From 7fb1515c93732f48894d37b317d68a01312c5ad3 Mon Sep 17 00:00:00 2001 From: Alejandro Saucedo Date: Sat, 27 Feb 2021 07:42:38 +0000 Subject: [PATCH] Added TensorOpTensorCreate tests --- single_include/kompute/Kompute.hpp | 1544 +--------------------------- test/TestOpTensorCreate.cpp | 205 +--- 2 files changed, 54 insertions(+), 1695 deletions(-) diff --git a/single_include/kompute/Kompute.hpp b/single_include/kompute/Kompute.hpp index 79bc6e1b4..b100f17dd 100755 --- a/single_include/kompute/Kompute.hpp +++ b/single_include/kompute/Kompute.hpp @@ -318,1546 +318,4 @@ static const unsigned char shaders_glsl_opmult_comp_spv[] = { 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, 0x04, 0x00, - 0x08, 0x00, 0x00, 0x00, 0x69, 0x6e, 0x64, 0x65, 0x78, 0x00, 0x00, 0x00, - 0x05, 0x00, 0x08, 0x00, 0x0b, 0x00, 0x00, 0x00, 0x67, 0x6c, 0x5f, 0x47, - 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- 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, - 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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 - -#include -#include - -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) - }; - - /** - * Default constructor with data provided which would be used to create the - * respective vulkan buffer and memory. - * - * @param data Non-zero-sized vector of data that will be used by the - * tensor - * @param tensorType Type for the tensor which is of type TensorTypes - */ - Tensor(std::shared_ptr physicalDevice, - std::shared_ptr device, - const std::vector& data, - const TensorTypes& tensorType = TensorTypes::eDevice); - - /** - * Destructor which is in charge of freeing vulkan resources unless they - * have been provided externally. - */ - ~Tensor(); - - /** - * Initialiser which calls the initialisation for all the respective tensors - * as well as creates the respective staging tensors. The staging tensors - * would only be created for the tensors of type TensorType::eDevice as - * otherwise there is no need to copy from host memory. - */ - void rebuild(const std::vector& data, - TensorTypes tensorType = TensorTypes::eDevice); - - /** - * Destroys and frees the GPU resources which include the buffer and memory. - */ - void destroy(); - - bool isInit(); - - /** - * Returns the vector of data currently contained by the Tensor. It is - * important to ensure that there is no out-of-sync data with the GPU - * memory. - * - * @return Reference to vector of elements representing the data in the - * tensor. - */ - std::vector& data(); - /** - * Overrides the subscript operator to expose the underlying data's - * subscript operator which in this case would be its underlying - * vector's. - * - * @param i The index where the element will be returned from. - * @return Returns the element in the position requested. - */ - float& operator[](int index); - /** - * 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 - */ - uint32_t size(); - - /** - * Retrieve the tensor type of the Tensor - * - * @return Tensor type of tensor - */ - TensorTypes tensorType(); - - /** - * 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 setData(const std::vector& data); - - /** - * 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(std::shared_ptr commandBuffer, - std::shared_ptr 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( - std::shared_ptr 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( - std::shared_ptr 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( - std::shared_ptr 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(); - /** - * Maps data from the Host Visible GPU memory into the data vector. It - * requires the Tensor to be of staging type for it to work. - */ - void mapDataFromHostMemory(); - /** - * Maps data from the data vector into the Host Visible GPU memory. It - * requires the tensor to be of staging type for it to work. - */ - void mapDataIntoHostMemory(); - - private: - // -------------- NEVER OWNED RESOURCES - std::shared_ptr mPhysicalDevice; - std::shared_ptr mDevice; - - // -------------- OPTIONALLY OWNED RESOURCES - std::shared_ptr mPrimaryBuffer; - bool mFreePrimaryBuffer = false; - std::shared_ptr mStagingBuffer; - bool mFreeStagingBuffer = false; - std::shared_ptr mPrimaryMemory; - bool mFreePrimaryMemory = false; - std::shared_ptr mStagingMemory; - bool mFreeStagingMemory = false; - - // -------------- ALWAYS OWNED RESOURCES - std::vector mData; - - TensorTypes mTensorType = TensorTypes::eDevice; - - void allocateMemoryCreateGPUResources(); // Creates the vulkan buffer - void createBuffer(std::shared_ptr buffer, - vk::BufferUsageFlags bufferUsageFlags); - void allocateBindMemory(std::shared_ptr buffer, - std::shared_ptr memory, - vk::MemoryPropertyFlags memoryPropertyFlags); - void copyBuffer(std::shared_ptr commandBuffer, - std::shared_ptr bufferFrom, - std::shared_ptr bufferTo, - vk::DeviceSize bufferSize, - vk::BufferCopy copyRegion, - bool createBarrier); - - // Private util functions - vk::BufferUsageFlags getPrimaryBufferUsageFlags(); - vk::MemoryPropertyFlags getPrimaryMemoryPropertyFlags(); - vk::BufferUsageFlags getStagingBufferUsageFlags(); - vk::MemoryPropertyFlags getStagingMemoryPropertyFlags(); - uint64_t memorySize(); -}; - -} // 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: - - /** - * Default constructor for Algorithm - * - * @param device The Vulkan device to use for creating resources - * @param commandBuffer The vulkan command buffer to bind the pipeline and - * shaders - */ - Algorithm( - std::shared_ptr device, - const std::vector>& tensors = {}, - const std::vector& spirv = {}, - const Workgroup& workgroup = {}, - const Constants& specializationConstants = {}, - const Constants& pushConstants = {}); - - /** - * Initialiser for the shader data provided to the algorithm as well as - * tensor parameters that will be used in shader. - * - * @param shaderFileData The bytes in spir-v format of the shader - * @tensorParams The Tensors to be used in the Algorithm / shader for - * @specalizationInstalces The specialization parameters to pass to the function - * processing - */ - void rebuild( - const std::vector>& tensors = {}, - const std::vector& 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 x Layout X dispatch value - * @param y Layout Y dispatch value - * @param z Layout Z dispatch value - */ - void recordDispatch(std::shared_ptr commandBuffer); - - bool isInit(); - - void setWorkgroup(const Workgroup& workgroup, uint32_t minSize = 1); - - const Workgroup& getWorkgroup(); - const Constants& getSpecializationConstants(); - const Constants& getPushConstants(); - const std::vector>& getTensors(); - - void destroy(); - -private: - // -------------- NEVER OWNED RESOURCES - std::shared_ptr mDevice; - std::vector> mTensors; - - // -------------- OPTIONALLY OWNED RESOURCES - std::shared_ptr mDescriptorSetLayout; - bool mFreeDescriptorSetLayout = false; - std::shared_ptr mDescriptorPool; - bool mFreeDescriptorPool = false; - std::shared_ptr mDescriptorSet; - bool mFreeDescriptorSet = false; - std::shared_ptr mShaderModule; - bool mFreeShaderModule = false; - std::shared_ptr mPipelineLayout; - bool mFreePipelineLayout = false; - std::shared_ptr mPipelineCache; - bool mFreePipelineCache = false; - std::shared_ptr mPipeline; - bool mFreePipeline = false; - - // -------------- ALWAYS OWNED RESOURCES - std::vector mSpirv; - Constants mSpecializationConstants; - Constants mPushConstants; - Workgroup mWorkgroup; - - bool mIsInit; - - // 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. - */ - virtual void record(std::shared_ptr 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. - */ - virtual void preEval() = 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. - */ - virtual void postEval() = 0; -}; - -} // End namespace kp - -namespace kp { - -/** - * Container of operations that can be sent to GPU as batch - */ -class Sequence: public std::enable_shared_from_this -{ - 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 - */ - Sequence(std::shared_ptr physicalDevice, - std::shared_ptr device, - std::shared_ptr computeQueue, - uint32_t queueIndex); - /** - * Destructor for sequence which is responsible for cleaning all subsequent - * owned operations. - */ - ~Sequence(); - - /** - */ - std::shared_ptr record(std::shared_ptr 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. - */ - template - std::shared_ptr - record(std::vector> tensors, TArgs&&... params) - { - KP_LOG_DEBUG("Kompute Sequence record function started"); - - static_assert(std::is_base_of::value, - "Kompute Sequence record(...) template only valid with " - "OpBase derived classes"); - - KP_LOG_DEBUG("Kompute Sequence creating OpBase derived class instance"); - std::shared_ptr op{ - new T(tensors, std::forward(params)...) }; - - return this->record(op); - } - template - std::shared_ptr - record(std::shared_ptr algorithm, TArgs&&... params) - { - KP_LOG_DEBUG("Kompute Sequence record function started"); - - static_assert(std::is_base_of::value, - "Kompute Sequence record(...) template only valid with " - "OpBase derived classes"); - - KP_LOG_DEBUG("Kompute Sequence creating OpBase derived class instance"); - std::shared_ptr op{ - new T(algorithm, std::forward(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 with a barrier. - * - * @return shared_ptr of the Sequence class itself - */ - std::shared_ptr eval(); - - std::shared_ptr eval(std::shared_ptr op); - - /** - * Eval sends all the recorded and stored operations in the vector of - * operations into the gpu as a submit job with a barrier. - * - * @return shared_ptr of the Sequence class itself - */ - // TODO: Aim to have only a single function with tensors/algorithm - template - std::shared_ptr - eval(std::vector> tensors, TArgs&&... params) - { - KP_LOG_DEBUG("Kompute Sequence record function started"); - - static_assert(std::is_base_of::value, - "Kompute Sequence record(...) template only valid with " - "OpBase derived classes"); - - KP_LOG_DEBUG("Kompute Sequence creating OpBase derived class instance"); - std::shared_ptr op{ - new T(tensors, std::forward(params)...) }; - - // TODO: Aim to be able to handle errors when returning without throw except - return this->eval(op); - } - // Needded as otherise can't use initialiser list - template - std::shared_ptr - eval(std::shared_ptr algorithm, TArgs&&... params) - { - KP_LOG_DEBUG("Kompute Sequence record function started"); - - static_assert(std::is_base_of::value, - "Kompute Sequence record(...) template only valid with " - "OpBase derived classes"); - - KP_LOG_DEBUG("Kompute Sequence creating OpBase derived class instance"); - std::shared_ptr op{ - new T(algorithm, std::forward(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 with a barrier. EvalAwait() must - * be called after to ensure the sequence is terminated correctly. - * - * @return Boolean stating whether execution was successful. - */ - std::shared_ptr evalAsync(); - std::shared_ptr evalAsync(std::shared_ptr op); - - /** - * Eval sends all the recorded and stored operations in the vector of - * operations into the gpu as a submit job with a barrier. - * - * @return shared_ptr of the Sequence class itself - */ - template - std::shared_ptr - evalAsync(std::vector> tensors, TArgs&&... params) - { - KP_LOG_DEBUG("Kompute Sequence record function started"); - - static_assert(std::is_base_of::value, - "Kompute Sequence record(...) template only valid with " - "OpBase derived classes"); - - KP_LOG_DEBUG("Kompute Sequence creating OpBase derived class instance"); - std::shared_ptr op{ - new T(tensors, std::forward(params)...) }; - - return this->evalAsync(op); - } - // Needed as otherwise it's not possible to use initializer lists - template - std::shared_ptr - evalAsync(std::shared_ptr algorithm, TArgs&&... params) - { - KP_LOG_DEBUG("Kompute Sequence record function started"); - - static_assert(std::is_base_of::value, - "Kompute Sequence record(...) template only valid with " - "OpBase derived classes"); - - KP_LOG_DEBUG("Kompute Sequence creating OpBase derived class instance"); - std::shared_ptr op{ - new T(algorithm, std::forward(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 Boolean stating whether execution was successful. - */ - std::shared_ptr evalAwait(uint64_t waitFor = UINT64_MAX); - - /** - * Clear function clears all operations currently recorded and starts recording again. - */ - void clear(); - - /** - * 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(); - - bool isInit(); - - /** - * 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 mPhysicalDevice = nullptr; - std::shared_ptr mDevice = nullptr; - std::shared_ptr mComputeQueue = nullptr; - uint32_t mQueueIndex = -1; - - // -------------- OPTIONALLY OWNED RESOURCES - std::shared_ptr mCommandPool = nullptr; - bool mFreeCommandPool = false; - std::shared_ptr mCommandBuffer = nullptr; - bool mFreeCommandBuffer = false; - - // -------------- ALWAYS OWNED RESOURCES - vk::Fence mFence; - std::vector> mOperations; - - // State - bool mRecording = false; - bool mIsRunning = false; - - // Create functions - void createCommandPool(); - void createCommandBuffer(); -}; - -} // 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::eStaging it will only map the data into host memory which will happen during preEval before the recorded commands are dispatched. This operation won't have any effect on TensorTypes::eStaging. -*/ -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 physicalDevice Vulkan physical device used to find device queues - * @param device Vulkan logical device for passing to Algorithm - * @param commandBuffer Vulkan Command Buffer to record commands into - * @param tensors Tensors that will be used to create in operation. - */ - OpTensorSyncDevice(const std::vector>& 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. - */ - void record(std::shared_ptr commandBuffer) override; - - /** - * Does not perform any preEval commands. - */ - virtual void preEval() override; - - /** - * Does not perform any postEval commands. - */ - virtual void postEval() override; - - private: - // -------------- ALWAYS OWNED RESOURCES - std::vector> mTensors; -}; - -} // End namespace kp - -#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 the user to provide the device - * they would like to create the resources on. - * - * @param physicalDeviceIndex The index of the physical device to use - * @param manageResources (Optional) Whether to manage the memory of the - * resources created and destroy when the manager is destroyed. - * @param familyQueueIndices (Optional) List of queue indices to add for - * explicit allocation - * @param totalQueues The total number of compute queues to create. - */ - Manager(uint32_t physicalDeviceIndex, - const std::vector& familyQueueIndices = {}); - - /** - * 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 instance, - std::shared_ptr physicalDevice, - std::shared_ptr device); - - /** - * Manager destructor which would ensure all owned resources are destroyed - * unless explicitly stated that resources should not be destroyed or freed. - */ - ~Manager(); - - /** - * Get or create a managed Sequence that will be contained by this manager. - * If the named sequence does not currently exist, it would be created and - * initialised. - * - * @param sequenceName The name for the named sequence to be retrieved or - * created - * @param queueIndex The queue to use from the available queues - * @return Shared pointer to the manager owned sequence resource - */ - std::shared_ptr sequence(uint32_t queueIndex = 0); - - /** - * Function that simplifies the common workflow of tensor creation and - * initialization. It will take the constructor parameters for a Tensor - * and will will us it to create a new Tensor and then create it. The - * tensor memory will then be managed and owned by the manager. - * - * @param data The data to initialize the tensor with - * @param tensorType The type of tensor to initialize - * @param syncDataToGPU Whether to sync the data to GPU memory - * @returns Initialized Tensor with memory Syncd to GPU device - */ - std::shared_ptr tensor( - const std::vector& data, - Tensor::TensorTypes tensorType = Tensor::TensorTypes::eDevice, - bool syncDataToGPU = true); - - std::shared_ptr algorithm( - const std::vector>& tensors = {}, - const std::vector& spirv = {}, - const Workgroup& workgroup = {}, - const Constants& specializationConstants = {}, - const Constants& pushConstants = {}); - - void destroy(); - void clear(); - - private: - // -------------- OPTIONALLY OWNED RESOURCES - std::shared_ptr mInstance = nullptr; - bool mFreeInstance = false; - std::shared_ptr mPhysicalDevice = nullptr; - std::shared_ptr mDevice = nullptr; - bool mFreeDevice = false; - - // -------------- ALWAYS OWNED RESOURCES - std::vector> mManagedTensors; - std::vector> mManagedSequences; - std::vector> mManagedAlgorithms; - - std::vector mComputeQueueFamilyIndices; - std::vector> 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& familyQueueIndices = {}, uint32_t hysicalDeviceIndex = 0); -}; - -} // End namespace kp - -#include - -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: - - OpAlgoDispatch(const std::shared_ptr& algorithm, bool skipAlgoInit = false); - - /** - * 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. - */ - virtual void record(std::shared_ptr commandBuffer) override; - - /** - * Does not perform any preEval commands. - */ - virtual void preEval() override; - - /** - * Executes after the recorded commands are submitted, and performs a copy - * of the GPU Device memory into the staging buffer so the output data can - * be retrieved. - */ - virtual void postEval() override; - -private: - // -------------- ALWAYS OWNED RESOURCES - std::shared_ptr mAlgorithm; -}; - -} // End namespace kp - -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 physicalDevice Vulkan physical device used to find device queues - * @param device Vulkan logical device for passing to Algorithm - * @param commandBuffer Vulkan Command Buffer to record commands into - * @param tensors Tensors that are to be used in this operation - * @param komputeWorkgroup Optional parameter to specify the layout for processing - */ - OpMult(std::vector> tensors, std::shared_ptr algorithm) - : OpAlgoDispatch(algorithm, true) - { - 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 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 { - -/** - 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 physicalDevice Vulkan physical device used to find device queues - * @param device Vulkan logical device for passing to Algorithm - * @param commandBuffer Vulkan Command Buffer to record commands into - * @param tensors Tensors that will be used to create in operation. - */ - OpTensorCopy(const std::vector>& 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. - */ - void record(std::shared_ptr commandBuffer) override; - - /** - * Does not perform any preEval commands. - */ - virtual void preEval() override; - - /** - * Copies the local vectors for all the tensors to sync the data with the gpu. - */ - virtual void postEval() override; - - private: - // -------------- ALWAYS OWNED RESOURCES - std::vector> 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::eStaging it will only map the data into host memory which will happen during preEval before the recorded commands are dispatched. This operation won't have any effect on TensorTypes::eStaging. -*/ -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 physicalDevice Vulkan physical device used to find device queues - * @param device Vulkan logical device for passing to Algorithm - * @param commandBuffer Vulkan Command Buffer to record commands into - * @param tensors Tensors that will be used to create in operation. - */ - OpTensorSyncLocal(const std::vector>& 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. - */ - void record(std::shared_ptr commandBuffer) override; - - /** - * Does not perform any preEval commands. - */ - virtual void preEval() override; - - /** - * For host tensors it performs the map command from the host memory into local memory. - */ - virtual void postEval() override; - - private: - // -------------- ALWAYS OWNED RESOURCES - std::vector> mTensors; -}; - -} // End namespace kp + 0x08, 0x00, 0x00, 0x00, 0x69, \ No newline at end of file diff --git a/test/TestOpTensorCreate.cpp b/test/TestOpTensorCreate.cpp index 0d7a26524..14153427e 100644 --- a/test/TestOpTensorCreate.cpp +++ b/test/TestOpTensorCreate.cpp @@ -3,155 +3,56 @@ #include "kompute/Kompute.hpp" -//TEST(TestOpTensorCreate, CreateSingleTensorSingleOp) -//{ -// std::vector testVecA{ 9, 8, 7 }; -// std::shared_ptr tensorA{ new kp::Tensor(testVecA) }; -// -// { -// kp::Manager mgr; -// -// mgr.rebuild({ tensorA }); -// -// EXPECT_TRUE(tensorA->isInit()); -// -// EXPECT_EQ(tensorA->data(), testVecA); -// } -// -// EXPECT_FALSE(tensorA->isInit()); -//} -// -//TEST(TestOpTensorCreate, CreateMultipleTensorSingleOp) -//{ -// -// kp::Manager mgr; -// -// std::vector testVecA{ 9, 8, 7 }; -// std::vector testVecB{ 6, 5, 4 }; -// -// std::shared_ptr tensorA{ new kp::Tensor(testVecA) }; -// std::shared_ptr tensorB{ new kp::Tensor(testVecB) }; -// -// mgr.rebuild({ tensorA, tensorB }); -// -// EXPECT_TRUE(tensorA->isInit()); -// EXPECT_TRUE(tensorB->isInit()); -// -// EXPECT_EQ(tensorA->data(), testVecA); -// EXPECT_EQ(tensorB->data(), testVecB); -//} -// -//TEST(TestOpTensorCreate, CreateMultipleTensorMultipleOp) -//{ -// -// kp::Manager mgr; -// -// std::vector testVecA{ 9, 8, 7 }; -// std::vector testVecB{ 6, 5, 4 }; -// -// std::shared_ptr tensorA{ new kp::Tensor(testVecA) }; -// std::shared_ptr tensorB{ new kp::Tensor(testVecB) }; -// -// mgr.rebuild({ tensorA }); -// mgr.rebuild({ tensorB }); -// -// EXPECT_TRUE(tensorA->isInit()); -// EXPECT_TRUE(tensorB->isInit()); -// -// EXPECT_EQ(tensorA->data(), testVecA); -// EXPECT_EQ(tensorB->data(), testVecB); -//} -// -//TEST(TestOpTensorCreate, TestTensorMemoryManagedByManagerDestroyed) -//{ -// -// std::vector testVecA{ 9, 8, 7 }; -// std::vector testVecB{ 6, 5, 4 }; -// -// std::shared_ptr tensorA{ new kp::Tensor(testVecA) }; -// std::shared_ptr tensorB{ new kp::Tensor(testVecB) }; -// -// { -// kp::Manager mgr; -// mgr.rebuild({ tensorA }); -// mgr.rebuild({ tensorB }); -// -// EXPECT_TRUE(tensorA->isInit()); -// EXPECT_TRUE(tensorB->isInit()); -// -// EXPECT_EQ(tensorA->data(), testVecA); -// EXPECT_EQ(tensorB->data(), testVecB); -// } -// -// EXPECT_FALSE(tensorA->isInit()); -// EXPECT_FALSE(tensorB->isInit()); -//} -// -//TEST(TestOpTensorCreate, TestTensorMemoryManagedByManagerNOTDestroyed) -//{ -// -// std::vector testVecA{ 9, 8, 7 }; -// std::vector testVecB{ 6, 5, 4 }; -// -// std::shared_ptr tensorA{ new kp::Tensor(testVecA) }; -// std::shared_ptr tensorB{ new kp::Tensor(testVecB) }; -// -// kp::Manager mgr; -// -// { -// mgr.rebuild({ tensorA }); -// mgr.rebuild({ tensorB }); -// -// EXPECT_TRUE(tensorA->isInit()); -// EXPECT_TRUE(tensorB->isInit()); -// -// EXPECT_EQ(tensorA->data(), testVecA); -// EXPECT_EQ(tensorB->data(), testVecB); -// } -// -// EXPECT_TRUE(tensorA->isInit()); -// EXPECT_TRUE(tensorB->isInit()); -//} -// -//TEST(TestOpTensorCreate, NoErrorIfTensorFreedBefore) -//{ -// -// std::vector testVecA{ 9, 8, 7 }; -// std::vector testVecB{ 6, 5, 4 }; -// -// std::shared_ptr tensorA{ new kp::Tensor(testVecA) }; -// std::shared_ptr tensorB{ new kp::Tensor(testVecB) }; -// -// kp::Manager mgr; -// -// mgr.rebuild({ tensorA }); -// mgr.rebuild({ tensorB }); -// -// EXPECT_TRUE(tensorA->isInit()); -// EXPECT_TRUE(tensorB->isInit()); -// -// EXPECT_EQ(tensorA->data(), testVecA); -// EXPECT_EQ(tensorB->data(), testVecB); -// -// tensorA->freeMemoryDestroyGPUResources(); -// tensorB->freeMemoryDestroyGPUResources(); -// EXPECT_FALSE(tensorA->isInit()); -// EXPECT_FALSE(tensorB->isInit()); -//} -// -//TEST(TestOpTensorCreate, ExceptionOnZeroSizeTensor) -//{ -// std::vector testVecA; -// -// std::shared_ptr tensorA{ new kp::Tensor(testVecA) }; -// -// kp::Manager mgr; -// -// try { -// mgr.rebuild({ tensorA }); -// } catch (const std::runtime_error& err) { -// // check exception -// ASSERT_TRUE(std::string(err.what()).find("zero-sized") != -// std::string::npos); -// } -//} +TEST(TestOpTensorCreate, CreateSingleTensorSingleOp) +{ + std::vector testVecA{ 9, 8, 7 }; + std::shared_ptr tensorA = nullptr; + + { + kp::Manager mgr; + + tensorA = mgr.tensor(testVecA); + + EXPECT_TRUE(tensorA->isInit()); + + EXPECT_EQ(tensorA->data(), testVecA); + } + + EXPECT_FALSE(tensorA->isInit()); +} + +TEST(TestOpTensorCreate, NoErrorIfTensorFreedBefore) +{ + + std::vector testVecA{ 9, 8, 7 }; + std::vector testVecB{ 6, 5, 4 }; + + kp::Manager mgr; + + std::shared_ptr tensorA = mgr.tensor(testVecA); + std::shared_ptr tensorB = mgr.tensor(testVecB); + + EXPECT_EQ(tensorA->data(), testVecA); + EXPECT_EQ(tensorB->data(), testVecB); + + tensorA->destroy(); + tensorB->destroy(); + + EXPECT_FALSE(tensorA->isInit()); + EXPECT_FALSE(tensorB->isInit()); +} + +TEST(TestOpTensorCreate, ExceptionOnZeroSizeTensor) +{ + std::vector testVecA; + + kp::Manager mgr; + + try { + std::shared_ptr tensorA = mgr.tensor(testVecA); + } catch (const std::runtime_error& err) { + // check exception + ASSERT_TRUE(std::string(err.what()).find("zero-sized") != + std::string::npos); + } +}