Updated examples
This commit is contained in:
Alejandro Saucedo
parent
63e220a8a4
commit
4fddf74ca7
11 changed files with 408 additions and 405 deletions
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@ -20,61 +20,62 @@ void KomputeModelML::train(std::vector<float> yData, std::vector<float> xIData,
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uint32_t ITERATIONS = 100;
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float learningRate = 0.1;
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std::shared_ptr<kp::Tensor> xI{ new kp::Tensor(xIData) };
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std::shared_ptr<kp::Tensor> xJ{ new kp::Tensor(xJData) };
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std::shared_ptr<kp::Tensor> y{ new kp::Tensor(yData) };
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std::shared_ptr<kp::Tensor> wIn{ new kp::Tensor({ 0.001, 0.001 }) };
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std::shared_ptr<kp::Tensor> wOutI{ new kp::Tensor(zerosData) };
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std::shared_ptr<kp::Tensor> wOutJ{ new kp::Tensor(zerosData) };
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std::shared_ptr<kp::Tensor> bIn{ new kp::Tensor({ 0 }) };
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std::shared_ptr<kp::Tensor> bOut{ new kp::Tensor(zerosData) };
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std::shared_ptr<kp::Tensor> lOut{ new kp::Tensor(zerosData) };
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std::vector<std::shared_ptr<kp::Tensor>> params = { xI, xJ, y,
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wIn, wOutI, wOutJ,
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bIn, bOut, lOut };
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{
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kp::Manager mgr;
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{
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mgr.rebuild(params);
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std::shared_ptr<kp::Tensor> xI = mgr.tensor(xIData);
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std::shared_ptr<kp::Tensor> xJ = mgr.tensor(xJData);
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std::shared_ptr<kp::Sequence> sq = mgr.sequence();
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std::shared_ptr<kp::Tensor> y = mgr.tensor(yData);
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// Record op algo base
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sq->begin();
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std::shared_ptr<kp::Tensor> wIn = mgr.tensor({ 0.001, 0.001 });
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std::shared_ptr<kp::Tensor> wOutI = mgr.tensor(zerosData);
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std::shared_ptr<kp::Tensor> wOutJ = mgr.tensor(zerosData);
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sq->record<kp::OpTensorSyncDevice>({ wIn, bIn });
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std::shared_ptr<kp::Tensor> bIn = mgr.tensor({ 0 });
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std::shared_ptr<kp::Tensor> bOut = mgr.tensor(zerosData);
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// Newer versions of Android are able to use shaderc to read raw string
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sq->record<kp::OpAlgoCreate>(
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params, kp::Shader::compile_source(LR_SHADER));
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std::shared_ptr<kp::Tensor> lOut = mgr.tensor(zerosData);
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sq->record<kp::OpTensorSyncLocal>({ wOutI, wOutJ, bOut, lOut });
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std::vector<std::shared_ptr<kp::Tensor>> params = { xI, xJ, y,
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wIn, wOutI, wOutJ,
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bIn, bOut, lOut };
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sq->end();
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std::vector<uint32_t> spirv(
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(uint32_t*)kp::shader_data::shaders_glsl_logisticregression_comp_spv,
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(uint32_t*)(kp::shader_data::shaders_glsl_logisticregression_comp_spv
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+ kp::shader_data::shaders_glsl_logisticregression_comp_spv_len));
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// Iterate across all expected iterations
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for (size_t i = 0; i < ITERATIONS; i++) {
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std::shared_ptr<kp::Algorithm> algo =
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mgr.algorithm(params, spirv, kp::Workgroup({ 5 }), kp::Constants({ 5.0 }));
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sq->eval();
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mgr.sequence()->eval<kp::OpTensorSyncDevice>(params);
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for (size_t j = 0; j < bOut->size(); j++) {
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wIn->data()[0] -= learningRate * wOutI->data()[j];
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wIn->data()[1] -= learningRate * wOutJ->data()[j];
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bIn->data()[0] -= learningRate * bOut->data()[j];
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}
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std::shared_ptr<kp::Sequence> sq = mgr.sequence()
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->record<kp::OpTensorSyncDevice>({ wIn, bIn })
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->record<kp::OpAlgoDispatch>(algo)
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->record<kp::OpTensorSyncLocal>({ wOutI, wOutJ, bOut, lOut });
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// Iterate across all expected iterations
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for (size_t i = 0; i < ITERATIONS; i++) {
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sq->eval();
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for (size_t j = 0; j < bOut->size(); j++) {
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wIn->data()[0] -= learningRate * wOutI->data()[j];
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wIn->data()[1] -= learningRate * wOutJ->data()[j];
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bIn->data()[0] -= learningRate * bOut->data()[j];
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}
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}
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}
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this->mWeights = kp::Tensor(wIn->data());
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this->mBias = kp::Tensor(bIn->data());
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KP_LOG_INFO("RESULT: <<<<<<<<<<<<<<<<<<<");
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KP_LOG_INFO("{}", wIn->data()[0]);
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KP_LOG_INFO("{}", wIn->data()[1]);
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KP_LOG_INFO("{}", bIn->data()[0]);
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this->mWeights = wIn;
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this->mBias = bIn;
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}
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}
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std::vector<float> KomputeModelML::predict(std::vector<float> xI, std::vector<float> xJ) {
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@ -88,9 +89,9 @@ std::vector<float> KomputeModelML::predict(std::vector<float> xI, std::vector<fl
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for (size_t i = 0; i < xI.size(); i++) {
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float xIVal = xI[i];
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float xJVal = xJ[i];
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float result = (xIVal * this->mWeights.data()[0]
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+ xJVal * this->mWeights.data()[1]
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+ this->mBias.data()[0]);
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float result = (xIVal * this->mWeights->data()[0]
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+ xJVal * this->mWeights->data()[1]
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+ this->mBias->data()[0]);
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// Instead of using sigmoid we'll just return full numbers
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float var = result > 0 ? 1 : 0;
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@ -103,13 +104,13 @@ std::vector<float> KomputeModelML::predict(std::vector<float> xI, std::vector<fl
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std::vector<float> KomputeModelML::get_params() {
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std::vector<float> retVector;
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if(this->mWeights.size() + this->mBias.size() == 0) {
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if(this->mWeights->size() + this->mBias->size() == 0) {
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return retVector;
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}
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retVector.push_back(this->mWeights.data()[0]);
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retVector.push_back(this->mWeights.data()[1]);
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retVector.push_back(this->mBias.data()[0]);
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retVector.push_back(this->mWeights->data()[0]);
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retVector.push_back(this->mWeights->data()[1]);
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retVector.push_back(this->mBias->data()[0]);
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retVector.push_back(99.0);
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return retVector;
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@ -4,6 +4,7 @@
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#include <vector>
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#include <string>
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#include <memory>
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#include "kompute/Kompute.hpp"
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@ -20,8 +21,8 @@ public:
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std::vector<float> get_params();
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private:
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kp::Tensor mWeights;
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kp::Tensor mBias;
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std::shared_ptr<kp::Tensor> mWeights;
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std::shared_ptr<kp::Tensor> mBias;
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};
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@ -37,11 +37,14 @@ int main()
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}
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)");
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mgr.evalOpDefault<kp::OpAlgoCreate>(
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{ tensorInA, tensorInB, tensorOut },
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kp::Shader::compile_source(shader));
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std::vector<std::shared_ptr<kp::Tensor>> params = { tensorInA, tensorInB, tensorOut };
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mgr.evalOpDefault<kp::OpTensorSyncLocal>({tensorOut});
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std::shared_ptr<kp::Algorithm> algo = mgr.algorithm(params, kp::Shader::compile_source(shader));
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mgr.sequence()
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->record<kp::OpTensorSyncDevice>(params)
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->record<kp::OpAlgoDispatch>(algo)
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->record<kp::OpTensorSyncLocal>(params);
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// prints "Output { 0 4 12 }"
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std::cout<< "Output: { ";
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@ -31,7 +31,7 @@ void KomputeSummatorNode::_init() {
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std::cout << "CALLING INIT" << std::endl;
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this->mPrimaryTensor = this->mManager.tensor({ 0.0 });
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this->mSecondaryTensor = this->mManager.tensor({ 0.0 });
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this->mSequence = this->mManager.sequence("AdditionSeq");
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this->mSequence = this->mManager.sequence();
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// We now record the steps in the sequence
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if (std::shared_ptr<kp::Sequence> sq = this->mSequence)
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@ -51,7 +51,11 @@ void KomputeSummatorNode::_init() {
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}
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)");
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sq->begin();
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std::shared_ptr<kp::Algorithm> algo =
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mgr.algorithm(
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{ this->mPrimaryTensor, this->mSecondaryTensor },
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kp::Shader::compile_source(shader));
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// First we ensure secondary tensor loads to GPU
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// No need to sync the primary tensor as it should not be changed
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@ -59,15 +63,12 @@ void KomputeSummatorNode::_init() {
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{ this->mSecondaryTensor });
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// Then we run the operation with both tensors
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sq->record<kp::OpAlgoCreate>(
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{ this->mPrimaryTensor, this->mSecondaryTensor },
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kp::Shader::compile_source(shader));
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sq->record<kp::OpAlgoDispatch>(algo)
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// We map the result back to local
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sq->record<kp::OpTensorSyncLocal>(
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{ this->mPrimaryTensor });
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sq->end();
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}
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else {
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throw std::runtime_error("Sequence pointer no longer available");
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@ -29,54 +29,41 @@ void KomputeModelMLNode::train(Array yArr, Array xIArr, Array xJArr) {
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uint32_t ITERATIONS = 100;
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float learningRate = 0.1;
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std::shared_ptr<kp::Tensor> xI{ new kp::Tensor(xIData) };
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std::shared_ptr<kp::Tensor> xJ{ new kp::Tensor(xJData) };
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std::shared_ptr<kp::Tensor> y{ new kp::Tensor(yData) };
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std::shared_ptr<kp::Tensor> wIn{ new kp::Tensor({ 0.001, 0.001 }) };
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std::shared_ptr<kp::Tensor> wOutI{ new kp::Tensor(zerosData) };
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std::shared_ptr<kp::Tensor> wOutJ{ new kp::Tensor(zerosData) };
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std::shared_ptr<kp::Tensor> bIn{ new kp::Tensor({ 0 }) };
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std::shared_ptr<kp::Tensor> bOut{ new kp::Tensor(zerosData) };
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std::shared_ptr<kp::Tensor> lOut{ new kp::Tensor(zerosData) };
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std::vector<std::shared_ptr<kp::Tensor>> params = { xI, xJ, y,
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wIn, wOutI, wOutJ,
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bIn, bOut, lOut };
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{
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kp::Manager mgr;
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mgr.rebuild(params);
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std::shared_ptr<kp::Tensor> xI = mgr.tensor(xIData);
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std::shared_ptr<kp::Tensor> xJ = mgr.tensor(xJData);
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std::shared_ptr<kp::Tensor> y = mgr.tensor(yData);
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std::shared_ptr<kp::Tensor> wIn = mgr.tensor({ 0.001, 0.001 });
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std::shared_ptr<kp::Tensor> wOutI = mgr.tensor(zerosData);
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std::shared_ptr<kp::Tensor> wOutJ = mgr.tensor(zerosData);
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std::shared_ptr<kp::Tensor> bIn = mgr.tensor({ 0 });
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std::shared_ptr<kp::Tensor> bOut = mgr.tensor(zerosData);
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std::shared_ptr<kp::Tensor> lOut = mgr.tensor(zerosData);
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std::vector<std::shared_ptr<kp::Tensor>> params = { xI, xJ, y,
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wIn, wOutI, wOutJ,
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bIn, bOut, lOut };
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{
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std::shared_ptr<kp::Sequence> sq = mgr.sequence();
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std::vector<uint32_t> spirv(
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(uint32_t*)kp::shader_data::shaders_glsl_logisticregression_comp_spv,
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(uint32_t*)(kp::shader_data::shaders_glsl_logisticregression_comp_spv
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+ kp::shader_data::shaders_glsl_logisticregression_comp_spv_len));
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// Record op algo base
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sq->begin();
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std::shared_ptr<kp::Algorithm> algo = mgr.algorithm(params, spirv);
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sq->record<kp::OpTensorSyncDevice>({ wIn, bIn });
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mgr.sequence()->eval<kp::OpTensorSyncDevice>(params);
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#ifdef KOMPUTE_ANDROID_SHADER_FROM_STRING
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// Newer versions of Android are able to use shaderc to read raw string
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sq->record<kp::OpAlgoCreate>(
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params, std::vector<char>(LR_SHADER.begin(), LR_SHADER.end()));
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#else
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// Older versions of Android require the SPIRV binary directly
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sq->record<kp::OpAlgoCreate>(
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params, std::vector<char>(
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kp::shader_data::shaders_glsl_logisticregression_comp_spv,
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kp::shader_data::shaders_glsl_logisticregression_comp_spv
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+ kp::shader_data::shaders_glsl_logisticregression_comp_spv_len
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));
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#endif
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sq->record<kp::OpTensorSyncLocal>({ wOutI, wOutJ, bOut, lOut });
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sq->end();
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std::shared_ptr<kp::Sequence> sq = mgr.sequence()
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->record<kp::OpTensorSyncDevice>({ wIn, bIn })
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->record<kp::OpAlgoDispatch>(algo)
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->record<kp::OpTensorSyncLocal>({ wOutI, wOutJ, bOut, lOut });
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// Iterate across all expected iterations
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for (size_t i = 0; i < ITERATIONS; i++) {
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@ -90,15 +77,15 @@ void KomputeModelMLNode::train(Array yArr, Array xIArr, Array xJArr) {
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}
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}
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}
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KP_LOG_INFO("RESULT: <<<<<<<<<<<<<<<<<<<");
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KP_LOG_INFO(wIn->data()[0]);
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KP_LOG_INFO(wIn->data()[1]);
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KP_LOG_INFO(bIn->data()[0]);
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this->mWeights = kp::Tensor(wIn->data());
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this->mBias = kp::Tensor(bIn->data());
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}
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KP_LOG_INFO("RESULT: <<<<<<<<<<<<<<<<<<<");
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KP_LOG_INFO(wIn->data()[0]);
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KP_LOG_INFO(wIn->data()[1]);
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KP_LOG_INFO(bIn->data()[0]);
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this->mWeights = kp::Tensor(wIn->data());
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this->mBias = kp::Tensor(bIn->data());
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}
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Array KomputeModelMLNode::predict(Array xI, Array xJ) {
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@ -33,54 +33,41 @@ void KomputeModelML::train(Array yArr, Array xIArr, Array xJArr) {
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uint32_t ITERATIONS = 100;
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float learningRate = 0.1;
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std::shared_ptr<kp::Tensor> xI{ new kp::Tensor(xIData) };
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std::shared_ptr<kp::Tensor> xJ{ new kp::Tensor(xJData) };
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std::shared_ptr<kp::Tensor> y{ new kp::Tensor(yData) };
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std::shared_ptr<kp::Tensor> wIn{ new kp::Tensor({ 0.001, 0.001 }) };
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std::shared_ptr<kp::Tensor> wOutI{ new kp::Tensor(zerosData) };
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std::shared_ptr<kp::Tensor> wOutJ{ new kp::Tensor(zerosData) };
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std::shared_ptr<kp::Tensor> bIn{ new kp::Tensor({ 0 }) };
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std::shared_ptr<kp::Tensor> bOut{ new kp::Tensor(zerosData) };
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std::shared_ptr<kp::Tensor> lOut{ new kp::Tensor(zerosData) };
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std::vector<std::shared_ptr<kp::Tensor>> params = { xI, xJ, y,
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wIn, wOutI, wOutJ,
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bIn, bOut, lOut };
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{
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kp::Manager mgr;
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std::shared_ptr<kp::Tensor> xI = mgr.tensor(xIData);
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std::shared_ptr<kp::Tensor> xJ = mgr.tensor(xJData);
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std::shared_ptr<kp::Tensor> y = mgr.tensor(yData);
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std::shared_ptr<kp::Tensor> wIn = mgr.tensor({ 0.001, 0.001 });
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std::shared_ptr<kp::Tensor> wOutI = mgr.tensor(zerosData);
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std::shared_ptr<kp::Tensor> wOutJ = mgr.tensor(zerosData);
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std::shared_ptr<kp::Tensor> bIn = mgr.tensor({ 0 });
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std::shared_ptr<kp::Tensor> bOut = mgr.tensor(zerosData);
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std::shared_ptr<kp::Tensor> lOut = mgr.tensor(zerosData);
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std::vector<std::shared_ptr<kp::Tensor>> params = { xI, xJ, y,
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wIn, wOutI, wOutJ,
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bIn, bOut, lOut };
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{
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mgr.rebuild(params);
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std::vector<uint32_t> spirv(
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(uint32_t*)kp::shader_data::shaders_glsl_logisticregression_comp_spv,
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(uint32_t*)(kp::shader_data::shaders_glsl_logisticregression_comp_spv
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+ kp::shader_data::shaders_glsl_logisticregression_comp_spv_len));
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std::shared_ptr<kp::Sequence> sq = mgr.sequence();
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std::shared_ptr<kp::Algorithm> algo = mgr.algorithm(params, spirv);
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// Record op algo base
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sq->begin();
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mgr.sequence()->eval<kp::OpTensorSyncDevice>(params);
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sq->record<kp::OpTensorSyncDevice>({ wIn, bIn });
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#ifdef KOMPUTE_ANDROID_SHADER_FROM_STRING
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// Newer versions of Android are able to use shaderc to read raw string
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sq->record<kp::OpAlgoCreate>(
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params, std::vector<char>(LR_SHADER.begin(), LR_SHADER.end()));
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#else
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// Older versions of Android require the SPIRV binary directly
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sq->record<kp::OpAlgoCreate>(
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params, std::vector<char>(
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kp::shader_data::shaders_glsl_logisticregression_comp_spv,
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kp::shader_data::shaders_glsl_logisticregression_comp_spv
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+ kp::shader_data::shaders_glsl_logisticregression_comp_spv_len
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));
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#endif
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sq->record<kp::OpTensorSyncLocal>({ wOutI, wOutJ, bOut, lOut });
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sq->end();
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std::shared_ptr<kp::Sequence> sq = mgr.sequence()
|
||||
->record<kp::OpTensorSyncDevice>({ wIn, bIn })
|
||||
->record<kp::OpAlgoDispatch>(algo)
|
||||
->record<kp::OpTensorSyncLocal>({ wOutI, wOutJ, bOut, lOut });
|
||||
|
||||
// Iterate across all expected iterations
|
||||
for (size_t i = 0; i < ITERATIONS; i++) {
|
||||
|
|
@ -94,15 +81,15 @@ void KomputeModelML::train(Array yArr, Array xIArr, Array xJArr) {
|
|||
}
|
||||
}
|
||||
}
|
||||
|
||||
KP_LOG_INFO("RESULT: <<<<<<<<<<<<<<<<<<<");
|
||||
KP_LOG_INFO(wIn->data()[0]);
|
||||
KP_LOG_INFO(wIn->data()[1]);
|
||||
KP_LOG_INFO(bIn->data()[0]);
|
||||
|
||||
this->mWeights = wIn;
|
||||
this->mBias = bIn;
|
||||
}
|
||||
|
||||
KP_LOG_INFO("RESULT: <<<<<<<<<<<<<<<<<<<");
|
||||
KP_LOG_INFO(wIn->data()[0]);
|
||||
KP_LOG_INFO(wIn->data()[1]);
|
||||
KP_LOG_INFO(bIn->data()[0]);
|
||||
|
||||
this->mWeights = kp::Tensor(wIn->data());
|
||||
this->mBias = kp::Tensor(bIn->data());
|
||||
}
|
||||
|
||||
Array KomputeModelML::predict(Array xI, Array xJ) {
|
||||
|
|
@ -116,9 +103,9 @@ Array KomputeModelML::predict(Array xI, Array xJ) {
|
|||
for (size_t i = 0; i < xI.size(); i++) {
|
||||
float xIVal = xI[i];
|
||||
float xJVal = xJ[i];
|
||||
float result = (xIVal * this->mWeights.data()[0]
|
||||
+ xJVal * this->mWeights.data()[1]
|
||||
+ this->mBias.data()[0]);
|
||||
float result = (xIVal * this->mWeights->data()[0]
|
||||
+ xJVal * this->mWeights->data()[1]
|
||||
+ this->mBias->data()[0]);
|
||||
|
||||
// Instead of using sigmoid we'll just return full numbers
|
||||
Variant var = result > 0 ? 1 : 0;
|
||||
|
|
@ -131,15 +118,15 @@ Array KomputeModelML::predict(Array xI, Array xJ) {
|
|||
Array KomputeModelML::get_params() {
|
||||
Array retArray;
|
||||
|
||||
KP_LOG_INFO(this->mWeights.size() + this->mBias.size());
|
||||
KP_LOG_INFO(this->mWeights->size() + this->mBias->size());
|
||||
|
||||
if(this->mWeights.size() + this->mBias.size() == 0) {
|
||||
if(this->mWeights->size() + this->mBias->size() == 0) {
|
||||
return retArray;
|
||||
}
|
||||
|
||||
retArray.push_back(this->mWeights.data()[0]);
|
||||
retArray.push_back(this->mWeights.data()[1]);
|
||||
retArray.push_back(this->mBias.data()[0]);
|
||||
retArray.push_back(this->mWeights->data()[0]);
|
||||
retArray.push_back(this->mWeights->data()[1]);
|
||||
retArray.push_back(this->mBias->data()[0]);
|
||||
retArray.push_back(99.0);
|
||||
|
||||
return retArray;
|
||||
|
|
|
|||
|
|
@ -28,8 +28,8 @@ public:
|
|||
static void _register_methods();
|
||||
|
||||
private:
|
||||
kp::Tensor mWeights;
|
||||
kp::Tensor mBias;
|
||||
std::shared_ptr<kp::Tensor> mWeights;
|
||||
std::shared_ptr<kp::Tensor> mBias;
|
||||
};
|
||||
|
||||
static std::string LR_SHADER = R"(
|
||||
|
|
|
|||
|
|
@ -15,44 +15,39 @@ int main()
|
|||
uint32_t ITERATIONS = 100;
|
||||
float learningRate = 0.1;
|
||||
|
||||
std::shared_ptr<kp::Tensor> xI{ new kp::Tensor({ 0, 1, 1, 1, 1 }) };
|
||||
std::shared_ptr<kp::Tensor> xJ{ new kp::Tensor({ 0, 0, 0, 1, 1 }) };
|
||||
kp::Manager mgr;
|
||||
|
||||
std::shared_ptr<kp::Tensor> y{ new kp::Tensor({ 0, 0, 0, 1, 1 }) };
|
||||
std::shared_ptr<kp::Tensor> xI = mgr.tensor({ 0, 1, 1, 1, 1 });
|
||||
std::shared_ptr<kp::Tensor> xJ = mgr.tensor({ 0, 0, 0, 1, 1 });
|
||||
|
||||
std::shared_ptr<kp::Tensor> wIn{ new kp::Tensor({ 0.001, 0.001 }) };
|
||||
std::shared_ptr<kp::Tensor> wOutI{ new kp::Tensor({ 0, 0, 0, 0, 0 }) };
|
||||
std::shared_ptr<kp::Tensor> wOutJ{ new kp::Tensor({ 0, 0, 0, 0, 0 }) };
|
||||
std::shared_ptr<kp::Tensor> y = mgr.tensor({ 0, 0, 0, 1, 1 });
|
||||
|
||||
std::shared_ptr<kp::Tensor> bIn{ new kp::Tensor({ 0 }) };
|
||||
std::shared_ptr<kp::Tensor> bOut{ new kp::Tensor({ 0, 0, 0, 0, 0 }) };
|
||||
std::shared_ptr<kp::Tensor> wIn = mgr.tensor({ 0.001, 0.001 });
|
||||
std::shared_ptr<kp::Tensor> wOutI = mgr.tensor({ 0, 0, 0, 0, 0 });
|
||||
std::shared_ptr<kp::Tensor> wOutJ = mgr.tensor({ 0, 0, 0, 0, 0 });
|
||||
|
||||
std::shared_ptr<kp::Tensor> lOut{ new kp::Tensor({ 0, 0, 0, 0, 0 }) };
|
||||
std::shared_ptr<kp::Tensor> bIn = mgr.tensor({ 0 });
|
||||
std::shared_ptr<kp::Tensor> bOut = mgr.tensor({ 0, 0, 0, 0, 0 });
|
||||
|
||||
std::shared_ptr<kp::Tensor> lOut = mgr.tensor({ 0, 0, 0, 0, 0 });
|
||||
|
||||
std::vector<std::shared_ptr<kp::Tensor>> params = { xI, xJ, y,
|
||||
wIn, wOutI, wOutJ,
|
||||
bIn, bOut, lOut };
|
||||
|
||||
kp::Manager mgr;
|
||||
|
||||
mgr.rebuild(params);
|
||||
|
||||
std::shared_ptr<kp::Sequence> sq = mgr.sequence();
|
||||
|
||||
// Record op algo base
|
||||
sq->begin();
|
||||
|
||||
sq->record<kp::OpTensorSyncDevice>({ wIn, bIn });
|
||||
|
||||
sq->record<kp::OpAlgoCreate>(
|
||||
params, std::vector<uint32_t>(
|
||||
std::vector<uint32_t> spirv(
|
||||
(uint32_t*)kp::shader_data::shaders_glsl_logisticregression_comp_spv,
|
||||
(uint32_t*)(kp::shader_data::shaders_glsl_logisticregression_comp_spv
|
||||
+ kp::shader_data::shaders_glsl_logisticregression_comp_spv_len)));
|
||||
+ kp::shader_data::shaders_glsl_logisticregression_comp_spv_len));
|
||||
|
||||
sq->record<kp::OpTensorSyncLocal>({ wOutI, wOutJ, bOut, lOut });
|
||||
std::shared_ptr<kp::Algorithm> algo = mgr.algorithm(params, spirv);
|
||||
|
||||
sq->end();
|
||||
mgr.sequence()->eval<kp::OpTensorSyncDevice>(params);
|
||||
|
||||
std::shared_ptr<kp::Sequence> sq = mgr.sequence()
|
||||
->record<kp::OpTensorSyncDevice>({ wIn, bIn })
|
||||
->record<kp::OpAlgoDispatch>(algo)
|
||||
->record<kp::OpTensorSyncLocal>({ wOutI, wOutJ, bOut, lOut });
|
||||
|
||||
// Iterate across all expected iterations
|
||||
for (size_t i = 0; i < ITERATIONS; i++) {
|
||||
|
|
|
|||
|
|
@ -647,12 +647,19 @@ extern py::object kp_debug, kp_info, kp_warning, kp_error;
|
|||
#define KP_LOG_DEBUG(...)
|
||||
#else
|
||||
#if defined(VK_USE_PLATFORM_ANDROID_KHR)
|
||||
#define KP_LOG_DEBUG(...) \
|
||||
((void)__android_log_print(ANDROID_LOG_DEBUG, KOMPUTE_LOG_TAG, fmt::format(__VA_ARGS__)))
|
||||
#define KP_LOG_DEBUG(...) \
|
||||
((void)__android_log_write( \
|
||||
ANDROID_LOG_DEBUG, KOMPUTE_LOG_TAG, fmt::format(__VA_ARGS__).c_str()))
|
||||
#elif defined(KOMPUTE_BUILD_PYTHON)
|
||||
#define KP_LOG_DEBUG(...) kp_debug(fmt::format(__VA_ARGS__))
|
||||
#else
|
||||
#define KP_LOG_DEBUG(...) fmt::print("[{} {}] [debug] [{}:{}] {}\n", __DATE__, __TIME__, __FILE__, __LINE__, fmt::format(__VA_ARGS__))
|
||||
#define KP_LOG_DEBUG(...) \
|
||||
fmt::print("[{} {}] [debug] [{}:{}] {}\n", \
|
||||
__DATE__, \
|
||||
__TIME__, \
|
||||
__FILE__, \
|
||||
__LINE__, \
|
||||
fmt::format(__VA_ARGS__))
|
||||
#endif // VK_USE_PLATFORM_ANDROID_KHR
|
||||
#endif // SPDLOG_ACTIVE_LEVEL > 1
|
||||
|
||||
|
|
@ -660,12 +667,19 @@ extern py::object kp_debug, kp_info, kp_warning, kp_error;
|
|||
#define KP_LOG_INFO(...)
|
||||
#else
|
||||
#if defined(VK_USE_PLATFORM_ANDROID_KHR)
|
||||
#define KP_LOG_INFO(...) \
|
||||
((void)__android_log_print(ANDROID_LOG_INFO, KOMPUTE_LOG_TAG, fmt::format(__VA_ARGS__)))
|
||||
#define KP_LOG_INFO(...) \
|
||||
((void)__android_log_write( \
|
||||
ANDROID_LOG_INFO, KOMPUTE_LOG_TAG, fmt::format(__VA_ARGS__).c_str()))
|
||||
#elif defined(KOMPUTE_BUILD_PYTHON)
|
||||
#define KP_LOG_INFO(...) kp_info(fmt::format(__VA_ARGS__))
|
||||
#else
|
||||
#define KP_LOG_INFO(...) fmt::print("[{} {}] [debug] [{}:{}] {}\n", __DATE__, __TIME__, __FILE__, __LINE__, fmt::format(__VA_ARGS__))
|
||||
#define KP_LOG_INFO(...) \
|
||||
fmt::print("[{} {}] [debug] [{}:{}] {}\n", \
|
||||
__DATE__, \
|
||||
__TIME__, \
|
||||
__FILE__, \
|
||||
__LINE__, \
|
||||
fmt::format(__VA_ARGS__))
|
||||
#endif // VK_USE_PLATFORM_ANDROID_KHR
|
||||
#endif // SPDLOG_ACTIVE_LEVEL > 2
|
||||
|
||||
|
|
@ -673,12 +687,19 @@ extern py::object kp_debug, kp_info, kp_warning, kp_error;
|
|||
#define KP_LOG_WARN(...)
|
||||
#else
|
||||
#if defined(VK_USE_PLATFORM_ANDROID_KHR)
|
||||
#define KP_LOG_WARN(...) \
|
||||
((void)__android_log_print(ANDROID_LOG_WARN, KOMPUTE_LOG_TAG, fmt::format(__VA_ARGS__)))
|
||||
#define KP_LOG_WARN(...) \
|
||||
((void)__android_log_write( \
|
||||
ANDROID_LOG_WARN, KOMPUTE_LOG_TAG, fmt::format(__VA_ARGS__).c_str()))
|
||||
#elif defined(KOMPUTE_BUILD_PYTHON)
|
||||
#define KP_LOG_WARN(...) kp_warning(fmt::format(__VA_ARGS__))
|
||||
#else
|
||||
#define KP_LOG_WARN(...) fmt::print("[{} {}] [debug] [{}:{}] {}\n", __DATE__, __TIME__, __FILE__, __LINE__, fmt::format(__VA_ARGS__))
|
||||
#define KP_LOG_WARN(...) \
|
||||
fmt::print("[{} {}] [debug] [{}:{}] {}\n", \
|
||||
__DATE__, \
|
||||
__TIME__, \
|
||||
__FILE__, \
|
||||
__LINE__, \
|
||||
fmt::format(__VA_ARGS__))
|
||||
#endif // VK_USE_PLATFORM_ANDROID_KHR
|
||||
#endif // SPDLOG_ACTIVE_LEVEL > 3
|
||||
|
||||
|
|
@ -686,12 +707,19 @@ extern py::object kp_debug, kp_info, kp_warning, kp_error;
|
|||
#define KP_LOG_ERROR(...)
|
||||
#else
|
||||
#if defined(VK_USE_PLATFORM_ANDROID_KHR)
|
||||
#define KP_LOG_ERROR(...) \
|
||||
((void)__android_log_print(ANDROID_LOG_ERROR, KOMPUTE_LOG_TAG, fmt::format(__VA_ARGS__)))
|
||||
#define KP_LOG_ERROR(...) \
|
||||
((void)__android_log_write( \
|
||||
ANDROID_LOG_ERROR, KOMPUTE_LOG_TAG, fmt::format(__VA_ARGS__).c_str()))
|
||||
#elif defined(KOMPUTE_BUILD_PYTHON)
|
||||
#define KP_LOG_ERROR(...) kp_error(fmt::format(__VA_ARGS__))
|
||||
#else
|
||||
#define KP_LOG_ERROR(...) fmt::print("[{} {}] [debug] [{}:{}] {}\n", __DATE__, __TIME__, __FILE__, __LINE__, fmt::format(__VA_ARGS__))
|
||||
#define KP_LOG_ERROR(...) \
|
||||
fmt::print("[{} {}] [debug] [{}:{}] {}\n", \
|
||||
__DATE__, \
|
||||
__TIME__, \
|
||||
__FILE__, \
|
||||
__LINE__, \
|
||||
fmt::format(__VA_ARGS__))
|
||||
#endif // VK_USE_PLATFORM_ANDROID_KHR
|
||||
#endif // SPDLOG_ACTIVE_LEVEL > 4
|
||||
#endif // KOMPUTE_SPDLOG_ENABLED
|
||||
|
|
@ -701,9 +729,9 @@ extern py::object kp_debug, kp_info, kp_warning, kp_error;
|
|||
#include <iostream>
|
||||
#include <vector>
|
||||
|
||||
#include <SPIRV/GlslangToSpv.h>
|
||||
#include <glslang/Include/ResourceLimits.h>
|
||||
#include <glslang/Public/ShaderLang.h>
|
||||
#include <SPIRV/GlslangToSpv.h>
|
||||
|
||||
namespace kp {
|
||||
|
||||
|
|
@ -711,157 +739,161 @@ namespace kp {
|
|||
// Has been adobted by:
|
||||
// https://github.com/KhronosGroup/glslang/blob/master/StandAlone/ResourceLimits.cpp
|
||||
const TBuiltInResource defaultResource = {
|
||||
/* .MaxLights = */ 0,
|
||||
/* .MaxClipPlanes = */ 0,
|
||||
/* .MaxTextureUnits = */ 0,
|
||||
/* .MaxTextureCoords = */ 0,
|
||||
/* .MaxVertexAttribs = */ 64,
|
||||
/* .MaxVertexUniformComponents = */ 4096,
|
||||
/* .MaxVaryingFloats = */ 64,
|
||||
/* .MaxVertexTextureImageUnits = */ 0,
|
||||
/* .MaxCombinedTextureImageUnits = */ 0,
|
||||
/* .MaxTextureImageUnits = */ 0,
|
||||
/* .MaxFragmentUniformComponents = */ 0,
|
||||
/* .MaxDrawBuffers = */ 0,
|
||||
/* .MaxVertexUniformVectors = */ 128,
|
||||
/* .MaxVaryingVectors = */ 8,
|
||||
/* .MaxFragmentUniformVectors = */ 0,
|
||||
/* .MaxVertexOutputVectors = */ 16,
|
||||
/* .MaxFragmentInputVectors = */ 0,
|
||||
/* .MinProgramTexelOffset = */ -8,
|
||||
/* .MaxProgramTexelOffset = */ 7,
|
||||
/* .MaxClipDistances = */ 8,
|
||||
/* .MaxComputeWorkGroupCountX = */ 65535,
|
||||
/* .MaxComputeWorkGroupCountY = */ 65535,
|
||||
/* .MaxComputeWorkGroupCountZ = */ 65535,
|
||||
/* .MaxComputeWorkGroupSizeX = */ 1024,
|
||||
/* .MaxComputeWorkGroupSizeY = */ 1024,
|
||||
/* .MaxComputeWorkGroupSizeZ = */ 64,
|
||||
/* .MaxComputeUniformComponents = */ 1024,
|
||||
/* .MaxComputeTextureImageUnits = */ 16,
|
||||
/* .MaxComputeImageUniforms = */ 8,
|
||||
/* .MaxComputeAtomicCounters = */ 8,
|
||||
/* .MaxComputeAtomicCounterBuffers = */ 1,
|
||||
/* .MaxVaryingComponents = */ 60,
|
||||
/* .MaxVertexOutputComponents = */ 64,
|
||||
/* .MaxGeometryInputComponents = */ 64,
|
||||
/* .MaxGeometryOutputComponents = */ 128,
|
||||
/* .MaxFragmentInputComponents = */ 0,
|
||||
/* .MaxImageUnits = */ 0,
|
||||
/* .MaxCombinedImageUnitsAndFragmentOutputs = */ 0,
|
||||
/* .MaxCombinedShaderOutputResources = */ 8,
|
||||
/* .MaxImageSamples = */ 0,
|
||||
/* .MaxVertexImageUniforms = */ 0,
|
||||
/* .MaxTessControlImageUniforms = */ 0,
|
||||
/* .MaxTessEvaluationImageUniforms = */ 0,
|
||||
/* .MaxGeometryImageUniforms = */ 0,
|
||||
/* .MaxFragmentImageUniforms = */ 0,
|
||||
/* .MaxCombinedImageUniforms = */ 0,
|
||||
/* .MaxGeometryTextureImageUnits = */ 0,
|
||||
/* .MaxGeometryOutputVertices = */ 256,
|
||||
/* .MaxGeometryTotalOutputComponents = */ 1024,
|
||||
/* .MaxGeometryUniformComponents = */ 1024,
|
||||
/* .MaxGeometryVaryingComponents = */ 64,
|
||||
/* .MaxTessControlInputComponents = */ 128,
|
||||
/* .MaxTessControlOutputComponents = */ 128,
|
||||
/* .MaxTessControlTextureImageUnits = */ 0,
|
||||
/* .MaxTessControlUniformComponents = */ 1024,
|
||||
/* .MaxTessControlTotalOutputComponents = */ 4096,
|
||||
/* .MaxTessEvaluationInputComponents = */ 128,
|
||||
/* .MaxTessEvaluationOutputComponents = */ 128,
|
||||
/* .MaxTessEvaluationTextureImageUnits = */ 16,
|
||||
/* .MaxTessEvaluationUniformComponents = */ 1024,
|
||||
/* .MaxTessPatchComponents = */ 120,
|
||||
/* .MaxPatchVertices = */ 32,
|
||||
/* .MaxTessGenLevel = */ 64,
|
||||
/* .MaxViewports = */ 16,
|
||||
/* .MaxVertexAtomicCounters = */ 0,
|
||||
/* .MaxTessControlAtomicCounters = */ 0,
|
||||
/* .MaxTessEvaluationAtomicCounters = */ 0,
|
||||
/* .MaxGeometryAtomicCounters = */ 0,
|
||||
/* .MaxFragmentAtomicCounters = */ 0,
|
||||
/* .MaxCombinedAtomicCounters = */ 8,
|
||||
/* .MaxAtomicCounterBindings = */ 1,
|
||||
/* .MaxVertexAtomicCounterBuffers = */ 0,
|
||||
/* .MaxTessControlAtomicCounterBuffers = */ 0,
|
||||
/* .MaxTessEvaluationAtomicCounterBuffers = */ 0,
|
||||
/* .MaxGeometryAtomicCounterBuffers = */ 0,
|
||||
/* .MaxFragmentAtomicCounterBuffers = */ 0,
|
||||
/* .MaxCombinedAtomicCounterBuffers = */ 1,
|
||||
/* .MaxAtomicCounterBufferSize = */ 16384,
|
||||
/* .MaxTransformFeedbackBuffers = */ 4,
|
||||
/* .MaxTransformFeedbackInterleavedComponents = */ 64,
|
||||
/* .MaxCullDistances = */ 8,
|
||||
/* .MaxCombinedClipAndCullDistances = */ 8,
|
||||
/* .MaxSamples = */ 4,
|
||||
/* .maxMeshOutputVerticesNV = */ 256,
|
||||
/* .maxMeshOutputPrimitivesNV = */ 512,
|
||||
/* .maxMeshWorkGroupSizeX_NV = */ 32,
|
||||
/* .maxMeshWorkGroupSizeY_NV = */ 1,
|
||||
/* .maxMeshWorkGroupSizeZ_NV = */ 1,
|
||||
/* .maxTaskWorkGroupSizeX_NV = */ 32,
|
||||
/* .maxTaskWorkGroupSizeY_NV = */ 1,
|
||||
/* .maxTaskWorkGroupSizeZ_NV = */ 1,
|
||||
/* .maxMeshViewCountNV = */ 4,
|
||||
/* .maxDualSourceDrawBuffersEXT = */ 1,
|
||||
/* .MaxLights = */ 0,
|
||||
/* .MaxClipPlanes = */ 0,
|
||||
/* .MaxTextureUnits = */ 0,
|
||||
/* .MaxTextureCoords = */ 0,
|
||||
/* .MaxVertexAttribs = */ 64,
|
||||
/* .MaxVertexUniformComponents = */ 4096,
|
||||
/* .MaxVaryingFloats = */ 64,
|
||||
/* .MaxVertexTextureImageUnits = */ 0,
|
||||
/* .MaxCombinedTextureImageUnits = */ 0,
|
||||
/* .MaxTextureImageUnits = */ 0,
|
||||
/* .MaxFragmentUniformComponents = */ 0,
|
||||
/* .MaxDrawBuffers = */ 0,
|
||||
/* .MaxVertexUniformVectors = */ 128,
|
||||
/* .MaxVaryingVectors = */ 8,
|
||||
/* .MaxFragmentUniformVectors = */ 0,
|
||||
/* .MaxVertexOutputVectors = */ 16,
|
||||
/* .MaxFragmentInputVectors = */ 0,
|
||||
/* .MinProgramTexelOffset = */ -8,
|
||||
/* .MaxProgramTexelOffset = */ 7,
|
||||
/* .MaxClipDistances = */ 8,
|
||||
/* .MaxComputeWorkGroupCountX = */ 65535,
|
||||
/* .MaxComputeWorkGroupCountY = */ 65535,
|
||||
/* .MaxComputeWorkGroupCountZ = */ 65535,
|
||||
/* .MaxComputeWorkGroupSizeX = */ 1024,
|
||||
/* .MaxComputeWorkGroupSizeY = */ 1024,
|
||||
/* .MaxComputeWorkGroupSizeZ = */ 64,
|
||||
/* .MaxComputeUniformComponents = */ 1024,
|
||||
/* .MaxComputeTextureImageUnits = */ 16,
|
||||
/* .MaxComputeImageUniforms = */ 8,
|
||||
/* .MaxComputeAtomicCounters = */ 8,
|
||||
/* .MaxComputeAtomicCounterBuffers = */ 1,
|
||||
/* .MaxVaryingComponents = */ 60,
|
||||
/* .MaxVertexOutputComponents = */ 64,
|
||||
/* .MaxGeometryInputComponents = */ 64,
|
||||
/* .MaxGeometryOutputComponents = */ 128,
|
||||
/* .MaxFragmentInputComponents = */ 0,
|
||||
/* .MaxImageUnits = */ 0,
|
||||
/* .MaxCombinedImageUnitsAndFragmentOutputs = */ 0,
|
||||
/* .MaxCombinedShaderOutputResources = */ 8,
|
||||
/* .MaxImageSamples = */ 0,
|
||||
/* .MaxVertexImageUniforms = */ 0,
|
||||
/* .MaxTessControlImageUniforms = */ 0,
|
||||
/* .MaxTessEvaluationImageUniforms = */ 0,
|
||||
/* .MaxGeometryImageUniforms = */ 0,
|
||||
/* .MaxFragmentImageUniforms = */ 0,
|
||||
/* .MaxCombinedImageUniforms = */ 0,
|
||||
/* .MaxGeometryTextureImageUnits = */ 0,
|
||||
/* .MaxGeometryOutputVertices = */ 256,
|
||||
/* .MaxGeometryTotalOutputComponents = */ 1024,
|
||||
/* .MaxGeometryUniformComponents = */ 1024,
|
||||
/* .MaxGeometryVaryingComponents = */ 64,
|
||||
/* .MaxTessControlInputComponents = */ 128,
|
||||
/* .MaxTessControlOutputComponents = */ 128,
|
||||
/* .MaxTessControlTextureImageUnits = */ 0,
|
||||
/* .MaxTessControlUniformComponents = */ 1024,
|
||||
/* .MaxTessControlTotalOutputComponents = */ 4096,
|
||||
/* .MaxTessEvaluationInputComponents = */ 128,
|
||||
/* .MaxTessEvaluationOutputComponents = */ 128,
|
||||
/* .MaxTessEvaluationTextureImageUnits = */ 16,
|
||||
/* .MaxTessEvaluationUniformComponents = */ 1024,
|
||||
/* .MaxTessPatchComponents = */ 120,
|
||||
/* .MaxPatchVertices = */ 32,
|
||||
/* .MaxTessGenLevel = */ 64,
|
||||
/* .MaxViewports = */ 16,
|
||||
/* .MaxVertexAtomicCounters = */ 0,
|
||||
/* .MaxTessControlAtomicCounters = */ 0,
|
||||
/* .MaxTessEvaluationAtomicCounters = */ 0,
|
||||
/* .MaxGeometryAtomicCounters = */ 0,
|
||||
/* .MaxFragmentAtomicCounters = */ 0,
|
||||
/* .MaxCombinedAtomicCounters = */ 8,
|
||||
/* .MaxAtomicCounterBindings = */ 1,
|
||||
/* .MaxVertexAtomicCounterBuffers = */ 0,
|
||||
/* .MaxTessControlAtomicCounterBuffers = */ 0,
|
||||
/* .MaxTessEvaluationAtomicCounterBuffers = */ 0,
|
||||
/* .MaxGeometryAtomicCounterBuffers = */ 0,
|
||||
/* .MaxFragmentAtomicCounterBuffers = */ 0,
|
||||
/* .MaxCombinedAtomicCounterBuffers = */ 1,
|
||||
/* .MaxAtomicCounterBufferSize = */ 16384,
|
||||
/* .MaxTransformFeedbackBuffers = */ 4,
|
||||
/* .MaxTransformFeedbackInterleavedComponents = */ 64,
|
||||
/* .MaxCullDistances = */ 8,
|
||||
/* .MaxCombinedClipAndCullDistances = */ 8,
|
||||
/* .MaxSamples = */ 4,
|
||||
/* .maxMeshOutputVerticesNV = */ 256,
|
||||
/* .maxMeshOutputPrimitivesNV = */ 512,
|
||||
/* .maxMeshWorkGroupSizeX_NV = */ 32,
|
||||
/* .maxMeshWorkGroupSizeY_NV = */ 1,
|
||||
/* .maxMeshWorkGroupSizeZ_NV = */ 1,
|
||||
/* .maxTaskWorkGroupSizeX_NV = */ 32,
|
||||
/* .maxTaskWorkGroupSizeY_NV = */ 1,
|
||||
/* .maxTaskWorkGroupSizeZ_NV = */ 1,
|
||||
/* .maxMeshViewCountNV = */ 4,
|
||||
/* .maxDualSourceDrawBuffersEXT = */ 1,
|
||||
|
||||
/* .limits = */
|
||||
{
|
||||
/* .nonInductiveForLoops = */ 1,
|
||||
/* .whileLoops = */ 1,
|
||||
/* .doWhileLoops = */ 1,
|
||||
/* .generalUniformIndexing = */ 1,
|
||||
/* .generalAttributeMatrixVectorIndexing = */ 1,
|
||||
/* .generalVaryingIndexing = */ 1,
|
||||
/* .generalSamplerIndexing = */ 1,
|
||||
/* .generalVariableIndexing = */ 1,
|
||||
/* .generalConstantMatrixVectorIndexing = */ 1,
|
||||
}
|
||||
};
|
||||
|
||||
/* .limits = */ {
|
||||
/* .nonInductiveForLoops = */ 1,
|
||||
/* .whileLoops = */ 1,
|
||||
/* .doWhileLoops = */ 1,
|
||||
/* .generalUniformIndexing = */ 1,
|
||||
/* .generalAttributeMatrixVectorIndexing = */ 1,
|
||||
/* .generalVaryingIndexing = */ 1,
|
||||
/* .generalSamplerIndexing = */ 1,
|
||||
/* .generalVariableIndexing = */ 1,
|
||||
/* .generalConstantMatrixVectorIndexing = */ 1,
|
||||
}};
|
||||
|
||||
/**
|
||||
Shader utily class with functions to compile and process glsl files.
|
||||
*/
|
||||
class Shader {
|
||||
public:
|
||||
class Shader
|
||||
{
|
||||
public:
|
||||
/**
|
||||
* Compile multiple sources with optional filenames. Currently this function
|
||||
* uses the glslang C++ interface which is not thread safe so this funciton
|
||||
* should not be called from multiple threads concurrently. If you have a
|
||||
* online shader processing multithreading use-case that can't use offline
|
||||
* online shader processing multithreading use-case that can't use offline
|
||||
* compilation please open an issue.
|
||||
*
|
||||
* @param sources A list of raw glsl shaders in string format
|
||||
* @param files A list of file names respective to each of the sources
|
||||
* @param entryPoint The function name to use as entry point
|
||||
* @param definitions List of pairs containing key value definitions
|
||||
* @param resourcesLimit A list that contains the resource limits for the GLSL compiler
|
||||
* @param resourcesLimit A list that contains the resource limits for the
|
||||
* GLSL compiler
|
||||
* @return The compiled SPIR-V binary in unsigned int32 format
|
||||
*/
|
||||
static std::vector<uint32_t> compile_sources(
|
||||
const std::vector<std::string>& sources,
|
||||
const std::vector<std::string>& files = {},
|
||||
const std::string& entryPoint = "main",
|
||||
std::vector<std::pair<std::string,std::string>> definitions = {},
|
||||
const TBuiltInResource& resources = defaultResource);
|
||||
const std::vector<std::string>& sources,
|
||||
const std::vector<std::string>& files = {},
|
||||
const std::string& entryPoint = "main",
|
||||
std::vector<std::pair<std::string, std::string>> definitions = {},
|
||||
const TBuiltInResource& resources = defaultResource);
|
||||
|
||||
/**
|
||||
* Compile a single glslang source from string value. Currently this function
|
||||
* uses the glslang C++ interface which is not thread safe so this funciton
|
||||
* should not be called from multiple threads concurrently. If you have a
|
||||
* online shader processing multithreading use-case that can't use offline
|
||||
* compilation please open an issue.
|
||||
* Compile a single glslang source from string value. Currently this
|
||||
* function uses the glslang C++ interface which is not thread safe so this
|
||||
* funciton should not be called from multiple threads concurrently. If you
|
||||
* have a online shader processing multithreading use-case that can't use
|
||||
* offline compilation please open an issue.
|
||||
*
|
||||
* @param source An individual raw glsl shader in string format
|
||||
* @param entryPoint The function name to use as entry point
|
||||
* @param definitions List of pairs containing key value definitions
|
||||
* @param resourcesLimit A list that contains the resource limits for the GLSL compiler
|
||||
* @param resourcesLimit A list that contains the resource limits for the
|
||||
* GLSL compiler
|
||||
* @return The compiled SPIR-V binary in unsigned int32 format
|
||||
*/
|
||||
static std::vector<uint32_t> compile_source(
|
||||
const std::string& source,
|
||||
const std::string& entryPoint = "main",
|
||||
std::vector<std::pair<std::string,std::string>> definitions = {},
|
||||
const TBuiltInResource& resources = defaultResource);
|
||||
|
||||
const std::string& source,
|
||||
const std::string& entryPoint = "main",
|
||||
std::vector<std::pair<std::string, std::string>> definitions = {},
|
||||
const TBuiltInResource& resources = defaultResource);
|
||||
};
|
||||
|
||||
}
|
||||
|
|
@ -919,7 +951,7 @@ class Tensor
|
|||
* otherwise there is no need to copy from host memory.
|
||||
*/
|
||||
void rebuild(const std::vector<float>& data,
|
||||
TensorTypes tensorType = TensorTypes::eDevice);
|
||||
TensorTypes tensorType = TensorTypes::eDevice);
|
||||
|
||||
/**
|
||||
* Destroys and frees the GPU resources which include the buffer and memory.
|
||||
|
|
@ -990,9 +1022,8 @@ class Tensor
|
|||
* @param createBarrier Whether to create a barrier that ensures the data is
|
||||
* copied before further operations. Default is true.
|
||||
*/
|
||||
void recordCopyFromStagingToDevice(
|
||||
const vk::CommandBuffer& commandBuffer,
|
||||
bool createBarrier);
|
||||
void recordCopyFromStagingToDevice(const vk::CommandBuffer& commandBuffer,
|
||||
bool createBarrier);
|
||||
|
||||
/**
|
||||
* Records a copy from the internal device memory to the staging memory
|
||||
|
|
@ -1003,9 +1034,8 @@ class Tensor
|
|||
* @param createBarrier Whether to create a barrier that ensures the data is
|
||||
* copied before further operations. Default is true.
|
||||
*/
|
||||
void recordCopyFromDeviceToStaging(
|
||||
const vk::CommandBuffer& commandBuffer,
|
||||
bool createBarrier);
|
||||
void recordCopyFromDeviceToStaging(const vk::CommandBuffer& commandBuffer,
|
||||
bool createBarrier);
|
||||
|
||||
/**
|
||||
* Records the buffer memory barrier into the command buffer which
|
||||
|
|
@ -1017,12 +1047,11 @@ class Tensor
|
|||
* @param scrStageMask Pipeline stage flags for source stage mask
|
||||
* @param dstStageMask Pipeline stage flags for destination stage mask
|
||||
*/
|
||||
void recordBufferMemoryBarrier(
|
||||
const vk::CommandBuffer& commandBuffer,
|
||||
vk::AccessFlagBits srcAccessMask,
|
||||
vk::AccessFlagBits dstAccessMask,
|
||||
vk::PipelineStageFlagBits srcStageMask,
|
||||
vk::PipelineStageFlagBits dstStageMask);
|
||||
void recordBufferMemoryBarrier(const vk::CommandBuffer& commandBuffer,
|
||||
vk::AccessFlagBits srcAccessMask,
|
||||
vk::AccessFlagBits dstAccessMask,
|
||||
vk::PipelineStageFlagBits srcStageMask,
|
||||
vk::PipelineStageFlagBits dstStageMask);
|
||||
|
||||
/**
|
||||
* Constructs a vulkan descriptor buffer info which can be used to specify
|
||||
|
|
@ -1070,11 +1099,11 @@ class Tensor
|
|||
std::shared_ptr<vk::DeviceMemory> memory,
|
||||
vk::MemoryPropertyFlags memoryPropertyFlags);
|
||||
void recordCopyBuffer(const vk::CommandBuffer& commandBuffer,
|
||||
std::shared_ptr<vk::Buffer> bufferFrom,
|
||||
std::shared_ptr<vk::Buffer> bufferTo,
|
||||
vk::DeviceSize bufferSize,
|
||||
vk::BufferCopy copyRegion,
|
||||
bool createBarrier);
|
||||
std::shared_ptr<vk::Buffer> bufferFrom,
|
||||
std::shared_ptr<vk::Buffer> bufferTo,
|
||||
vk::DeviceSize bufferSize,
|
||||
vk::BufferCopy copyRegion,
|
||||
bool createBarrier);
|
||||
|
||||
// Private util functions
|
||||
vk::BufferUsageFlags getPrimaryBufferUsageFlags();
|
||||
|
|
@ -1094,8 +1123,7 @@ namespace kp {
|
|||
*/
|
||||
class Algorithm
|
||||
{
|
||||
public:
|
||||
|
||||
public:
|
||||
/**
|
||||
* Default constructor for Algorithm
|
||||
*
|
||||
|
|
@ -1103,12 +1131,11 @@ public:
|
|||
* @param commandBuffer The vulkan command buffer to bind the pipeline and
|
||||
* shaders
|
||||
*/
|
||||
Algorithm(
|
||||
std::shared_ptr<vk::Device> device,
|
||||
const std::vector<std::shared_ptr<Tensor>>& tensors = {},
|
||||
const std::vector<uint32_t>& spirv = {},
|
||||
const Workgroup& workgroup = {},
|
||||
const Constants& specializationConstants = {});
|
||||
Algorithm(std::shared_ptr<vk::Device> device,
|
||||
const std::vector<std::shared_ptr<Tensor>>& tensors = {},
|
||||
const std::vector<uint32_t>& spirv = {},
|
||||
const Workgroup& workgroup = {},
|
||||
const Constants& specializationConstants = {});
|
||||
|
||||
/**
|
||||
* Initialiser for the shader data provided to the algorithm as well as
|
||||
|
|
@ -1116,14 +1143,13 @@ public:
|
|||
*
|
||||
* @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
|
||||
* @specalizationInstalces The specialization parameters to pass to the
|
||||
* function processing
|
||||
*/
|
||||
void rebuild(
|
||||
const std::vector<std::shared_ptr<Tensor>>& tensors = {},
|
||||
const std::vector<uint32_t>& spirv = {},
|
||||
const Workgroup& workgroup = {},
|
||||
const Constants& specializationConstants = {});
|
||||
void rebuild(const std::vector<std::shared_ptr<Tensor>>& tensors = {},
|
||||
const std::vector<uint32_t>& spirv = {},
|
||||
const Workgroup& workgroup = {},
|
||||
const Constants& specializationConstants = {});
|
||||
|
||||
/**
|
||||
* Destructor for Algorithm which is responsible for freeing and desroying
|
||||
|
|
@ -1143,7 +1169,8 @@ public:
|
|||
|
||||
void bindCore(const vk::CommandBuffer& commandBuffer);
|
||||
|
||||
void bindPush(const vk::CommandBuffer& commandBuffer, const Constants& pushConstants);
|
||||
void bindPush(const vk::CommandBuffer& commandBuffer,
|
||||
const Constants& pushConstants);
|
||||
|
||||
bool isInit();
|
||||
|
||||
|
|
@ -1155,7 +1182,7 @@ public:
|
|||
|
||||
void destroy();
|
||||
|
||||
private:
|
||||
private:
|
||||
// -------------- NEVER OWNED RESOURCES
|
||||
std::shared_ptr<vk::Device> mDevice;
|
||||
std::vector<std::shared_ptr<Tensor>> mTensors;
|
||||
|
|
@ -1489,7 +1516,7 @@ namespace kp {
|
|||
/**
|
||||
* Container of operations that can be sent to GPU as batch
|
||||
*/
|
||||
class Sequence: public std::enable_shared_from_this<Sequence>
|
||||
class Sequence : public std::enable_shared_from_this<Sequence>
|
||||
{
|
||||
public:
|
||||
/**
|
||||
|
|
@ -1526,8 +1553,9 @@ class Sequence: public std::enable_shared_from_this<Sequence>
|
|||
* which allows for extensible configurations on initialisation.
|
||||
*/
|
||||
template<typename T, typename... TArgs>
|
||||
std::shared_ptr<Sequence>
|
||||
record(std::vector<std::shared_ptr<Tensor>> tensors, TArgs&&... params)
|
||||
std::shared_ptr<Sequence> record(
|
||||
std::vector<std::shared_ptr<Tensor>> tensors,
|
||||
TArgs&&... params)
|
||||
{
|
||||
KP_LOG_DEBUG("Kompute Sequence record function started");
|
||||
|
||||
|
|
@ -1536,14 +1564,13 @@ class Sequence: public std::enable_shared_from_this<Sequence>
|
|||
"OpBase derived classes");
|
||||
|
||||
KP_LOG_DEBUG("Kompute Sequence creating OpBase derived class instance");
|
||||
std::shared_ptr<T> op{
|
||||
new T(tensors, std::forward<TArgs>(params)...) };
|
||||
std::shared_ptr<T> op{ new T(tensors, std::forward<TArgs>(params)...) };
|
||||
|
||||
return this->record(op);
|
||||
}
|
||||
template<typename T, typename... TArgs>
|
||||
std::shared_ptr<Sequence>
|
||||
record(std::shared_ptr<Algorithm> algorithm, TArgs&&... params)
|
||||
std::shared_ptr<Sequence> record(std::shared_ptr<Algorithm> algorithm,
|
||||
TArgs&&... params)
|
||||
{
|
||||
KP_LOG_DEBUG("Kompute Sequence record function started");
|
||||
|
||||
|
|
@ -1552,8 +1579,8 @@ class Sequence: public std::enable_shared_from_this<Sequence>
|
|||
"OpBase derived classes");
|
||||
|
||||
KP_LOG_DEBUG("Kompute Sequence creating OpBase derived class instance");
|
||||
std::shared_ptr<T> op{
|
||||
new T(algorithm, std::forward<TArgs>(params)...) };
|
||||
std::shared_ptr<T> op{ new T(algorithm,
|
||||
std::forward<TArgs>(params)...) };
|
||||
|
||||
return this->record(op);
|
||||
}
|
||||
|
|
@ -1576,8 +1603,8 @@ class Sequence: public std::enable_shared_from_this<Sequence>
|
|||
*/
|
||||
// TODO: Aim to have only a single function with tensors/algorithm
|
||||
template<typename T, typename... TArgs>
|
||||
std::shared_ptr<Sequence>
|
||||
eval(std::vector<std::shared_ptr<Tensor>> tensors, TArgs&&... params)
|
||||
std::shared_ptr<Sequence> eval(std::vector<std::shared_ptr<Tensor>> tensors,
|
||||
TArgs&&... params)
|
||||
{
|
||||
KP_LOG_DEBUG("Kompute Sequence record function started");
|
||||
|
||||
|
|
@ -1586,16 +1613,16 @@ class Sequence: public std::enable_shared_from_this<Sequence>
|
|||
"OpBase derived classes");
|
||||
|
||||
KP_LOG_DEBUG("Kompute Sequence creating OpBase derived class instance");
|
||||
std::shared_ptr<T> op{
|
||||
new T(tensors, std::forward<TArgs>(params)...) };
|
||||
std::shared_ptr<T> op{ new T(tensors, std::forward<TArgs>(params)...) };
|
||||
|
||||
// TODO: Aim to be able to handle errors when returning without throw except
|
||||
// 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<typename T, typename... TArgs>
|
||||
std::shared_ptr<Sequence>
|
||||
eval(std::shared_ptr<Algorithm> algorithm, TArgs&&... params)
|
||||
std::shared_ptr<Sequence> eval(std::shared_ptr<Algorithm> algorithm,
|
||||
TArgs&&... params)
|
||||
{
|
||||
KP_LOG_DEBUG("Kompute Sequence record function started");
|
||||
|
||||
|
|
@ -1604,8 +1631,8 @@ class Sequence: public std::enable_shared_from_this<Sequence>
|
|||
"OpBase derived classes");
|
||||
|
||||
KP_LOG_DEBUG("Kompute Sequence creating OpBase derived class instance");
|
||||
std::shared_ptr<T> op{
|
||||
new T(algorithm, std::forward<TArgs>(params)...) };
|
||||
std::shared_ptr<T> op{ new T(algorithm,
|
||||
std::forward<TArgs>(params)...) };
|
||||
|
||||
return this->eval(op);
|
||||
}
|
||||
|
|
@ -1627,8 +1654,9 @@ class Sequence: public std::enable_shared_from_this<Sequence>
|
|||
* @return shared_ptr<Sequence> of the Sequence class itself
|
||||
*/
|
||||
template<typename T, typename... TArgs>
|
||||
std::shared_ptr<Sequence>
|
||||
evalAsync(std::vector<std::shared_ptr<Tensor>> tensors, TArgs&&... params)
|
||||
std::shared_ptr<Sequence> evalAsync(
|
||||
std::vector<std::shared_ptr<Tensor>> tensors,
|
||||
TArgs&&... params)
|
||||
{
|
||||
KP_LOG_DEBUG("Kompute Sequence record function started");
|
||||
|
||||
|
|
@ -1637,15 +1665,14 @@ class Sequence: public std::enable_shared_from_this<Sequence>
|
|||
"OpBase derived classes");
|
||||
|
||||
KP_LOG_DEBUG("Kompute Sequence creating OpBase derived class instance");
|
||||
std::shared_ptr<T> op{
|
||||
new T(tensors, std::forward<TArgs>(params)...) };
|
||||
std::shared_ptr<T> op{ new T(tensors, std::forward<TArgs>(params)...) };
|
||||
|
||||
return this->evalAsync(op);
|
||||
}
|
||||
// Needed as otherwise it's not possible to use initializer lists
|
||||
template<typename T, typename... TArgs>
|
||||
std::shared_ptr<Sequence>
|
||||
evalAsync(std::shared_ptr<Algorithm> algorithm, TArgs&&... params)
|
||||
std::shared_ptr<Sequence> evalAsync(std::shared_ptr<Algorithm> algorithm,
|
||||
TArgs&&... params)
|
||||
{
|
||||
KP_LOG_DEBUG("Kompute Sequence record function started");
|
||||
|
||||
|
|
@ -1654,8 +1681,8 @@ class Sequence: public std::enable_shared_from_this<Sequence>
|
|||
"OpBase derived classes");
|
||||
|
||||
KP_LOG_DEBUG("Kompute Sequence creating OpBase derived class instance");
|
||||
std::shared_ptr<T> op{
|
||||
new T(algorithm, std::forward<TArgs>(params)...) };
|
||||
std::shared_ptr<T> op{ new T(algorithm,
|
||||
std::forward<TArgs>(params)...) };
|
||||
|
||||
return this->evalAsync(op);
|
||||
}
|
||||
|
|
@ -1670,7 +1697,8 @@ class Sequence: public std::enable_shared_from_this<Sequence>
|
|||
std::shared_ptr<Sequence> evalAwait(uint64_t waitFor = UINT64_MAX);
|
||||
|
||||
/**
|
||||
* Clear function clears all operations currently recorded and starts recording again.
|
||||
* Clear function clears all operations currently recorded and starts
|
||||
* recording again.
|
||||
*/
|
||||
void clear();
|
||||
|
||||
|
|
@ -1821,10 +1849,10 @@ class Manager
|
|||
Tensor::TensorTypes tensorType = Tensor::TensorTypes::eDevice);
|
||||
|
||||
std::shared_ptr<Algorithm> algorithm(
|
||||
const std::vector<std::shared_ptr<Tensor>>& tensors = {},
|
||||
const std::vector<uint32_t>& spirv = {},
|
||||
const Workgroup& workgroup = {},
|
||||
const Constants& specializationConstants = {});
|
||||
const std::vector<std::shared_ptr<Tensor>>& tensors = {},
|
||||
const std::vector<uint32_t>& spirv = {},
|
||||
const Workgroup& workgroup = {},
|
||||
const Constants& specializationConstants = {});
|
||||
|
||||
void destroy();
|
||||
void clear();
|
||||
|
|
@ -1856,7 +1884,8 @@ class Manager
|
|||
|
||||
// Create functions
|
||||
void createInstance();
|
||||
void createDevice(const std::vector<uint32_t>& familyQueueIndices = {}, uint32_t hysicalDeviceIndex = 0);
|
||||
void createDevice(const std::vector<uint32_t>& familyQueueIndices = {},
|
||||
uint32_t hysicalDeviceIndex = 0);
|
||||
};
|
||||
|
||||
} // End namespace kp
|
||||
|
|
|
|||
|
|
@ -61,8 +61,8 @@ extern py::object kp_debug, kp_info, kp_warning, kp_error;
|
|||
#else
|
||||
#if defined(VK_USE_PLATFORM_ANDROID_KHR)
|
||||
#define KP_LOG_DEBUG(...) \
|
||||
((void)__android_log_print( \
|
||||
ANDROID_LOG_DEBUG, KOMPUTE_LOG_TAG, fmt::format(__VA_ARGS__)))
|
||||
((void)__android_log_write( \
|
||||
ANDROID_LOG_DEBUG, KOMPUTE_LOG_TAG, fmt::format(__VA_ARGS__).c_str()))
|
||||
#elif defined(KOMPUTE_BUILD_PYTHON)
|
||||
#define KP_LOG_DEBUG(...) kp_debug(fmt::format(__VA_ARGS__))
|
||||
#else
|
||||
|
|
@ -81,8 +81,8 @@ extern py::object kp_debug, kp_info, kp_warning, kp_error;
|
|||
#else
|
||||
#if defined(VK_USE_PLATFORM_ANDROID_KHR)
|
||||
#define KP_LOG_INFO(...) \
|
||||
((void)__android_log_print( \
|
||||
ANDROID_LOG_INFO, KOMPUTE_LOG_TAG, fmt::format(__VA_ARGS__)))
|
||||
((void)__android_log_write( \
|
||||
ANDROID_LOG_INFO, KOMPUTE_LOG_TAG, fmt::format(__VA_ARGS__).c_str()))
|
||||
#elif defined(KOMPUTE_BUILD_PYTHON)
|
||||
#define KP_LOG_INFO(...) kp_info(fmt::format(__VA_ARGS__))
|
||||
#else
|
||||
|
|
@ -101,8 +101,8 @@ extern py::object kp_debug, kp_info, kp_warning, kp_error;
|
|||
#else
|
||||
#if defined(VK_USE_PLATFORM_ANDROID_KHR)
|
||||
#define KP_LOG_WARN(...) \
|
||||
((void)__android_log_print( \
|
||||
ANDROID_LOG_WARN, KOMPUTE_LOG_TAG, fmt::format(__VA_ARGS__)))
|
||||
((void)__android_log_write( \
|
||||
ANDROID_LOG_WARN, KOMPUTE_LOG_TAG, fmt::format(__VA_ARGS__).c_str()))
|
||||
#elif defined(KOMPUTE_BUILD_PYTHON)
|
||||
#define KP_LOG_WARN(...) kp_warning(fmt::format(__VA_ARGS__))
|
||||
#else
|
||||
|
|
@ -121,8 +121,8 @@ extern py::object kp_debug, kp_info, kp_warning, kp_error;
|
|||
#else
|
||||
#if defined(VK_USE_PLATFORM_ANDROID_KHR)
|
||||
#define KP_LOG_ERROR(...) \
|
||||
((void)__android_log_print( \
|
||||
ANDROID_LOG_ERROR, KOMPUTE_LOG_TAG, fmt::format(__VA_ARGS__)))
|
||||
((void)__android_log_write( \
|
||||
ANDROID_LOG_ERROR, KOMPUTE_LOG_TAG, fmt::format(__VA_ARGS__).c_str()))
|
||||
#elif defined(KOMPUTE_BUILD_PYTHON)
|
||||
#define KP_LOG_ERROR(...) kp_error(fmt::format(__VA_ARGS__))
|
||||
#else
|
||||
|
|
|
|||
|
|
@ -1,7 +1,6 @@
|
|||
#pragma once
|
||||
|
||||
#include "kompute/Core.hpp"
|
||||
|
||||
#include "kompute/Tensor.hpp"
|
||||
#include "kompute/Algorithm.hpp"
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue