Fixing deleted sequence from header
Signed-off-by: Alejandro Saucedo <axsauze@gmail.com>
This commit is contained in:
Alejandro Saucedo
parent
3d320ff687
commit
559b83e07f
1 changed files with 520 additions and 0 deletions
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@ -1793,3 +1793,523 @@ class OpMult : public OpAlgoDispatch
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};
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} // End namespace kp
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// SPDX-License-Identifier: Apache-2.0
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namespace kp {
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/**
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* Container of operations that can be sent to GPU as batch
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*/
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class Sequence : public std::enable_shared_from_this<Sequence>
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{
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public:
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/**
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* Main constructor for sequence which requires core vulkan components to
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* generate all dependent resources.
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*
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* @param physicalDevice Vulkan physical device
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* @param device Vulkan logical device
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* @param computeQueue Vulkan compute queue
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* @param queueIndex Vulkan compute queue index in device
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* @param totalTimestamps Maximum number of timestamps to allocate
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*/
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Sequence(std::shared_ptr<vk::PhysicalDevice> physicalDevice,
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std::shared_ptr<vk::Device> device,
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std::shared_ptr<vk::Queue> computeQueue,
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uint32_t queueIndex,
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uint32_t totalTimestamps = 0);
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/**
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* Destructor for sequence which is responsible for cleaning all subsequent
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* owned operations.
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*/
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~Sequence();
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/**
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* Record function for operation to be added to the GPU queue in batch. This
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* template requires classes to be derived from the OpBase class. This
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* function also requires the Sequence to be recording, otherwise it will
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* not be able to add the operation.
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*
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* @param op Object derived from kp::BaseOp that will be recoreded by the
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* sequence which will be used when the operation is evaluated.
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* @return shared_ptr<Sequence> of the Sequence class itself
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*/
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std::shared_ptr<Sequence> record(std::shared_ptr<OpBase> op);
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/**
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* Record function for operation to be added to the GPU queue in batch. This
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* template requires classes to be derived from the OpBase class. This
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* function also requires the Sequence to be recording, otherwise it will
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* not be able to add the operation.
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*
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* @param tensors Vector of tensors to use for the operation
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* @param TArgs Template parameters that are used to initialise operation
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* which allows for extensible configurations on initialisation.
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* @return shared_ptr<Sequence> of the Sequence class itself
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*/
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template<typename T, typename... TArgs>
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std::shared_ptr<Sequence> record(
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std::vector<std::shared_ptr<Tensor>> tensors,
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TArgs&&... params)
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{
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std::shared_ptr<T> op{ new T(tensors, std::forward<TArgs>(params)...) };
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return this->record(op);
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}
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/**
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* Record function for operation to be added to the GPU queue in batch. This
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* template requires classes to be derived from the OpBase class. This
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* function also requires the Sequence to be recording, otherwise it will
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* not be able to add the operation.
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*
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* @param algorithm Algorithm to use for the record often used for OpAlgo
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* operations
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* @param TArgs Template parameters that are used to initialise operation
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* which allows for extensible configurations on initialisation.
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* @return shared_ptr<Sequence> of the Sequence class itself
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*/
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template<typename T, typename... TArgs>
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std::shared_ptr<Sequence> record(std::shared_ptr<Algorithm> algorithm,
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TArgs&&... params)
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{
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std::shared_ptr<T> op{ new T(algorithm,
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std::forward<TArgs>(params)...) };
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return this->record(op);
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}
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/**
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* Eval sends all the recorded and stored operations in the vector of
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* operations into the gpu as a submit job synchronously (with a barrier).
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*
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* @return shared_ptr<Sequence> of the Sequence class itself
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*/
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std::shared_ptr<Sequence> eval();
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/**
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* Resets all the recorded and stored operations, records the operation
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* provided and submits into the gpu as a submit job synchronously (with a
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* barrier).
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*
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* @return shared_ptr<Sequence> of the Sequence class itself
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*/
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std::shared_ptr<Sequence> eval(std::shared_ptr<OpBase> op);
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/**
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* Eval sends all the recorded and stored operations in the vector of
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* operations into the gpu as a submit job with a barrier.
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*
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* @param tensors Vector of tensors to use for the operation
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* @param TArgs Template parameters that are used to initialise operation
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* which allows for extensible configurations on initialisation.
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* @return shared_ptr<Sequence> of the Sequence class itself
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*/
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template<typename T, typename... TArgs>
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std::shared_ptr<Sequence> eval(std::vector<std::shared_ptr<Tensor>> tensors,
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TArgs&&... params)
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{
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std::shared_ptr<T> op{ new T(tensors, std::forward<TArgs>(params)...) };
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return this->eval(op);
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}
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/**
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* Eval sends all the recorded and stored operations in the vector of
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* operations into the gpu as a submit job with a barrier.
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*
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* @param algorithm Algorithm to use for the record often used for OpAlgo
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* operations
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* @param TArgs Template parameters that are used to initialise operation
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* which allows for extensible configurations on initialisation.
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* @return shared_ptr<Sequence> of the Sequence class itself
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*/
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template<typename T, typename... TArgs>
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std::shared_ptr<Sequence> eval(std::shared_ptr<Algorithm> algorithm,
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TArgs&&... params)
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{
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std::shared_ptr<T> op{ new T(algorithm,
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std::forward<TArgs>(params)...) };
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return this->eval(op);
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}
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/**
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* Eval Async sends all the recorded and stored operations in the vector of
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* operations into the gpu as a submit job without a barrier. EvalAwait()
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* must ALWAYS be called after to ensure the sequence is terminated
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* correctly.
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*
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* @return Boolean stating whether execution was successful.
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*/
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std::shared_ptr<Sequence> evalAsync();
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/**
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* Clears currnet operations to record provided one in the vector of
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* operations into the gpu as a submit job without a barrier. EvalAwait()
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* must ALWAYS be called after to ensure the sequence is terminated
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* correctly.
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*
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* @return Boolean stating whether execution was successful.
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*/
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std::shared_ptr<Sequence> evalAsync(std::shared_ptr<OpBase> op);
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/**
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* Eval sends all the recorded and stored operations in the vector of
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* operations into the gpu as a submit job with a barrier.
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*
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* @param tensors Vector of tensors to use for the operation
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* @param TArgs Template parameters that are used to initialise operation
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* which allows for extensible configurations on initialisation.
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* @return shared_ptr<Sequence> of the Sequence class itself
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*/
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template<typename T, typename... TArgs>
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std::shared_ptr<Sequence> evalAsync(
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std::vector<std::shared_ptr<Tensor>> tensors,
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TArgs&&... params)
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{
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std::shared_ptr<T> op{ new T(tensors, std::forward<TArgs>(params)...) };
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return this->evalAsync(op);
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}
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/**
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* Eval sends all the recorded and stored operations in the vector of
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* operations into the gpu as a submit job with a barrier.
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*
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* @param algorithm Algorithm to use for the record often used for OpAlgo
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* operations
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* @param TArgs Template parameters that are used to initialise operation
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* which allows for extensible configurations on initialisation.
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* @return shared_ptr<Sequence> of the Sequence class itself
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*/
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template<typename T, typename... TArgs>
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std::shared_ptr<Sequence> evalAsync(std::shared_ptr<Algorithm> algorithm,
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TArgs&&... params)
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{
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std::shared_ptr<T> op{ new T(algorithm,
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std::forward<TArgs>(params)...) };
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return this->evalAsync(op);
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}
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/**
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* Eval Await waits for the fence to finish processing and then once it
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* finishes, it runs the postEval of all operations.
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*
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* @param waitFor Number of milliseconds to wait before timing out.
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* @return shared_ptr<Sequence> of the Sequence class itself
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*/
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std::shared_ptr<Sequence> evalAwait(uint64_t waitFor = UINT64_MAX);
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/**
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* Clear function clears all operations currently recorded and starts
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* recording again.
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*/
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void clear();
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/**
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* Return the timestamps that were latched at the beginning and
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* after each operation during the last eval() call.
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*/
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std::vector<std::uint64_t> getTimestamps();
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/**
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* Begins recording commands for commands to be submitted into the command
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* buffer.
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*
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* @return Boolean stating whether execution was successful.
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*/
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void begin();
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/**
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* Ends the recording and stops recording commands when the record command
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* is sent.
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*
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* @return Boolean stating whether execution was successful.
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*/
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void end();
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/**
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* Returns true if the sequence is currently in recording activated.
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*
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* @return Boolean stating if recording ongoing.
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*/
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bool isRecording();
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/**
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* Returns true if the sequence has been initialised, and it's based on the
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* GPU resources being refrenced.
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*
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* @return Boolean stating if is initialized
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*/
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bool isInit();
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/**
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* Clears command buffer and triggers re-record of all the current
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* operations saved, which is useful if the underlying kp::Tensors or
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* kp::Algorithms are modified and need to be re-recorded.
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*/
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void rerecord();
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/**
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* Returns true if the sequence is currently running - mostly used for async
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* workloads.
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*
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* @return Boolean stating if currently running.
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*/
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bool isRunning();
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/**
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* Destroys and frees the GPU resources which include the buffer and memory
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* and sets the sequence as init=False.
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*/
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void destroy();
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private:
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// -------------- NEVER OWNED RESOURCES
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std::shared_ptr<vk::PhysicalDevice> mPhysicalDevice = nullptr;
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std::shared_ptr<vk::Device> mDevice = nullptr;
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std::shared_ptr<vk::Queue> mComputeQueue = nullptr;
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uint32_t mQueueIndex = -1;
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// -------------- OPTIONALLY OWNED RESOURCES
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std::shared_ptr<vk::CommandPool> mCommandPool = nullptr;
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bool mFreeCommandPool = false;
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std::shared_ptr<vk::CommandBuffer> mCommandBuffer = nullptr;
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bool mFreeCommandBuffer = false;
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// -------------- ALWAYS OWNED RESOURCES
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vk::Fence mFence;
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std::vector<std::shared_ptr<OpBase>> mOperations;
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std::shared_ptr<vk::QueryPool> timestampQueryPool = nullptr;
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// State
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bool mRecording = false;
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bool mIsRunning = false;
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// Create functions
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void createCommandPool();
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void createCommandBuffer();
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void createTimestampQueryPool(uint32_t totalTimestamps);
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};
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} // End namespace kp
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// SPDX-License-Identifier: Apache-2.0
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#include <set>
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#include <unordered_map>
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#define KP_DEFAULT_SESSION "DEFAULT"
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namespace kp {
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/**
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Base orchestrator which creates and manages device and child components
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*/
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class Manager
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{
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public:
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/**
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Base constructor and default used which creates the base resources
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including choosing the device 0 by default.
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*/
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Manager();
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/**
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* Similar to base constructor but allows for further configuration to use
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* when creating the Vulkan resources.
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*
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* @param physicalDeviceIndex The index of the physical device to use
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* @param familyQueueIndices (Optional) List of queue indices to add for
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* explicit allocation
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* @param desiredExtensions The desired extensions to load from
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* physicalDevice
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*/
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Manager(uint32_t physicalDeviceIndex,
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const std::vector<uint32_t>& familyQueueIndices = {},
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const std::vector<std::string>& desiredExtensions = {});
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/**
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* Manager constructor which allows your own vulkan application to integrate
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* with the kompute use.
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*
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* @param instance Vulkan compute instance to base this application
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* @param physicalDevice Vulkan physical device to use for application
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* @param device Vulkan logical device to use for all base resources
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* @param physicalDeviceIndex Index for vulkan physical device used
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*/
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Manager(std::shared_ptr<vk::Instance> instance,
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std::shared_ptr<vk::PhysicalDevice> physicalDevice,
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std::shared_ptr<vk::Device> device);
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/**
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* Manager destructor which would ensure all owned resources are destroyed
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* unless explicitly stated that resources should not be destroyed or freed.
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*/
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~Manager();
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/**
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* Create a managed sequence that will be destroyed by this manager
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* if it hasn't been destroyed by its reference count going to zero.
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*
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* @param queueIndex The queue to use from the available queues
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* @param nrOfTimestamps The maximum number of timestamps to allocate.
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* If zero (default), disables latching of timestamps.
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* @returns Shared pointer with initialised sequence
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*/
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std::shared_ptr<Sequence> sequence(uint32_t queueIndex = 0,
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uint32_t totalTimestamps = 0);
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/**
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* Create a managed tensor that will be destroyed by this manager
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* if it hasn't been destroyed by its reference count going to zero.
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*
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* @param data The data to initialize the tensor with
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* @param tensorType The type of tensor to initialize
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* @returns Shared pointer with initialised tensor
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*/
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template<typename T>
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std::shared_ptr<TensorT<T>> tensorT(
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const std::vector<T>& data,
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Tensor::TensorTypes tensorType = Tensor::TensorTypes::eDevice)
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{
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KP_LOG_DEBUG("Kompute Manager tensor creation triggered");
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std::shared_ptr<TensorT<T>> tensor{ new kp::TensorT<T>(
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this->mPhysicalDevice, this->mDevice, data, tensorType) };
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if (this->mManageResources) {
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this->mManagedTensors.push_back(tensor);
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}
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return tensor;
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}
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std::shared_ptr<TensorT<float>> tensor(
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const std::vector<float>& data,
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Tensor::TensorTypes tensorType = Tensor::TensorTypes::eDevice)
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{
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return this->tensorT<float>(data, tensorType);
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}
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std::shared_ptr<Tensor> tensor(
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void* data,
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uint32_t elementTotalCount,
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uint32_t elementMemorySize,
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const Tensor::TensorDataTypes& dataType,
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Tensor::TensorTypes tensorType = Tensor::TensorTypes::eDevice)
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{
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std::shared_ptr<Tensor> tensor{ new kp::Tensor(this->mPhysicalDevice,
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this->mDevice,
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data,
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elementTotalCount,
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elementMemorySize,
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dataType,
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tensorType) };
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if (this->mManageResources) {
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this->mManagedTensors.push_back(tensor);
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}
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return tensor;
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}
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std::shared_ptr<Algorithm> algorithm(
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const std::vector<std::shared_ptr<Tensor>>& tensors = {},
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const std::vector<uint32_t>& spirv = {},
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const Workgroup& workgroup = {},
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const std::vector<float>& specializationConstants = {},
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const std::vector<float>& pushConstants = {})
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{
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return this->algorithm<>(tensors, spirv, workgroup, specializationConstants, pushConstants);
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}
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/**
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* Create a managed algorithm that will be destroyed by this manager
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* if it hasn't been destroyed by its reference count going to zero.
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*
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* @param tensors (optional) The tensors to initialise the algorithm with
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* @param spirv (optional) The SPIRV bytes for the algorithm to dispatch
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* @param workgroup (optional) kp::Workgroup for algorithm to use, and
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* defaults to (tensor[0].size(), 1, 1)
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* @param specializationConstants (optional) kp::Constant to use for
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* specialization constants, and defaults to an empty constant
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* @param pushConstants (optional) kp::Constant to use for push constants,
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* and defaults to an empty constant
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* @returns Shared pointer with initialised algorithm
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*/
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template<typename S = float, typename P = float>
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std::shared_ptr<Algorithm> algorithm(
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const std::vector<std::shared_ptr<Tensor>>& tensors,
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const std::vector<uint32_t>& spirv,
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const Workgroup& workgroup,
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const std::vector<S>& specializationConstants,
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const std::vector<P>& pushConstants)
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{
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KP_LOG_DEBUG("Kompute Manager algorithm creation triggered");
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std::shared_ptr<Algorithm> algorithm{ new kp::Algorithm(
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this->mDevice,
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tensors,
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spirv,
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workgroup,
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specializationConstants,
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pushConstants) };
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if (this->mManageResources) {
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this->mManagedAlgorithms.push_back(algorithm);
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}
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return algorithm;
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}
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/**
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* Destroy the GPU resources and all managed resources by manager.
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**/
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void destroy();
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/**
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* Run a pseudo-garbage collection to release all the managed resources
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* that have been already freed due to these reaching to zero ref count.
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**/
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void clear();
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/**
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* Information about the current device.
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*
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* @return vk::PhysicalDeviceProperties containing information about the device
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**/
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vk::PhysicalDeviceProperties getDeviceProperties() const;
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/**
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* List the devices available in the current vulkan instance.
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*
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* @return vector of physical devices containing their respective properties
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**/
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std::vector<vk::PhysicalDevice> listDevices() const;
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private:
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// -------------- OPTIONALLY OWNED RESOURCES
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std::shared_ptr<vk::Instance> mInstance = nullptr;
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bool mFreeInstance = false;
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std::shared_ptr<vk::PhysicalDevice> mPhysicalDevice = nullptr;
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std::shared_ptr<vk::Device> mDevice = nullptr;
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bool mFreeDevice = false;
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// -------------- 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
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue