Updated formatting
Signed-off-by: Alejandro Saucedo <axsauze@gmail.com>
This commit is contained in:
Alejandro Saucedo
parent
5308141d0e
commit
797ba87c2a
12 changed files with 161 additions and 112 deletions
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@ -23,8 +23,8 @@ Algorithm::isInit()
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void
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Algorithm::destroy()
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{
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// We don't have to free memory on destroy as it's freed by the commandBuffer destructor
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// if (this->mPushConstantsData) {
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// We don't have to free memory on destroy as it's freed by the
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// commandBuffer destructor if (this->mPushConstantsData) {
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// free(this->mPushConstantsData);
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// }
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// if (this->mSpecializationConstantsData) {
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@ -238,7 +238,8 @@ Algorithm::createPipeline()
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if (this->mPushConstantsSize) {
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pushConstantRange.setStageFlags(vk::ShaderStageFlagBits::eCompute);
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pushConstantRange.setOffset(0);
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pushConstantRange.setSize(this->mPushConstantsDataTypeMemorySize * this->mPushConstantsSize);
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pushConstantRange.setSize(this->mPushConstantsDataTypeMemorySize *
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this->mPushConstantsSize);
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pipelineLayoutInfo.setPushConstantRangeCount(1);
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pipelineLayoutInfo.setPPushConstantRanges(&pushConstantRange);
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@ -254,7 +255,8 @@ Algorithm::createPipeline()
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for (uint32_t i = 0; i < this->mSpecializationConstantsSize; i++) {
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vk::SpecializationMapEntry specializationEntry(
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static_cast<uint32_t>(i),
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static_cast<uint32_t>(this->mSpecializationConstantsDataTypeMemorySize * i),
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static_cast<uint32_t>(
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this->mSpecializationConstantsDataTypeMemorySize * i),
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this->mSpecializationConstantsDataTypeMemorySize);
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specializationEntries.push_back(specializationEntry);
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@ -265,7 +267,8 @@ Algorithm::createPipeline()
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vk::SpecializationInfo specializationInfo(
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static_cast<uint32_t>(specializationEntries.size()),
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specializationEntries.data(),
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this->mSpecializationConstantsDataTypeMemorySize * this->mSpecializationConstantsSize,
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this->mSpecializationConstantsDataTypeMemorySize *
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this->mSpecializationConstantsSize,
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this->mSpecializationConstantsData);
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vk::PipelineShaderStageCreateInfo shaderStage(
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@ -339,12 +342,14 @@ Algorithm::recordBindPush(const vk::CommandBuffer& commandBuffer)
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{
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if (this->mPushConstantsSize) {
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KP_LOG_DEBUG("Kompute Algorithm binding push constants memory size: {}",
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this->mPushConstantsSize * this->mPushConstantsDataTypeMemorySize);
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this->mPushConstantsSize *
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this->mPushConstantsDataTypeMemorySize);
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commandBuffer.pushConstants(*this->mPipelineLayout,
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vk::ShaderStageFlagBits::eCompute,
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0,
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this->mPushConstantsSize * this->mPushConstantsDataTypeMemorySize,
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this->mPushConstantsSize *
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this->mPushConstantsDataTypeMemorySize,
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this->mPushConstantsData);
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}
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}
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@ -288,22 +288,22 @@ Manager::createDevice(const std::vector<uint32_t>& familyQueueIndices,
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}
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this->mFreeDevice = true;
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// Getting an integer that says how many vuklan devices we have
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uint32_t deviceCount = 0;
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this->mInstance->enumeratePhysicalDevices(&deviceCount, nullptr);
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// This means there are no devices at all
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if (deviceCount == 0) {
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throw std::runtime_error("Failed to find GPUs with Vulkan support! "
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"Maybe you haven't installed vulkan drivers?");
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}
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// This means that we're exceeding our device limit, for
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// example if we have 2 devices, just physicalDeviceIndex
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// 0 and 1 are acceptable. Hence, physicalDeviceIndex should
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// always be less than deviceCount, else we raise an error
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if ( !(deviceCount > physicalDeviceIndex) ) {
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if (!(deviceCount > physicalDeviceIndex)) {
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throw std::runtime_error("There is no such physical index or device, "
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"please use your existing device");
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}
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@ -33,9 +33,9 @@ OpAlgoDispatch::record(const vk::CommandBuffer& commandBuffer)
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if (this->mPushConstantsSize) {
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this->mAlgorithm->setPushConstants(
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this->mPushConstantsData,
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this->mPushConstantsSize,
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this->mPushConstantsDataTypeMemorySize);
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this->mPushConstantsData,
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this->mPushConstantsSize,
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this->mPushConstantsDataTypeMemorySize);
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}
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this->mAlgorithm->recordBindCore(commandBuffer);
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@ -24,12 +24,13 @@ class Algorithm
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* @param spirv (optional) The spirv code to use to create the algorithm
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* @param workgroup (optional) The kp::Workgroup to use for the dispatch
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* which defaults to kp::Workgroup(tensor[0].size(), 1, 1) if not set.
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* @param specializationConstants (optional) The templatable param is to be used to
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* initialize the specialization constants which cannot be changed once set.
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* @param pushConstants (optional) This templatable param is to be used when
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* initializing the pipeline, which set the size of the push constants -
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* these can be modified but all new values must have the same data type and length
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* as otherwise it will result in errors.
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* @param specializationConstants (optional) The templatable param is to be
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* used to initialize the specialization constants which cannot be changed
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* once set.
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* @param pushConstants (optional) This templatable param is to be used
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* when initializing the pipeline, which set the size of the push constants
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* - these can be modified but all new values must have the same data type
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* and length as otherwise it will result in errors.
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*/
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template<typename S = float, typename P = float>
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Algorithm(std::shared_ptr<vk::Device> device,
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@ -44,15 +45,20 @@ class Algorithm
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this->mDevice = device;
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if (tensors.size() && spirv.size()) {
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KP_LOG_INFO("Kompute Algorithm initialising with tensor size: {} and "
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"spirv size: {}",
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tensors.size(),
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spirv.size());
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this->rebuild(
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tensors, spirv, workgroup, specializationConstants, pushConstants);
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KP_LOG_INFO(
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"Kompute Algorithm initialising with tensor size: {} and "
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"spirv size: {}",
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tensors.size(),
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spirv.size());
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this->rebuild(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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} else {
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KP_LOG_INFO("Kompute Algorithm constructor with empty tensors and or "
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"spirv so not rebuilding vulkan components");
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KP_LOG_INFO(
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"Kompute Algorithm constructor with empty tensors and or "
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"spirv so not rebuilding vulkan components");
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}
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}
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@ -64,8 +70,9 @@ class Algorithm
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* @param spirv The spirv code to use to create the algorithm
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* @param workgroup (optional) The kp::Workgroup to use for the dispatch
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* which defaults to kp::Workgroup(tensor[0].size(), 1, 1) if not set.
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* @param specializationConstants (optional) The std::vector<float> to use to
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* initialize the specialization constants which cannot be changed once set.
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* @param specializationConstants (optional) The std::vector<float> to use
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* to initialize the specialization constants which cannot be changed once
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* set.
