWriter
{
public:
virtual ~IGpuKernelWriter() = default;
virtual void set_IdSpace(int32_t id) = 0;
virtual void import_tile(const std::string& dst, const IVectorTile *src) = 0;
virtual void declare_argument(const std::string& name, const TensorInfo& tensor) = 0;
virtual void declare_tile(const std::string& name, const TileInfo& info) = 0;
virtual void declare_const_tile(const std::string& name, const std::vector<std::vector<std::string>>& in, DataType dt) = 0;
virtual void write_text(const std::string& x) = 0;
virtual void compound_statement_begin() = 0;
virtual void compound_statement_end() = 0;
// Operations
virtual void op_get_global_id(const Operand& dst_var, int32_t dim) = 0;
virtual void op_get_global_coord(const Operand& dst, const Operand& step, const TensorOperand& tensor, int32_t dim) = 0;
virtual void op_get_global_batch(const Operand& dst, const TensorOperand& tensor) = 0;
virtual void op_get_global_size(const Operand& dst_var, int32_t dim) = 0;
virtual void op_binary_expression(const Operand& dst, const Operand &lhs, BinaryOp op, const Operand &rhs) = 0;
virtual void op_assign(const Operand& dst_name, const Operand& src_name) = 0;
virtual void op_scalar_function(const Operand& dst_name, const Operand& src_name, ScalarUnaryFunction func) = 0;
virtual void op_if(const Operand& lhs, BinaryOp op, const Operand& rhs) = 0;
virtual void op_for_loop(const Operand& var_name, BinaryOp cond_op, const Operand& cond_value, AssignmentOp update_op, const Operand& update_value) = 0;
virtual void op_load_indirect(const TensorOperand& tensor, const Operand& dst, const Operand& x, const Operand& y_indirect, const Operand& z, const Operand& b = Operand("0", OperandType::ScalarInt32)) = 0;
virtual void op_load_immediate(const TensorOperand& tensor, const Operand& dst, const Operand& x, const Operand& y, const Operand& z, const Operand& b = Operand("0", OperandType::ScalarInt32), const Operand& dilation_y = Operand("1", OperandType::ScalarInt32)) = 0;
virtual void op_store_immediate(const TensorOperand& tensor, const Operand& src, const Operand& x, const Operand& y, const Operand& z, const Operand& b = Operand("0", OperandType::ScalarInt32)) = 0;
virtual void op_cast_expression(const Operand& dst, const Operand &src, ConvertPolicy policy) = 0;
virtual void op_return() = 0;
// virtual void op_else() = 0;
// virtual void op_elseif() = 0;
// Utils
// It is the process of converting
virtual void util_get_indirect_buffer(const Operand& dst, const TensorOperand& tensor, const Operand& x, const Operand& y, const Operand& x_off, const Operand& y_off) = 0;
};
enum class GpuLoadStoreType
{
Load = 1,
Store = 2
};
class IGpuLoadStoreHelperWriter
{
public:
IGpuLoadStoreHelperWriter(IGpuKernelWriter *x, GpuTensor3dMapper mapper, GpuLoadStoreType type) : _writer(x), _mapper(mapper), _type(type) {}
IGpuLoadStoreHelperWriter(const IGpuLoadStoreHelperWriter &) = default;
IGpuLoadStoreHelperWriter &operator=(const IGpuLoadStoreHelperWriter &) = default;
virtual ~IGpuLoadStoreHelperWriter() = default;
virtual void initialize(IVectorTile *dst, IVectorTile *x, IVectorTile *z, IVectorTile *b) = 0;
virtual void write(const std::pair<int32_t, std::string>& y) = 0;
virtual void finalize() = 0;
protected:
IGpuKernelWriter* _writer;
GpuTensor3dMapper _mapper;
GpuLoadStoreType _type;
};
class ClLoadStoreBufferHelperWriter : public IGpuLoadStoreHelperWriter
{
public:
ClLoadStoreBufferHelperWriter(IGpuKernelWriter *x, const GpuTensor3dMapper& mapper, GpuLoadStoreType type) : IGpuLoadStoreHelperWriter(x, mapper, type)
{
}
ClLoadStoreBufferHelperWriter(const ClLoadStoreBufferHelperWriter &) = default;
ClLoadStoreBufferHelperWriter &operator=(const ClLoadStoreBufferHelperWriter &) = default;
static bool validate(IGpuKernelWriter *x, GpuTensor3dMapper mapper, GpuLoadStoreType type, IVectorTile *dst)
{
CKW_UNUSED(x, type, dst);
if(mapper.gpu_sampler().storage != GpuSamplerTensorStorage::BufferUint8Ptr)
{
return false;
}
return true;
}
void initialize(IVectorTile *dst, IVectorTile *x, IVectorTile *z, IVectorTile *b) override
{
assert(validate(_writer, _mapper, _type, dst));
_dst = dst;
_ls_width_full = dst->format().w;
_coord_x = x->scalar(0, 0).str;
_coord_z = z->scalar(0, 0).str;
_coord_b = b->scalar(0, 0).str;
_coord_orig_z = _coord_z;
out_of_bound_initialize_x(_coord_x);
out_of_bound_initialize_z(_coord_z);
/*
meaning of else:
- x: partial load/store
- y: no load/store operation
- z: no load/store operation
if(x)
{
if(z)
{
if(y)
{
// full load/store width
}
else
{
// no load/store
}
}
else
{
// no load/store
}
}
else
{
if(z)
{
if(y)
{
// partial load/store width
}
else
{
// no load/store
}
}
else
{
// no load/store
}
}
*/
}
void write(const std::pair<int32_t, std::string>& y) override
{
int32_t idx_y = y.first;
std::string coord_y = y.second;
// The only check required is on Y.
out_of_bound_initialize_y(coord_y);
const std::string dst = _dst->vector(idx_y).str;
const std::string address = to_ls_buffer_address(_coord_x, coord_y, _coord_z, _coord_b);
const std::string ls_buf = to_ls_buffer(_type, _ls_width_full, dst, address);
_writer->write_text(ls_buf);
_writer->write_text(";\n");
out_of_bound_finalize_y(dst);
// The left over load/store will be written in the finalize stage
if(_ls_width_part.size() != 0)
{
int32_t w = 0;
for(auto &p : _ls_width_part)
{
const std::string dst0 = _dst->vector(w, p, idx_y).str;
const std::string coord_x = _coord_x + " + " + std::to_string(w);
const std::string address = to_ls_buffer_address(coord_x, coord_y, _coord_z, _coord_b);
const std::string ls_buf0 = to_ls_buffer(_type, p, dst0, address);
_leftovers_x.push_back(std::make_pair(std::make_pair(dst0, coord_y), ls_buf0));
w += p;
}
}
}
void finalize() override
{
out_of_bound_finalize_z();
out_of_bound_finalize_x();
}
private:
IVectorTile* _dst { nullptr };
int32_t _ls_width_full { 0 };
std::vector<int32_t> _ls_width_part { };
std::vector<std::pair<std::pair<std::string, std::string>, std::string>> _leftovers_x {};
std::string _coord_x {};
std::string _coord_z {};
std::string _coord_orig_z {};
std::string _coord_b {};
void out_of_bound_initialize_x(std::string& coord)
{
if(_mapper.gpu_sampler().address_mode_x == TensorSamplerAddressModeX::OverlappingMin)
{
auto tensor_format = _mapper.tensor_argument()->format();
auto shape = tensor_format.shape;
_ls_width_part = decompose_leftover_ls_vector_width(shape[0] % _ls_width_full);
if(_ls_width_part.size() != 0)
{
_writer->write_text("if(" + coord + " > 0)\n");
_writer->compound_statement_begin();
}
}
};
void out_of_bound_finalize_x()
{
if(_mapper.gpu_sampler().address_mode_x == TensorSamplerAddressModeX::OverlappingMin)
{
if(_ls_width_part.size() != 0)
{
_writer->compound_statement_end();
_writer->write_text("else\n");
_writer->compound_statement_begin();
out_of_bound_initialize_z(_coord_orig_z);
for(auto &i : _leftovers_x)
{
out_of_bound_initialize_y(i.first.second);
_writer->write_text(i.second);
_writer->write_text(";\n");
out_of_bound_finalize_y(i.first.first);
}
out_of_bound_finalize_z();
_writer->compound_statement_end();
}
}
};
void out_of_bound_initialize_y(std::string& coord)
{
std::string max = "";
const auto address_mode_y = _mapper.gpu_sampler().address_mode_y;
switch(address_mode_y)
{
case TensorSamplerAddressModeY::Skip:
case TensorSamplerAddressModeY::ClampToBorder:
// NOTE: This line should not be moved outside of the switch statement.
// The reason for that is because when we query the component, the component is marked as used
// and added to the list of arguments of the kernel. Since, not in all cases this component is required,
// we should request the component only when used
max = _mapper.tensor_component_y();
_writer->write_text("if((" + coord + " >= 0) && (" + coord + " < " + max + "))\n");
_writer->compound_statement_begin();
break;
case TensorSamplerAddressModeY::SkipMinEdgeOnly:
case TensorSamplerAddressModeY::ClampToBorderMinEdgeOnly:
_writer->write_text("if(" + coord + " >= 0)\n");
_writer->compound_statement_begin();
break;
case TensorSamplerAddressModeY::SkipMaxEdgeOnly:
case TensorSamplerAddressModeY::ClampToBorderMaxEdgeOnly:
max = _mapper.tensor_component_y();
_writer->write_text("if(" + coord + " < " + max + ")\n");
_writer->compound_statement_begin();
break;
case TensorSamplerAddressModeY::ClampToNearest:
max = _mapper.tensor_component_y();
coord = "clamp(" + coord + ", 0, " + max + " - 1)";
break;
case TensorSamplerAddressModeY::ClampToMaxEdgeOnly:
max = _mapper.tensor_component_y();
coord = "min(" + coord + ", " + max + " - 1)";
break;
case TensorSamplerAddressModeY::ClampToMinEdgeOnly:
coord = "max(" + coord + ", 0)";
break;
case TensorSamplerAddressModeY::None:
break;
default:
std::cout << "Unsupported address mode for write_out_of_bound_check_yz" << std::endl;
assert(false);
}
};
void out_of_bound_finalize_y(const std::string& dst)
{
const auto address_mode_y = _mapper.gpu_sampler().address_mode_y;
switch(address_mode_y)
{
case TensorSamplerAddressModeY::ClampToBorder:
case TensorSamplerAddressModeY::ClampToBorderMaxEdgeOnly:
case TensorSamplerAddressModeY::ClampToBorderMinEdgeOnly:
case TensorSamplerAddressModeY::Skip:
case TensorSamplerAddressModeY::SkipMaxEdgeOnly:
case TensorSamplerAddressModeY::SkipMinEdgeOnly:
_writer->compound_statement_end();
break;
default:
assert(false);
}
switch(address_mode_y)
{
case TensorSamplerAddressModeY::ClampToBorder:
case TensorSamplerAddressModeY::ClampToBorderMinEdgeOnly:
case TensorSamplerAddressModeY::ClampToBorderMaxEdgeOnly:
_writer->write_text("else\n");
_writer->compound_statement_begin();
_writer->write_text(dst);
_writer->write_text(" = 0.0f;\n");
_writer->compound_statement_end();
break;
default:
assert(false);
}
};
void out_of_bound_initialize_z(std::string& coord)
{
std::string max = "";
const auto address_mode_z = _mapper.gpu_sampler().address_mode_z;
switch(address_mode_z)
{
case TensorSamplerAddressModeZ::Skip:
max = _mapper.tensor_component_z();
_writer->write_text("if((" + coord + " >= 0) && (" + coord + " < " + max + "))\n");
_writer->compound_statement_begin();
break;
case TensorSamplerAddressModeZ::SkipMinEdgeOnly:
_writer->write_text("if(" + coord + " >= 0)\n");
_writer->compound_statement_begin();
break;
case TensorSamplerAddressModeZ::SkipMaxEdgeOnly:
max = _mapper.tensor_component_z();
_writer->write_text("if(" + coord + " < " + max + ")\n");
_writer->compound_statement_begin();
break;
case TensorSamplerAddressModeZ::ClampToNearest:
max = _mapper.tensor_component_z();
coord = "clamp(" + coord + ", 0, " + max + " - 1)";
break;
case TensorSamplerAddressModeZ::ClampToMaxEdgeOnly:
max = _mapper.tensor_component_z();
coord = "min(" + coord + ", " + max + " - 1)";
break;
case TensorSamplerAddressModeZ::ClampToMinEdgeOnly:
coord = "max(" + coord + ", 0)";
break;
case TensorSamplerAddressModeZ::None:
break;
default:
std::cout << "Unsupported address mode for write_out_of_bound_check_yz" << std::endl;
assert(false);
}
};
void out_of_bound_finalize_z()
{
const auto address_mode_z = _mapper.gpu_sampler().address_mode_z;
switch(address_mode_z)
{
case TensorSamplerAddressModeZ::Skip:
case TensorSamplerAddressModeZ::SkipMinEdgeOnly:
case TensorSamplerAddressModeZ::SkipMaxEdgeOnly:
_writer->compound_statement_end();
break;
default:
assert(false);
}
};
std::vector<int32_t> decompose_leftover_ls_vector_width(int32_t ls_leftover_vector_width) const
{
std::vector<int32_t> x;
switch(ls_leftover_vector_width)
{
case 0:
break;
case 1:
case 2:
case 3:
case 4:
case 8:
case 16:
x.push_back(ls_leftover_vector_width);
break;
case 5:
x.push_back(4);
x.push_back(1);
break;
case 6:
x.push_back(4);
x.push_back(2);
break;
case 7:
x.push_back(4);
x.push_back(3);
break;
case 9:
x.push_back(8);
x.push_back(1);
break;
case 10:
x.push_back(8);
x.push_back(2);
break;
case 11:
x.push_back(8);
x.push_back(3);
break;
case 12:
x.push_back(8);
x.push_back(4);
break;
case 13:
x.push_back(8);
x.push_back(4);
x.push_back(1);
break;
case 14:
x.push_back(8);
x.push_back(4);
x.push_back(2);
break;
case 15:
x.push_back(8);
x.push_back(4);
x.push_back(3);
break;
default:
assert(false);
}
return x;
}
std::string to_ls_buffer(GpuLoadStoreType type, int32_t vector_width, const std::string& data, const std::string& address)
{
switch(type)
{
case GpuLoadStoreType::Load:
if(vector_width != 1)
{
return data + " = vload" + std::to_string(vector_width) + "(0, " + address + ")";
}
else
{
return data + " = *(" + address + ")";
}
break;
case GpuLoadStoreType::Store:
if(vector_width != 1)
{
return "vstore" + std::to_string(vector_width) + "(" + data + ", 0, " + address + ")";
}
else
{
return "*(" + address + ") = " + data;
}
break;
default:
std::cout << "Unsupported GpuLoadStoreType" << std::endl;
assert(false);
return "";
}
}
std::string to_ls_buffer_address(const std::string& x, const std::string& y, const std::string& z, const std::string& b) const
{
auto tensor_storage = static_cast<GpuTensorStorage>(_mapper.gpu_sampler().storage);
assert(tensor_storage == GpuTensorStorage::BufferUint8Ptr);
const std::string ptr_buf = _mapper.tensor_argument()->storage(tensor_storage);
const std::string dst_type = get_cl_data_type(_dst->format().dt, 1);
std::string address;
address += "(__global ";
address += dst_type;
address += "*)(";
address += ptr_buf;
if(x != "0" && (_mapper.is_one_component_x() != true))
{
address += " + (";
address += x + ") * sizeof(" + dst_type + ")";
}
if(y != "0" && (_mapper.is_one_component_y() != true))
{
const std::string stride_y = _mapper.tensor_component_stride_y();
address += " + (";
address += y + ")";
address += " * ";
address += stride_y;
}
if(z != "0" && (_mapper.is_one_component_z() != true))
{
const std::string stride_z = _mapper.tensor_component_stride_z();
address += " + (";
address += z + ")";
address += " * ";
address += stride_z;
}
if(b != "0" && (_mapper.is_one_component_batch() != true))
{
const std::string stride_b = _mapper.tensor_component_stride_batch();
address += " + (";
address += b + ")";
address += " * ";
address += stride_b;
}
address += ")";
return address;
}
};
class ClLoadStoreImage2dHelperWriter : public IGpuLoadStoreHelperWriter
{
public:
static bool validate(IGpuKernelWriter *x, const GpuTensor3dMapper& mapper, GpuLoadStoreType type, IVectorTile *dst)
{
CKW_UNUSED(x);
if(dst->format().w != 4)
{
return false;
}
if(mapper.gpu_sampler().address_mode_x != TensorSamplerAddressModeX::None)
{
return false;
}
if(mapper.gpu_sampler().address_mode_z != TensorSamplerAddressModeZ::None)
{
return false;
}
if(mapper.gpu_sampler().storage != GpuSamplerTensorStorage::Image2dReadOnly && type == GpuLoadStoreType::Load)
{
return false;
}
if(mapper.gpu_sampler().storage != GpuSamplerTensorStorage::Image2dWriteOnly && type == GpuLoadStoreType::Store)
{
return false;
}
if((dst->format().dt != DataType::Fp32) && (dst->format().dt != DataType::Fp16))
{
return false;
}
return true;
/*
- x: Only GpuSamplerAddressModeX::None is supported and vector length = 4
- z: Only GpuSamplerAddressModeZ::None is supported
*/
}
ClLoadStoreImage2dHelperWriter(IGpuKernelWriter *x, const GpuTensor3dMapper& mapper, GpuLoadStoreType type) : IGpuLoadStoreHelperWriter(x, mapper, type)
{
}
ClLoadStoreImage2dHelperWriter(const ClLoadStoreImage2dHelperWriter &) = default;
ClLoadStoreImage2dHelperWriter &operator=(const ClLoadStoreImage2dHelperWriter &) = default;
void initialize(IVectorTile *dst, IVectorTile *x, IVectorTile *z, IVectorTile *b) override
{
assert(validate(_writer, _mapper, _type, dst));
_dst = dst;
_ls_width_full = dst->format().w;
_coord_x = x->scalar(0, 0).str;
_coord_z = z->scalar(0, 0).str;
_coord_b = b->scalar(0, 0).str;
/*
if(y)
{
// full load/store width
}
else
{
// no load/store
}
*/
}
void write(const std::pair<int32_t, std::string>& y) override
{
int32_t idx_y = y.first;
std::string coord_y = y.second;
// The only check required is on Y.
out_of_bound_initialize_y(coord_y);
const std::string dst = _dst->vector(idx_y).str;
const std::string sampler = to_ls_image2d_sampler();
const std::string coord = to_ls_image2d_coord(_coord_x, coord_y, _coord_z, _coord_b);
const std::string ls_buf = to_ls_image2d(_type, _ls_width_full, dst, sampler, coord);
_writer->write_text(ls_buf);
_writer->write_text(";\n");
out_of_bound_finalize_y(dst);
}
void finalize() override
{
}
private:
IVectorTile* _dst { nullptr };
int32_t _ls_width_full { 0 };
std::string _coord_x {};
std::string _coord_z {};
std::string _coord_b {};
void out_of_bound_initialize_y(std::string& coord)
{
std::string max = "";
const auto address_mode_y = _mapper.gpu_sampler().address_mode_y;
switch(address_mode_y)
{
case TensorSamplerAddressModeY::Skip:
max = _mapper.tensor_component_y();
_writer->write_text("if((" + coord + " >= 0) && (" + coord + " < " + max + "))\n");
_writer->compound_statement_begin();
break;
case TensorSamplerAddressModeY::SkipMinEdgeOnly:
_writer->write_text("if(" + coord + " >= 0)\n");
_writer->compound_statement_begin();
break;
case TensorSamplerAddressModeY::SkipMaxEdgeOnly:
max = _mapper.tensor_component_y();
_writer->write_text("if(" + coord + " < " + max + ")\n");
_writer->compound_statement_begin();
break;
case TensorSamplerAddressModeY::ClampToBorder:
case TensorSamplerAddressModeY::ClampToBorderMinEdgeOnly:
case TensorSamplerAddressModeY::ClampToBorderMaxEdgeOnly:
case TensorSamplerAddressModeY::ClampToNearest:
case TensorSamplerAddressModeY::ClampToMaxEdgeOnly:
case TensorSamplerAddressModeY::ClampToMinEdgeOnly:
case TensorSamplerAddressModeY::None:
break;
default:
std::cout << "Unsupported address mode for write_out_of_bound_check_y" << std::endl;
assert(false);
}
};
void out_of_bound_finalize_y(const std::string& dst)
{
CKW_UNUSED(dst);
const auto address_mode_y = _mapper.gpu_sampler().address_mode_y;
switch(address_mode_y)
{
case TensorSamplerAddressModeY::Skip:
case TensorSamplerAddressModeY::SkipMinEdgeOnly:
case TensorSamplerAddressModeY::SkipMaxEdgeOnly:
_writer->compound_statement_end();
break;
default:
assert(false);
}
};
std::string to_ls_image2d(GpuLoadStoreType type, int32_t vector_width, const std::string& data, const std::string& sampler, const std::string& coord)
{
CKW_UNUSED(vector_width);
auto tensor_storage = static_cast<GpuTensorStorage>(_mapper.gpu_sampler().storage);
const std::string image2d_obj = _mapper.tensor_argument()->storage(tensor_storage);
// const DataType dt = _dst->format().dt;
const std::string post_fix = _dst->format().dt == DataType::Fp32? "f" : "h";
switch(type)
{
case GpuLoadStoreType::Load:
return data + " = read_image" + post_fix + "(" + image2d_obj + ", " + sampler + ", " + coord + ")";
break;
case GpuLoadStoreType::Store:
return "write_image" + post_fix + "(" + image2d_obj + ", " + coord + ", " + data + ")";
default:
assert(false);
std::cout << "Unsupported GpuLoadStoreType" << std::endl;
assert(false);
return "";
}
}
std::string to_ls_image2d_sampler() const
{
const auto address_mode_y = _mapper.gpu_sampler().address_mode_y;
switch(address_mode_y)
{
case TensorSamplerAddressModeY::None:
return "CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_NONE | CLK_FILTER_NEAREST";
case TensorSamplerAddressModeY::Skip:
case TensorSamplerAddressModeY::SkipMinEdgeOnly:
case TensorSamplerAddressModeY::SkipMaxEdgeOnly:
case TensorSamplerAddressModeY::ClampToBorder:
case TensorSamplerAddressModeY::ClampToBorderMinEdgeOnly:
case TensorSamplerAddressModeY::ClampToBorderMaxEdgeOnly:
return "CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST";
case TensorSamplerAddressModeY::ClampToNearest:
case TensorSamplerAddressModeY::ClampToMaxEdgeOnly:
case TensorSamplerAddressModeY::ClampToMinEdgeOnly:
return "CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP_TO_EDGE | CLK_FILTER_NEAREST";
default:
std::cout << "Unsupported address_mode_coord" << std::endl;
assert(false);
return "";
}
}
std::string to_ls_image2d_coord(const std::string& x, const std::string& y, const std::string& z, const std::string& b) const
{
std::string coord_x = "(" + x + ") >> 2";
std::string coord_y = "(";
if(y != "0" && (_mapper.is_one_component_y() != true))
{
coord_y += y;
}
if(z != "0" && (_mapper.is_one_component_z() != true))
{
const std::string dim = _mapper.tensor_component_y();
coord_y += " + (";
coord_y += z + ")";
coord_y += " * ";
coord_y += dim;
}
if(b != "0" && (_mapper.is_one_component_batch() != true))
{
const std::string dim0 = _mapper.tensor_component_y();
const std::string dim1 = _mapper.tensor_component_z();
coord_y += " + (";
coord_y += b + ")";
coord_y += " * ";
coord_y += dim0;
coord_y += " * ";
coord_y += dim1;
}
coord_y += ")";
return "(int2)(" + coord_x + ", " + coord_y + ")";
}
};
/** IGpuLoadStoreHelperWriter factory class */
class ClLoadStoreHelperWriterFactory final
{
public:
/** Static method to call the IGpuLoadStoreHelperWriter class accordingly with the tensor storage set in the mapper
*
*
* @return IGpuLoadStoreHelperWriter
*/
static std::unique_ptr<IGpuLoadStoreHelperWriter> create(IGpuKernelWriter *x, const GpuTensor3dMapper& mapper, GpuLoadStoreType type)
{
const auto tensor_storage = mapper.gpu_sampler().storage;
switch(tensor_storage)
{
case GpuSamplerTensorStorage::BufferUint8Ptr:
return std::make_unique<ClLoadStoreBufferHelperWriter>(x, mapper, type);
case GpuSamplerTensorStorage::Image2dReadOnly:
case GpuSamplerTensorStorage::Image2dWriteOnly:
return std::make_unique<ClLoadStoreImage2dHelperWriter>(x, mapper, type);
default:
std::cout << "Unsupported Gpu tensor storage" << std::endl;
assert(false);
return nullptr;
}
}
};
// This utility method needs to go in utils.h
inline bool is_tile_scalar(IVectorTile* x)
{
return x->format().w == 1 && x->format().h == 1;
}
class ClKernelWriter : public IGpuKernelWriter
{
public:
ClKernelWriter(GpuKernelWriterAttribute *attr, GpuKernelWriterDataHolder *x)
{
_data = x;
_attr = attr;
}
ClKernelWriter(const ClKernelWriter &) = default;
ClKernelWriter &operator=(const ClKernelWriter &) = default;
// A IdSpaced ID is a term used to describe a fragment that is registered in ICode to ensure
// there are no conflicts or ambiguity in the code
void set_IdSpace(int32_t id) override
{
_data->tiles.set_IdSpace(id);
_data->arguments.set_IdSpace(id);
}
void import_tile(const std::string& dst_name, const IVectorTile *src) override
{
_data->tiles.insert(dst_name, src);
}
void declare_argument(const std::string& name, const TensorInfo& tensor) override
{
assert(_data->arguments[name] == nullptr);
_data->arguments.insert(name, tensor, _attr->return_tensor_component_by_value);
}
void declare_tile(const std::string& name, const TileInfo& format) override
{
assert(_data->tiles[name] == nullptr);
_data->tiles.insert(name, format);
IVectorTile *x = _data->tiles[name];
for(auto &t : x->underlying_source_variables())
{
_data->code += t.type.str + " " + t.str + ";\n";
}
}
void declare_const_tile(const std::string& name, const std::vector<std::vector<std::string>>& in, DataType dt) override
{
assert(_data->tiles[name] == nullptr);
_data->tiles.insert(name, in, dt);
// Note: A constant does not need to be declared in the code
}
void write_text(const std::string& x) override
{
_data->code += x;
}
void compound_statement_begin() override
{
_data->tiles.increment_registry_level();
_data->code += "{\n";
}
void compound_statement_end() override
{
_data->tiles.decrement_registry_level();
_data->code += "}\n";
}
void op_get_global_id(const Operand& dst_var, int32_t dim) override
{
assert(dst_var.type() == OperandType::Tile);
assert(_data->tiles.has_tile(dst_var.value()));
assert(_data->tiles[dst_var.value()]->format().w == 1 &&
_data->tiles[dst_var.value()]->format().h == 1); // It must be a scalar variable
auto var = _data->tiles[dst_var.value()];
_data->code += var->scalar(0, 0).str;
_data->code += " = get_global_id(";
_data->code += std::to_string(dim);
_data->code += ");\n";
};
void op_get_global_coord(const Operand& o_dst, const Operand& o_step, const TensorOperand& o_tensor, int32_t dim) override
{
OperandUnpacker operands(_data->tiles, _data->arguments);
auto dst = operands.unpack(o_dst);
auto step = operands.unpack(o_step);
// Validation: Check that x, y and z are scalar
TensorOperandUnpacker tensor_operands(_data->arguments);
auto tensor = tensor_operands.unpack(o_tensor);
auto gpu_sampler = o_tensor.sampler();
GpuTensor3dMapper mapper(tensor, gpu_sampler);
switch (dim)
{
case 0:
if(mapper.is_one_component_x())
{
_data->code += dst->scalar(0, 0).str;
_data->code += " = 0;\n";
}
else
{
if(mapper.gpu_sampler().address_mode_x == TensorSamplerAddressModeX::OverlappingMin)
{
// Validation: Check: fixed tensor shape
// TO BE CHANGED
_data->code += dst->scalar(0, 0).str;
_data->code += " = get_global_id(0) * ";
_data->code += step->scalar(0, 0).str;
_data->code += ";\n";
}
else
{
_data->code += dst->scalar(0, 0).str;
_data->code += " = get_global_id(0) * ";
_data->code += step->scalar(0, 0).str;
_data->code += ";\n";
}
}
break;
case 1:
if(mapper.is_one_component_y())
{
_data->code += dst->scalar(0, 0).str;
_data->code += " = 0;\n";
}
else
{
if(mapper.gpu_sampler().address_mode_y == TensorSamplerAddressModeY::OverlappingMin)
{
}
else
{
_data->code += dst->scalar(0, 0).str;
_data->code += " = get_global_id(1) * ";
_data->code += step->scalar(0, 0).str;
_data->code += ";\n";
}
}
break;
case 2:
if(mapper.is_one_component_z())
{
_data->code += dst->scalar(0, 0).str;
_data->code += " = 0;\n";
}
else
{
_data->code += dst->scalar(0, 0).str;
_data->code += " = get_global_id(2) * ";
_data->code += step->scalar(0, 0).str;
_data->code += ";\n";
}
break;
default:
break;
}
};
void op_get_global_batch(const Operand& o_dst, const TensorOperand& o_tensor) override
{
OperandUnpacker operands(_data->tiles, _data->arguments);
auto dst = operands.unpack(o_dst);
TensorOperandUnpacker tensor_operands(_data->arguments);
auto tensor = tensor_operands.unpack(o_tensor);
auto gpu_sampler = o_tensor.sampler();
GpuTensor3dMapper mapper(tensor, gpu_sampler);
if(mapper.is_one_component_batch())
{
_data->code += dst->scalar(0, 0).str;
_data->code += " = 0;\n";
}
else
{
std::cout << "Unsupported batched computation" << std::endl;
assert(false);
}
};
void op_get_global_size(const Operand& dst_var, int32_t dim) override
{
assert(dst_var.type() == OperandType::Tile);
assert(_data->tiles.has_tile(dst_var.value()));
assert(_data->tiles[dst_var.value()]->format().w == 1 &&
_data->tiles[dst_var.value()]->format().h == 1); // It must be a scalar variable
auto var = _data->tiles[dst_var.value()];
_data->code += var->scalar(0, 0).str;
_data->code += " = get_global_size(";
_data->code += std::to_string(dim);
_data->code += ");\n";
}
void op_binary_expression(const Operand& dst_name, const Operand& lhs_name, BinaryOp op, const Operand& rhs_name) override
{
OperandUnpacker operands(_data->tiles, _data->arguments);
auto lhs = operands.unpack(lhs_name);
auto rhs = operands.unpack(rhs_name);
auto dst = operands.unpack(dst_name);
const int32_t dst_w = dst->format().w;
const int32_t dst_h = dst->format().h;
assert(lhs != nullptr);
const int32_t lhs_w = lhs->format().w;
const int32_t rhs_w = rhs->format().w;
if(op == BinaryOp::MatMul_Nt_T)
{
assert((dst->format().dt == DataType::Fp32) || (dst->format().dt == DataType::Fp16));
for(int32_t y = 0; y < dst_h; ++y)
{
for(int32_t x = 0; x < dst_w; ++x)
{
for(int32_t k = 0; k < lhs_w; ++k)
{
_data->code += dst->scalar(x, y).str;
_data->code += " = fma(";
_data->code += lhs->scalar(k, y).str;
_data->code += ", ";
_data->code += rhs->scalar(k, x).str;
_data->code += ", ";
_data->code += dst->scalar(x, y).str;
_data->code += ");\n";
}
}
}
return;
}
bool broadcast_lhs_x = dst_w != 1 && lhs_w == 1;
bool broadcast_rhs_x = dst_w != 1 && rhs_w == 1;
std::string lhs_prefix = broadcast_lhs_x? "(" + dst->underlying_source_variables()[0].type.str + ")" : "";
std::string rhs_prefix = broadcast_rhs_x? "(" + dst->underlying_source_variables()[0].type.str + ")" : "";
std::string op_str = to_string(op);
// Broadcasting on Y is automatic
for(int32_t y = 0; y < dst_h; ++y)
{
_data->code += dst->vector(y).str;
_data->code += " = ";
_data->code += lhs_prefix + lhs->vector(y).str;
_data->code += " ";
_data->code += op_str;
_data->code += " ";
_data->code += rhs_prefix + rhs->vector(y).str;
_data->code += ";\n";
}
};
void op_cast_expression(const Operand& o_dst, const Operand &o_src, ConvertPolicy policy) override
{
CKW_UNUSED(policy);
OperandUnpacker operands(_data->tiles, _data->arguments);
auto src = operands.unpack(o_src);
auto dst = operands.unpack(o_dst);
// const int32_t dst_w = dst->format().w;
const int32_t dst_h = dst->format().h;
const std::string dt = dst->scalar(0, 0).type.str;
// Broadcasting on Y is automatic
for(int32_t y = 0; y < dst_h; ++y)
{
_data->code += dst->vector(y).str;
_data->code += " = convert_" + dt + "(";
_data->code += src->vector(y).str;
_data->code += ");\n";
}
};
void op_assign(const Operand& dst_name, const Operand& src_name) override
{
OperandUnpacker operands(_data->tiles, _data->arguments);
auto src = operands.unpack(src_name);
auto dst = operands.unpack(dst_name);
const int32_t dst_w = dst->format().w;
const int32_t dst_h = dst->format().h;
const int32_t src_w = src->format().w;
// const int32_t src_h = src->format().h;
const std::string dt = dst->scalar(0, 0).type.str;
bool broadcast_src_x = dst_w != 1 && src_w == 1;
std::string src_prefix = broadcast_src_x? "(" + dt + ")" : "";
// Broadcasting on Y is automatic
for(int32_t y = 0; y < dst_h; ++y)
{
_data->code += dst->vector(y).str;
_data->code += " = ";
_data->code += src_prefix + src->vector(y).str;
_data->code += ";\n";
}
}
void op_scalar_function(const Operand& dst_name, const Operand& src_name, ScalarUnaryFunction func) override
{
OperandUnpacker operands(_data->tiles, _data->arguments);
auto src = operands.unpack(src_name);
auto dst = operands.unpack(dst_name);
const int32_t dst_w = dst->format().w;
const int32_t dst_h = dst->format().h;
const int32_t src_w = src->format().w;
// const int32_t src_h = src->format().h;
const std::string dt = dst->scalar(0, 0).type.str;
bool broadcast_src_x = dst_w != 1 && src_w == 1;
std::string src_prefix = broadcast_src_x? "(" + dt + ")" : "";
// Broadcasting on Y is automatic
for(int32_t y = 0; y < dst_h; ++y)
{
_data->code += dst->vector(y).str;
_data->code += " = ";
switch(func)
{
case ScalarUnaryFunction::Exp:
_data->code += "exp(";
break;
default:
CKW_ASSERT(false);
}
_data->code += src_prefix + src->vector(y).str;
_data->code += ");\n";
}
}
void op_if(const Operand& o_lhs, BinaryOp op, const Operand& o_rhs) override
{
OperandUnpacker operands(_data->tiles, _data->arguments);
auto lhs = operands.unpack(o_lhs);
auto rhs = operands.unpack(o_rhs);
assert(is_tile_scalar(lhs));
assert(is_tile_scalar(rhs));
_data->code += "if(";
_data->code += lhs->scalar(0, 0).str;
_data->code += " ";
_data->code += to_string(op);
_data->code += " ";
_data->code += rhs->scalar(0, 0).str;
_data->code += ")\n";
}
void op_for_loop(const Operand& var_name, BinaryOp cond_op, const Operand& cond_value_name, AssignmentOp update_op, const Operand& update_value_name) override
{
OperandUnpacker operands(_data->tiles, _data->arguments);
auto var = operands.unpack(var_name);
auto cond_value = operands.unpack(cond_value_name);
auto update_value = operands.unpack(update_value_name);
const int32_t dst_w = var->format().w;
const int32_t dst_h = var->format().h;
// It must be a scalar variable
CKW_UNUSED(dst_w, dst_h);
assert(dst_w == 1);
assert(dst_h == 1);
_data->code += "for(; " ;
_data->code += var->scalar(0, 0).str;
_data->code += " ";
_data->code += to_string(cond_op);
_data->code += " " + cond_value->scalar(0, 0).str + "; ";
_data->code += var->scalar(0, 0).str;
_data->code += " ";
_data->code += to_string(update_op);
_data->code += " " + update_value->scalar(0, 0).str + ")";
_data->code += "\n";
}
void op_load_immediate(const TensorOperand& o_tensor, const Operand& o_dst, const Operand& o_x, const Operand& o_y, const Operand& o_z, const Operand& o_batch_idx, const Operand& dilation_y) override
{
OperandUnpacker operands(_data->tiles, _data->arguments);
auto dst = operands.unpack(o_dst);
auto x = operands.unpack(o_x);
auto y = operands.unpack(o_y);
auto z = operands.unpack(o_z);
auto dil_y = operands.unpack(dilation_y);
auto b = operands.unpack(o_batch_idx);
TensorOperandUnpacker tensor_operands(_data->arguments);
auto tensor = tensor_operands.unpack(o_tensor);
auto gpu_sampler = o_tensor.sampler();
GpuTensor3dMapper mapper(tensor, gpu_sampler);
auto load_writer = ClLoadStoreHelperWriterFactory::create(this, mapper, GpuLoadStoreType::Load);
// Initialize the constant part
load_writer->initialize(dst, x, z, b);
for(int i = 0; i < dst->format().h; ++i)
{
std::string coord_y = y->scalar(0, 0).str + " + " + std::to_string(i);
if(dil_y->scalar(0, 0).str != "1")
{
coord_y += " * " + dil_y->scalar(0, 0).str;
}
load_writer->write(std::make_pair(i, coord_y));
}
load_writer->finalize();
}
void op_load_indirect(const TensorOperand& o_tensor, const Operand& o_dst, const Operand& o_x, const Operand& o_indirect_h, const Operand& o_z, const Operand& o_batch_idx) override
{
OperandUnpacker operands(_data->tiles, _data->arguments);
auto dst = operands.unpack(o_dst);
auto x = operands.unpack(o_x);
auto y_ind = operands.unpack(o_indirect_h);
auto z = operands.unpack(o_z);
auto b = operands.unpack(o_batch_idx);
TensorOperandUnpacker tensor_operands(_data->arguments);
auto tensor = tensor_operands.unpack(o_tensor);
auto gpu_sampler = o_tensor.sampler();
GpuTensor3dMapper mapper(tensor, gpu_sampler);
auto load_writer = ClLoadStoreHelperWriterFactory::create(this, mapper, GpuLoadStoreType::Load);
// Initialize the constant part
load_writer->initialize(dst, x, z, b);
for(int i = 0; i < dst->format().h; ++i)
{
load_writer->write(std::make_pair(i, y_ind->scalar(0, i).str));
}
load_writer->finalize();
}
void op_store_immediate(const TensorOperand& tensor_name, const Operand& src_name, const Operand& x_name, const Operand& y_name, const Operand& z_name, const Operand& batch_index_name) override
{
OperandUnpacker operands(_data->tiles, _data->arguments);
auto src = operands.unpack(src_name);
auto x = operands.unpack(x_name);
auto y = operands.unpack(y_name);
auto z = operands.unpack(z_name);
auto b = operands.unpack(batch_index_name);
TensorOperandUnpacker tensor_operands(_data->arguments);
auto tensor = tensor_operands.unpack(tensor_name);
auto gpu_sampler = tensor_name.sampler();
GpuTensor3dMapper mapper(tensor, gpu_sampler);
auto store_writer = ClLoadStoreHelperWriterFactory::create(this, mapper, GpuLoadStoreType::Store);
// Initialize the constant part
store_writer->initialize(src, x, z, b);
int32_t tile_h = src->format().h;
for(int m0 = tile_h - 1; m0 >= 0; m0--)
{
store_writer->write(std::make_pair(m0, y->scalar(0, 0).str + " + " + std::to_string(m0)));
}
store_writer->finalize();
}
void op_return() override
{
_data->code += "return;\n";
}
void util_get_indirect_buffer(const Operand& o_dst, const TensorOperand& o_tensor, const Operand& o_x, const Operand& o_y, const Operand& o_x_off, const Operand& o_y_off) override
{
OperandUnpacker operands(_data->tiles, _data->arguments);
auto dst = operands.unpack(o_dst);
auto x = operands.unpack(o_x);
auto y = operands.unpack(o_y);
auto x_off = operands.unpack(o_x_off);
auto y_off = operands.unpack(o_y_off);
TensorOperandUnpacker tensor_operands(_data->arguments);
auto tensor = tensor_operands.unpack(o_tensor);
assert(dst->format().w == 1);
assert(x->format().w == 1);
assert(y->format().w == 1);
assert(x_off->format().w == 1);
assert(y_off->format().w == 1);
assert(dst->format().dt == DataType::Int32);
assert(x->format().dt == DataType::Int32);
assert(y->format().dt == DataType::Int32);
assert(x_off->format().dt == DataType::Int32);
assert(y_off->format().dt == DataType::Int32);
const std::string width = tensor->component(TensorComponent::W);
const std::string height = tensor->component(TensorComponent::H);
const std::string wxh = tensor->component(TensorComponent::WxH);
/*
int x_s;
int y_s;
x_s = (xi_0 + x_k);
y_s = (yi_0 + y_k);
mi_0 = x_s + y_s * width + b * widthxheight;
mi_0 = select(-1, mi_0, x_s >= 0);
mi_0 = select(-1, mi_0, y_s >= 0);
mi_0 = select(-1, mi_0, x_s < 128);
mi_0 = select(-1, mi_0, y_s < 128);
*/
compound_statement_begin();
declare_tile("_x_s", TileInfo(DataType::Int32));
declare_tile("_y_s", TileInfo(DataType::Int32));
auto x_s = operands.unpack(Operand("_x_s"));
auto y_s = operands.unpack(Operand("_y_s"));
for(int i = 0; i < dst->format().h; ++i)
{
// x_s = (xi_0 + x_k);
// y_s = (yi_0 + y_k);
_data->code += x_s->scalar(0, i).str;
_data->code += " = (";
_data->code += x->scalar(0, i).str;
_data->code += " + ";
_data->code += x_off->scalar(0, i).str;
_data->code += ");\n";
_data->code += y_s->scalar(0, i).str;
_data->code += " = (";
_data->code += y->scalar(0, i).str;
_data->code += " + ";
_data->code += y_off->scalar(0, i).str;
_data->code += ");\n";
// mi_0 = x_s + y_s * width;
_data->code += dst->scalar(0, i).str;
_data->code += " = ";
_data->code += x_s->scalar(0, i).str;
_data->code += " + ";
_data->code += y_s->scalar(0, i).str;
_data->code += " * " + width + ";\n";
// mi_0 = select(wxh, mi_0, x_s >= 0);
_data->code += dst->scalar(0, i).str;
_data->code += " = select(-1, ";
_data->code += dst->scalar(0, i).str;
_data->code += ", ";
_data->code += x_s->scalar(0, i).str;
_data->code += " >= 0);\n";
// mi_0 = select(wxh, mi_0, y_s >= 0);
_data->code += dst->scalar(0, i).str;
_data->code += " = select(-1, ";
_data->code += dst->scalar(0, i).str;
_data->code += ", ";
_data->code += y_s->scalar(0, i).str;
_data->code += " >= 0);\n";
// mi_0 = select(wxh, mi_0, x_s < width);
_data->code += dst->scalar(0, i).str;
_data->code += " = select(-1, ";
_data->code += dst->scalar(0, i).str;
_data->code += ", ";
_data->code += x_s->scalar(0, i).str;
_data->code += " < ";
_data->code += width + ");\n";
// mi_0 = select(wxh, mi_0, y_s < height);
_data->code += dst->scalar(0, i).str;
_data->code += " = select(-1, ";
_data->code += dst->scalar(0, i).str;
_data->code += ", ";
_data->code += y_s->scalar(0, i).str;
_data->code += " < ";
_data->code += height + ");\n";
}
compound_statement_end();
}
private:
GpuKernelWriterDataHolder* _data { nullptr };
GpuKernelWriterAttribute * _attr { nullptr };
};
/** IGpuKernelWriter factory class */
class GpuKernelWriterFactory final
{
public:
/** Static method to call the IGpuKernelWriter class accordingly with the Gpu programming language
*
* @param[in] gpu GPU target
*
* @return IGpuKernelWriter
*/
static std::unique_ptr<IGpuKernelWriter> create(GpuKernelWriterAttribute *attr, GpuKernelWriterDataHolder *x)
{
switch(x->programming_language())
{
case GpuTargetLanguage::OpenCL:
return std::make_unique<ClKernelWriter>(attr, x);
default:
std::cout << "Unsupported Gpu programming language" << std::endl;
assert(false);
return nullptr;
}
}
};
inline int32_t adjust_step(TensorSamplerFormat tensor_format, int32_t step, const TensorInfo *tensor_info_id, int32_t idx)
{
auto tensor = tensor_info_id->shape;
int32_t dim[3] = {0};
switch(tensor_format)
{
case TensorSamplerFormat::C_W_H:
dim[0] = tensor[0];
dim[1] = tensor[1];
dim[2] = tensor[2];
break;
case TensorSamplerFormat::C_WH_1:
dim[0] = tensor[0];
dim[1] = tensor[1] * tensor[2];
dim[2] = 1;
break;
default:
std::cout << "Unsupported tensor format" << std::endl;
assert(false);
break;
}
return std::min(step, dim[idx]);
}
} // namespace prototype
} // namespace ckw
#endif // CKW_PROTOTYPE_SRC_PROTOTYPE_H