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llama-cpp-turboquant/single_include/kompute/Kompute.hpp
Alejandro Saucedo a2ee928f4c Updated tests and rebased
2021-03-07 14:09:56 +00:00

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#pragma once
/*
THIS FILE HAS BEEN AUTOMATICALLY GENERATED - DO NOT EDIT
---
Copyright 2020 The Institute for Ethical AI & Machine Learning
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
#ifndef SHADEROP_SHADEROPMULT_HPP
#define SHADEROP_SHADEROPMULT_HPP
namespace kp {
namespace shader_data {
static const unsigned char shaders_glsl_opmult_comp_spv[] = {
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0x1c, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x41, 0x00, 0x06, 0x00,
0x1d, 0x00, 0x00, 0x00, 0x1e, 0x00, 0x00, 0x00, 0x1b, 0x00, 0x00, 0x00,
0x16, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00,
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0x08, 0x00, 0x00, 0x00, 0x41, 0x00, 0x06, 0x00, 0x1d, 0x00, 0x00, 0x00,
0x25, 0x00, 0x00, 0x00, 0x23, 0x00, 0x00, 0x00, 0x16, 0x00, 0x00, 0x00,
0x24, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, 0x10, 0x00, 0x00, 0x00,
0x26, 0x00, 0x00, 0x00, 0x25, 0x00, 0x00, 0x00, 0x85, 0x00, 0x05, 0x00,
0x10, 0x00, 0x00, 0x00, 0x27, 0x00, 0x00, 0x00, 0x1f, 0x00, 0x00, 0x00,
0x26, 0x00, 0x00, 0x00, 0x41, 0x00, 0x06, 0x00, 0x1d, 0x00, 0x00, 0x00,
0x28, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x16, 0x00, 0x00, 0x00,
0x17, 0x00, 0x00, 0x00, 0x3e, 0x00, 0x03, 0x00, 0x28, 0x00, 0x00, 0x00,
0x27, 0x00, 0x00, 0x00, 0xfd, 0x00, 0x01, 0x00, 0x38, 0x00, 0x01, 0x00
};
static const unsigned int shaders_glsl_opmult_comp_spv_len = 1464;
}
}
#endif // define SHADEROP_SHADEROPMULT_HPP
/*
THIS FILE HAS BEEN AUTOMATICALLY GENERATED - DO NOT EDIT
---
Copyright 2020 The Institute for Ethical AI & Machine Learning
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
#ifndef SHADEROP_SHADERLOGISTICREGRESSION_HPP
#define SHADEROP_SHADERLOGISTICREGRESSION_HPP
namespace kp {
namespace shader_data {
static const unsigned char shaders_glsl_logisticregression_comp_spv[] = {
0x03, 0x02, 0x23, 0x07, 0x00, 0x00, 0x01, 0x00, 0x0a, 0x00, 0x08, 0x00,
0xae, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x11, 0x00, 0x02, 0x00,
0x01, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x06, 0x00, 0x01, 0x00, 0x00, 0x00,
0x47, 0x4c, 0x53, 0x4c, 0x2e, 0x73, 0x74, 0x64, 0x2e, 0x34, 0x35, 0x30,
0x00, 0x00, 0x00, 0x00, 0x0e, 0x00, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00,
0x01, 0x00, 0x00, 0x00, 0x0f, 0x00, 0x06, 0x00, 0x05, 0x00, 0x00, 0x00,
0x04, 0x00, 0x00, 0x00, 0x6d, 0x61, 0x69, 0x6e, 0x00, 0x00, 0x00, 0x00,
0x41, 0x00, 0x00, 0x00, 0x10, 0x00, 0x06, 0x00, 0x04, 0x00, 0x00, 0x00,
0x11, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x03, 0x00, 0x02, 0x00, 0x00, 0x00,
0xc2, 0x01, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00, 0x04, 0x00, 0x00, 0x00,
0x6d, 0x61, 0x69, 0x6e, 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x05, 0x00,
0x0a, 0x00, 0x00, 0x00, 0x73, 0x69, 0x67, 0x6d, 0x6f, 0x69, 0x64, 0x28,
0x66, 0x31, 0x3b, 0x00, 0x05, 0x00, 0x03, 0x00, 0x09, 0x00, 0x00, 0x00,
0x7a, 0x00, 0x00, 0x00, 0x05, 0x00, 0x08, 0x00, 0x12, 0x00, 0x00, 0x00,
0x69, 0x6e, 0x66, 0x65, 0x72, 0x65, 0x6e, 0x63, 0x65, 0x28, 0x76, 0x66,
0x32, 0x3b, 0x76, 0x66, 0x32, 0x3b, 0x66, 0x31, 0x3b, 0x00, 0x00, 0x00,
0x05, 0x00, 0x03, 0x00, 0x0f, 0x00, 0x00, 0x00, 0x78, 0x00, 0x00, 0x00,
0x05, 0x00, 0x03, 0x00, 0x10, 0x00, 0x00, 0x00, 0x77, 0x00, 0x00, 0x00,
0x05, 0x00, 0x03, 0x00, 0x11, 0x00, 0x00, 0x00, 0x62, 0x00, 0x00, 0x00,
0x05, 0x00, 0x08, 0x00, 0x17, 0x00, 0x00, 0x00, 0x63, 0x61, 0x6c, 0x63,
0x75, 0x6c, 0x61, 0x74, 0x65, 0x4c, 0x6f, 0x73, 0x73, 0x28, 0x66, 0x31,
0x3b, 0x66, 0x31, 0x3b, 0x00, 0x00, 0x00, 0x00, 0x05, 0x00, 0x04, 0x00,
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0x08, 0x00, 0x00, 0x00, 0x37, 0x00, 0x03, 0x00, 0x07, 0x00, 0x00, 0x00,
0x09, 0x00, 0x00, 0x00, 0xf8, 0x00, 0x02, 0x00, 0x0b, 0x00, 0x00, 0x00,
0x3d, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x1a, 0x00, 0x00, 0x00,
0x09, 0x00, 0x00, 0x00, 0x7f, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00,
0x1b, 0x00, 0x00, 0x00, 0x1a, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x06, 0x00,
0x06, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
0x1b, 0x00, 0x00, 0x00, 0x1b, 0x00, 0x00, 0x00, 0x81, 0x00, 0x05, 0x00,
0x06, 0x00, 0x00, 0x00, 0x1d, 0x00, 0x00, 0x00, 0x19, 0x00, 0x00, 0x00,
0x1c, 0x00, 0x00, 0x00, 0x88, 0x00, 0x05, 0x00, 0x06, 0x00, 0x00, 0x00,
0x1e, 0x00, 0x00, 0x00, 0x19, 0x00, 0x00, 0x00, 0x1d, 0x00, 0x00, 0x00,
0xfe, 0x00, 0x02, 0x00, 0x1e, 0x00, 0x00, 0x00, 0x38, 0x00, 0x01, 0x00,
0x36, 0x00, 0x05, 0x00, 0x06, 0x00, 0x00, 0x00, 0x12, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x0e, 0x00, 0x00, 0x00, 0x37, 0x00, 0x03, 0x00,
0x0d, 0x00, 0x00, 0x00, 0x0f, 0x00, 0x00, 0x00, 0x37, 0x00, 0x03, 0x00,
0x0d, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x37, 0x00, 0x03, 0x00,
0x07, 0x00, 0x00, 0x00, 0x11, 0x00, 0x00, 0x00, 0xf8, 0x00, 0x02, 0x00,
0x13, 0x00, 0x00, 0x00, 0x3b, 0x00, 0x04, 0x00, 0x07, 0x00, 0x00, 0x00,
0x21, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, 0x3b, 0x00, 0x04, 0x00,
0x07, 0x00, 0x00, 0x00, 0x27, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00,
0x3b, 0x00, 0x04, 0x00, 0x07, 0x00, 0x00, 0x00, 0x28, 0x00, 0x00, 0x00,
0x07, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, 0x0c, 0x00, 0x00, 0x00,
0x22, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00,
0x0c, 0x00, 0x00, 0x00, 0x23, 0x00, 0x00, 0x00, 0x0f, 0x00, 0x00, 0x00,
0x94, 0x00, 0x05, 0x00, 0x06, 0x00, 0x00, 0x00, 0x24, 0x00, 0x00, 0x00,
0x22, 0x00, 0x00, 0x00, 0x23, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00,
0x06, 0x00, 0x00, 0x00, 0x25, 0x00, 0x00, 0x00, 0x11, 0x00, 0x00, 0x00,
0x81, 0x00, 0x05, 0x00, 0x06, 0x00, 0x00, 0x00, 0x26, 0x00, 0x00, 0x00,
0x24, 0x00, 0x00, 0x00, 0x25, 0x00, 0x00, 0x00, 0x3e, 0x00, 0x03, 0x00,
0x21, 0x00, 0x00, 0x00, 0x26, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00,
0x06, 0x00, 0x00, 0x00, 0x29, 0x00, 0x00, 0x00, 0x21, 0x00, 0x00, 0x00,
0x3e, 0x00, 0x03, 0x00, 0x28, 0x00, 0x00, 0x00, 0x29, 0x00, 0x00, 0x00,
0x39, 0x00, 0x05, 0x00, 0x06, 0x00, 0x00, 0x00, 0x2a, 0x00, 0x00, 0x00,
0x0a, 0x00, 0x00, 0x00, 0x28, 0x00, 0x00, 0x00, 0x3e, 0x00, 0x03, 0x00,
0x27, 0x00, 0x00, 0x00, 0x2a, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00,
0x06, 0x00, 0x00, 0x00, 0x2b, 0x00, 0x00, 0x00, 0x27, 0x00, 0x00, 0x00,
0xfe, 0x00, 0x02, 0x00, 0x2b, 0x00, 0x00, 0x00, 0x38, 0x00, 0x01, 0x00,
0x36, 0x00, 0x05, 0x00, 0x06, 0x00, 0x00, 0x00, 0x17, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x37, 0x00, 0x03, 0x00,
0x07, 0x00, 0x00, 0x00, 0x15, 0x00, 0x00, 0x00, 0x37, 0x00, 0x03, 0x00,
0x07, 0x00, 0x00, 0x00, 0x16, 0x00, 0x00, 0x00, 0xf8, 0x00, 0x02, 0x00,
0x18, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00,
0x2e, 0x00, 0x00, 0x00, 0x16, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00,
0x06, 0x00, 0x00, 0x00, 0x2f, 0x00, 0x00, 0x00, 0x15, 0x00, 0x00, 0x00,
0x0c, 0x00, 0x06, 0x00, 0x06, 0x00, 0x00, 0x00, 0x30, 0x00, 0x00, 0x00,
0x01, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, 0x2f, 0x00, 0x00, 0x00,
0x85, 0x00, 0x05, 0x00, 0x06, 0x00, 0x00, 0x00, 0x31, 0x00, 0x00, 0x00,
0x2e, 0x00, 0x00, 0x00, 0x30, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00,
0x06, 0x00, 0x00, 0x00, 0x32, 0x00, 0x00, 0x00, 0x16, 0x00, 0x00, 0x00,
0x83, 0x00, 0x05, 0x00, 0x06, 0x00, 0x00, 0x00, 0x33, 0x00, 0x00, 0x00,
0x19, 0x00, 0x00, 0x00, 0x32, 0x00, 0x00, 0x00, 0x3d, 0x00, 0x04, 0x00,
0x06, 0x00, 0x00, 0x00, 0x34, 0x00, 0x00, 0x00, 0x15, 0x00, 0x00, 0x00,
0x83, 0x00, 0x05, 0x00, 0x06, 0x00, 0x00, 0x00, 0x35, 0x00, 0x00, 0x00,
0x19, 0x00, 0x00, 0x00, 0x34, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x06, 0x00,
0x06, 0x00, 0x00, 0x00, 0x36, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
0x1c, 0x00, 0x00, 0x00, 0x35, 0x00, 0x00, 0x00, 0x85, 0x00, 0x05, 0x00,
0x06, 0x00, 0x00, 0x00, 0x37, 0x00, 0x00, 0x00, 0x33, 0x00, 0x00, 0x00,
0x36, 0x00, 0x00, 0x00, 0x81, 0x00, 0x05, 0x00, 0x06, 0x00, 0x00, 0x00,
0x38, 0x00, 0x00, 0x00, 0x31, 0x00, 0x00, 0x00, 0x37, 0x00, 0x00, 0x00,
0x7f, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x39, 0x00, 0x00, 0x00,
0x38, 0x00, 0x00, 0x00, 0xfe, 0x00, 0x02, 0x00, 0x39, 0x00, 0x00, 0x00,
0x38, 0x00, 0x01, 0x00
};
static const unsigned int shaders_glsl_logisticregression_comp_spv_len = 4816;
}
}
#endif // define SHADEROP_SHADERLOGISTICREGRESSION_HPP
#if VK_USE_PLATFORM_ANDROID_KHR
#include <android/log.h>
#include <kompute_vk_ndk_wrapper.hpp>
// VK_NO_PROTOTYPES required before vulkan import but after wrapper.hpp
#undef VK_NO_PROTOTYPES
static const char* KOMPUTE_LOG_TAG = "KomputeLog";
#endif
#include <fmt/core.h>
#include <vulkan/vulkan.hpp>
// Typedefs to simplify interaction with core types
namespace kp {
typedef std::array<uint32_t, 3> Workgroup;
typedef std::vector<float> Constants;
}
// Must be after vulkan is included
#ifndef KOMPUTE_VK_API_VERSION
#ifndef KOMPUTE_VK_API_MAJOR_VERSION
#define KOMPUTE_VK_API_MAJOR_VERSION 1
#endif // KOMPUTE_VK_API_MAJOR_VERSION
#ifndef KOMPUTE_VK_API_MINOR_VERSION
#define KOMPUTE_VK_API_MINOR_VERSION 1
#endif // KOMPUTE_VK_API_MINOR_VERSION
#define KOMPUTE_VK_API_VERSION \
VK_MAKE_VERSION( \
KOMPUTE_VK_API_MAJOR_VERSION, KOMPUTE_VK_API_MINOR_VERSION, 0)
#endif // KOMPUTE_VK_API_VERSION
// SPDLOG_ACTIVE_LEVEL must be defined before spdlog.h import
#ifndef SPDLOG_ACTIVE_LEVEL
#if DEBUG
#define SPDLOG_ACTIVE_LEVEL SPDLOG_LEVEL_DEBUG
#else
#define SPDLOG_ACTIVE_LEVEL SPDLOG_LEVEL_INFO
#endif
#endif
#if defined(KOMPUTE_BUILD_PYTHON)
#include <pybind11/pybind11.h>
namespace py = pybind11;
// from python/src/main.cpp
extern py::object kp_debug, kp_info, kp_warning, kp_error;
#endif
#ifndef KOMPUTE_LOG_OVERRIDE
#if KOMPUTE_ENABLE_SPDLOG
#include <spdlog/spdlog.h>
#define KP_LOG_DEBUG(...) SPDLOG_DEBUG(__VA_ARGS__)
#define KP_LOG_INFO(...) SPDLOG_INFO(__VA_ARGS__)
#define KP_LOG_WARN(...) SPDLOG_WARN(__VA_ARGS__)
#define KP_LOG_ERROR(...) SPDLOG_ERROR(__VA_ARGS__)
#else
#include <iostream>
#if SPDLOG_ACTIVE_LEVEL > 1
#define KP_LOG_DEBUG(...)
#else
#if defined(VK_USE_PLATFORM_ANDROID_KHR)
#define KP_LOG_DEBUG(...) \
((void)__android_log_write( \
ANDROID_LOG_DEBUG, KOMPUTE_LOG_TAG, fmt::format(__VA_ARGS__).c_str()))
#elif defined(KOMPUTE_BUILD_PYTHON)
#define KP_LOG_DEBUG(...) kp_debug(fmt::format(__VA_ARGS__))
#else
#define KP_LOG_DEBUG(...) \
fmt::print("[{} {}] [debug] [{}:{}] {}\n", \
__DATE__, \
__TIME__, \
__FILE__, \
__LINE__, \
fmt::format(__VA_ARGS__))
#endif // VK_USE_PLATFORM_ANDROID_KHR
#endif // SPDLOG_ACTIVE_LEVEL > 1
#if SPDLOG_ACTIVE_LEVEL > 2
#define KP_LOG_INFO(...)
#else
#if defined(VK_USE_PLATFORM_ANDROID_KHR)
#define KP_LOG_INFO(...) \
((void)__android_log_write( \
ANDROID_LOG_INFO, KOMPUTE_LOG_TAG, fmt::format(__VA_ARGS__).c_str()))
#elif defined(KOMPUTE_BUILD_PYTHON)
#define KP_LOG_INFO(...) kp_info(fmt::format(__VA_ARGS__))
#else
#define KP_LOG_INFO(...) \
fmt::print("[{} {}] [debug] [{}:{}] {}\n", \
__DATE__, \
__TIME__, \
__FILE__, \
__LINE__, \
fmt::format(__VA_ARGS__))
#endif // VK_USE_PLATFORM_ANDROID_KHR
#endif // SPDLOG_ACTIVE_LEVEL > 2
#if SPDLOG_ACTIVE_LEVEL > 3
#define KP_LOG_WARN(...)
#else
#if defined(VK_USE_PLATFORM_ANDROID_KHR)
#define KP_LOG_WARN(...) \
((void)__android_log_write( \
ANDROID_LOG_WARN, KOMPUTE_LOG_TAG, fmt::format(__VA_ARGS__).c_str()))
#elif defined(KOMPUTE_BUILD_PYTHON)
#define KP_LOG_WARN(...) kp_warning(fmt::format(__VA_ARGS__))
#else
#define KP_LOG_WARN(...) \
fmt::print("[{} {}] [debug] [{}:{}] {}\n", \
__DATE__, \
__TIME__, \
__FILE__, \
__LINE__, \
fmt::format(__VA_ARGS__))
#endif // VK_USE_PLATFORM_ANDROID_KHR
#endif // SPDLOG_ACTIVE_LEVEL > 3
#if SPDLOG_ACTIVE_LEVEL > 4
#define KP_LOG_ERROR(...)
#else
#if defined(VK_USE_PLATFORM_ANDROID_KHR)
#define KP_LOG_ERROR(...) \
((void)__android_log_write( \
ANDROID_LOG_ERROR, KOMPUTE_LOG_TAG, fmt::format(__VA_ARGS__).c_str()))
#elif defined(KOMPUTE_BUILD_PYTHON)
#define KP_LOG_ERROR(...) kp_error(fmt::format(__VA_ARGS__))
#else
#define KP_LOG_ERROR(...) \
fmt::print("[{} {}] [debug] [{}:{}] {}\n", \
__DATE__, \
__TIME__, \
__FILE__, \
__LINE__, \
fmt::format(__VA_ARGS__))
#endif // VK_USE_PLATFORM_ANDROID_KHR
#endif // SPDLOG_ACTIVE_LEVEL > 4
#endif // KOMPUTE_SPDLOG_ENABLED
#endif // KOMPUTE_LOG_OVERRIDE
#if !defined(KOMPUTE_DISABLE_SHADER_UTILS) || !KOMPUTE_DISABLE_SHADER_UTILS
#include <iostream>
#include <vector>
#include <SPIRV/GlslangToSpv.h>
#include <glslang/Include/ResourceLimits.h>
#include <glslang/Public/ShaderLang.h>
namespace kp {
/**
Shader utily class with functions to compile and process glsl files.
*/
class Shader
{
public:
// The default resource limit for the GLSL compiler, can be overwritten
// Has been adopted by:
// https://github.com/KhronosGroup/glslang/blob/master/StandAlone/ResourceLimits.cpp
const static TBuiltInResource defaultResource;
/**
* Compile multiple sources with optional filenames. Currently this function
* uses the glslang C++ interface which is not thread safe so this funciton
* should not be called from multiple threads concurrently. If you have a
* online shader processing multithreading use-case that can't use offline
* compilation please open an issue.
*
* @param sources A list of raw glsl shaders in string format
* @param files A list of file names respective to each of the sources
* @param entryPoint The function name to use as entry point
* @param definitions List of pairs containing key value definitions
* @param resourcesLimit A list that contains the resource limits for the
* GLSL compiler
* @return The compiled SPIR-V binary in unsigned int32 format
*/
static std::vector<uint32_t> compileSources(
const std::vector<std::string>& sources,
const std::vector<std::string>& files = {},
const std::string& entryPoint = "main",
std::vector<std::pair<std::string, std::string>> definitions = {},
const TBuiltInResource& resources = Shader::defaultResource);
/**
* Compile a single glslang source from string value. Currently this
* function uses the glslang C++ interface which is not thread safe so this
* funciton should not be called from multiple threads concurrently. If you
* have a online shader processing multithreading use-case that can't use
* offline compilation please open an issue.
*
* @param source An individual raw glsl shader in string format
* @param entryPoint The function name to use as entry point
* @param definitions List of pairs containing key value definitions
* @param resourcesLimit A list that contains the resource limits for the
* GLSL compiler
* @return The compiled SPIR-V binary in unsigned int32 format
*/
static std::vector<uint32_t> compileSource(
const std::string& source,
const std::string& entryPoint = "main",
std::vector<std::pair<std::string, std::string>> definitions = {},
const TBuiltInResource& resources = Shader::defaultResource);
};
}
#endif // DKOMPUTE_DISABLE_SHADER_UTILS
namespace kp {
/**
* Structured data used in GPU operations.
*
* Tensors are the base building block in Kompute to perform operations across
* GPUs. Each tensor would have a respective Vulkan memory and buffer, which
* would be used to store their respective data. The tensors can be used for GPU
* data storage or transfer.
*/
class Tensor
{
public:
/**
* Type for tensors created: Device allows memory to be transferred from
* staging buffers. Staging are host memory visible. Storage are device
* visible but are not set up to transfer or receive data (only for shader
* storage).
*/
enum class TensorTypes
{
eDevice = 0, ///< Type is device memory, source and destination
eHost = 1, ///< Type is host memory, source and destination
eStorage = 2, ///< Type is Device memory (only)
};
enum class TensorDataTypes
{
eBool = 0,
eInt = 1,
eUnsignedInt = 2,
eFloat = 3,
eDouble = 4,
};
/**
* Constructor with data provided which would be used to create the
* respective vulkan buffer and memory.
*
* @param physicalDevice The physical device to use to fetch properties
* @param device The device to use to create the buffer and memory from
* @param data Non-zero-sized vector of data that will be used by the
* tensor
* @param tensorTypes Type for the tensor which is of type TensorTypes
*/
Tensor(std::shared_ptr<vk::PhysicalDevice> physicalDevice,
std::shared_ptr<vk::Device> device,
void* data,
uint32_t elementTotalCount,
uint32_t elementMemorySize,
const TensorDataTypes& dataType,
const TensorTypes& tensorType = TensorTypes::eDevice);
/**
* Destructor which is in charge of freeing vulkan resources unless they
* have been provided externally.
*/
virtual ~Tensor();
/**
* Function to trigger reinitialisation of the tensor buffer and memory with
* new data as well as new potential device type.
*
* @param data Vector of data to use to initialise vector from
* @param tensorType The type to use for the tensor
*/
void rebuild(void* data,
uint32_t elementTotalCount,
uint32_t elementMemorySize);
/**
* Destroys and frees the GPU resources which include the buffer and memory.
*/
void destroy();
/**
* Check whether tensor is initialized based on the created gpu resources.
*
* @returns Boolean stating whether tensor is initialized
*/
bool isInit();
/**
* Retrieve the tensor type of the Tensor
*
* @return Tensor type of tensor
*/
TensorTypes tensorType();
/**
* Records a copy from the memory of the tensor provided to the current
* thensor. This is intended to pass memory into a processing, to perform
* a staging buffer transfer, or to gather output (between others).
*
* @param commandBuffer Vulkan Command Buffer to record the commands into
* @param copyFromTensor Tensor to copy the data from
* @param createBarrier Whether to create a barrier that ensures the data is
* copied before further operations. Default is true.
*/
void recordCopyFrom(const vk::CommandBuffer& commandBuffer,
std::shared_ptr<Tensor> copyFromTensor,
bool createBarrier);
/**
* Records a copy from the internal staging memory to the device memory
* using an optional barrier to wait for the operation. This function would
* only be relevant for kp::Tensors of type eDevice.
*
* @param commandBuffer Vulkan Command Buffer to record the commands into
* @param createBarrier Whether to create a barrier that ensures the data is
* copied before further operations. Default is true.
*/
void recordCopyFromStagingToDevice(const vk::CommandBuffer& commandBuffer,
bool createBarrier);
/**
* Records a copy from the internal device memory to the staging memory
* using an optional barrier to wait for the operation. This function would
* only be relevant for kp::Tensors of type eDevice.
*
* @param commandBuffer Vulkan Command Buffer to record the commands into
* @param createBarrier Whether to create a barrier that ensures the data is
* copied before further operations. Default is true.
*/
void recordCopyFromDeviceToStaging(const vk::CommandBuffer& commandBuffer,
bool createBarrier);
/**
* Records the buffer memory barrier into the command buffer which
* ensures that relevant data transfers are carried out correctly.
*
* @param commandBuffer Vulkan Command Buffer to record the commands into
* @param srcAccessMask Access flags for source access mask
* @param dstAccessMask Access flags for destination access mask
* @param scrStageMask Pipeline stage flags for source stage mask
* @param dstStageMask Pipeline stage flags for destination stage mask
*/
void recordBufferMemoryBarrier(const vk::CommandBuffer& commandBuffer,
vk::AccessFlagBits srcAccessMask,
vk::AccessFlagBits dstAccessMask,
vk::PipelineStageFlagBits srcStageMask,
vk::PipelineStageFlagBits dstStageMask);
/**
* Constructs a vulkan descriptor buffer info which can be used to specify
* and reference the underlying buffer component of the tensor without
* exposing it.
*
* @return Descriptor buffer info with own buffer
*/
vk::DescriptorBufferInfo constructDescriptorBufferInfo();
/**
* Returns the size/magnitude of the Tensor, which will be the total number
* of elements across all dimensions
*
* @return Unsigned integer representing the total number of elements
*/
// TODO: move to cpp
uint32_t size() {
return this->mSize;
}
// TODO: move to cpp
uint32_t dataTypeMemorySize() {
return this->mDataTypeMemorySize;
}
// TODO: move to cpp
uint32_t memorySize() {
return this->mSize * this->mDataTypeMemorySize;
}
/**
* Retrieve the underlying data type of the Tensor
*
* @return Data type of tensor of type kp::Tensor::TensorDataTypes
*/
TensorDataTypes dataType() {
return this->mDataType;
}
void* rawData() {
return this->mRawData;
}
// TODO: move to cpp
template <typename T>
T* data() {
return (T*)this->mRawData;
}
template <typename T>
std::vector<T> vector() {
return { (T*)this->mRawData, ((T*)this->mRawData) + this->size() };
}
/**
* Sets / resets the vector data of the tensor. This function does not
* perform any copies into GPU memory and is only performed on the host.
*/
void setRawData(const void* data)
{
// Copy data
memcpy(this->mRawData, data, this->memorySize());
}
protected:
// -------------- ALWAYS OWNED RESOURCES
TensorTypes mTensorType;
TensorDataTypes mDataType;
uint32_t mSize;
uint32_t mDataTypeMemorySize;
void* mRawData;
private:
void mapRawData() {
KP_LOG_DEBUG("Kompute Tensor mapping data from host buffer");
std::shared_ptr<vk::DeviceMemory> hostVisibleMemory = nullptr;
if (this->mTensorType == TensorTypes::eHost) {
hostVisibleMemory = this->mPrimaryMemory;
} else if (this->mTensorType == TensorTypes::eDevice) {
hostVisibleMemory = this->mStagingMemory;
} else {
KP_LOG_WARN(
"Kompute Tensor mapping data not supported on storage tensor");
return;
}
vk::DeviceSize bufferSize = this->memorySize();
// Given we request coherent host memory we don't need to invalidate / flush
this->mRawData = this->mDevice->mapMemory(
*hostVisibleMemory, 0, bufferSize, vk::MemoryMapFlags());
vk::MappedMemoryRange mappedMemoryRange(*hostVisibleMemory, 0, bufferSize);
}
void unmapRawData() {
KP_LOG_DEBUG("Kompute Tensor mapping data from host buffer");
std::shared_ptr<vk::DeviceMemory> hostVisibleMemory = nullptr;
if (this->mTensorType == TensorTypes::eHost) {
hostVisibleMemory = this->mPrimaryMemory;
} else if (this->mTensorType == TensorTypes::eDevice) {
hostVisibleMemory = this->mStagingMemory;
} else {
KP_LOG_WARN(
"Kompute Tensor mapping data not supported on storage tensor");
return;
}
vk::DeviceSize bufferSize = this->memorySize();
vk::MappedMemoryRange mappedRange(*hostVisibleMemory, 0, bufferSize);
this->mDevice->flushMappedMemoryRanges(1, &mappedRange);
this->mDevice->unmapMemory(*hostVisibleMemory);
}
// -------------- NEVER OWNED RESOURCES
std::shared_ptr<vk::PhysicalDevice> mPhysicalDevice;
std::shared_ptr<vk::Device> mDevice;
// -------------- OPTIONALLY OWNED RESOURCES
std::shared_ptr<vk::Buffer> mPrimaryBuffer;
bool mFreePrimaryBuffer = false;
std::shared_ptr<vk::Buffer> mStagingBuffer;
bool mFreeStagingBuffer = false;
std::shared_ptr<vk::DeviceMemory> mPrimaryMemory;
bool mFreePrimaryMemory = false;
std::shared_ptr<vk::DeviceMemory> mStagingMemory;
bool mFreeStagingMemory = false;
void allocateMemoryCreateGPUResources(); // Creates the vulkan buffer
void createBuffer(std::shared_ptr<vk::Buffer> buffer,
vk::BufferUsageFlags bufferUsageFlags);
void allocateBindMemory(std::shared_ptr<vk::Buffer> buffer,
std::shared_ptr<vk::DeviceMemory> memory,
vk::MemoryPropertyFlags memoryPropertyFlags);
void recordCopyBuffer(const vk::CommandBuffer& commandBuffer,
std::shared_ptr<vk::Buffer> bufferFrom,
std::shared_ptr<vk::Buffer> bufferTo,
vk::DeviceSize bufferSize,
vk::BufferCopy copyRegion,
bool createBarrier);
// Private util functions
vk::BufferUsageFlags getPrimaryBufferUsageFlags();
vk::MemoryPropertyFlags getPrimaryMemoryPropertyFlags();
vk::BufferUsageFlags getStagingBufferUsageFlags();
vk::MemoryPropertyFlags getStagingMemoryPropertyFlags();
};
// TODO: Limit T to be only float, bool, double, etc
template <typename T>
class TensorT: public Tensor
{
public:
TensorT(std::shared_ptr<vk::PhysicalDevice> physicalDevice,
std::shared_ptr<vk::Device> device,
const std::vector<T>& data,
const TensorTypes& tensorType = TensorTypes::eDevice)
: Tensor(physicalDevice,
device,
(void*)data.data(),
data.size(),
sizeof(T),
this->dataType(),
tensorType)
{
KP_LOG_DEBUG("Kompute TensorT constructor with data size {}", data.size());
}
~TensorT() {
KP_LOG_DEBUG("Kompute TensorT destructor");
}
T* data() {
return (T*)this->mRawData;
}
std::vector<T> vector() {
return { (T*)this->mRawData, ((T*)this->mRawData) + this->size() };
}
T& operator[](int index) {
return *(((T*)this->mRawData) + index);
}
void setData(const std::vector<T>& data) {
KP_LOG_DEBUG("Kompute TensorT setting data with data size {}", data.size());
if (data.size() != this->mSize) {
throw std::runtime_error(
"Kompute TensorT Cannot set data of different sizes");
}
Tensor::setRawData(data.data());
}
TensorDataTypes dataType();
};
} // End namespace kp
namespace kp {
/**
Abstraction for compute shaders that are run on top of tensors grouped via
ParameterGroups (which group descriptorsets)
*/
class Algorithm
{
public:
/**
* Main constructor for algorithm with configuration parameters to create
* the underlying resources.
*
* @param device The Vulkan device to use for creating resources
* @param tensors (optional) The tensors to use to create the descriptor resources
* @param spirv (optional) The spirv code to use to create the algorithm
* @param workgroup (optional) The kp::Workgroup to use for the dispatch which defaults to
* kp::Workgroup(tensor[0].size(), 1, 1) if not set.
* @param specializationConstants (optional) The kp::Constants to use to initialize
* the specialization constants which cannot be changed once set.
* @param pushConstants (optional) The kp::Constants to use when initializing the
* pipeline, which set the size of the push constants - these can be modified but
* all new values must have the same vector size as this initial value.
*/
Algorithm(std::shared_ptr<vk::Device> device,
const std::vector<std::shared_ptr<Tensor>>& tensors = {},
const std::vector<uint32_t>& spirv = {},
const Workgroup& workgroup = {},
const Constants& specializationConstants = {},
const Constants& pushConstants = {});
/**
* Rebuild function to reconstruct algorithm with configuration parameters to create
* the underlying resources.
*
* @param tensors The tensors to use to create the descriptor resources
* @param spirv The spirv code to use to create the algorithm
* @param workgroup (optional) The kp::Workgroup to use for the dispatch which defaults to
* kp::Workgroup(tensor[0].size(), 1, 1) if not set.
* @param specializationConstants (optional) The kp::Constants to use to initialize
* the specialization constants which cannot be changed once set.
* @param pushConstants (optional) The kp::Constants to use when initializing the
* pipeline, which set the size of the push constants - these can be modified but
* all new values must have the same vector size as this initial value.
*/
void rebuild(const std::vector<std::shared_ptr<Tensor>>& tensors,
const std::vector<uint32_t>& spirv,
const Workgroup& workgroup = {},
const Constants& specializationConstants = {},
const Constants& pushConstants = {});
/**
* Destructor for Algorithm which is responsible for freeing and desroying
* respective pipelines and owned parameter groups.
*/
~Algorithm();
/**
* Records the dispatch function with the provided template parameters or
* alternatively using the size of the tensor by default.
*
* @param commandBuffer Command buffer to record the algorithm resources to
*/
void recordDispatch(const vk::CommandBuffer& commandBuffer);
/**
* Records command that binds the "core" algorithm components which consist of
* binding the pipeline and binding the descriptorsets.
*
* @param commandBuffer Command buffer to record the algorithm resources to
*/
void recordBindCore(const vk::CommandBuffer& commandBuffer);
/**
* Records command that binds the push constants to the command buffer provided
* - it is required that the pushConstants provided are of the same size as the
* ones provided during initialization.
*
* @param commandBuffer Command buffer to record the algorithm resources to
*/
void recordBindPush(const vk::CommandBuffer& commandBuffer);
/**
* function that checks all the gpu resource components to verify if these have
* been created and returns true if all are valid.
*
* @returns returns true if the algorithm is currently initialized.
*/
bool isInit();
/**
* Sets the work group to use in the recordDispatch
*
* @param workgroup The kp::Workgroup value to use to update the algorithm. It
* must have a value greater than 1 on the x value (index 1) otherwise it will
* be initialized on the size of the first tensor (ie. this->mTensor[0]->size())
*/
void setWorkgroup(const Workgroup& workgroup, uint32_t minSize = 1);
/**
* Sets the push constants to the new value provided to use in the next bindPush()
*
* @param The kp::Constant to use to set the push constants to use in the next
* bindPush(...) calls. The constants provided must be of the same size as the
* ones created during initialization.
*/
void setPush(const Constants& pushConstants);
/**
* Gets the current workgroup from the algorithm.
*
* @param The kp::Constant to use to set the push constants to use in the next
* bindPush(...) calls. The constants provided must be of the same size as the
* ones created during initialization.
*/
const Workgroup& getWorkgroup();
/**
* Gets the specialization constants of the current algorithm.
*
* @returns The kp::Constants currently set for specialization constants
*/
const Constants& getSpecializationConstants();
/**
* Gets the specialization constants of the current algorithm.
*
* @returns The kp::Constants currently set for push constants
*/
const Constants& getPush();
/**
* Gets the current tensors that are used in the algorithm.
*
* @returns The list of tensors used in the algorithm.
*/
const std::vector<std::shared_ptr<Tensor>>& getTensors();
void destroy();
private:
// -------------- NEVER OWNED RESOURCES
std::shared_ptr<vk::Device> mDevice;
std::vector<std::shared_ptr<Tensor>> mTensors;
// -------------- OPTIONALLY OWNED RESOURCES
std::shared_ptr<vk::DescriptorSetLayout> mDescriptorSetLayout;
bool mFreeDescriptorSetLayout = false;
std::shared_ptr<vk::DescriptorPool> mDescriptorPool;
bool mFreeDescriptorPool = false;
std::shared_ptr<vk::DescriptorSet> mDescriptorSet;
bool mFreeDescriptorSet = false;
std::shared_ptr<vk::ShaderModule> mShaderModule;
bool mFreeShaderModule = false;
std::shared_ptr<vk::PipelineLayout> mPipelineLayout;
bool mFreePipelineLayout = false;
std::shared_ptr<vk::PipelineCache> mPipelineCache;
bool mFreePipelineCache = false;
std::shared_ptr<vk::Pipeline> mPipeline;
bool mFreePipeline = false;
// -------------- ALWAYS OWNED RESOURCES
std::vector<uint32_t> mSpirv;
Constants mSpecializationConstants;
Constants mPushConstants;
Workgroup mWorkgroup;
// Create util functions
void createShaderModule();
void createPipeline();
// Parameters
void createParameters();
};
} // End namespace kp
namespace kp {
/**
* Base Operation which provides the high level interface that Kompute
* operations implement in order to perform a set of actions in the GPU.
*
* Operations can perform actions on tensors, and optionally can also own an
* Algorithm with respective parameters. kp::Operations with kp::Algorithms
* would inherit from kp::OpBaseAlgo.
*/
class OpBase
{
public:
/**
* Default destructor for OpBase class. This OpBase destructor class should
* always be called to destroy and free owned resources unless it is
* intended to destroy the resources in the parent class.
*/
virtual ~OpBase()
{
KP_LOG_DEBUG("Kompute OpBase destructor started");
}
/**
* The record function is intended to only send a record command or run
* commands that are expected to record operations that are to be submitted
* as a batch into the GPU.
*
* @param commandBuffer The command buffer to record the command into.
*/
virtual void record(const vk::CommandBuffer& commandBuffer) = 0;
/**
* Pre eval is called before the Sequence has called eval and submitted the commands to
* the GPU for processing, and can be used to perform any per-eval setup steps
* required as the computation iteration begins. It's worth noting that
* there are situations where eval can be called multiple times, so the
* resources that are created should be idempotent in case it's called multiple
* times in a row.
*
* @param commandBuffer The command buffer to record the command into.
*/
virtual void preEval(const vk::CommandBuffer& commandBuffer) = 0;
/**
* Post eval is called after the Sequence has called eval and submitted the commands to
* the GPU for processing, and can be used to perform any tear-down steps
* required as the computation iteration finishes. It's worth noting that
* there are situations where eval can be called multiple times, so the
* resources that are destroyed should not require a re-init unless explicitly
* provided by the user.
*
* @param commandBuffer The command buffer to record the command into.
*/
virtual void postEval(const vk::CommandBuffer& commandBuffer) = 0;
};
} // End namespace kp
namespace kp {
/**
* Operation that copies the data from the first tensor to the rest of the tensors
* provided, using a record command for all the vectors. This operation does not
* own/manage the memory of the tensors passed to it. The operation must only
* receive tensors of type
*/
class OpTensorCopy : public OpBase
{
public:
/**
* Default constructor with parameters that provides the core vulkan resources
* and the tensors that will be used in the operation.
*
* @param tensors Tensors that will be used to create in operation.
*/
OpTensorCopy(const std::vector<std::shared_ptr<Tensor>>& tensors);
/**
* Default destructor. This class does not manage memory so it won't be
* expecting the parent to perform a release.
*/
~OpTensorCopy() override;
/**
* Records the copy commands from the first tensor into all the other
* tensors provided. Also optionally records a barrier.
*
* @param commandBuffer The command buffer to record the command into.
*/
void record(const vk::CommandBuffer& commandBuffer) override;
/**
* Does not perform any preEval commands.
*
* @param commandBuffer The command buffer to record the command into.
*/
virtual void preEval(const vk::CommandBuffer& commandBuffer) override;
/**
* Copies the local vectors for all the tensors to sync the data with the gpu.
*
* @param commandBuffer The command buffer to record the command into.
*/
virtual void postEval(const vk::CommandBuffer& commandBuffer) override;
private:
// -------------- ALWAYS OWNED RESOURCES
std::vector<std::shared_ptr<Tensor>> mTensors;
};
} // End namespace kp
namespace kp {
/**
* Operation that syncs tensor's device by mapping local data into the device memory.
* For TensorTypes::eDevice it will use a record operation for the memory to be syncd
* into GPU memory which means that the operation will be done in sync with GPU commands.
* For TensorTypes::eHost it will only map the data into host memory which will
* happen during preEval before the recorded commands are dispatched.
*/
class OpTensorSyncDevice : public OpBase
{
public:
/**
* Default constructor with parameters that provides the core vulkan resources
* and the tensors that will be used in the operation. The tensos provided cannot
* be of type TensorTypes::eStorage.
*
* @param tensors Tensors that will be used to create in operation.
*/
OpTensorSyncDevice(const std::vector<std::shared_ptr<Tensor>>& tensors);
/**
* Default destructor. This class does not manage memory so it won't be expecting the parent to perform a release.
*/
~OpTensorSyncDevice() override;
/**
* For device tensors, it records the copy command for the tensor to copy the
* data from its staging to device memory.
*
* @param commandBuffer The command buffer to record the command into.
*/
void record(const vk::CommandBuffer& commandBuffer) override;
/**
* Does not perform any preEval commands.
*
* @param commandBuffer The command buffer to record the command into.
*/
virtual void preEval(const vk::CommandBuffer& commandBuffer) override;
/**
* Does not perform any postEval commands.
*
* @param commandBuffer The command buffer to record the command into.
*/
virtual void postEval(const vk::CommandBuffer& commandBuffer) override;
private:
// -------------- ALWAYS OWNED RESOURCES
std::vector<std::shared_ptr<Tensor>> mTensors;
};
} // End namespace kp
namespace kp {
/**
* Operation that syncs tensor's local memory by mapping device data into the
* local CPU memory. For TensorTypes::eDevice it will use a record operation
* for the memory to be syncd into GPU memory which means that the operation
* will be done in sync with GPU commands. For TensorTypes::eHost it will
* only map the data into host memory which will happen during preEval before
* the recorded commands are dispatched.
*/
class OpTensorSyncLocal : public OpBase
{
public:
/**
* Default constructor with parameters that provides the core vulkan resources
* and the tensors that will be used in the operation. The tensors provided
* cannot be of type TensorTypes::eStorage.
*
* @param tensors Tensors that will be used to create in operation.
*/
OpTensorSyncLocal(const std::vector<std::shared_ptr<Tensor>>& tensors);
/**
* Default destructor. This class does not manage memory so it won't be expecting
* the parent to perform a release.
*/
~OpTensorSyncLocal() override;
/**
* For device tensors, it records the copy command for the tensor to copy the
* data from its device to staging memory.
*
* @param commandBuffer The command buffer to record the command into.
*/
void record(const vk::CommandBuffer& commandBuffer) override;
/**
* Does not perform any preEval commands.
*
* @param commandBuffer The command buffer to record the command into.
*/
virtual void preEval(const vk::CommandBuffer& commandBuffer) override;
/**
* For host tensors it performs the map command from the host memory into local memory.
*
* @param commandBuffer The command buffer to record the command into.
*/
virtual void postEval(const vk::CommandBuffer& commandBuffer) override;
private:
// -------------- ALWAYS OWNED RESOURCES
std::vector<std::shared_ptr<Tensor>> mTensors;
};
} // End namespace kp
namespace kp {
/**
* Operation that provides a general abstraction that simplifies the use of
* algorithm and parameter components which can be used with shaders.
* By default it enables the user to provide a dynamic number of tensors
* which are then passed as inputs.
*/
class OpAlgoDispatch : public OpBase
{
public:
/**
* Constructor that stores the algorithm to use as well as the relevant
* push constants to override when recording.
*
* @param algorithm The algorithm object to use for dispatch
* @param pushConstants The push constants to use for override
*/
OpAlgoDispatch(const std::shared_ptr<kp::Algorithm>& algorithm,
const kp::Constants& pushConstants = {});
/**
* Default destructor, which is in charge of destroying the algorithm
* components but does not destroy the underlying tensors
*/
virtual ~OpAlgoDispatch() override;
/**
* This records the commands that are to be sent to the GPU. This includes
* the barriers that ensure the memory has been copied before going in and
* out of the shader, as well as the dispatch operation that sends the
* shader processing to the gpu. This function also records the GPU memory
* copy of the output data for the staging buffer so it can be read by the
* host.
*
* @param commandBuffer The command buffer to record the command into.
*/
virtual void record(const vk::CommandBuffer& commandBuffer) override;
/**
* Does not perform any preEval commands.
*
* @param commandBuffer The command buffer to record the command into.
*/
virtual void preEval(const vk::CommandBuffer& commandBuffer) override;
/**
* Does not perform any postEval commands.
*
* @param commandBuffer The command buffer to record the command into.
*/
virtual void postEval(const vk::CommandBuffer& commandBuffer) override;
private:
// -------------- ALWAYS OWNED RESOURCES
std::shared_ptr<Algorithm> mAlgorithm;
Constants mPushConstants;
};
} // End namespace kp
#include <fstream>
namespace kp {
/**
* Operation that performs multiplication on two tensors and outpus on third
* tensor.
*/
class OpMult : public OpAlgoDispatch
{
public:
/**
* Default constructor with parameters that provides the bare minimum
* requirements for the operations to be able to create and manage their
* sub-components.
*
* @param tensors Tensors that are to be used in this operation
* @param algorithm An algorithm that will be overridden with the OpMult
* shader data and the tensors provided which are expected to be 3
*/
OpMult(std::vector<std::shared_ptr<Tensor>> tensors, std::shared_ptr<Algorithm> algorithm)
: OpAlgoDispatch(algorithm)
{
KP_LOG_DEBUG("Kompute OpMult constructor with params");
if (tensors.size() != 3) {
throw std::runtime_error("Kompute OpMult expected 3 tensors but got " + tensors.size());
}
std::vector<uint32_t> spirv(
(uint32_t*)shader_data::shaders_glsl_opmult_comp_spv,
(uint32_t*)(shader_data::shaders_glsl_opmult_comp_spv +
kp::shader_data::shaders_glsl_opmult_comp_spv_len));
algorithm->rebuild(tensors, spirv);
}
/**
* Default destructor, which is in charge of destroying the algorithm
* components but does not destroy the underlying tensors
*/
virtual ~OpMult() override {
KP_LOG_DEBUG("Kompute OpMult destructor started");
}
};
} // End namespace kp
namespace kp {
/**
* Container of operations that can be sent to GPU as batch
*/
class Sequence : public std::enable_shared_from_this<Sequence>
{
public:
/**
* Main constructor for sequence which requires core vulkan components to
* generate all dependent resources.
*
* @param physicalDevice Vulkan physical device
* @param device Vulkan logical device
* @param computeQueue Vulkan compute queue
* @param queueIndex Vulkan compute queue index in device
* @param totalTimestamps Maximum number of timestamps to allocate
*/
Sequence(std::shared_ptr<vk::PhysicalDevice> physicalDevice,
std::shared_ptr<vk::Device> device,
std::shared_ptr<vk::Queue> computeQueue,
uint32_t queueIndex,
uint32_t totalTimestamps = 0);
/**
* Destructor for sequence which is responsible for cleaning all subsequent
* owned operations.
*/
~Sequence();
/**
* Record function for operation to be added to the GPU queue in batch. This
* template requires classes to be derived from the OpBase class. This
* function also requires the Sequence to be recording, otherwise it will
* not be able to add the operation.
*
* @param op Object derived from kp::BaseOp that will be recoreded by the sequence
* which will be used when the operation is evaluated.
* @return shared_ptr<Sequence> of the Sequence class itself
*/
std::shared_ptr<Sequence> record(std::shared_ptr<OpBase> op);
/**
* Record function for operation to be added to the GPU queue in batch. This
* template requires classes to be derived from the OpBase class. This
* function also requires the Sequence to be recording, otherwise it will
* not be able to add the operation.
*
* @param tensors Vector of tensors to use for the operation
* @param TArgs Template parameters that are used to initialise operation
* which allows for extensible configurations on initialisation.
* @return shared_ptr<Sequence> of the Sequence class itself
*/
template<typename T, typename... TArgs>
std::shared_ptr<Sequence> record(
std::vector<std::shared_ptr<Tensor>> tensors, TArgs&&... params)
{
std::shared_ptr<T> op{ new T(tensors, std::forward<TArgs>(params)...) };
return this->record(op);
}
/**
* Record function for operation to be added to the GPU queue in batch. This
* template requires classes to be derived from the OpBase class. This
* function also requires the Sequence to be recording, otherwise it will
* not be able to add the operation.
*
* @param algorithm Algorithm to use for the record often used for OpAlgo
* operations
* @param TArgs Template parameters that are used to initialise operation
* which allows for extensible configurations on initialisation.
* @return shared_ptr<Sequence> of the Sequence class itself
*/
template<typename T, typename... TArgs>
std::shared_ptr<Sequence> record(std::shared_ptr<Algorithm> algorithm,
TArgs&&... params)
{
std::shared_ptr<T> op{ new T(algorithm,
std::forward<TArgs>(params)...) };
return this->record(op);
}
/**
* Eval sends all the recorded and stored operations in the vector of
* operations into the gpu as a submit job synchronously (with a barrier).
*
* @return shared_ptr<Sequence> of the Sequence class itself
*/
std::shared_ptr<Sequence> eval();
/**
* Resets all the recorded and stored operations, records the operation
* provided and submits into the gpu as a submit job synchronously (with a barrier).
*
* @return shared_ptr<Sequence> of the Sequence class itself
*/
std::shared_ptr<Sequence> eval(std::shared_ptr<OpBase> op);
/**
* Eval sends all the recorded and stored operations in the vector of
* operations into the gpu as a submit job with a barrier.
*
* @param tensors Vector of tensors to use for the operation
* @param TArgs Template parameters that are used to initialise operation
* which allows for extensible configurations on initialisation.
* @return shared_ptr<Sequence> of the Sequence class itself
*/
template<typename T, typename... TArgs>
std::shared_ptr<Sequence> eval(std::vector<std::shared_ptr<Tensor>> tensors,
TArgs&&... params)
{
std::shared_ptr<T> op{ new T(tensors, std::forward<TArgs>(params)...) };
return this->eval(op);
}
/**
* Eval sends all the recorded and stored operations in the vector of
* operations into the gpu as a submit job with a barrier.
*
* @param algorithm Algorithm to use for the record often used for OpAlgo
* operations
* @param TArgs Template parameters that are used to initialise operation
* which allows for extensible configurations on initialisation.
* @return shared_ptr<Sequence> of the Sequence class itself
*/
template<typename T, typename... TArgs>
std::shared_ptr<Sequence> eval(std::shared_ptr<Algorithm> algorithm,
TArgs&&... params)
{
std::shared_ptr<T> op{ new T(algorithm,
std::forward<TArgs>(params)...) };
return this->eval(op);
}
/**
* Eval Async sends all the recorded and stored operations in the vector of
* operations into the gpu as a submit job without a barrier. EvalAwait() must
* ALWAYS be called after to ensure the sequence is terminated correctly.
*
* @return Boolean stating whether execution was successful.
*/
std::shared_ptr<Sequence> evalAsync();
/**
* Clears currnet operations to record provided one in the vector of
* operations into the gpu as a submit job without a barrier. EvalAwait() must
* ALWAYS be called after to ensure the sequence is terminated correctly.
*
* @return Boolean stating whether execution was successful.
*/
std::shared_ptr<Sequence> evalAsync(std::shared_ptr<OpBase> op);
/**
* Eval sends all the recorded and stored operations in the vector of
* operations into the gpu as a submit job with a barrier.
*
* @param tensors Vector of tensors to use for the operation
* @param TArgs Template parameters that are used to initialise operation
* which allows for extensible configurations on initialisation.
* @return shared_ptr<Sequence> of the Sequence class itself
*/
template<typename T, typename... TArgs>
std::shared_ptr<Sequence> evalAsync(
std::vector<std::shared_ptr<Tensor>> tensors,
TArgs&&... params)
{
std::shared_ptr<T> op{ new T(tensors, std::forward<TArgs>(params)...) };
return this->evalAsync(op);
}
/**
* Eval sends all the recorded and stored operations in the vector of
* operations into the gpu as a submit job with a barrier.
*
* @param algorithm Algorithm to use for the record often used for OpAlgo
* operations
* @param TArgs Template parameters that are used to initialise operation
* which allows for extensible configurations on initialisation.
* @return shared_ptr<Sequence> of the Sequence class itself
*/
template<typename T, typename... TArgs>
std::shared_ptr<Sequence> evalAsync(std::shared_ptr<Algorithm> algorithm,
TArgs&&... params)
{
std::shared_ptr<T> op{ new T(algorithm,
std::forward<TArgs>(params)...) };
return this->evalAsync(op);
}
/**
* Eval Await waits for the fence to finish processing and then once it
* finishes, it runs the postEval of all operations.
*
* @param waitFor Number of milliseconds to wait before timing out.
* @return shared_ptr<Sequence> of the Sequence class itself
*/
std::shared_ptr<Sequence> evalAwait(uint64_t waitFor = UINT64_MAX);
/**
* Clear function clears all operations currently recorded and starts
* recording again.
*/
void clear();
/**
* Return the timestamps that were latched at the beginning and
* after each operation during the last eval() call.
*/
std::vector<std::uint64_t> getTimestamps();
/**
* Begins recording commands for commands to be submitted into the command
* buffer.
*
* @return Boolean stating whether execution was successful.
*/
void begin();
/**
* Ends the recording and stops recording commands when the record command
* is sent.
*
* @return Boolean stating whether execution was successful.
*/
void end();
/**
* Returns true if the sequence is currently in recording activated.
*
* @return Boolean stating if recording ongoing.
*/
bool isRecording();
/**
* Returns true if the sequence has been initialised, and it's based on the
* GPU resources being refrenced.
*
* @return Boolean stating if is initialized
*/
bool isInit();
/**
* Clears command buffer and triggers re-record of all the current operations
* saved, which is useful if the underlying kp::Tensors or kp::Algorithms
* are modified and need to be re-recorded.
*/
void rerecord();
/**
* Returns true if the sequence is currently running - mostly used for async
* workloads.
*
* @return Boolean stating if currently running.
*/
bool isRunning();
/**
* Destroys and frees the GPU resources which include the buffer and memory
* and sets the sequence as init=False.
*/
void destroy();
private:
// -------------- NEVER OWNED RESOURCES
std::shared_ptr<vk::PhysicalDevice> mPhysicalDevice = nullptr;
std::shared_ptr<vk::Device> mDevice = nullptr;
std::shared_ptr<vk::Queue> mComputeQueue = nullptr;
uint32_t mQueueIndex = -1;
// -------------- OPTIONALLY OWNED RESOURCES
std::shared_ptr<vk::CommandPool> mCommandPool = nullptr;
bool mFreeCommandPool = false;
std::shared_ptr<vk::CommandBuffer> mCommandBuffer = nullptr;
bool mFreeCommandBuffer = false;
// -------------- ALWAYS OWNED RESOURCES
vk::Fence mFence;
std::vector<std::shared_ptr<OpBase>> mOperations;
std::shared_ptr<vk::QueryPool> timestampQueryPool = nullptr;
// State
bool mRecording = false;
bool mIsRunning = false;
// Create functions
void createCommandPool();
void createCommandBuffer();
void createTimestampQueryPool(uint32_t totalTimestamps);
};
} // End namespace kp
#include <set>
#include <unordered_map>
#define KP_DEFAULT_SESSION "DEFAULT"
namespace kp {
/**
Base orchestrator which creates and manages device and child components
*/
class Manager
{
public:
/**
Base constructor and default used which creates the base resources
including choosing the device 0 by default.
*/
Manager();
/**
* Similar to base constructor but allows for further configuration to use when
* creating the Vulkan resources.
*
* @param physicalDeviceIndex The index of the physical device to use
* @param familyQueueIndices (Optional) List of queue indices to add for
* explicit allocation
* @param desiredExtensions The desired extensions to load from physicalDevice
*/
Manager(uint32_t physicalDeviceIndex,
const std::vector<uint32_t>& familyQueueIndices = {},
const std::vector<std::string>& desiredExtensions = {});
/**
* Manager constructor which allows your own vulkan application to integrate
* with the vulkan kompute use.
*
* @param instance Vulkan compute instance to base this application
* @param physicalDevice Vulkan physical device to use for application
* @param device Vulkan logical device to use for all base resources
* @param physicalDeviceIndex Index for vulkan physical device used
*/
Manager(std::shared_ptr<vk::Instance> instance,
std::shared_ptr<vk::PhysicalDevice> physicalDevice,
std::shared_ptr<vk::Device> device);
/**
* Manager destructor which would ensure all owned resources are destroyed
* unless explicitly stated that resources should not be destroyed or freed.
*/
~Manager();
/**
* Create a managed sequence that will be destroyed by this manager
* if it hasn't been destroyed by its reference count going to zero.
*
* @param queueIndex The queue to use from the available queues
* @param nrOfTimestamps The maximum number of timestamps to allocate.
* If zero (default), disables latching of timestamps.
* @returns Shared pointer with initialised sequence
*/
std::shared_ptr<Sequence> sequence(uint32_t queueIndex = 0, uint32_t nrOfTimestamps = 0);
/**
* Create a managed tensor that will be destroyed by this manager
* if it hasn't been destroyed by its reference count going to zero.
*
* @param data The data to initialize the tensor with
* @param tensorType The type of tensor to initialize
* @returns Shared pointer with initialised tensor
*/
template <typename T>
std::shared_ptr<TensorT<T>> tensorT(
const std::vector<T>& data,
Tensor::TensorTypes tensorType = Tensor::TensorTypes::eDevice)
{
KP_LOG_DEBUG("Kompute Manager tensor creation triggered");
std::shared_ptr<TensorT<T>> tensor{ new kp::TensorT<T>(
this->mPhysicalDevice, this->mDevice, data, tensorType) };
if (this->mManageResources) {
this->mManagedTensors.push_back(tensor);
}
return tensor;
}
std::shared_ptr<TensorT<float>> tensor(
const std::vector<float>& data,
Tensor::TensorTypes tensorType = Tensor::TensorTypes::eDevice)
{
return this->tensorT<float>(data, tensorType);
}
std::shared_ptr<Tensor> tensor(
void* data,
uint32_t elementTotalCount,
uint32_t elementMemorySize,
const Tensor::TensorDataTypes& dataType,
Tensor::TensorTypes tensorType = Tensor::TensorTypes::eDevice)
{
std::shared_ptr<Tensor> tensor{ new kp::Tensor(
this->mPhysicalDevice, this->mDevice, data, elementTotalCount, elementMemorySize, dataType, tensorType) };
if (this->mManageResources) {
this->mManagedTensors.push_back(tensor);
}
return tensor;
}
/**
* Create a managed algorithm that will be destroyed by this manager
* if it hasn't been destroyed by its reference count going to zero.
*
* @param tensors (optional) The tensors to initialise the algorithm with
* @param spirv (optional) The SPIRV bytes for the algorithm to dispatch
* @param workgroup (optional) kp::Workgroup for algorithm to use, and
* defaults to (tensor[0].size(), 1, 1)
* @param specializationConstants (optional) kp::Constant to use for
* specialization constants, and defaults to an empty constant
* @param pushConstants (optional) kp::Constant to use for push constants,
* and defaults to an empty constant
* @returns Shared pointer with initialised algorithm
*/
std::shared_ptr<Algorithm> algorithm(
const std::vector<std::shared_ptr<Tensor>>& tensors = {},
const std::vector<uint32_t>& spirv = {},
const Workgroup& workgroup = {},
const Constants& specializationConstants = {},
const Constants& pushConstants = {});
/**
* Destroy the GPU resources and all managed resources by manager.
**/
void destroy();
/**
* Run a pseudo-garbage collection to release all the managed resources
* that have been already freed due to these reaching to zero ref count.
**/
void clear();
private:
// -------------- OPTIONALLY OWNED RESOURCES
std::shared_ptr<vk::Instance> mInstance = nullptr;
bool mFreeInstance = false;
std::shared_ptr<vk::PhysicalDevice> mPhysicalDevice = nullptr;
std::shared_ptr<vk::Device> mDevice = nullptr;
bool mFreeDevice = false;
// -------------- ALWAYS OWNED RESOURCES
std::vector<std::weak_ptr<Tensor>> mManagedTensors;
std::vector<std::weak_ptr<Sequence>> mManagedSequences;
std::vector<std::weak_ptr<Algorithm>> mManagedAlgorithms;
std::vector<uint32_t> mComputeQueueFamilyIndices;
std::vector<std::shared_ptr<vk::Queue>> mComputeQueues;
bool mManageResources = false;
#if DEBUG
#ifndef KOMPUTE_DISABLE_VK_DEBUG_LAYERS
vk::DebugReportCallbackEXT mDebugReportCallback;
vk::DispatchLoaderDynamic mDebugDispatcher;
#endif
#endif
// Create functions
void createInstance();
void createDevice(const std::vector<uint32_t>& familyQueueIndices = {},
uint32_t hysicalDeviceIndex = 0,
const std::vector<std::string>& desiredExtensions = {});
};
} // End namespace kp
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