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* @param pushConstants (optional) The std::vector<float> to use when
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* initializing the pipeline, which set the size of the push constants -
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* these can be modified but all new values must have the same vector size
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@ -87,11 +94,14 @@ class Algorithm
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if (this->mSpecializationConstantsData) {
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free(this->mSpecializationConstantsData);
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}
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uint32_t memorySize = sizeof(decltype(specializationConstants.back()));
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uint32_t memorySize =
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sizeof(decltype(specializationConstants.back()));
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uint32_t size = specializationConstants.size();
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uint32_t totalSize = size * memorySize;
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this->mSpecializationConstantsData = malloc(totalSize);
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memcpy(this->mSpecializationConstantsData, specializationConstants.data(), totalSize);
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memcpy(this->mSpecializationConstantsData,
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specializationConstants.data(),
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totalSize);
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this->mSpecializationConstantsDataTypeMemorySize = memorySize;
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this->mSpecializationConstantsSize = size;
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}
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@ -109,11 +119,11 @@ class Algorithm
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this->mPushConstantsSize = size;
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}
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this->setWorkgroup(workgroup,
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this->mTensors.size() ? this->mTensors[0]->size() : 1);
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this->setWorkgroup(
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workgroup, this->mTensors.size() ? this->mTensors[0]->size() : 1);
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// Descriptor pool is created first so if available then destroy all before
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// rebuild
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// Descriptor pool is created first so if available then destroy all
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// before rebuild
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if (this->isInit()) {
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this->destroy();
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}
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@ -176,9 +186,9 @@ class Algorithm
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* Sets the push constants to the new value provided to use in the next
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* bindPush()
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*
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* @param pushConstants The templatable vector is to be used to set the push constants to use in the
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* next bindPush(...) calls. The constants provided must be of the same size
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* as the ones created during initialization.
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* @param pushConstants The templatable vector is to be used to set the push
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* constants to use in the next bindPush(...) calls. The constants provided
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* must be of the same size as the ones created during initialization.
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*/
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template<typename T>
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void setPushConstants(const std::vector<T>& pushConstants)
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@ -193,21 +203,24 @@ class Algorithm
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* Sets the push constants to the new value provided to use in the next
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* bindPush() with the raw memory block location and memory size to be used.
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*
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* @param data The raw data point to copy the data from, without modifying the pointer.
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* @param data The raw data point to copy the data from, without modifying
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* the pointer.
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* @param size The number of data elements provided in the data
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* @param memorySize The memory size of each of the data elements in bytes.
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*/
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void setPushConstants(void* data, uint32_t size, uint32_t memorySize) {
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void setPushConstants(void* data, uint32_t size, uint32_t memorySize)
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{
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uint32_t totalSize = memorySize * size;
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uint32_t previousTotalSize = this->mPushConstantsDataTypeMemorySize * this->mPushConstantsSize;
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uint32_t previousTotalSize =
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this->mPushConstantsDataTypeMemorySize * this->mPushConstantsSize;
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if (totalSize != previousTotalSize) {
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throw std::runtime_error(
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fmt::format("Kompute Algorithm push "
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"constant total memory size provided is {} but expected {} bytes",
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totalSize,
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previousTotalSize));
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throw std::runtime_error(fmt::format(
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"Kompute Algorithm push "
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"constant total memory size provided is {} but expected {} bytes",
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totalSize,
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previousTotalSize));
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}
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if (this->mPushConstantsData) {
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free(this->mPushConstantsData);
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@ -230,13 +243,15 @@ class Algorithm
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/**
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* Gets the specialization constants of the current algorithm.
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*
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* @returns The std::vector<float> currently set for specialization constants
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* @returns The std::vector<float> currently set for specialization
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* constants
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*/
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template<typename T>
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const std::vector<T> getSpecializationConstants()
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{
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return { (T*)this->mSpecializationConstantsData,
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((T*)this->mSpecializationConstantsData) + this->mSpecializationConstantsSize };
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((T*)this->mSpecializationConstantsData) +
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this->mSpecializationConstantsSize };
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}
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/**
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* Gets the specialization constants of the current algorithm.
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@ -247,7 +262,7 @@ class Algorithm
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const std::vector<T> getPushConstants()
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{
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return { (T*)this->mPushConstantsData,
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((T*)this->mPushConstantsData) + this->mPushConstantsSize };
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((T*)this->mPushConstantsData) + this->mPushConstantsSize };
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}
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/**
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* Gets the current tensors that are used in the algorithm.
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@ -124,8 +124,9 @@ class Manager
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}
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/**
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* Default non-template function that can be used to create algorithm objects
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* which provides default types to the push and spec constants as floats.
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* Default non-template function that can be used to create algorithm
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* objects which provides default types to the push and spec constants as
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* floats.
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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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@ -144,7 +145,8 @@ class Manager
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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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return this->algorithm<>(
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tensors, spirv, workgroup, specializationConstants, pushConstants);
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}
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/**
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@ -155,10 +157,10 @@ class Manager
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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) templatable vector parameter to use for
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* specialization constants, and defaults to an empty constant
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* @param pushConstants (optional) templatable vector parameter to use for push constants,
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* and defaults to an empty constant
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* @param specializationConstants (optional) templatable vector parameter to
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* use for specialization constants, and defaults to an empty constant
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* @param pushConstants (optional) templatable vector parameter to use for
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* push constants, 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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@ -200,7 +202,8 @@ class Manager
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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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* @return vk::PhysicalDeviceProperties containing information about the
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*device
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**/
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vk::PhysicalDeviceProperties getDeviceProperties() const;
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@ -211,7 +214,6 @@ class Manager
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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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@ -168,8 +168,10 @@ TEST(TestAsyncOperations, TestManagerAsyncExecution)
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// AMD Drivers in Windows may see an error in this line due to timeout.
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// In order to fix this, it requires a change on Windows registries.
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// More details on this can be found here: https://docs.substance3d.com/spdoc/gpu-drivers-crash-with-long-computations-128745489.html
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// Context on solution discussed in github: https://github.com/KomputeProject/kompute/issues/196#issuecomment-808866505
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// More details on this can be found here:
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// https://docs.substance3d.com/spdoc/gpu-drivers-crash-with-long-computations-128745489.html
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// Context on solution discussed in github:
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// https://github.com/KomputeProject/kompute/issues/196#issuecomment-808866505
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sq1->evalAsync<kp::OpAlgoDispatch>(algo1);
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sq2->evalAsync<kp::OpAlgoDispatch>(algo2);
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@ -232,8 +234,10 @@ TEST(TestAsyncOperations, TestManagerAsyncExecutionTimeout)
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// AMD Drivers in Windows may see an error in this line due to timeout.
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// In order to fix this, it requires a change on Windows registries.
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// More details on this can be found here: https://docs.substance3d.com/spdoc/gpu-drivers-crash-with-long-computations-128745489.html
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// Context on solution discussed in github: https://github.com/KomputeProject/kompute/issues/196#issuecomment-808866505
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// More details on this can be found here:
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// https://docs.substance3d.com/spdoc/gpu-drivers-crash-with-long-computations-128745489.html
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// Context on solution discussed in github:
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// https://github.com/KomputeProject/kompute/issues/196#issuecomment-808866505
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sq1->evalAsync<kp::OpAlgoDispatch>(algo1);
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sq2->evalAsync<kp::OpAlgoDispatch>(algo2);
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@ -245,7 +249,8 @@ TEST(TestAsyncOperations, TestManagerAsyncExecutionTimeout)
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std::chrono::duration_cast<std::chrono::microseconds>(endSync - startSync)
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.count();
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// The time should several orders of magnitude smaller (in this 100k instead of 1m ns)
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// The time should several orders of magnitude smaller (in this 100k instead
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// of 1m ns)
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EXPECT_LT(duration, 100000);
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sq1->evalAsync<kp::OpTensorSyncLocal>({ tensorA, tensorB });
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@ -126,8 +126,8 @@ TEST(TestLogisticRegression, TestMainLogisticRegressionManualCopy)
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kp::shader_data::
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shaders_glsl_logisticregression_comp_spv_len));
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std::shared_ptr<kp::Algorithm> algorithm =
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mgr.algorithm(params, spirv, kp::Workgroup(), std::vector<float>({ 5.0 }));
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std::shared_ptr<kp::Algorithm> algorithm = mgr.algorithm(
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params, spirv, kp::Workgroup(), std::vector<float>({ 5.0 }));
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std::shared_ptr<kp::Sequence> sq =
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mgr.sequence()
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@ -87,7 +87,8 @@ TEST(TestManager, TestClearDestroy)
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{
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std::shared_ptr<kp::TensorT<float>> tensorLHS = mgr.tensor({ 0, 1, 2 });
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std::shared_ptr<kp::TensorT<float>> tensorRHS = mgr.tensor({ 2, 4, 6 });
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std::shared_ptr<kp::TensorT<float>> tensorOutput = mgr.tensor({ 0, 0, 0 });
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std::shared_ptr<kp::TensorT<float>> tensorOutput =
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mgr.tensor({ 0, 0, 0 });
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std::vector<std::shared_ptr<kp::Tensor>> params = { tensorLHS,
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tensorRHS,
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@ -53,11 +53,8 @@ TEST(TestMultipleAlgoExecutions, TestEndToEndFunctionality)
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std::vector<float> pushConstsA({ 2.0 });
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std::vector<float> pushConstsB({ 3.0 });
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auto algorithm = mgr.algorithm(params,
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compileSource(shader),
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workgroup,
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specConsts,
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pushConstsA);
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auto algorithm = mgr.algorithm(
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params, compileSource(shader), workgroup, specConsts, pushConstsA);
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// 3. Run operation with string shader synchronously
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mgr.sequence()
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@ -97,11 +94,11 @@ TEST(TestMultipleAlgoExecutions, SingleSequenceRecord)
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{
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// A sharedMemoryBarrier is required as the shader is not thread-safe:w
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std::shared_ptr<kp::OpMemoryBarrier> shaderBarrier{
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new kp::OpMemoryBarrier({ tensorA },
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vk::AccessFlagBits::eTransferRead,
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vk::AccessFlagBits::eShaderWrite,
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vk::PipelineStageFlagBits::eComputeShader,
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vk::PipelineStageFlagBits::eComputeShader)
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new kp::OpMemoryBarrier({ tensorA },
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vk::AccessFlagBits::eTransferRead,
|
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vk::AccessFlagBits::eShaderWrite,
|
||||
vk::PipelineStageFlagBits::eComputeShader,
|
||||
vk::PipelineStageFlagBits::eComputeShader)
|
||||
};
|
||||
|
||||
mgr.sequence()
|
||||
|
|
@ -266,11 +263,8 @@ TEST(TestMultipleAlgoExecutions, TestAlgorithmUtilFunctions)
|
|||
std::vector<float> specConsts({ 2 });
|
||||
std::vector<float> pushConsts({ 2.0 });
|
||||
|
||||
auto algorithm = mgr.algorithm(params,
|
||||
compileSource(shader),
|
||||
workgroup,
|
||||
specConsts,
|
||||
pushConsts);
|
||||
auto algorithm = mgr.algorithm(
|
||||
params, compileSource(shader), workgroup, specConsts, pushConsts);
|
||||
|
||||
EXPECT_EQ(algorithm->getWorkgroup(), workgroup);
|
||||
EXPECT_EQ(algorithm->getPushConstants<float>(), pushConsts);
|
||||
|
|
|
|||
|
|
@ -44,8 +44,10 @@ TEST(TestPushConstants, TestConstantsAlgoDispatchOverride)
|
|||
// We need to run this in sequence to avoid race condition
|
||||
// We can't use atomicAdd as swiftshader doesn't support it for
|
||||
// float
|
||||
sq->eval<kp::OpAlgoDispatch>(algo, std::vector<float>{ 0.1, 0.2, 0.3 });
|
||||
sq->eval<kp::OpAlgoDispatch>(algo, std::vector<float>{ 0.3, 0.2, 0.1 });
|
||||
sq->eval<kp::OpAlgoDispatch>(algo,
|
||||
std::vector<float>{ 0.1, 0.2, 0.3 });
|
||||
sq->eval<kp::OpAlgoDispatch>(algo,
|
||||
std::vector<float>{ 0.3, 0.2, 0.1 });
|
||||
sq->eval<kp::OpTensorSyncLocal>({ tensor });
|
||||
|
||||
EXPECT_EQ(tensor->vector(), std::vector<float>({ 0.4, 0.4, 0.4 }));
|
||||
|
|
@ -90,7 +92,8 @@ TEST(TestPushConstants, TestConstantsAlgoDispatchNoOverride)
|
|||
// We can't use atomicAdd as swiftshader doesn't support it for
|
||||
// float
|
||||
sq->eval<kp::OpAlgoDispatch>(algo);
|
||||
sq->eval<kp::OpAlgoDispatch>(algo, std::vector<float>{ 0.3, 0.2, 0.1 });
|
||||
sq->eval<kp::OpAlgoDispatch>(algo,
|
||||
std::vector<float>{ 0.3, 0.2, 0.1 });
|
||||
sq->eval<kp::OpTensorSyncLocal>({ tensor });
|
||||
|
||||
EXPECT_EQ(tensor->vector(), std::vector<float>({ 0.4, 0.4, 0.4 }));
|
||||
|
|
@ -156,22 +159,22 @@ TEST(TestPushConstants, TestConstantsWrongSize)
|
|||
// pa[1] += pcs.y;
|
||||
// pa[2] += pcs.z;
|
||||
// })");
|
||||
//
|
||||
//
|
||||
// std::vector<uint32_t> spirv = compileSource(shader);
|
||||
//
|
||||
//
|
||||
// std::shared_ptr<kp::Sequence> sq = nullptr;
|
||||
//
|
||||
//
|
||||
// {
|
||||
// kp::Manager mgr;
|
||||
//
|
||||
//
|
||||
// std::shared_ptr<kp::TensorT<float>> tensor =
|
||||
// mgr.tensor({ 0, 0, 0 });
|
||||
//
|
||||
//
|
||||
// std::shared_ptr<kp::Algorithm> algo = mgr.algorithm(
|
||||
// { tensor }, spirv, kp::Workgroup({ 1 }), {}, { 0.0 });
|
||||
//
|
||||
//
|
||||
// sq = mgr.sequence()->record<kp::OpTensorSyncDevice>({ tensor });
|
||||
//
|
||||
//
|
||||
// EXPECT_THROW(sq->record<kp::OpAlgoDispatch>(
|
||||
// algo, std::vector<uint32_t>{ 1, 2, 3 }),
|
||||
// std::runtime_error);
|
||||
|
|
@ -197,7 +200,8 @@ TEST(TestPushConstants, TestConstantsMixedTypes)
|
|||
pa[2] += pcs.z;
|
||||
})");
|
||||
|
||||
struct TestConsts{
|
||||
struct TestConsts
|
||||
{
|
||||
float x;
|
||||
uint32_t y;
|
||||
int32_t z;
|
||||
|
|
@ -213,16 +217,19 @@ TEST(TestPushConstants, TestConstantsMixedTypes)
|
|||
std::shared_ptr<kp::TensorT<float>> tensor =
|
||||
mgr.tensorT<float>({ 0, 0, 0 });
|
||||
|
||||
std::shared_ptr<kp::Algorithm> algo = mgr.algorithm<float, TestConsts>(
|
||||
{ tensor }, spirv, kp::Workgroup({ 1 }), {}, {{ 0, 0, 0 }});
|
||||
std::shared_ptr<kp::Algorithm> algo =
|
||||
mgr.algorithm<float, TestConsts>(
|
||||
{ tensor }, spirv, kp::Workgroup({ 1 }), {}, { { 0, 0, 0 } });
|
||||
|
||||
sq = mgr.sequence()->eval<kp::OpTensorSyncDevice>({ tensor });
|
||||
|
||||
// We need to run this in sequence to avoid race condition
|
||||
// We can't use atomicAdd as swiftshader doesn't support it for
|
||||
// float
|
||||
sq->eval<kp::OpAlgoDispatch>(algo, std::vector<TestConsts>{{ 15.32, 2147483650, 10 }});
|
||||
sq->eval<kp::OpAlgoDispatch>(algo, std::vector<TestConsts>{{ 30.32, 2147483650, -3 }});
|
||||
sq->eval<kp::OpAlgoDispatch>(
|
||||
algo, std::vector<TestConsts>{ { 15.32, 2147483650, 10 } });
|
||||
sq->eval<kp::OpAlgoDispatch>(
|
||||
algo, std::vector<TestConsts>{ { 30.32, 2147483650, -3 } });
|
||||
sq->eval<kp::OpTensorSyncLocal>({ tensor });
|
||||
|
||||
EXPECT_EQ(tensor->vector(), std::vector<float>({ 45.64, 1300, 7 }));
|
||||
|
|
@ -258,16 +265,19 @@ TEST(TestPushConstants, TestConstantsInt)
|
|||
std::shared_ptr<kp::TensorT<int32_t>> tensor =
|
||||
mgr.tensorT<int32_t>({ -1, -1, -1 });
|
||||
|
||||
std::shared_ptr<kp::Algorithm> algo = mgr.algorithm<int32_t , int32_t>(
|
||||
{ tensor }, spirv, kp::Workgroup({ 1 }), {}, {{ 0, 0, 0 }});
|
||||
std::shared_ptr<kp::Algorithm> algo =
|
||||
mgr.algorithm<int32_t, int32_t>(
|
||||
{ tensor }, spirv, kp::Workgroup({ 1 }), {}, { { 0, 0, 0 } });
|
||||
|
||||
sq = mgr.sequence()->eval<kp::OpTensorSyncDevice>({ tensor });
|
||||
|
||||
// We need to run this in sequence to avoid race condition
|
||||
// We can't use atomicAdd as swiftshader doesn't support it for
|
||||
// float
|
||||
sq->eval<kp::OpAlgoDispatch>(algo, std::vector<int32_t>{{ -1, -1, -1 }});
|
||||
sq->eval<kp::OpAlgoDispatch>(algo, std::vector<int32_t>{{ -1, -1, -1 }});
|
||||
sq->eval<kp::OpAlgoDispatch>(
|
||||
algo, std::vector<int32_t>{ { -1, -1, -1 } });
|
||||
sq->eval<kp::OpAlgoDispatch>(
|
||||
algo, std::vector<int32_t>{ { -1, -1, -1 } });
|
||||
sq->eval<kp::OpTensorSyncLocal>({ tensor });
|
||||
|
||||
EXPECT_EQ(tensor->vector(), std::vector<int32_t>({ -3, -3, -3 }));
|
||||
|
|
@ -303,19 +313,25 @@ TEST(TestPushConstants, TestConstantsUnsignedInt)
|
|||
std::shared_ptr<kp::TensorT<uint32_t>> tensor =
|
||||
mgr.tensorT<uint32_t>({ 0, 0, 0 });
|
||||
|
||||
std::shared_ptr<kp::Algorithm> algo = mgr.algorithm<uint32_t , uint32_t>(
|
||||
{ tensor }, spirv, kp::Workgroup({ 1 }), {}, {{ 0, 0, 0 }});
|
||||
std::shared_ptr<kp::Algorithm> algo =
|
||||
mgr.algorithm<uint32_t, uint32_t>(
|
||||
{ tensor }, spirv, kp::Workgroup({ 1 }), {}, { { 0, 0, 0 } });
|
||||
|
||||
sq = mgr.sequence()->eval<kp::OpTensorSyncDevice>({ tensor });
|
||||
|
||||
// We need to run this in sequence to avoid race condition
|
||||
// We can't use atomicAdd as swiftshader doesn't support it for
|
||||
// float
|
||||
sq->eval<kp::OpAlgoDispatch>(algo, std::vector<uint32_t>{{ 2147483650, 2147483650, 2147483650 }});
|
||||
sq->eval<kp::OpAlgoDispatch>(algo, std::vector<uint32_t>{{ 5, 5, 5 }});
|
||||
sq->eval<kp::OpAlgoDispatch>(
|
||||
algo,
|
||||
std::vector<uint32_t>{ { 2147483650, 2147483650, 2147483650 } });
|
||||
sq->eval<kp::OpAlgoDispatch>(algo,
|
||||
std::vector<uint32_t>{ { 5, 5, 5 } });
|
||||
sq->eval<kp::OpTensorSyncLocal>({ tensor });
|
||||
|
||||
EXPECT_EQ(tensor->vector(), std::vector<uint32_t>({ 2147483655, 2147483655, 2147483655 }));
|
||||
EXPECT_EQ(
|
||||
tensor->vector(),
|
||||
std::vector<uint32_t>({ 2147483655, 2147483655, 2147483655 }));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -349,18 +365,29 @@ TEST(TestPushConstants, TestConstantsDouble)
|
|||
mgr.tensorT<double>({ 0, 0, 0 });
|
||||
|
||||
std::shared_ptr<kp::Algorithm> algo = mgr.algorithm<double, double>(
|
||||
{ tensor }, spirv, kp::Workgroup({ 1 }), {}, {{ 0, 0, 0 }});
|
||||
{ tensor }, spirv, kp::Workgroup({ 1 }), {}, { { 0, 0, 0 } });
|
||||
|
||||
sq = mgr.sequence()->eval<kp::OpTensorSyncDevice>({ tensor });
|
||||
|
||||
// We need to run this in sequence to avoid race condition
|
||||
// We can't use atomicAdd as swiftshader doesn't support it for
|
||||
// float
|
||||
sq->eval<kp::OpAlgoDispatch>(algo, std::vector<double>{{ 1.1111222233334444, 2.1111222233334444, 3.1111222233334444 }});
|
||||
sq->eval<kp::OpAlgoDispatch>(algo, std::vector<double>{{ 1.1111222233334444, 2.1111222233334444, 3.1111222233334444 }});
|
||||
sq->eval<kp::OpAlgoDispatch>(
|
||||
algo,
|
||||
std::vector<double>{ { 1.1111222233334444,
|
||||
2.1111222233334444,
|
||||
3.1111222233334444 } });
|
||||
sq->eval<kp::OpAlgoDispatch>(
|
||||
algo,
|
||||
std::vector<double>{ { 1.1111222233334444,
|
||||
2.1111222233334444,
|
||||
3.1111222233334444 } });
|
||||
sq->eval<kp::OpTensorSyncLocal>({ tensor });
|
||||
|
||||
EXPECT_EQ(tensor->vector(), std::vector<double>({ 2.2222444466668888, 4.2222444466668888, 6.2222444466668888 }));
|
||||
EXPECT_EQ(tensor->vector(),
|
||||
std::vector<double>({ 2.2222444466668888,
|
||||
4.2222444466668888,
|
||||
6.2222444466668888 }));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -101,4 +101,3 @@ TEST(TestSpecializationConstants, TestConstantsInt)
|
|||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -33,7 +33,8 @@ TEST(TestTensor, DataTypes)
|
|||
{
|
||||
std::vector<uint32_t> vec{ 0, 1, 2 };
|
||||
std::shared_ptr<kp::TensorT<uint32_t>> tensor = mgr.tensorT(vec);
|
||||
EXPECT_EQ(tensor->dataType(), kp::Tensor::TensorDataTypes::eUnsignedInt);
|
||||
EXPECT_EQ(tensor->dataType(),
|
||||
kp::Tensor::TensorDataTypes::eUnsignedInt);
|
||||
}
|
||||
|
||||
{
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue