Merge pull request #164 from EthicalML/160_op_memory

Amend memory hierarchy to enable for push constants and functional interface for more flexible operations

.ccls

@@ -19,6 +19,7 @@
 -I./external/googletest/googletest/include/
 -I./external/glslang/
 -I./external/spdlog/include/
+-I./external/fmt/include/
 -I./src/include/
 -I./single_include/
 -I./vk_ndk_wrapper_include/

Makefile

@@ -57,7 +57,6 @@ MK_KOMPUTE_EXTRA_CXX_FLAGS ?= ""
 mk_cmake:
 	cmake \
 		-Bbuild \
-		$(MK_CMAKE_EXTRA_FLAGS) \
 		-DKOMPUTE_EXTRA_CXX_FLAGS=$(MK_KOMPUTE_EXTRA_CXX_FLAGS) \
 		-DCMAKE_BUILD_TYPE=$(MK_BUILD_TYPE) \
 		-DCMAKE_INSTALL_PREFIX=$(MK_INSTALL_PATH) \
@@ -69,6 +68,7 @@ mk_cmake:
 		-DKOMPUTE_OPT_BUILD_SINGLE_HEADER=1 \
 		-DKOMPUTE_OPT_ENABLE_SPDLOG=1 \
 		-DKOMPUTE_OPT_CODE_COVERAGE=1 \
+		$(MK_CMAKE_EXTRA_FLAGS) \
 		-G "Unix Makefiles"
 
 mk_build_all:

README.md

@@ -56,35 +56,65 @@ int main() {
     // 2. Create and initialise Kompute Tensors through manager
     auto tensorInA = mgr.tensor({ 2., 2., 2. });
     auto tensorInB = mgr.tensor({ 1., 2., 3. });
-    auto tensorOut = mgr.tensor({ 0., 0., 0. });
+    auto tensorOutA = mgr.tensor({ 0., 0., 0. });
+    auto tensorOutB = mgr.tensor({ 0., 0., 0. });
 
-    // 3. Specify "multiply shader" code (can also be raw string, spir-v bytes or file path)
+    std::vector<std::shared_ptr<kp::Tensor>> params = {tensorInA, tensorInB, tensorOutA, tensorOutB};
-    std::string shaderString = (R"(
+
+    // 3. Create algorithm based on shader (supports buffers & push/spec constants)
+    std::string shader = (R"(
         #version 450
 
         layout (local_size_x = 1) in;
 
         // The input tensors bind index is relative to index in parameter passed
-        layout(set = 0, binding = 0) buffer bina { float tina[]; };
+        layout(set = 0, binding = 0) buffer buf_in_a { float in_a[]; };
-        layout(set = 0, binding = 1) buffer binb { float tinb[]; };
+        layout(set = 0, binding = 1) buffer buf_in_b { float in_b[]; };
-        layout(set = 0, binding = 2) buffer bout { float tout[]; };
+        layout(set = 0, binding = 2) buffer buf_out_a { float out_a[]; };
+        layout(set = 0, binding = 3) buffer buf_out_b { float out_b[]; };
+
+        // Kompute supports push constants updated on dispatch
+        layout(push_constant) uniform PushConstants {
+            float val;
+        } push_const;
+
+        // Kompute also supports spec constants on initalization
+        layout(constant_id = 0) const float const_one = 0;
 
         void main() {
             uint index = gl_GlobalInvocationID.x;
-            tout[index] = tina[index] * tinb[index];
+            out_a[index] += in_a[index] * in_b[index];
+            out_b[index] += const_one * push_const.val;
         }
     )");
 
-    // 3. Run operation with string shader synchronously
+    kp::Workgroup workgroup({3, 1, 1});
-    mgr.evalOpDefault<kp::OpAlgoBase>(
+    kp::Constants specConsts({ 2 });
-        { tensorInA, tensorInB, tensorOut },
-        kp::Shader::compile_source(shaderString));
 
-    // 4. Map results back from GPU memory to print the results
+    auto algorithm = mgr.algorithm(params, kp::Shader::compile_source(shader), workgroup, specConsts);
-    mgr.evalOpDefault<kp::OpTensorSyncLocal>({ tensorInA, tensorInB, tensorOut });
 
-    // Prints the output which is Output: { 2, 4, 6 }
+    kp::Constants pushConstsA({ 2.0 });
-    for (const float& elem : tensorOut->data()) std::cout << elem << "  ";
+    kp::Constants pushConstsB({ 3.0 });
+
+    // 4. Run operation synchronously using sequence
+    mgr.sequence()
+        ->record<kp::OpTensorSyncDevice>(params)
+        ->record<kp::OpAlgoDispatch>(algorithm, pushConstsA)
+        ->record<kp::OpAlgoDispatch>(algorithm, pushConstsB)
+        ->eval();
+
+    // 5. Sync results from the GPU asynchronously
+    sq = mgr.sequence()
+    sq->evalAsync<kp::OpTensorSyncLocal>(params);
+
+    // ... Do other work asynchronously whilst GPU finishes
+
+    sq->evalAwait();
+
+    // Prints the first output which is: { 4, 8, 12 }
+    for (const float& elem : tensorOutA->data()) std::cout << elem << "  ";
+    // Prints the second output which is: { 10, 10, 10 }
+    for (const float& elem : tensorOutB->data()) std::cout << elem << "  ";
 }
 
 ```
@@ -94,34 +124,72 @@ int main() {
 The [Python package](https://kompute.cc/overview/python-package.html) provides a [high level interactive interface](https://kompute.cc/overview/python-reference.html) that enables for experimentation whilst ensuring high performance and fast development workflows.
 
 ```python
+
 # 1. Create Kompute Manager with default settings (device 0 and first compute compatible queue)
-mgr = Manager()
+mgr = kp.Manager()
 
-# 2. Create and initialise Kompute Tensors (can be initialized with List[] or np.Array)
+# 2. Create and initialise Kompute Tensors through manager
-tensor_in_a = Tensor([2, 2, 2])
+tensor_in_a = mgr.tensor([2, 2, 2])
-tensor_in_b = Tensor([1, 2, 3])
+tensor_in_b = mgr.tensor([1, 2, 3])
-tensor_out = Tensor([0, 0, 0])
+tensor_out_a = mgr.tensor([0, 0, 0])
+tensor_out_b = mgr.tensor([0, 0, 0])
 
-mgr.eval_tensor_create_def([tensor_in_a, tensor_in_b, tensor_out])
+params = [tensor_in_a, tensor_in_b, tensor_out_a, tensor_out_b]
 
-# 3. Specify "multiply shader" code (can also be raw string, spir-v bytes or file path)
+# 3. Create algorithm based on shader (supports buffers & push/spec constants)
-@python2shader
+shader = """
-def compute_shader_multiply(index=("input", "GlobalInvocationId", ivec3),
+    #version 450
-                            data1=("buffer", 0, Array(f32)),
-                            data2=("buffer", 1, Array(f32)),
-                            data3=("buffer", 2, Array(f32))):
-    i = index.x
-    data3[i] = data1[i] * data2[i]
 
-# 4. Run multiplication operation synchronously
+    layout (local_size_x = 1) in;
-mgr.eval_algo_data_def(
-    [tensor_in_a, tensor_in_b, tensor_out], compute_shader_multiply.to_spirv())
 
-# 5. Map results back from GPU memory to print the results
+    // The input tensors bind index is relative to index in parameter passed
-mgr.eval_tensor_sync_local_def([tensor_out])
+    layout(set = 0, binding = 0) buffer buf_in_a { float in_a[]; };
+    layout(set = 0, binding = 1) buffer buf_in_b { float in_b[]; };
+    layout(set = 0, binding = 2) buffer buf_out_a { float out_a[]; };
+    layout(set = 0, binding = 3) buffer buf_out_b { float out_b[]; };
+
+    // Kompute supports push constants updated on dispatch
+    layout(push_constant) uniform PushConstants {
+        float val;
+    } push_const;
+
+    // Kompute also supports spec constants on initalization
+    layout(constant_id = 0) const float const_one = 0;
+
+    void main() {
+        uint index = gl_GlobalInvocationID.x;
+        out_a[index] += in_a[index] * in_b[index];
+        out_b[index] += const_one * push_const.val;
+    }
+"""
+
+workgroup = (3, 1, 1)
+spec_consts = [2]
+push_consts_a = [2]
+push_consts_b = [3]
+
+algo = mgr.algorithm(params, kp.Shader.compile_source(shader), workgroup, spec_consts)
+
+# 4. Run operation synchronously using sequence
+(mgr.sequence()
+    .record(kp.OpTensorSyncDevice(params))
+    .record(kp.OpAlgoDispatch(algo, push_consts_a))
+    .record(kp.OpAlgoDispatch(algo, push_consts_b))
+    .eval())
+
+# 5. Sync results from the GPU asynchronously
+sq = mgr.sequence()
+sq.eval_async(kp.OpTensorSyncLocal(params))
+
+# ... Do other work asynchronously whilst GPU finishes
+
+sq.eval_await()
+
+# Prints the first output which is: { 4, 8, 12 }
+print(tensor_out_a)
+# Prints the first output which is: { 10, 10, 10 }
+print(tensor_out_b)
 
-# Prints [2.0, 4.0, 6.0]
-print(tensor_out.data())
 ```
 
 ### Interactive Notebooks & Hands on Videos
@@ -199,7 +267,7 @@ The core architecture of Kompute includes the following:
 * [Kompute Sequence](https://kompute.cc/overview/reference.html#sequence) - Container of operations that can be sent to GPU as batch
 * [Kompute Operation (Base)](https://kompute.cc/overview/reference.html#algorithm) - Base class from which all operations inherit
 * [Kompute Tensor](https://kompute.cc/overview/reference.html#tensor) - Tensor structured data used in GPU operations
-* [Kompute Algorithm](https://kompute.cc/overview/reference.html#algorithm) - Abstraction for (shader) code executed in the GPU
+* [Kompute Algorithm](https://kompute.cc/overview/reference.html#algorithm) - Abstraction for (shader) logic executed in the GPU
 
 To see a full breakdown you can read further in the [C++ Class Reference](https://kompute.cc/overview/reference.html).
 

examples/android/android-simple/app/src/main/cpp/KomputeModelML.cpp

@@ -20,61 +20,62 @@ void KomputeModelML::train(std::vector<float> yData, std::vector<float> xIData,
     uint32_t ITERATIONS = 100;
     float learningRate = 0.1;
 
-    std::shared_ptr<kp::Tensor> xI{ new kp::Tensor(xIData) };
-    std::shared_ptr<kp::Tensor> xJ{ new kp::Tensor(xJData) };
-
-    std::shared_ptr<kp::Tensor> y{ new kp::Tensor(yData) };
-
-    std::shared_ptr<kp::Tensor> wIn{ new kp::Tensor({ 0.001, 0.001 }) };
-    std::shared_ptr<kp::Tensor> wOutI{ new kp::Tensor(zerosData) };
-    std::shared_ptr<kp::Tensor> wOutJ{ new kp::Tensor(zerosData) };
-
-    std::shared_ptr<kp::Tensor> bIn{ new kp::Tensor({ 0 }) };
-    std::shared_ptr<kp::Tensor> bOut{ new kp::Tensor(zerosData) };
-
-    std::shared_ptr<kp::Tensor> lOut{ new kp::Tensor(zerosData) };
-
-    std::vector<std::shared_ptr<kp::Tensor>> params = { xI,  xJ,    y,
-                                                        wIn, wOutI, wOutJ,
-                                                        bIn, bOut,  lOut };
-
     {
         kp::Manager mgr;
 
-        {
+        std::shared_ptr<kp::Tensor> xI = mgr.tensor(xIData);
-            mgr.rebuild(params);
+        std::shared_ptr<kp::Tensor> xJ = mgr.tensor(xJData);
 
-            std::shared_ptr<kp::Sequence> sq = mgr.sequence();
+        std::shared_ptr<kp::Tensor> y = mgr.tensor(yData);
 
-            // Record op algo base
+        std::shared_ptr<kp::Tensor> wIn = mgr.tensor({ 0.001, 0.001 });
-            sq->begin();
+        std::shared_ptr<kp::Tensor> wOutI = mgr.tensor(zerosData);
+        std::shared_ptr<kp::Tensor> wOutJ = mgr.tensor(zerosData);
 
-            sq->record<kp::OpTensorSyncDevice>({ wIn, bIn });
+        std::shared_ptr<kp::Tensor> bIn = mgr.tensor({ 0 });
+        std::shared_ptr<kp::Tensor> bOut = mgr.tensor(zerosData);
 
-            // Newer versions of Android are able to use shaderc to read raw string
+        std::shared_ptr<kp::Tensor> lOut = mgr.tensor(zerosData);
-            sq->record<kp::OpAlgoBase>(
-                    params, kp::Shader::compile_source(LR_SHADER));
 
-            sq->record<kp::OpTensorSyncLocal>({ wOutI, wOutJ, bOut, lOut });
+        std::vector<std::shared_ptr<kp::Tensor>> params = { xI,  xJ,    y,
+                                                            wIn, wOutI, wOutJ,
+                                                            bIn, bOut,  lOut };
 
-            sq->end();
+        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));
 
-            // Iterate across all expected iterations
+        std::shared_ptr<kp::Algorithm> algo =
-            for (size_t i = 0; i < ITERATIONS; i++) {
+                mgr.algorithm(params, spirv, kp::Workgroup({ 5 }), kp::Constants({ 5.0 }));
 
-                sq->eval();
+        mgr.sequence()->eval<kp::OpTensorSyncDevice>(params);
 
-                for (size_t j = 0; j < bOut->size(); j++) {
+        std::shared_ptr<kp::Sequence> sq = mgr.sequence()
-                    wIn->data()[0] -= learningRate * wOutI->data()[j];
+            ->record<kp::OpTensorSyncDevice>({ wIn, bIn })
-                    wIn->data()[1] -= learningRate * wOutJ->data()[j];
+            ->record<kp::OpAlgoDispatch>(algo)
-                    bIn->data()[0] -= learningRate * bOut->data()[j];
+            ->record<kp::OpTensorSyncLocal>({ wOutI, wOutJ, bOut, lOut });
-                }
+
+        // Iterate across all expected iterations
+        for (size_t i = 0; i < ITERATIONS; i++) {
+
+            sq->eval();
+
+            for (size_t j = 0; j < bOut->size(); j++) {
+                wIn->data()[0] -= learningRate * wOutI->data()[j];
+                wIn->data()[1] -= learningRate * wOutJ->data()[j];
+                bIn->data()[0] -= learningRate * bOut->data()[j];
             }
         }
-    }
 
-    this->mWeights = kp::Tensor(wIn->data());
+        KP_LOG_INFO("RESULT: <<<<<<<<<<<<<<<<<<<");
-    this->mBias = kp::Tensor(bIn->data());
+        KP_LOG_INFO("{}", wIn->data()[0]);
+        KP_LOG_INFO("{}", wIn->data()[1]);
+        KP_LOG_INFO("{}", bIn->data()[0]);
+
+        this->mWeights = wIn;
+        this->mBias = bIn;
+    }
 }
 
 std::vector<float> KomputeModelML::predict(std::vector<float> xI, std::vector<float> xJ) {
@@ -88,9 +89,9 @@ std::vector<float> KomputeModelML::predict(std::vector<float> xI, std::vector<fl
     for (size_t i = 0; i < xI.size(); i++) {
         float xIVal = xI[i];
         float xJVal = xJ[i];
-        float result = (xIVal * this->mWeights.data()[0]
+        float result = (xIVal * this->mWeights->data()[0]
-                        + xJVal * this->mWeights.data()[1]
+                        + xJVal * this->mWeights->data()[1]
-                        + this->mBias.data()[0]);
+                        + this->mBias->data()[0]);
 
         // Instead of using sigmoid we'll just return full numbers
         float var = result > 0 ? 1 : 0;
@@ -103,13 +104,13 @@ std::vector<float> KomputeModelML::predict(std::vector<float> xI, std::vector<fl
 std::vector<float> KomputeModelML::get_params() {
     std::vector<float> retVector;
 
-    if(this->mWeights.size() + this->mBias.size() == 0) {
+    if(this->mWeights->size() + this->mBias->size() == 0) {
         return retVector;
     }
 
-    retVector.push_back(this->mWeights.data()[0]);
+    retVector.push_back(this->mWeights->data()[0]);
-    retVector.push_back(this->mWeights.data()[1]);
+    retVector.push_back(this->mWeights->data()[1]);
-    retVector.push_back(this->mBias.data()[0]);
+    retVector.push_back(this->mBias->data()[0]);
     retVector.push_back(99.0);
 
     return retVector;

examples/android/android-simple/app/src/main/cpp/KomputeModelML.hpp

@@ -4,6 +4,7 @@
 
 #include <vector>
 #include <string>
+#include <memory>
 
 #include "kompute/Kompute.hpp"
 
@@ -20,8 +21,8 @@ public:
     std::vector<float> get_params();
 
 private:
-    kp::Tensor mWeights;
+    std::shared_ptr<kp::Tensor> mWeights;
-    kp::Tensor mBias;
+    std::shared_ptr<kp::Tensor> mBias;
 
 };
 

examples/array_multiplication/src/Main.cpp

@@ -37,11 +37,14 @@ int main()
         }
       )");
 
-    mgr.evalOpDefault<kp::OpAlgoBase>(
+    std::vector<std::shared_ptr<kp::Tensor>> params = { tensorInA, tensorInB, tensorOut };
-            { tensorInA, tensorInB, tensorOut },
-            kp::Shader::compile_source(shader));
 
-    mgr.evalOpDefault<kp::OpTensorSyncLocal>({tensorOut});
+    std::shared_ptr<kp::Algorithm> algo = mgr.algorithm(params, kp::Shader::compile_source(shader));
+
+    mgr.sequence()
+        ->record<kp::OpTensorSyncDevice>(params)
+        ->record<kp::OpAlgoDispatch>(algo)
+        ->record<kp::OpTensorSyncLocal>(params);
 
     // prints "Output {  0  4  12  }"
     std::cout<< "Output: {  ";

examples/godot_examples/custom_module/kompute_summator/KomputeSummatorNode.cpp

@@ -31,7 +31,7 @@ void KomputeSummatorNode::_init() {
     std::cout << "CALLING INIT" << std::endl;
     this->mPrimaryTensor = this->mManager.tensor({ 0.0 });
     this->mSecondaryTensor = this->mManager.tensor({ 0.0 });
-    this->mSequence = this->mManager.sequence("AdditionSeq");
+    this->mSequence = this->mManager.sequence();
 
     // We now record the steps in the sequence
     if (std::shared_ptr<kp::Sequence> sq = this->mSequence)
@@ -51,7 +51,11 @@ void KomputeSummatorNode::_init() {
             }
         )");
 
-        sq->begin();
+        std::shared_ptr<kp::Algorithm> algo =
+          mgr.algorithm(
+                { this->mPrimaryTensor, this->mSecondaryTensor },
+                kp::Shader::compile_source(shader));
+
 
         // First we ensure secondary tensor loads to GPU
         // No need to sync the primary tensor as it should not be changed
@@ -59,15 +63,12 @@ void KomputeSummatorNode::_init() {
                 { this->mSecondaryTensor });
 
         // Then we run the operation with both tensors
-        sq->record<kp::OpAlgoBase>(
+        sq->record<kp::OpAlgoDispatch>(algo)
-            { this->mPrimaryTensor, this->mSecondaryTensor }, 
-            kp::Shader::compile_source(shader));
 
         // We map the result back to local 
         sq->record<kp::OpTensorSyncLocal>(
                 { this->mPrimaryTensor });
 
-        sq->end();
     }
     else {
         throw std::runtime_error("Sequence pointer no longer available");

examples/godot_examples/gdnative_shared/src/KomputeSummator.cpp

@@ -56,7 +56,7 @@ void KomputeSummator::_init() {
                 { this->mSecondaryTensor });
 
         // Then we run the operation with both tensors
-        this->mSequence->record<kp::OpAlgoBase>(
+        this->mSequence->record<kp::OpAlgoCreate>(
             { this->mPrimaryTensor, this->mSecondaryTensor }, 
             kp::Shader::compile_source(shader));
 

examples/godot_logistic_regression/custom_module/kompute_model_ml/KomputeModelMLNode.cpp

@@ -29,54 +29,41 @@ void KomputeModelMLNode::train(Array yArr, Array xIArr, Array xJArr) {
     uint32_t ITERATIONS = 100;
     float learningRate = 0.1;
 
-    std::shared_ptr<kp::Tensor> xI{ new kp::Tensor(xIData) };
-    std::shared_ptr<kp::Tensor> xJ{ new kp::Tensor(xJData) };
-
-    std::shared_ptr<kp::Tensor> y{ new kp::Tensor(yData) };
-
-    std::shared_ptr<kp::Tensor> wIn{ new kp::Tensor({ 0.001, 0.001 }) };
-    std::shared_ptr<kp::Tensor> wOutI{ new kp::Tensor(zerosData) };
-    std::shared_ptr<kp::Tensor> wOutJ{ new kp::Tensor(zerosData) };
-
-    std::shared_ptr<kp::Tensor> bIn{ new kp::Tensor({ 0 }) };
-    std::shared_ptr<kp::Tensor> bOut{ new kp::Tensor(zerosData) };
-
-    std::shared_ptr<kp::Tensor> lOut{ new kp::Tensor(zerosData) };
-
-    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::Tensor> xI = mgr.tensor(xIData);
+        std::shared_ptr<kp::Tensor> xJ = mgr.tensor(xJData);
+
+        std::shared_ptr<kp::Tensor> y = mgr.tensor(yData);
+
+        std::shared_ptr<kp::Tensor> wIn = mgr.tensor({ 0.001, 0.001 });
+        std::shared_ptr<kp::Tensor> wOutI = mgr.tensor(zerosData);
+        std::shared_ptr<kp::Tensor> wOutJ = mgr.tensor(zerosData);
+
+        std::shared_ptr<kp::Tensor> bIn = mgr.tensor({ 0 });
+        std::shared_ptr<kp::Tensor> bOut = mgr.tensor(zerosData);
+
+        std::shared_ptr<kp::Tensor> lOut = mgr.tensor(zerosData);
+
+        std::vector<std::shared_ptr<kp::Tensor>> params = { xI,  xJ,    y,
+                                                            wIn, wOutI, wOutJ,
+                                                            bIn, bOut,  lOut };
 
         {
-            std::shared_ptr<kp::Sequence> sq = mgr.sequence();
+            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));
 
-            // Record op algo base
+            std::shared_ptr<kp::Algorithm> algo = mgr.algorithm(params, spirv);
-            sq->begin();
 
-            sq->record<kp::OpTensorSyncDevice>({ wIn, bIn });
+            mgr.sequence()->eval<kp::OpTensorSyncDevice>(params);
 
-#ifdef KOMPUTE_ANDROID_SHADER_FROM_STRING
+            std::shared_ptr<kp::Sequence> sq = mgr.sequence()
-            // Newer versions of Android are able to use shaderc to read raw string
+                ->record<kp::OpTensorSyncDevice>({ wIn, bIn })
-            sq->record<kp::OpAlgoBase>(
+                ->record<kp::OpAlgoDispatch>(algo)
-                    params, std::vector<char>(LR_SHADER.begin(), LR_SHADER.end()));
+                ->record<kp::OpTensorSyncLocal>({ wOutI, wOutJ, bOut, lOut });
-#else
-            // Older versions of Android require the SPIRV binary directly
-            sq->record<kp::OpAlgoBase>(
-                    params, std::vector<char>(
-                            kp::shader_data::shaders_glsl_logisticregression_comp_spv,
-                            kp::shader_data::shaders_glsl_logisticregression_comp_spv
-                                + kp::shader_data::shaders_glsl_logisticregression_comp_spv_len
-                    ));
-#endif
-
-            sq->record<kp::OpTensorSyncLocal>({ wOutI, wOutJ, bOut, lOut });
-
-            sq->end();
 
             // Iterate across all expected iterations
             for (size_t i = 0; i < ITERATIONS; i++) {
@@ -90,15 +77,15 @@ void KomputeModelMLNode::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 = kp::Tensor(wIn->data());
+        this->mBias = kp::Tensor(bIn->data());
     }
-
-    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 KomputeModelMLNode::predict(Array xI, Array xJ) {

examples/godot_logistic_regression/gdnative_shared/src/KomputeModelML.cpp

@@ -33,54 +33,41 @@ void KomputeModelML::train(Array yArr, Array xIArr, Array xJArr) {
     uint32_t ITERATIONS = 100;
     float learningRate = 0.1;
 
-    std::shared_ptr<kp::Tensor> xI{ new kp::Tensor(xIData) };
-    std::shared_ptr<kp::Tensor> xJ{ new kp::Tensor(xJData) };
-
-    std::shared_ptr<kp::Tensor> y{ new kp::Tensor(yData) };
-
-    std::shared_ptr<kp::Tensor> wIn{ new kp::Tensor({ 0.001, 0.001 }) };
-    std::shared_ptr<kp::Tensor> wOutI{ new kp::Tensor(zerosData) };
-    std::shared_ptr<kp::Tensor> wOutJ{ new kp::Tensor(zerosData) };
-
-    std::shared_ptr<kp::Tensor> bIn{ new kp::Tensor({ 0 }) };
-    std::shared_ptr<kp::Tensor> bOut{ new kp::Tensor(zerosData) };
-
-    std::shared_ptr<kp::Tensor> lOut{ new kp::Tensor(zerosData) };
-
-    std::vector<std::shared_ptr<kp::Tensor>> params = { xI,  xJ,    y,
-                                                        wIn, wOutI, wOutJ,
-                                                        bIn, bOut,  lOut };
-
     {
         kp::Manager mgr;
 
+        std::shared_ptr<kp::Tensor> xI = mgr.tensor(xIData);
+        std::shared_ptr<kp::Tensor> xJ = mgr.tensor(xJData);
+
+        std::shared_ptr<kp::Tensor> y = mgr.tensor(yData);
+
+        std::shared_ptr<kp::Tensor> wIn = mgr.tensor({ 0.001, 0.001 });
+        std::shared_ptr<kp::Tensor> wOutI = mgr.tensor(zerosData);
+        std::shared_ptr<kp::Tensor> wOutJ = mgr.tensor(zerosData);
+
+        std::shared_ptr<kp::Tensor> bIn = mgr.tensor({ 0 });
+        std::shared_ptr<kp::Tensor> bOut = mgr.tensor(zerosData);
+
+        std::shared_ptr<kp::Tensor> lOut = mgr.tensor(zerosData);
+
+        std::vector<std::shared_ptr<kp::Tensor>> params = { xI,  xJ,    y,
+                                                            wIn, wOutI, wOutJ,
+                                                            bIn, bOut,  lOut };
+
         {
-            mgr.rebuild(params);
+            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));
 
-            std::shared_ptr<kp::Sequence> sq = mgr.sequence();
+            std::shared_ptr<kp::Algorithm> algo = mgr.algorithm(params, spirv);
 
-            // Record op algo base
+            mgr.sequence()->eval<kp::OpTensorSyncDevice>(params);
-            sq->begin();
 
-            sq->record<kp::OpTensorSyncDevice>({ wIn, bIn });
+            std::shared_ptr<kp::Sequence> sq = mgr.sequence()
-
+                ->record<kp::OpTensorSyncDevice>({ wIn, bIn })
-#ifdef KOMPUTE_ANDROID_SHADER_FROM_STRING
+                ->record<kp::OpAlgoDispatch>(algo)
-            // Newer versions of Android are able to use shaderc to read raw string
+                ->record<kp::OpTensorSyncLocal>({ wOutI, wOutJ, bOut, lOut });
-            sq->record<kp::OpAlgoBase>(
-                    params, std::vector<char>(LR_SHADER.begin(), LR_SHADER.end()));
-#else
-            // Older versions of Android require the SPIRV binary directly
-            sq->record<kp::OpAlgoBase>(
-                    params, std::vector<char>(
-                            kp::shader_data::shaders_glsl_logisticregression_comp_spv,
-                            kp::shader_data::shaders_glsl_logisticregression_comp_spv
-                                + kp::shader_data::shaders_glsl_logisticregression_comp_spv_len
-                    ));
-#endif
-
-            sq->record<kp::OpTensorSyncLocal>({ wOutI, wOutJ, bOut, lOut });
-
-            sq->end();
 
             // 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]
+        float result = (xIVal * this->mWeights->data()[0]
-                + xJVal * this->mWeights.data()[1]
+                + xJVal * this->mWeights->data()[1]
-                + this->mBias.data()[0]);
+                + 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()[0]);
-    retArray.push_back(this->mWeights.data()[1]);
+    retArray.push_back(this->mWeights->data()[1]);
-    retArray.push_back(this->mBias.data()[0]);
+    retArray.push_back(this->mBias->data()[0]);
     retArray.push_back(99.0);
 
     return retArray;

examples/godot_logistic_regression/gdnative_shared/src/KomputeModelML.hpp

@@ -28,8 +28,8 @@ public:
     static void _register_methods();
 
 private:
-    kp::Tensor mWeights;
+    std::shared_ptr<kp::Tensor> mWeights;
-    kp::Tensor mBias;
+    std::shared_ptr<kp::Tensor> mBias;
 };
 
 static std::string LR_SHADER = R"(

examples/logistic_regression/src/Main.cpp

@@ -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 }) };
+    kp::Manager mgr;
-    std::shared_ptr<kp::Tensor> xJ{ new kp::Tensor({ 0, 0, 0, 1, 1 }) };
 
-    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> y = mgr.tensor({ 0, 0, 0, 1, 1 });
-    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> bIn{ new kp::Tensor({ 0 }) };
+    std::shared_ptr<kp::Tensor> wIn = mgr.tensor({ 0.001, 0.001 });
-    std::shared_ptr<kp::Tensor> bOut{ new kp::Tensor({ 0, 0, 0, 0, 0 }) };
+    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;
+    std::vector<uint32_t> spirv(
-
-    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::OpAlgoBase>(
-        params, std::vector<uint32_t>(
                 (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++) {

python/src/docstrings.hpp

@@ -266,23 +266,23 @@ The type of tensor to initialize @param syncDataToGPU Whether to sync
 the data to GPU memory @returns Initialized Tensor with memory Syncd
 to GPU device)doc";
 
-static const char *__doc_kp_OpAlgoBase =
+static const char *__doc_kp_OpAlgoCreate =
 R"doc(Operation that provides a general abstraction that simplifies the use
 of algorithm and parameter components which can be used with shaders.
 By default it enables the user to provide a dynamic number of tensors
 which are then passed as inputs.)doc";
 
-static const char *__doc_kp_OpAlgoBase_KomputeWorkgroup = R"doc()doc";
+static const char *__doc_kp_OpAlgoCreate_KomputeWorkgroup = R"doc()doc";
 
-static const char *__doc_kp_OpAlgoBase_KomputeWorkgroup_x = R"doc()doc";
+static const char *__doc_kp_OpAlgoCreate_KomputeWorkgroup_x = R"doc()doc";
 
-static const char *__doc_kp_OpAlgoBase_KomputeWorkgroup_y = R"doc()doc";
+static const char *__doc_kp_OpAlgoCreate_KomputeWorkgroup_y = R"doc()doc";
 
-static const char *__doc_kp_OpAlgoBase_KomputeWorkgroup_z = R"doc()doc";
+static const char *__doc_kp_OpAlgoCreate_KomputeWorkgroup_z = R"doc()doc";
 
-static const char *__doc_kp_OpAlgoBase_OpAlgoBase = R"doc(Base constructor, should not be used unless explicitly intended.)doc";
+static const char *__doc_kp_OpAlgoCreate_OpAlgoCreate = R"doc(Base constructor, should not be used unless explicitly intended.)doc";
 
-static const char *__doc_kp_OpAlgoBase_OpAlgoBase_2 =
+static const char *__doc_kp_OpAlgoCreate_OpAlgoCreate_2 =
 R"doc(Default constructor with parameters that provides the bare minimum
 requirements for the operations to be able to create and manage their
 sub-components.
@@ -295,7 +295,7 @@ shaderFilePath Optional parameter to specify the shader to load
 (either in spirv or raw format) @param komputeWorkgroup Optional
 parameter to specify the layout for processing)doc";
 
-static const char *__doc_kp_OpAlgoBase_OpAlgoBase_3 =
+static const char *__doc_kp_OpAlgoCreate_OpAlgoCreate_3 =
 R"doc(Constructor that enables a file to be passed to the operation with the
 contents of the shader. This can be either in raw format or in
 compiled SPIR-V binary format.
@@ -308,7 +308,7 @@ shaderFilePath Parameter to specify the shader to load (either in
 spirv or raw format) @param komputeWorkgroup Optional parameter to
 specify the layout for processing)doc";
 
-static const char *__doc_kp_OpAlgoBase_OpAlgoBase_4 =
+static const char *__doc_kp_OpAlgoCreate_OpAlgoCreate_4 =
 R"doc(Constructor that enables raw shader data to be passed to the main
 operation which can be either in raw shader glsl code or in compiled
 SPIR-V binary.
@@ -321,37 +321,37 @@ shaderDataRaw Optional parameter to specify the shader data either in
 binary or raw form @param komputeWorkgroup Optional parameter to
 specify the layout for processing)doc";
 
-static const char *__doc_kp_OpAlgoBase_fetchSpirvBinaryData = R"doc()doc";
+static const char *__doc_kp_OpAlgoCreate_fetchSpirvBinaryData = R"doc()doc";
 
-static const char *__doc_kp_OpAlgoBase_init =
+static const char *__doc_kp_OpAlgoCreate_init =
 R"doc(The init function is responsible for the initialisation of the
 algorithm component based on the parameters specified, and allows for
 extensibility on the options provided. Further dependent classes can
 perform more specific checks such as ensuring tensors provided are
 initialised, etc.)doc";
 
-static const char *__doc_kp_OpAlgoBase_mAlgorithm = R"doc()doc";
+static const char *__doc_kp_OpAlgoCreate_mAlgorithm = R"doc()doc";
 
-static const char *__doc_kp_OpAlgoBase_mFreeAlgorithm = R"doc()doc";
+static const char *__doc_kp_OpAlgoCreate_mFreeAlgorithm = R"doc()doc";
 
-static const char *__doc_kp_OpAlgoBase_mKomputeWorkgroup = R"doc()doc";
+static const char *__doc_kp_OpAlgoCreate_mKomputeWorkgroup = R"doc()doc";
 
-static const char *__doc_kp_OpAlgoBase_mShaderDataRaw =
+static const char *__doc_kp_OpAlgoCreate_mShaderDataRaw =
 R"doc(< Optional member variable which can be provided to contain either the
 raw shader content or the spirv binary content)doc";
 
-static const char *__doc_kp_OpAlgoBase_mShaderFilePath =
+static const char *__doc_kp_OpAlgoCreate_mShaderFilePath =
-R"doc(< Optional member variable which can be provided for the OpAlgoBase to
+R"doc(< Optional member variable which can be provided for the OpAlgoCreate to
 find the data automatically and load for processing)doc";
 
-static const char *__doc_kp_OpAlgoBase_postEval =
+static const char *__doc_kp_OpAlgoCreate_postEval =
 R"doc(Executes after the recorded commands are submitted, and performs a
 copy of the GPU Device memory into the staging buffer so the output
 data can be retrieved.)doc";
 
-static const char *__doc_kp_OpAlgoBase_preEval = R"doc(Does not perform any preEval commands.)doc";
+static const char *__doc_kp_OpAlgoCreate_preEval = R"doc(Does not perform any preEval commands.)doc";
 
-static const char *__doc_kp_OpAlgoBase_record =
+static const char *__doc_kp_OpAlgoCreate_record =
 R"doc(This records the commands that are to be sent to the GPU. This
 includes the barriers that ensure the memory has been copied before
 going in and out of the shader, as well as the dispatch operation that

python/src/main.cpp

@@ -4,6 +4,8 @@
 
 #include <kompute/Kompute.hpp>
 
+#include "fmt/ranges.h"
+
 #include "docstrings.hpp"
 
 namespace py = pybind11;
@@ -23,8 +25,7 @@ PYBIND11_MODULE(kp, m) {
 
     py::module_ np = py::module_::import("numpy");
 
-
+    py::enum_<kp::Tensor::TensorTypes>(m, "TensorTypes")
-    py::enum_<kp::Tensor::TensorTypes>(m, "TensorTypes", DOC(kp, Tensor, TensorTypes))
         .value("device", kp::Tensor::TensorTypes::eDevice, "Tensor holding data in GPU memory.")
         .value("host", kp::Tensor::TensorTypes::eHost, "Tensor used for CPU visible GPU data.")
         .value("storage", kp::Tensor::TensorTypes::eStorage, "Tensor with host visible gpu memory.")
@@ -53,22 +54,32 @@ PYBIND11_MODULE(kp, m) {
             py::arg("sources"), py::arg("files") = std::vector<std::string>(), py::arg("entryPoint") = "main", py::arg("definitions") = std::vector<std::pair<std::string,std::string>>() );
 #endif // KOMPUTE_DISABLE_SHADER_UTILS
 
+    py::class_<kp::OpBase, std::shared_ptr<kp::OpBase>>(m, "OpBase");
+
+    py::class_<kp::OpTensorSyncDevice, std::shared_ptr<kp::OpTensorSyncDevice>>(m, "OpTensorSyncDevice", py::base<kp::OpBase>())
+        .def(py::init<const std::vector<std::shared_ptr<kp::Tensor>>&>());
+
+    py::class_<kp::OpTensorSyncLocal, std::shared_ptr<kp::OpTensorSyncLocal>>(m, "OpTensorSyncLocal", py::base<kp::OpBase>())
+        .def(py::init<const std::vector<std::shared_ptr<kp::Tensor>>&>());
+
+    py::class_<kp::OpTensorCopy, std::shared_ptr<kp::OpTensorCopy>>(m, "OpTensorCopy", py::base<kp::OpBase>())
+        .def(py::init<const std::vector<std::shared_ptr<kp::Tensor>>&>());
+
+    py::class_<kp::OpAlgoDispatch, std::shared_ptr<kp::OpAlgoDispatch>>(m, "OpAlgoDispatch", py::base<kp::OpBase>())
+        .def(py::init<const std::shared_ptr<kp::Algorithm>&,const kp::Constants&>(),
+                py::arg("algorithm"), py::arg("push_consts") = kp::Constants());
+
+    py::class_<kp::OpMult, std::shared_ptr<kp::OpMult>>(m, "OpMult", py::base<kp::OpBase>())
+        .def(py::init<const std::vector<std::shared_ptr<kp::Tensor>>&,const std::shared_ptr<kp::Algorithm>&>());
+
+    py::class_<kp::Algorithm, std::shared_ptr<kp::Algorithm>>(m, "Algorithm")
+        .def("get_tensors", &kp::Algorithm::getTensors)
+        .def("destroy", &kp::Algorithm::destroy)
+        .def("get_spec_consts", &kp::Algorithm::getSpecializationConstants)
+        .def("is_init", &kp::Algorithm::isInit);
+
     py::class_<kp::Tensor, std::shared_ptr<kp::Tensor>>(m, "Tensor", DOC(kp, Tensor))
-        .def(py::init(
+        .def("data", [](kp::Tensor& self) {
-            [np](const py::array_t<float> data, kp::Tensor::TensorTypes tensor_type) {
-                const py::array_t<float> flatdata = np.attr("ravel")(data);
-                const py::buffer_info info        = flatdata.request();
-                const float* ptr                  = (float*) info.ptr;
-                return std::unique_ptr<kp::Tensor>(
-                    new kp::Tensor(std::vector<float>(ptr, ptr+flatdata.size()), tensor_type)
-                );
-            }), 
-            "Construct Tensor with an array as initial data and an optional kp.TensorType (default:device).",
-            py::arg("data"),
-            py::arg("tensor_type") = kp::Tensor::TensorTypes::eDevice
-            )
-        .def("data", &kp::Tensor::data, DOC(kp, Tensor, data))
-        .def("numpy", [](kp::Tensor& self) {
                 return py::array(self.data().size(), self.data().data());
             }, "Returns stored data as a new numpy array.")
         .def("__getitem__", [](kp::Tensor &self, size_t index) -> float { return self.data()[index]; },
@@ -105,221 +116,50 @@ PYBIND11_MODULE(kp, m) {
         .def("__len__", &kp::Tensor::size, "Retrieves the size of the Tensor data as per the local Tensor memory.")
         .def("tensor_type", &kp::Tensor::tensorType, "Retreves the memory type of the tensor.")
         .def("is_init", &kp::Tensor::isInit, "Checks whether the tensor GPU memory has been initialised.")
-        .def("map_data_from_host", &kp::Tensor::mapDataFromHostMemory, "Maps data into GPU memory from tensor local data.")
+        .def("destroy", &kp::Tensor::destroy, "Destroy tensor GPU resources.");
-        .def("map_data_into_host", &kp::Tensor::mapDataIntoHostMemory, "Maps data from GPU memory into tensor local data.");
-
 
     py::class_<kp::Sequence, std::shared_ptr<kp::Sequence>>(m, "Sequence")
-        .def("init", &kp::Sequence::init, DOC(kp, Sequence, init))
+        .def("record", [](kp::Sequence& self, std::shared_ptr<kp::OpBase> op) { return self.record(op); })
-        
+        .def("eval", [](kp::Sequence& self) { return self.eval(); })
-        // record
+        .def("eval", [](kp::Sequence& self, std::shared_ptr<kp::OpBase> op) { return self.eval(op); })
-        .def("begin", &kp::Sequence::begin, DOC(kp, Sequence, begin))
+        .def("eval_async", [](kp::Sequence& self) { return self.eval(); })
-        .def("end", &kp::Sequence::end, DOC(kp, Sequence, end))
+        .def("eval_async", [](kp::Sequence& self, std::shared_ptr<kp::OpBase> op) { return self.evalAsync(op); })
-        
+        .def("eval_await", [](kp::Sequence& self) { return self.evalAwait(); })
-        // eval
+        .def("eval_await", [](kp::Sequence& self, uint32_t wait) { return self.evalAwait(wait); })
-        .def("eval", &kp::Sequence::eval, DOC(kp, Sequence, eval))
+        .def("is_recording", &kp::Sequence::isRecording)
-        .def("eval_async", &kp::Sequence::evalAsync, DOC(kp, Sequence, evalAsync))
+        .def("is_running", &kp::Sequence::isRunning)
-        .def("eval_await", &kp::Sequence::evalAwait, DOC(kp, Sequence, evalAwait))
+        .def("is_init", &kp::Sequence::isInit)
-        
+        .def("clear", &kp::Sequence::clear)
-        // status
+        .def("destroy", &kp::Sequence::destroy);
-        .def("is_running", &kp::Sequence::isRunning, DOC(kp, Sequence, isRunning))
+
-        .def("is_rec", &kp::Sequence::isRecording, DOC(kp, Sequence, isRecording))
+    py::class_<kp::Manager, std::shared_ptr<kp::Manager>>(m, "Manager")
-        .def("is_init", &kp::Sequence::isInit, DOC(kp, Sequence, isInit))
+        .def(py::init())
-        
+        .def(py::init<uint32_t>())
-        // record
+        .def(py::init<uint32_t,const std::vector<uint32_t>&>())
-        .def("record_tensor_copy", &kp::Sequence::record<kp::OpTensorCopy>, DOC(kp, Sequence, record))
+        .def("sequence", &kp::Manager::sequence, py::arg("queueIndex") = 0)
-        .def("record_tensor_sync_device", &kp::Sequence::record<kp::OpTensorSyncDevice>,
+        .def("tensor", [np](kp::Manager& self,
-            "Records operation to sync tensor from local memory to GPU memory")
+                            const py::array_t<float> data,
-        .def("record_tensor_sync_local", &kp::Sequence::record<kp::OpTensorSyncLocal>,
+                            kp::Tensor::TensorTypes tensor_type) {
-            "Records operation to sync tensor(s) from GPU memory to local memory")
+                const py::array_t<float> flatdata = np.attr("ravel")(data);
-        .def("record_algo_file", &kp::Sequence::record<
+                const py::buffer_info info        = flatdata.request();
-                                    kp::OpAlgoBase,
+                const float* ptr                  = (float*) info.ptr;
-                                    const std::string&,
+                return self.tensor(std::vector<float>(ptr, ptr+flatdata.size()), tensor_type);
-                                    kp::Workgroup,
-                                    kp::Constants>,
-        "Records an operation using a custom shader provided from a shader path",
-            py::arg("tensors"), py::arg("data"), py::arg("workgroup") = kp::Workgroup(), py::arg("constants") = kp::Constants() )
-        .def("record_algo_data", [](kp::Sequence &self,
-                                    std::vector<std::shared_ptr<kp::Tensor>> tensors,
-                                    py::bytes &bytes,
-                                    kp::Workgroup workgroup,
-                                    kp::Constants constants) -> bool {
-                // Bytes have to be converted into std::vector
-                py::buffer_info info(py::buffer(bytes).request());
-                const char *data = reinterpret_cast<const char *>(info.ptr);
-                size_t length = static_cast<size_t>(info.size);
-                return self.record<kp::OpAlgoBase>(
-                    tensors, std::vector<uint32_t>((uint32_t*)data, (uint32_t*)(data + length)), workgroup, constants);
             },
-            "Records an operation using a custom shader provided as spirv bytes",
+            "Tensor initialisation function with data and tensor type",
-            py::arg("tensors"), py::arg("bytes"), py::arg("workgroup") = kp::Workgroup(), py::arg("constants") = kp::Constants() );
+            py::arg("data"), py::arg("tensor_type") = kp::Tensor::TensorTypes::eDevice)
-
+        .def("algorithm", [](kp::Manager& self,
-
+                             const std::vector<std::shared_ptr<kp::Tensor>>& tensors,
-    py::class_<kp::Manager>(m, "Manager")
+                             const py::bytes& spirv,
-        .def(py::init(), "Default initializer uses device 0 and first compute compatible GPU queueFamily")
+                             const kp::Workgroup& workgroup,
-        .def(py::init(
+                             const kp::Constants& spec_consts) {
-            [](uint32_t physicalDeviceIndex) {
+                    py::buffer_info info(py::buffer(spirv).request());
-                return std::unique_ptr<kp::Manager>(new kp::Manager(physicalDeviceIndex));
+                    const char *data = reinterpret_cast<const char *>(info.ptr);
-            }), "Manager initialiser can provide specified device index but will use first compute compatible GPU queueFamily")
+                    size_t length = static_cast<size_t>(info.size);
-        .def(py::init(
+                    std::vector<uint32_t> spirvVec((uint32_t*)data, (uint32_t*)(data + length));
-            [](uint32_t physicalDeviceIndex, const std::vector<uint32_t>& familyQueueIndices) {
+                    return self.algorithm(tensors, spirvVec, workgroup, spec_consts);
-                return std::unique_ptr<kp::Manager>(new kp::Manager(physicalDeviceIndex, familyQueueIndices));
-            }), "Manager initialiser can provide specified device and array of GPU queueFamilies to load.")
-        .def("sequence", &kp::Manager::sequence,
-                py::arg("name") = "", py::arg("queueIndex") = 0, "Get or create a sequence with specific name and specified index of available queues")
-        .def("tensor", &kp::Manager::tensor, 
-                py::arg("data"), py::arg("tensorType") = kp::Tensor::TensorTypes::eDevice, py::arg("syncDataToGPU") = true,
-                "Build and initialise tensor")
-        .def("rebuild", py::overload_cast<std::vector<std::shared_ptr<kp::Tensor>>, bool>(&kp::Manager::rebuild),
-                py::arg("tensors"), py::arg("syncDataToGPU") = true,
-                "Build and initialise list of tensors")
-        .def("rebuild", py::overload_cast<std::shared_ptr<kp::Tensor>, bool>(&kp::Manager::rebuild),
-                py::arg("tensor"), py::arg("syncDataToGPU") = true,
-                "Build and initialise tensor")
-        .def("destroy", py::overload_cast<std::shared_ptr<kp::Tensor>>(&kp::Manager::destroy),
-                py::arg("tensor"), DOC(kp, Manager, destroy))
-        .def("destroy", py::overload_cast<std::vector<std::shared_ptr<kp::Tensor>>>(&kp::Manager::destroy),
-                py::arg("tensors"), DOC(kp, Manager, destroy, 2))
-        .def("destroy", py::overload_cast<std::vector<std::shared_ptr<kp::Sequence>>>(&kp::Manager::destroy),
-                py::arg("sequences"), DOC(kp, Manager, destroy, 3))
-        .def("destroy", py::overload_cast<std::shared_ptr<kp::Sequence>>(&kp::Manager::destroy),
-                py::arg("sequence"), DOC(kp, Manager, destroy, 4))
-        .def("destroy", py::overload_cast<const std::string &>(&kp::Manager::destroy),
-                py::arg("sequenceName"), DOC(kp, Manager, destroy, 5))
-        .def("destroy", py::overload_cast<const std::vector<std::string>&>(&kp::Manager::destroy),
-                py::arg("sequenceNames"), DOC(kp, Manager, destroy, 6))
-        // temporary backwards compatibility
-        .def("eval_tensor_create_def",[](kp::Manager& self, std::vector<std::shared_ptr<kp::Tensor>> tensors, bool syncDataToGPU) -> void {
-                    kp_error("IMPORTANT: eval_tensor_create_def is depricated! Please use Manager.rebuild instead as function will be removed soon.");
-                    self.rebuild(tensors, syncDataToGPU);
                 },
-                py::arg("tensors"), py::arg("syncDataToGPU") = true,
+            "Algorithm initialisation function",
-                "Temporary backwards compatibility for tensor creation function which will be removed in the next version.")
+            py::arg("tensors"), py::arg("spirv"), py::arg("workgroup") = kp::Workgroup(), py::arg("spec_consts") = kp::Constants());
-
-        // Await functions
-        .def("eval_await", &kp::Manager::evalOpAwait,
-                py::arg("sequenceName"), py::arg("waitFor") = UINT64_MAX,
-                "Awaits for asynchronous operation on a named Sequence")
-        .def("eval_await_def", &kp::Manager::evalOpAwaitDefault,
-                py::arg("waitFor") = UINT64_MAX, "Awaits for asynchronous operation on the last anonymous Sequence created")
-        
-        // eval default
-        .def("eval_tensor_copy_def", &kp::Manager::evalOpDefault<kp::OpTensorCopy>,
-            "Evaluates operation to copy one tensor to one or many tensors with new anonymous Sequence")
-        .def("eval_tensor_sync_device_def", &kp::Manager::evalOpDefault<kp::OpTensorSyncDevice>,
-            "Evaluates operation to sync tensor from local memory to GPU memory with new anonymous Sequence")
-        .def("eval_tensor_sync_local_def", &kp::Manager::evalOpDefault<kp::OpTensorSyncLocal>,
-            "Evaluates operation to sync tensor(s) from GPU memory to local memory with new anonymous Sequence")
-        .def("eval_algo_file_def", &kp::Manager::evalOpDefault<
-                                    kp::OpAlgoBase,
-                                    const std::string&,
-                                    kp::Workgroup,
-                                    kp::Constants>,
-            "Evaluates an operation using a custom shader provided from a shader path with new anonymous Sequence",
-            py::arg("tensors"), py::arg("data"), py::arg("workgroup") = kp::Workgroup(), py::arg("constants") = kp::Constants() )
-        .def("eval_algo_data_def", [](kp::Manager &self,
-                                    std::vector<std::shared_ptr<kp::Tensor>> tensors,
-                                    py::bytes &bytes,
-                                    kp::Workgroup workgroup,
-                                    kp::Constants constants) {
-                // Bytes have to be converted into std::vector
-                py::buffer_info info(py::buffer(bytes).request());
-                const char *data = reinterpret_cast<const char *>(info.ptr);
-                size_t length = static_cast<size_t>(info.size);
-                self.evalOpDefault<kp::OpAlgoBase>(
-                    tensors, std::vector<uint32_t>((uint32_t*)data, (uint32_t*)(data + length)), workgroup, constants);
-            },
-            "Evaluates an operation using a custom shader provided as spirv bytes with new anonymous Sequence",
-            py::arg("tensors"), py::arg("bytes"), py::arg("workgroup") = kp::Workgroup(), py::arg("constants") = kp::Constants() )
-        
-        // eval
-        .def("eval_tensor_copy", &kp::Manager::evalOp<kp::OpTensorCopy>,
-            "Evaluates operation to copy one tensor to one or many tensors with explicitly named Sequence")
-        .def("eval_tensor_sync_device", &kp::Manager::evalOp<kp::OpTensorSyncDevice>,
-            "Evaluates operation to sync tensor from local memory to GPU memory with explicitly named Sequence")
-        .def("eval_tensor_sync_local", &kp::Manager::evalOp<kp::OpTensorSyncLocal>,
-            "Evaluates operation to sync tensor(s) from GPU memory to local memory with explicitly named Sequence")
-        .def("eval_algo_file", &kp::Manager::evalOp<
-                                    kp::OpAlgoBase,
-                                    const std::string&,
-                                    kp::Workgroup,
-                                    kp::Constants>,
-            "Evaluates an operation using a custom shader provided from a shader path with explicitly named Sequence",
-            py::arg("tensors"), py::arg("sequence_name"), py::arg("data"),py::arg("workgroup") = kp::Workgroup(), py::arg("constants") = kp::Constants() )
-        .def("eval_algo_data", [](kp::Manager &self,
-                                    std::vector<std::shared_ptr<kp::Tensor>> tensors,
-                                    std::string sequenceName,
-                                    py::bytes &bytes,
-                                    kp::Workgroup workgroup,
-                                    kp::Constants constants) {
-                // Bytes have to be converted into std::vector
-                py::buffer_info info(py::buffer(bytes).request());
-                const char *data = reinterpret_cast<const char *>(info.ptr);
-                size_t length = static_cast<size_t>(info.size);
-                self.evalOp<kp::OpAlgoBase>(
-                    tensors, sequenceName, std::vector<uint32_t>((uint32_t*)data, (uint32_t*)(data + length)), workgroup, constants);
-            },
-            "Evaluates an operation using a custom shader provided as spirv bytes with explicitly named Sequence",
-            py::arg("tensors"), py::arg("sequence_name"), py::arg("bytes"), py::arg("workgroup") = kp::Workgroup(), py::arg("constants") = kp::Constants() )
-        
-        // eval async default
-        .def("eval_async_tensor_copy_def", &kp::Manager::evalOpAsyncDefault<kp::OpTensorCopy>,
-            "Evaluates asynchronously operation to copy one tensor to one or many tensors with anonymous Sequence")
-        .def("eval_async_tensor_sync_device_def", &kp::Manager::evalOpAsyncDefault<kp::OpTensorSyncDevice>,
-            "Evaluates asynchronously operation to sync tensor from local memory to GPU memory with anonymous Sequence")
-        .def("eval_async_tensor_sync_local_def", &kp::Manager::evalOpAsyncDefault<kp::OpTensorSyncLocal>,
-            "Evaluates asynchronously operation to sync tensor(s) from GPU memory to local memory with anonymous Sequence")
-        .def("eval_async_algo_file_def", &kp::Manager::evalOpAsyncDefault<
-                                    kp::OpAlgoBase,
-                                    const std::string&,
-                                    kp::Workgroup,
-                                    kp::Constants>,
-            "Evaluates asynchronously an operation using a custom shader provided from a shader path with anonymous Sequence",
-            py::arg("tensors"), py::arg("data"), py::arg("workgroup") = kp::Workgroup(), py::arg("constants") = kp::Constants() )
-        .def("eval_async_algo_data_def", [](kp::Manager &self,
-                                    std::vector<std::shared_ptr<kp::Tensor>> tensors,
-                                    py::bytes &bytes,
-                                    kp::Workgroup workgroup,
-                                    kp::Constants constants) {
-                // Bytes have to be converted into std::vector
-                py::buffer_info info(py::buffer(bytes).request());
-                const char *data = reinterpret_cast<const char *>(info.ptr);
-                size_t length = static_cast<size_t>(info.size);
-                self.evalOpAsyncDefault<kp::OpAlgoBase>(
-                    tensors, std::vector<uint32_t>((uint32_t*)data, (uint32_t*)(data + length)), workgroup, constants);
-            },
-            "Evaluates asynchronously an operation using a custom shader provided as raw string or spirv bytes with anonymous Sequence",
-            py::arg("tensors"), py::arg("bytes"), py::arg("workgroup") = kp::Workgroup(), py::arg("constants") = kp::Constants() )
-        
-        // eval async
-        .def("eval_async_tensor_copy", &kp::Manager::evalOpAsync<kp::OpTensorCopy>,
-            "Evaluates asynchronously operation to copy one tensor to one or many tensors with explicitly named Sequence")
-        .def("eval_async_tensor_sync_device", &kp::Manager::evalOpAsync<kp::OpTensorSyncDevice>,
-            "Evaluates asynchronously operation to sync tensor from local memory to GPU memory with explicitly named Sequence")
-        .def("eval_async_tensor_sync_local", &kp::Manager::evalOpAsync<kp::OpTensorSyncLocal>,
-            "Evaluates asynchronously operation to sync tensor(s) from GPU memory to local memory with explicitly named Sequence")
-        .def("eval_async_algo_file", &kp::Manager::evalOpAsync<
-                                    kp::OpAlgoBase,
-                                    const std::string&,
-                                    kp::Workgroup,
-                                    kp::Constants>,
-            "Evaluates asynchronously an operation using a custom shader provided from a shader path with explicitly named Sequence",
-            py::arg("tensors"), py::arg("sequence_name"), py::arg("data"), py::arg("workgroup") = kp::Workgroup(), py::arg("constants") = kp::Constants() )
-        .def("eval_async_algo_data", [](kp::Manager &self,
-                                    std::vector<std::shared_ptr<kp::Tensor>> tensors,
-                                    std::string sequenceName,
-                                    py::bytes &bytes,
-                                    kp::Workgroup workgroup,
-                                    kp::Constants constants) {
-                // Bytes have to be converted into std::vector
-                py::buffer_info info(py::buffer(bytes).request());
-                const char *data = reinterpret_cast<const char *>(info.ptr);
-                size_t length = static_cast<size_t>(info.size);
-                self.evalOpAsync<kp::OpAlgoBase>(
-                    tensors, sequenceName, std::vector<uint32_t>((uint32_t*)data, (uint32_t*)(data + length)), workgroup, constants);
-            },
-            "Evaluates asynchronously an operation using a custom shader provided as raw string or spirv bytes with explicitly named Sequence",
-            py::arg("tensors"), py::arg("sequence_name"), py::arg("bytes"), py::arg("workgroup") = kp::Workgroup(), py::arg("constants") = kp::Constants() );
 
 #ifdef VERSION_INFO
     m.attr("__version__") = VERSION_INFO;

python/test/test_array_multiplication.py

@@ -9,29 +9,26 @@ def test_array_multiplication():
     mgr = kp.Manager()
 
     # 2. Create Kompute Tensors to hold data
-    tensor_in_a = kp.Tensor([2, 2, 2])
+    tensor_in_a = mgr.tensor([2, 2, 2])
-    tensor_in_b = kp.Tensor([1, 2, 3])
+    tensor_in_b = mgr.tensor([1, 2, 3])
-    tensor_out = kp.Tensor([0, 0, 0])
+    tensor_out = mgr.tensor([0, 0, 0])
 
-    # 3. Initialise the Kompute Tensors in the GPU
+    params = [tensor_in_a, tensor_in_b, tensor_out]
-    mgr.rebuild([tensor_in_a, tensor_in_b, tensor_out])
 
     # 4. Define the multiplication shader code to run on the GPU
     @ps.python2shader
-    def compute_shader_multiply(index=("input", "GlobalInvocationId", ps.ivec3),
+    def compute_mult(index=("input", "GlobalInvocationId", ps.ivec3),
                                 data1=("buffer", 0, ps.Array(ps.f32)),
                                 data2=("buffer", 1, ps.Array(ps.f32)),
                                 data3=("buffer", 2, ps.Array(ps.f32))):
         i = index.x
         data3[i] = data1[i] * data2[i]
 
-    # 5. Run shader code against our previously defined tensors
+    (mgr.sequence()
-    mgr.eval_algo_data_def(
+        .record(kp.OpTensorSyncDevice(params))
-      [tensor_in_a, tensor_in_b, tensor_out],
+        .record(kp.OpAlgoDispatch(mgr.algorithm(params, compute_mult.to_spirv())))
-      compute_shader_multiply.to_spirv())
+        .record(kp.OpTensorSyncLocal([tensor_out]))
+        .eval())
 
-    # 6. Sync tensor data from GPU back to local
+    assert tensor_out.data().tolist() == [2.0, 4.0, 6.0]
-    mgr.eval_tensor_sync_local_def([tensor_out])
+    assert np.all(tensor_out.data() == [2.0, 4.0, 6.0])
-
-    assert tensor_out.data() == [2.0, 4.0, 6.0]
-    assert np.all(tensor_out.numpy() == [2.0, 4.0, 6.0])

python/test/test_kompute.py

@@ -7,25 +7,86 @@ import pyshader as ps
 
 DIRNAME = os.path.dirname(os.path.abspath(__file__))
 
-def test_opalgobase_file():
+kp_log = logging.getLogger("kp")
-    """
-    Test basic OpMult operation
-    """
 
-    tensor_in_a = kp.Tensor([2, 2, 2])
+# TODO: Add example with file
-    tensor_in_b = kp.Tensor([1, 2, 3])
+#def test_opalgobase_file():
-    tensor_out = kp.Tensor([0, 0, 0])
+#    """
+#    Test basic OpMult operation
+#    """
+#
+#    tensor_in_a = kp.Tensor([2, 2, 2])
+#    tensor_in_b = kp.Tensor([1, 2, 3])
+#    tensor_out = kp.Tensor([0, 0, 0])
+#
+#    mgr = kp.Manager()
+#    mgr.rebuild([tensor_in_a, tensor_in_b, tensor_out])
+#
+#    shader_path = os.path.join(DIRNAME, "../../shaders/glsl/opmult.comp.spv")
+#
+#    mgr.eval_algo_file_def([tensor_in_a, tensor_in_b, tensor_out], shader_path)
+#
+#    mgr.eval_tensor_sync_local_def([tensor_out])
+#
+#    assert tensor_out.data() == [2.0, 4.0, 6.0]
+
+def test_end_to_end():
 
     mgr = kp.Manager()
-    mgr.rebuild([tensor_in_a, tensor_in_b, tensor_out])
 
-    shader_path = os.path.join(DIRNAME, "../../shaders/glsl/opmult.comp.spv")
+    tensor_in_a = mgr.tensor([2, 2, 2])
+    tensor_in_b = mgr.tensor([1, 2, 3])
+    tensor_out_a = mgr.tensor([0, 0, 0])
+    tensor_out_b = mgr.tensor([0, 0, 0])
 
-    mgr.eval_algo_file_def([tensor_in_a, tensor_in_b, tensor_out], shader_path)
+    params = [tensor_in_a, tensor_in_b, tensor_out_a, tensor_out_b]
 
-    mgr.eval_tensor_sync_local_def([tensor_out])
+    shader = """
+        #version 450
 
-    assert tensor_out.data() == [2.0, 4.0, 6.0]
+        layout (local_size_x = 1) in;
+
+        // The input tensors bind index is relative to index in parameter passed
+        layout(set = 0, binding = 0) buffer buf_in_a { float in_a[]; };
+        layout(set = 0, binding = 1) buffer buf_in_b { float in_b[]; };
+        layout(set = 0, binding = 2) buffer buf_out_a { float out_a[]; };
+        layout(set = 0, binding = 3) buffer buf_out_b { float out_b[]; };
+
+        // Kompute supports push constants updated on dispatch
+        layout(push_constant) uniform PushConstants {
+            float val;
+        } push_const;
+
+        // Kompute also supports spec constants on initalization
+        layout(constant_id = 0) const float const_one = 0;
+
+        void main() {
+            uint index = gl_GlobalInvocationID.x;
+            out_a[index] += in_a[index] * in_b[index];
+            out_b[index] += const_one * push_const.val;
+        }
+    """
+
+    workgroup = (3, 1, 1)
+    spec_consts = [2]
+    push_consts_a = [2]
+    push_consts_b = [3]
+
+    algo = mgr.algorithm(params, kp.Shader.compile_source(shader), workgroup, spec_consts)
+
+    (mgr.sequence()
+        .record(kp.OpTensorSyncDevice(params))
+        .record(kp.OpAlgoDispatch(algo, push_consts_a))
+        .record(kp.OpAlgoDispatch(algo, push_consts_b))
+        .eval())
+
+    sq = mgr.sequence()
+    sq.eval_async(kp.OpTensorSyncLocal(params))
+
+    sq.eval_await()
+
+    assert tensor_out_a.data().tolist() == [4, 8, 12]
+    assert tensor_out_b.data().tolist() == [10, 10, 10]
 
 
 def test_shader_str():
@@ -47,67 +108,120 @@ void main()
 }
     """
 
-    tensor_in_a = kp.Tensor([2, 2, 2])
-    tensor_in_b = kp.Tensor([1, 2, 3])
-    tensor_out = kp.Tensor([0, 0, 0])
-
-    mgr = kp.Manager()
-    mgr.rebuild([tensor_in_a, tensor_in_b, tensor_out])
-
     spirv = kp.Shader.compile_source(shader)
 
-    mgr.eval_algo_data_def([tensor_in_a, tensor_in_b, tensor_out], spirv)
+    mgr = kp.Manager()
 
-    mgr.eval_tensor_sync_local_def([tensor_out])
+    tensor_in_a = mgr.tensor([2, 2, 2])
+    tensor_in_b = mgr.tensor([1, 2, 3])
+    tensor_out = mgr.tensor([0, 0, 0])
 
-    assert tensor_out.data() == [2.0, 4.0, 6.0]
+    params = [tensor_in_a, tensor_in_b, tensor_out]
+
+    algo = mgr.algorithm(params, spirv)
+
+    (mgr.sequence()
+        .record(kp.OpTensorSyncDevice(params))
+        .record(kp.OpAlgoDispatch(algo))
+        .record(kp.OpTensorSyncLocal(params))
+        .eval())
+
+    assert tensor_out.data().tolist() == [2.0, 4.0, 6.0]
 
 def test_sequence():
     """
     Test basic OpAlgoBase operation
     """
-    mgr = kp.Manager(0, [2])
 
-    tensor_in_a = kp.Tensor([2, 2, 2])
+    shader = """
-    tensor_in_b = kp.Tensor([1, 2, 3])
+        #version 450
-    tensor_out = kp.Tensor([0, 0, 0])
+        layout(set = 0, binding = 0) buffer tensorLhs {float valuesLhs[];};
+        layout(set = 0, binding = 1) buffer tensorRhs {float valuesRhs[];};
+        layout(set = 0, binding = 2) buffer tensorOutput { float valuesOutput[];};
+        layout (local_size_x = 1, local_size_y = 1, local_size_z = 1) in;
 
-    mgr.rebuild([tensor_in_a, tensor_in_b, tensor_out])
+        void main()
+        {
+            uint index = gl_GlobalInvocationID.x;
+            valuesOutput[index] = valuesLhs[index] * valuesRhs[index];
+        }
+    """
 
-    shader_path = os.path.abspath(os.path.join(DIRNAME, "../../shaders/glsl/opmult.comp.spv"))
+    spirv = kp.Shader.compile_source(shader)
-    mgr.eval_async_algo_file_def([tensor_in_a, tensor_in_b, tensor_out], shader_path)
 
-    mgr.eval_await_def()
+    mgr = kp.Manager(0)
 
-    seq = mgr.sequence("op")
+    tensor_in_a = mgr.tensor([2, 2, 2])
-    seq.begin()
+    tensor_in_b = mgr.tensor([1, 2, 3])
-    seq.record_tensor_sync_local([tensor_in_a])
+    tensor_out = mgr.tensor([0, 0, 0])
-    seq.record_tensor_sync_local([tensor_in_b])
-    seq.record_tensor_sync_local([tensor_out])
-    seq.end()
-    seq.eval()
 
-    mgr.destroy("op")
+    params = [tensor_in_a, tensor_in_b, tensor_out]
 
-    assert seq.is_init() == False
+    algo = mgr.algorithm(params, spirv)
 
-    assert tensor_out.data() == [2.0, 4.0, 6.0]
+    sq = mgr.sequence()
-    assert np.all(tensor_out.numpy() == [2.0, 4.0, 6.0])
 
-    mgr.destroy(tensor_in_a)
+    sq.record(kp.OpTensorSyncDevice(params))
-    mgr.destroy([tensor_in_b, tensor_out])
+    sq.record(kp.OpAlgoDispatch(algo))
+    sq.record(kp.OpTensorSyncLocal(params))
+
+    sq.eval()
+
+    assert sq.is_init() == True
+
+    sq.destroy()
+
+    assert sq.is_init() == False
+
+    assert tensor_out.data().tolist() == [2.0, 4.0, 6.0]
+    assert np.all(tensor_out.data() == [2.0, 4.0, 6.0])
+
+    tensor_in_a.destroy()
+    tensor_in_b.destroy()
+    tensor_out.destroy()
 
     assert tensor_in_a.is_init() == False
     assert tensor_in_b.is_init() == False
     assert tensor_out.is_init() == False
 
+def test_pushconsts():
+
+    spirv = kp.Shader.compile_source("""
+          #version 450
+          layout(push_constant) uniform PushConstants {
+            float x;
+            float y;
+            float z;
+          } pcs;
+          layout (local_size_x = 1) in;
+          layout(set = 0, binding = 0) buffer a { float pa[]; };
+          void main() {
+              pa[0] += pcs.x;
+              pa[1] += pcs.y;
+              pa[2] += pcs.z;
+          }
+    """)
+
+    mgr = kp.Manager()
+
+    tensor = mgr.tensor([0, 0, 0])
+
+    algo = mgr.algorithm([tensor], spirv, (1, 1, 1))
+
+    (mgr.sequence()
+        .record(kp.OpTensorSyncDevice([tensor]))
+        .record(kp.OpAlgoDispatch(algo, [0.1, 0.2, 0.3]))
+        .record(kp.OpAlgoDispatch(algo, [0.3, 0.2, 0.1]))
+        .record(kp.OpTensorSyncLocal([tensor]))
+        .eval())
+
+    assert np.all(tensor.data() == np.array([0.4, 0.4, 0.4], dtype=np.float32))
+
 def test_workgroup():
     mgr = kp.Manager(0)
 
-    tensor_a = kp.Tensor(np.zeros([16,8]))
+    tensor_a = mgr.tensor(np.zeros([16,8]))
-    tensor_b = kp.Tensor(np.zeros([16,8]))
+    tensor_b = mgr.tensor(np.zeros([16,8]))
-
-    mgr.rebuild([tensor_a, tensor_b])
 
     @ps.python2shader
     def compute_shader_wg(gl_idx=("input", "GlobalInvocationId", ps.ivec3),
@@ -119,50 +233,17 @@ def test_workgroup():
         data1[i] = f32(gl_idx.x)
         data2[i] = f32(gl_idx.y)
 
-    seq = mgr.sequence("new")
+    algo = mgr.algorithm([tensor_a, tensor_b], compute_shader_wg.to_spirv(), (16,8,1))
-    seq.begin()
-    seq.record_algo_data([tensor_a, tensor_b], compute_shader_wg.to_spirv(), workgroup=(16,8,1))
-    seq.end()
-    seq.eval()
 
-    mgr.destroy(seq)
+    (mgr.sequence()
+        .record(kp.OpTensorSyncDevice([tensor_a, tensor_b]))
+        .record(kp.OpAlgoDispatch(algo))
+        .record(kp.OpTensorSyncLocal([tensor_a, tensor_b]))
+        .eval())
 
-    assert seq.is_init() == False
+    print(tensor_a.data())
-
+    print(tensor_b.data())
-    mgr.eval_tensor_sync_local_def([tensor_a, tensor_b])
-
-    print(tensor_a.numpy())
-    print(tensor_b.numpy())
-
-    assert np.all(tensor_a.numpy() == np.stack([np.arange(16)]*8, axis=1).ravel())
-    assert np.all(tensor_b.numpy() == np.stack([np.arange(8)]*16, axis=0).ravel())
-
-    mgr.destroy([tensor_a, tensor_b])
-
-    assert tensor_a.is_init() == False
-    assert tensor_b.is_init() == False
-
-
-def test_tensor_rebuild_backwards_compat():
-    """
-    Test basic OpMult operation
-    """
-
-    tensor_in_a = kp.Tensor([2, 2, 2])
-    tensor_in_b = kp.Tensor([1, 2, 3])
-    tensor_out = kp.Tensor([0, 0, 0])
-
-    mgr = kp.Manager()
-
-    mgr.eval_tensor_create_def([tensor_in_a, tensor_in_b, tensor_out])
-
-    shader_path = os.path.abspath(os.path.join(DIRNAME, "../../shaders/glsl/opmult.comp.spv"))
-    mgr.eval_async_algo_file_def([tensor_in_a, tensor_in_b, tensor_out], shader_path)
-    mgr.eval_await_def()
-
-    mgr.eval_tensor_sync_local_def([tensor_out])
-
-    assert tensor_out.data() == [2.0, 4.0, 6.0]
-    assert np.all(tensor_out.numpy() == [2.0, 4.0, 6.0])
 
+    assert np.all(tensor_a.data() == np.stack([np.arange(16)]*8, axis=1).ravel())
+    assert np.all(tensor_b.data() == np.stack([np.arange(8)]*16, axis=0).ravel())
 

python/test/test_logistic_regression.py

@@ -46,45 +46,39 @@ def test_logistic_regression():
     mgr = kp.Manager(0)
 
     # First we create input and ouput tensors for shader
-    tensor_x_i = kp.Tensor([0.0, 1.0, 1.0, 1.0, 1.0])
+    tensor_x_i = mgr.tensor([0.0, 1.0, 1.0, 1.0, 1.0])
-    tensor_x_j = kp.Tensor([0.0, 0.0, 0.0, 1.0, 1.0])
+    tensor_x_j = mgr.tensor([0.0, 0.0, 0.0, 1.0, 1.0])
 
-    tensor_y = kp.Tensor([0.0, 0.0, 0.0, 1.0, 1.0])
+    tensor_y = mgr.tensor([0.0, 0.0, 0.0, 1.0, 1.0])
 
-    tensor_w_in = kp.Tensor([0.001, 0.001])
+    tensor_w_in = mgr.tensor([0.001, 0.001])
-    tensor_w_out_i = kp.Tensor([0.0, 0.0, 0.0, 0.0, 0.0])
+    tensor_w_out_i = mgr.tensor([0.0, 0.0, 0.0, 0.0, 0.0])
-    tensor_w_out_j = kp.Tensor([0.0, 0.0, 0.0, 0.0, 0.0])
+    tensor_w_out_j = mgr.tensor([0.0, 0.0, 0.0, 0.0, 0.0])
 
-    tensor_b_in = kp.Tensor([0.0])
+    tensor_b_in = mgr.tensor([0.0])
-    tensor_b_out = kp.Tensor([0.0, 0.0, 0.0, 0.0, 0.0])
+    tensor_b_out = mgr.tensor([0.0, 0.0, 0.0, 0.0, 0.0])
 
-    tensor_l_out = kp.Tensor([0.0, 0.0, 0.0, 0.0, 0.0])
+    tensor_l_out = mgr.tensor([0.0, 0.0, 0.0, 0.0, 0.0])
 
-    tensor_m = kp.Tensor([ tensor_y.size() ])
+    tensor_m = mgr.tensor([ tensor_y.size() ])
 
     # We store them in an array for easier interaction
     params = [tensor_x_i, tensor_x_j, tensor_y, tensor_w_in, tensor_w_out_i,
         tensor_w_out_j, tensor_b_in, tensor_b_out, tensor_l_out, tensor_m]
 
-    mgr.rebuild(params)
+    mgr.sequence().eval(kp.OpTensorSyncDevice(params))
 
     # Create a managed sequence
     sq = mgr.sequence()
 
-    # Clear previous operations and begin recording for new operations
-    sq.begin()
-
     # Record operation to sync memory from local to GPU memory
-    sq.record_tensor_sync_device([tensor_w_in, tensor_b_in])
+    sq.record(kp.OpTensorSyncDevice([tensor_w_in, tensor_b_in]))
 
     # Record operation to execute GPU shader against all our parameters
-    sq.record_algo_data(params, compute_shader.to_spirv())
+    sq.record(kp.OpAlgoDispatch(mgr.algorithm(params, compute_shader.to_spirv())))
 
     # Record operation to sync memory from GPU to local memory
-    sq.record_tensor_sync_local([tensor_w_out_i, tensor_w_out_j, tensor_b_out, tensor_l_out])
+    sq.record(kp.OpTensorSyncLocal([tensor_w_out_i, tensor_w_out_j, tensor_b_out, tensor_l_out]))
-
-    # Stop recording operations
-    sq.end()
 
     ITERATIONS = 100
     learning_rate = 0.1

single_include/AggregateHeaders.cpp

@@ -1,16 +1,15 @@
 #pragma once
-#include "kompute/Core.hpp"
-#include "kompute/Shader.hpp"
 #include "kompute/shaders/shaderopmult.hpp"
 #include "kompute/shaders/shaderlogisticregression.hpp"
-#include "kompute/Manager.hpp"
+#include "kompute/Core.hpp"
-#include "kompute/Sequence.hpp"
+#include "kompute/Shader.hpp"
+#include "kompute/Tensor.hpp"
+#include "kompute/Algorithm.hpp"
 #include "kompute/operations/OpBase.hpp"
-#include "kompute/operations/OpAlgoBase.hpp"
-#include "kompute/operations/OpAlgoLhsRhsOut.hpp"
-#include "kompute/operations/OpMult.hpp"
 #include "kompute/operations/OpTensorCopy.hpp"
 #include "kompute/operations/OpTensorSyncDevice.hpp"
 #include "kompute/operations/OpTensorSyncLocal.hpp"
-#include "kompute/Algorithm.hpp"
+#include "kompute/operations/OpAlgoDispatch.hpp"
-#include "kompute/Tensor.hpp"
+#include "kompute/operations/OpMult.hpp"
+#include "kompute/Sequence.hpp"
+#include "kompute/Manager.hpp"

src/Algorithm.cpp

@@ -4,138 +4,174 @@
 
 namespace kp {
 
-Algorithm::Algorithm()
-{
-    KP_LOG_DEBUG("Kompute Algorithm base constructor");
-}
-
 Algorithm::Algorithm(std::shared_ptr<vk::Device> device,
-                     std::shared_ptr<vk::CommandBuffer> commandBuffer,
+                     const std::vector<std::shared_ptr<Tensor>>& tensors,
+                     const std::vector<uint32_t>& spirv,
+                     const Workgroup& workgroup,
                      const Constants& specializationConstants)
 {
     KP_LOG_DEBUG("Kompute Algorithm Constructor with device");
 
     this->mDevice = device;
-    this->mCommandBuffer = commandBuffer;
+
-    this->mSpecializationConstants = specializationConstants;
+    if (tensors.size() && spirv.size()) {
+        KP_LOG_INFO("Kompute Algorithm initialising with tensor size: {} and "
+                    "spirv size: {}",
+                    tensors.size(),
+                    spirv.size());
+        this->rebuild(tensors, spirv, workgroup, specializationConstants);
+    } else {
+        KP_LOG_INFO("Kompute Algorithm constructor with empty tensors and or "
+                    "spirv so not rebuilding vulkan components");
+    }
 }
 
 Algorithm::~Algorithm()
 {
     KP_LOG_DEBUG("Kompute Algorithm Destructor started");
 
+    this->destroy();
+}
+
+void
+Algorithm::rebuild(const std::vector<std::shared_ptr<Tensor>>& tensors,
+                   const std::vector<uint32_t>& spirv,
+                   const Workgroup& workgroup,
+                   const Constants& specializationConstants)
+{
+    KP_LOG_DEBUG("Kompute Algorithm rebuild started");
+
+    this->mTensors = tensors;
+    this->mSpirv = spirv;
+    this->mSpecializationConstants = specializationConstants;
+    this->setWorkgroup(workgroup,
+                       this->mTensors.size() ? this->mTensors[0]->size() : 1);
+
+    // Descriptor pool is created first so if available then destroy all before
+    // rebuild
+    if (this->isInit()) {
+        this->destroy();
+    }
+
+    this->createParameters();
+    this->createShaderModule();
+    this->createPipeline();
+}
+
+bool
+Algorithm::isInit()
+{
+    return this->mPipeline && this->mPipelineCache && this->mPipelineLayout &&
+           this->mDescriptorPool && this->mDescriptorSet &&
+           this->mDescriptorSetLayout && this->mShaderModule;
+}
+
+void
+Algorithm::destroy()
+{
+
     if (!this->mDevice) {
-        KP_LOG_ERROR(
+        KP_LOG_WARN("Kompute Algorithm destroy function reached with null "
-          "Kompute Algorithm destructor reached with null Device pointer");
+                    "Device pointer");
         return;
     }
 
-    if (this->mFreePipeline) {
+    if (this->mFreePipeline && this->mPipeline) {
         KP_LOG_DEBUG("Kompute Algorithm Destroying pipeline");
         if (!this->mPipeline) {
-            KP_LOG_ERROR("Kompute Algorithm Error requested to destroy "
+            KP_LOG_WARN("Kompute Algorithm Error requested to destroy "
-                         "pipeline but it is null");
+                        "pipeline but it is null");
         }
         this->mDevice->destroy(
           *this->mPipeline,
           (vk::Optional<const vk::AllocationCallbacks>)nullptr);
+        this->mPipeline = nullptr;
     }
 
-    if (this->mFreePipelineCache) {
+    if (this->mFreePipelineCache && this->mPipelineCache) {
         KP_LOG_DEBUG("Kompute Algorithm Destroying pipeline cache");
         if (!this->mPipelineCache) {
-            KP_LOG_ERROR("Kompute Algorithm Error requested to destroy "
+            KP_LOG_WARN("Kompute Algorithm Error requested to destroy "
-                         "pipeline cache but it is null");
+                        "pipeline cache but it is null");
         }
         this->mDevice->destroy(
           *this->mPipelineCache,
           (vk::Optional<const vk::AllocationCallbacks>)nullptr);
+        this->mPipelineCache = nullptr;
     }
 
-    if (this->mFreePipelineLayout) {
+    if (this->mFreePipelineLayout && this->mPipelineLayout) {
         KP_LOG_DEBUG("Kompute Algorithm Destroying pipeline layout");
         if (!this->mPipelineLayout) {
-            KP_LOG_ERROR("Kompute Algorithm Error requested to destroy "
+            KP_LOG_WARN("Kompute Algorithm Error requested to destroy "
-                         "pipeline layout but it is null");
+                        "pipeline layout but it is null");
         }
         this->mDevice->destroy(
           *this->mPipelineLayout,
           (vk::Optional<const vk::AllocationCallbacks>)nullptr);
+        this->mPipelineLayout = nullptr;
     }
 
-    if (this->mFreeShaderModule) {
+    if (this->mFreeShaderModule && this->mShaderModule) {
         KP_LOG_DEBUG("Kompute Algorithm Destroying shader module");
         if (!this->mShaderModule) {
-            KP_LOG_ERROR("Kompute Algorithm Error requested to destroy shader "
+            KP_LOG_WARN("Kompute Algorithm Error requested to destroy shader "
-                         "module but it is null");
+                        "module but it is null");
         }
         this->mDevice->destroy(
           *this->mShaderModule,
           (vk::Optional<const vk::AllocationCallbacks>)nullptr);
+        this->mShaderModule = nullptr;
     }
 
-    if (this->mFreeDescriptorSet) {
+    // We don't call freeDescriptorSet as the descriptor pool is not created
-        KP_LOG_DEBUG("Kompute Algorithm Freeing Descriptor Set");
+    // with VK_DESCRIPTOR_POOL_CREATE_FREE_DESCRIPTOR_SET_BIT more at
-        if (!this->mDescriptorSet) {
+    // (https://www.khronos.org/registry/vulkan/specs/1.0/html/vkspec.html#VUID-vkFreeDescriptorSets-descriptorPool-00312))
-            KP_LOG_ERROR(
+    // if (this->mFreeDescriptorSet && this->mDescriptorSet) {
-              "Kompute Algorithm Error requested to free descriptor set");
+    //    KP_LOG_DEBUG("Kompute Algorithm Freeing Descriptor Set");
-        }
+    //    if (!this->mDescriptorSet) {
-        this->mDevice->freeDescriptorSets(
+    //        KP_LOG_WARN(
-          *this->mDescriptorPool, 1, this->mDescriptorSet.get());
+    //          "Kompute Algorithm Error requested to free descriptor set");
-    }
+    //    }
+    //    this->mDevice->freeDescriptorSets(
+    //      *this->mDescriptorPool, 1, this->mDescriptorSet.get());
+    //    this->mDescriptorSet = nullptr;
+    //}
 
-    if (this->mFreeDescriptorSetLayout) {
+    if (this->mFreeDescriptorSetLayout && this->mDescriptorSetLayout) {
         KP_LOG_DEBUG("Kompute Algorithm Destroying Descriptor Set Layout");
         if (!this->mDescriptorSetLayout) {
-            KP_LOG_ERROR("Kompute Algorithm Error requested to destroy "
+            KP_LOG_WARN("Kompute Algorithm Error requested to destroy "
-                         "descriptor set layout but it is null");
+                        "descriptor set layout but it is null");
         }
         this->mDevice->destroy(
           *this->mDescriptorSetLayout,
           (vk::Optional<const vk::AllocationCallbacks>)nullptr);
+        this->mDescriptorSetLayout = nullptr;
     }
 
-    if (this->mFreeDescriptorPool) {
+    if (this->mFreeDescriptorPool && this->mDescriptorPool) {
         KP_LOG_DEBUG("Kompute Algorithm Destroying Descriptor Pool");
         if (!this->mDescriptorPool) {
-            KP_LOG_ERROR("Kompute Algorithm Error requested to destroy "
+            KP_LOG_WARN("Kompute Algorithm Error requested to destroy "
-                         "descriptor pool but it is null");
+                        "descriptor pool but it is null");
         }
         this->mDevice->destroy(
           *this->mDescriptorPool,
           (vk::Optional<const vk::AllocationCallbacks>)nullptr);
+        this->mDescriptorPool = nullptr;
     }
 }
 
 void
-Algorithm::init(const std::vector<uint32_t>& shaderFileData,
+Algorithm::createParameters()
-                std::vector<std::shared_ptr<Tensor>> tensorParams)
-{
-    KP_LOG_DEBUG("Kompute Algorithm init started");
-
-    this->createParameters(tensorParams);
-    this->createShaderModule(shaderFileData);
-
-    for (std::shared_ptr<Tensor> tensor : tensorParams) {
-        this->mSpecializationConstants.push_back(tensor->size());
-    }
-
-    this->createPipeline();
-}
-
-void
-Algorithm::createDescriptorPool()
-{}
-
-void
-Algorithm::createParameters(std::vector<std::shared_ptr<Tensor>>& tensorParams)
 {
     KP_LOG_DEBUG("Kompute Algorithm createParameters started");
 
     std::vector<vk::DescriptorPoolSize> descriptorPoolSizes = {
         vk::DescriptorPoolSize(
           vk::DescriptorType::eStorageBuffer,
-          static_cast<uint32_t>(tensorParams.size()) // Descriptor count
+          static_cast<uint32_t>(this->mTensors.size()) // Descriptor count
           )
     };
 
@@ -152,7 +188,7 @@ Algorithm::createParameters(std::vector<std::shared_ptr<Tensor>>& tensorParams)
     this->mFreeDescriptorPool = true;
 
     std::vector<vk::DescriptorSetLayoutBinding> descriptorSetBindings;
-    for (size_t i = 0; i < tensorParams.size(); i++) {
+    for (size_t i = 0; i < this->mTensors.size(); i++) {
         descriptorSetBindings.push_back(
           vk::DescriptorSetLayoutBinding(i, // Binding index
                                          vk::DescriptorType::eStorageBuffer,
@@ -184,11 +220,11 @@ Algorithm::createParameters(std::vector<std::shared_ptr<Tensor>>& tensorParams)
     this->mFreeDescriptorSet = true;
 
     KP_LOG_DEBUG("Kompute Algorithm updating descriptor sets");
-    for (size_t i = 0; i < tensorParams.size(); i++) {
+    for (size_t i = 0; i < this->mTensors.size(); i++) {
         std::vector<vk::WriteDescriptorSet> computeWriteDescriptorSets;
 
         vk::DescriptorBufferInfo descriptorBufferInfo =
-          tensorParams[i]->constructDescriptorBufferInfo();
+          this->mTensors[i]->constructDescriptorBufferInfo();
 
         computeWriteDescriptorSets.push_back(
           vk::WriteDescriptorSet(*this->mDescriptorSet,
@@ -207,17 +243,17 @@ Algorithm::createParameters(std::vector<std::shared_ptr<Tensor>>& tensorParams)
 }
 
 void
-Algorithm::createShaderModule(const std::vector<uint32_t>& shaderFileData)
+Algorithm::createShaderModule()
 {
     KP_LOG_DEBUG("Kompute Algorithm createShaderModule started");
 
-    vk::ShaderModuleCreateInfo shaderModuleInfo(
+    vk::ShaderModuleCreateInfo shaderModuleInfo(vk::ShaderModuleCreateFlags(),
-      vk::ShaderModuleCreateFlags(),
+                                                sizeof(uint32_t) *
-      sizeof(uint32_t) * shaderFileData.size(),
+                                                  this->mSpirv.size(),
-      shaderFileData.data());
+                                                this->mSpirv.data());
 
     KP_LOG_DEBUG("Kompute Algorithm Creating shader module. ShaderFileSize: {}",
-                 shaderFileData.size());
+                 this->mSpirv.size());
     this->mFreeShaderModule = true;
     this->mShaderModule = std::make_shared<vk::ShaderModule>();
     this->mDevice->createShaderModule(
@@ -246,14 +282,14 @@ Algorithm::createPipeline()
 
     for (uint32_t i = 0; i < this->mSpecializationConstants.size(); i++) {
         vk::SpecializationMapEntry specializationEntry(
-           static_cast<uint32_t>(i),
+          static_cast<uint32_t>(i),
-           static_cast<uint32_t>(sizeof(float) * i),
+          static_cast<uint32_t>(sizeof(float) * i),
-           sizeof(float));
+          sizeof(float));
 
         specializationEntries.push_back(specializationEntry);
     }
 
-    // This passes ownership of the memory so we remove ownership from 
+    // This passes ownership of the memory so we remove ownership from
     // specialization container by using "transferDataOwnership"
     vk::SpecializationInfo specializationInfo(
       static_cast<uint32_t>(specializationEntries.size()),
@@ -289,32 +325,109 @@ Algorithm::createPipeline()
         throw std::runtime_error("Failed to create pipeline result: " +
                                  vk::to_string(pipelineResult.result));
     }
+
+    vk::Pipeline& pipeline = pipelineResult.value;
+    this->mPipeline = std::make_shared<vk::Pipeline>(pipeline);
+    this->mFreePipeline = true;
 #else
-    vk::Pipeline pipelineResult =
+    vk::Pipeline pipeline =
       this->mDevice->createComputePipeline(*this->mPipelineCache, pipelineInfo);
+    this->mPipeline = std::make_shared<vk::Pipeline>(pipeline);
     this->mFreePipeline = true;
 #endif
 
-    this->mFreePipeline = true;
+    // TODO: Update to consistent
-    this->mPipeline = std::make_shared<vk::Pipeline>(pipelineResult);
+    // this->mPipeline = std::make_shared<vk::Pipeline>();
+    // this->mDevice->createComputePipelines(
+    //         *this->mPipelineCache, 1, &pipelineInfo, nullptr,
+    //         this->mPipeline.get());
+
+    KP_LOG_DEBUG("Kompute Algorithm Create Pipeline Success");
 }
 
 void
-Algorithm::recordDispatch(uint32_t x, uint32_t y, uint32_t z)
+Algorithm::bindCore(const vk::CommandBuffer& commandBuffer)
 {
-    KP_LOG_DEBUG("Kompute Algorithm calling record dispatch");
+    KP_LOG_DEBUG("Kompute Algorithm binding pipeline");
 
-    this->mCommandBuffer->bindPipeline(vk::PipelineBindPoint::eCompute,
+    commandBuffer.bindPipeline(vk::PipelineBindPoint::eCompute,
-                                       *this->mPipeline);
+                               *this->mPipeline);
 
-    this->mCommandBuffer->bindDescriptorSets(vk::PipelineBindPoint::eCompute,
+    KP_LOG_DEBUG("Kompute Algorithm binding descriptor sets");
-                                             *this->mPipelineLayout,
+
-                                             0, // First set
+    commandBuffer.bindDescriptorSets(vk::PipelineBindPoint::eCompute,
-                                             *this->mDescriptorSet,
+                                     *this->mPipelineLayout,
-                                             nullptr // Dispatcher
+                                     0, // First set
+                                     *this->mDescriptorSet,
+                                     nullptr // Dispatcher
     );
+}
 
-    this->mCommandBuffer->dispatch(x, y, z);
+void
+Algorithm::bindPush(const vk::CommandBuffer& commandBuffer,
+                    const Constants& pushConstants)
+{
+    if (pushConstants.size()) {
+        KP_LOG_DEBUG("Kompute Algorithm binding push constants size: {}",
+                     pushConstants.size());
+
+        commandBuffer.pushConstants(*this->mPipelineLayout,
+                                    vk::ShaderStageFlagBits::eCompute,
+                                    0,
+                                    pushConstants.size() * sizeof(float),
+                                    pushConstants.data());
+    }
+}
+
+void
+Algorithm::recordDispatch(const vk::CommandBuffer& commandBuffer)
+{
+    KP_LOG_DEBUG("Kompute Algorithm recording dispatch");
+
+    commandBuffer.dispatch(
+      this->mWorkgroup[0], this->mWorkgroup[1], this->mWorkgroup[2]);
+}
+
+void
+Algorithm::setWorkgroup(const Workgroup& workgroup, uint32_t minSize)
+{
+
+    KP_LOG_INFO("Kompute OpAlgoCreate setting dispatch size");
+
+    // The dispatch size is set up based on either explicitly provided template
+    // parameters or by default it would take the shape and size of the tensors
+    if (workgroup[0] > 0) {
+        // If at least the x value is provided we use mainly the parameters
+        // provided
+        this->mWorkgroup = { workgroup[0],
+                             workgroup[1] > 0 ? workgroup[1] : 1,
+                             workgroup[2] > 0 ? workgroup[2] : 1 };
+    } else {
+        this->mWorkgroup = { minSize, 1, 1 };
+    }
+
+    KP_LOG_INFO("Kompute OpAlgoCreate set dispatch size X: {}, Y: {}, Z: {}",
+                this->mWorkgroup[0],
+                this->mWorkgroup[1],
+                this->mWorkgroup[2]);
+}
+
+const Workgroup&
+Algorithm::getWorkgroup()
+{
+    return this->mWorkgroup;
+}
+
+const Constants&
+Algorithm::getSpecializationConstants()
+{
+    return this->mSpecializationConstants;
+}
+
+const std::vector<std::shared_ptr<Tensor>>&
+Algorithm::getTensors()
+{
+    return this->mTensors;
 }
 
 }

src/CMakeLists.txt

@@ -151,8 +151,7 @@ if(NOT KOMPUTE_OPT_DISABLE_SHADER_UTILS)
             # HLSL
             # glslang includes OGLCompiler, OSDependent, MachineIndependent
             glslang
-            SPIRV
+            SPIRV)
-            glslang-default-resource-limits)
     else()
         find_package(glslang CONFIG REQUIRED)
 
@@ -164,9 +163,8 @@ if(NOT KOMPUTE_OPT_DISABLE_SHADER_UTILS)
             # Not including hlsl support
             # glslang::HLSL
             # Adding explicit dependencies to match above
-            glslang
+            glslang::glslang
-            SPIRV
+            glslang::SPIRV)
-            glslang-default-resource-limits)
     endif()
 endif()
 

src/Manager.cpp

@@ -31,26 +31,34 @@ Manager::Manager()
 Manager::Manager(uint32_t physicalDeviceIndex,
                  const std::vector<uint32_t>& familyQueueIndices)
 {
-    this->mPhysicalDeviceIndex = physicalDeviceIndex;
+    this->mManageResources = true;
 
     this->createInstance();
-    this->createDevice(familyQueueIndices);
+    this->createDevice(familyQueueIndices, physicalDeviceIndex);
 }
 
 Manager::Manager(std::shared_ptr<vk::Instance> instance,
                  std::shared_ptr<vk::PhysicalDevice> physicalDevice,
-                 std::shared_ptr<vk::Device> device,
+                 std::shared_ptr<vk::Device> device)
-                 uint32_t physicalDeviceIndex)
 {
+    this->mManageResources = false;
+
     this->mInstance = instance;
     this->mPhysicalDevice = physicalDevice;
     this->mDevice = device;
-    this->mPhysicalDeviceIndex = physicalDeviceIndex;
 }
 
 Manager::~Manager()
 {
     KP_LOG_DEBUG("Kompute Manager Destructor started");
+    this->destroy();
+}
+
+void
+Manager::destroy()
+{
+
+    KP_LOG_DEBUG("Kompute Manager destroy() started");
 
     if (this->mDevice == nullptr) {
         KP_LOG_ERROR(
@@ -58,24 +66,34 @@ Manager::~Manager()
         return;
     }
 
-    if (this->mManagedSequences.size()) {
+    if (this->mManageResources && this->mManagedSequences.size()) {
         KP_LOG_DEBUG("Kompute Manager explicitly running destructor for "
                      "managed sequences");
-        for (const std::pair<std::string, std::shared_ptr<Sequence>>& sqPair :
+        for (const std::weak_ptr<Sequence>& weakSq : this->mManagedSequences) {
-             this->mManagedSequences) {
+            if (std::shared_ptr<Sequence> sq = weakSq.lock()) {
-            sqPair.second->freeMemoryDestroyGPUResources();
+                sq->destroy();
+            }
         }
         this->mManagedSequences.clear();
     }
 
-    if (this->mManagedTensors.size()) {
+    if (this->mManageResources && this->mManagedAlgorithms.size()) {
-        KP_LOG_DEBUG("Kompute Manager explicitly freeing tensors");
+        KP_LOG_DEBUG("Kompute Manager explicitly freeing algorithms");
-        for (const std::shared_ptr<Tensor>& tensor : this->mManagedTensors) {
+        for (const std::weak_ptr<Algorithm>& weakAlgorithm :
-            if (!tensor->isInit()) {
+             this->mManagedAlgorithms) {
-                KP_LOG_ERROR("Kompute Manager attempted to free managed tensor "
+            if (std::shared_ptr<Algorithm> algorithm = weakAlgorithm.lock()) {
-                             "but not tensor is not initialised");
+                algorithm->destroy();
+            }
+        }
+        this->mManagedAlgorithms.clear();
+    }
+
+    if (this->mManageResources && this->mManagedTensors.size()) {
+        KP_LOG_DEBUG("Kompute Manager explicitly freeing tensors");
+        for (const std::weak_ptr<Tensor>& weakTensor : this->mManagedTensors) {
+            if (std::shared_ptr<Tensor> tensor = weakTensor.lock()) {
+                tensor->destroy();
             }
-            tensor->freeMemoryDestroyGPUResources();
         }
         this->mManagedTensors.clear();
     }
@@ -84,6 +102,7 @@ Manager::~Manager()
         KP_LOG_INFO("Destroying device");
         this->mDevice->destroy(
           (vk::Optional<const vk::AllocationCallbacks>)nullptr);
+        this->mDevice = nullptr;
         KP_LOG_DEBUG("Kompute Manager Destroyed Device");
     }
 
@@ -106,39 +125,11 @@ Manager::~Manager()
     if (this->mFreeInstance) {
         this->mInstance->destroy(
           (vk::Optional<const vk::AllocationCallbacks>)nullptr);
+        this->mInstance = nullptr;
         KP_LOG_DEBUG("Kompute Manager Destroyed Instance");
     }
 }
 
-std::shared_ptr<Sequence>
-Manager::sequence(std::string sequenceName, uint32_t queueIndex)
-{
-    KP_LOG_DEBUG("Kompute Manager sequence() with sequenceName: {} "
-                 "and queueIndex: {}",
-                 sequenceName,
-                 queueIndex);
-
-    std::shared_ptr<Sequence> sq = nullptr;
-
-    std::unordered_map<std::string, std::shared_ptr<Sequence>>::iterator found =
-      this->mManagedSequences.find(sequenceName);
-
-    if (found == this->mManagedSequences.end()) {
-        std::shared_ptr<Sequence> sq =
-          std::make_shared<Sequence>(this->mPhysicalDevice,
-                                     this->mDevice,
-                                     this->mComputeQueues[queueIndex],
-                                     this->mComputeQueueFamilyIndices[queueIndex]);
-        sq->init();
-
-        this->mManagedSequences.insert({ sequenceName, sq });
-
-        return sq;
-    } else {
-        return found->second;
-    }
-}
-
 void
 Manager::createInstance()
 {
@@ -225,7 +216,31 @@ Manager::createInstance()
 }
 
 void
-Manager::createDevice(const std::vector<uint32_t>& familyQueueIndices)
+Manager::clear()
+{
+    if (this->mManageResources) {
+        this->mManagedTensors.erase(
+          std::remove_if(begin(this->mManagedTensors),
+                         end(this->mManagedTensors),
+                         [](std::weak_ptr<Tensor> t) { return t.expired(); }),
+          end(this->mManagedTensors));
+        this->mManagedAlgorithms.erase(
+          std::remove_if(
+            begin(this->mManagedAlgorithms),
+            end(this->mManagedAlgorithms),
+            [](std::weak_ptr<Algorithm> t) { return t.expired(); }),
+          end(this->mManagedAlgorithms));
+        this->mManagedSequences.erase(
+          std::remove_if(begin(this->mManagedSequences),
+                         end(this->mManagedSequences),
+                         [](std::weak_ptr<Sequence> t) { return t.expired(); }),
+          end(this->mManagedSequences));
+    }
+}
+
+void
+Manager::createDevice(const std::vector<uint32_t>& familyQueueIndices,
+                      uint32_t physicalDeviceIndex)
 {
 
     KP_LOG_DEBUG("Kompute Manager creating Device");
@@ -233,7 +248,7 @@ Manager::createDevice(const std::vector<uint32_t>& familyQueueIndices)
     if (this->mInstance == nullptr) {
         throw std::runtime_error("Kompute Manager instance is null");
     }
-    if (this->mPhysicalDeviceIndex < 0) {
+    if (physicalDeviceIndex < 0) {
         throw std::runtime_error(
           "Kompute Manager physical device index not provided");
     }
@@ -243,8 +258,7 @@ Manager::createDevice(const std::vector<uint32_t>& familyQueueIndices)
     std::vector<vk::PhysicalDevice> physicalDevices =
       this->mInstance->enumeratePhysicalDevices();
 
-    vk::PhysicalDevice physicalDevice =
+    vk::PhysicalDevice physicalDevice = physicalDevices[physicalDeviceIndex];
-      physicalDevices[this->mPhysicalDeviceIndex];
 
     this->mPhysicalDevice =
       std::make_shared<vk::PhysicalDevice>(physicalDevice);
@@ -253,7 +267,7 @@ Manager::createDevice(const std::vector<uint32_t>& familyQueueIndices)
       physicalDevice.getProperties();
 
     KP_LOG_INFO("Using physical device index {} found {}",
-                this->mPhysicalDeviceIndex,
+                physicalDeviceIndex,
                 physicalDeviceProperties.deviceName);
 
     if (!familyQueueIndices.size()) {
@@ -329,150 +343,55 @@ Manager::createDevice(const std::vector<uint32_t>& familyQueueIndices)
 }
 
 std::shared_ptr<Tensor>
-Manager::tensor(
+Manager::tensor(const std::vector<float>& data, Tensor::TensorTypes tensorType)
-  const std::vector<float>& data,
-  Tensor::TensorTypes tensorType,
-  bool syncDataToGPU)
 {
-    KP_LOG_DEBUG("Kompute Manager tensor triggered");
+    KP_LOG_DEBUG("Kompute Manager tensor creation triggered");
 
-    KP_LOG_DEBUG("Kompute Manager creating new tensor shared ptr");
+    std::shared_ptr<Tensor> tensor{ new kp::Tensor(
-    std::shared_ptr<Tensor> tensor =
+      this->mPhysicalDevice, this->mDevice, data, tensorType) };
-      std::make_shared<Tensor>(kp::Tensor(data, tensorType));
 
-    tensor->init(this->mPhysicalDevice, this->mDevice);
+    if (this->mManageResources) {
-
+        this->mManagedTensors.push_back(tensor);
-    if (syncDataToGPU) {
-        this->evalOpDefault<OpTensorSyncDevice>({ tensor });
     }
-    this->mManagedTensors.insert(tensor);
 
     return tensor;
 }
 
-void
+std::shared_ptr<Algorithm>
-Manager::rebuild(std::vector<std::shared_ptr<kp::Tensor>> tensors,
+Manager::algorithm(const std::vector<std::shared_ptr<Tensor>>& tensors,
-                    bool syncDataToGPU)
+                   const std::vector<uint32_t>& spirv,
+                   const Workgroup& workgroup,
+                   const Constants& specializationConstants)
 {
-    KP_LOG_DEBUG("Kompute Manager rebuild triggered");
-    for (std::shared_ptr<Tensor> tensor : tensors) {
 
-        // False syncData to run all tensors at once instead one by one
+    KP_LOG_DEBUG("Kompute Manager algorithm creation triggered");
-        this->rebuild(tensor, false);
+
+    std::shared_ptr<Algorithm> algorithm{ new kp::Algorithm(
+      this->mDevice, tensors, spirv, workgroup, specializationConstants) };
+
+    if (this->mManageResources) {
+        this->mManagedAlgorithms.push_back(algorithm);
     }
 
-    if (syncDataToGPU) {
+    return algorithm;
-        this->evalOpDefault<OpTensorSyncDevice>(tensors);
-    }
 }
 
-void
+std::shared_ptr<Sequence>
-Manager::rebuild(std::shared_ptr<kp::Tensor> tensor,
+Manager::sequence(uint32_t queueIndex)
-                    bool syncDataToGPU)
 {
-    KP_LOG_DEBUG("Kompute Manager rebuild Tensor triggered");
+    KP_LOG_DEBUG("Kompute Manager sequence() with queueIndex: {}", queueIndex);
 
-    if (tensor->isInit()) {
+    std::shared_ptr<Sequence> sq{ new kp::Sequence(
-        tensor->freeMemoryDestroyGPUResources();
+      this->mPhysicalDevice,
+      this->mDevice,
+      this->mComputeQueues[queueIndex],
+      this->mComputeQueueFamilyIndices[queueIndex]) };
+
+    if (this->mManageResources) {
+        this->mManagedSequences.push_back(sq);
     }
 
-    tensor->init(this->mPhysicalDevice, this->mDevice);
+    return sq;
-
-    std::set<std::shared_ptr<Tensor>>::iterator it =
-      this->mManagedTensors.find(tensor);
-    if (it == this->mManagedTensors.end()) {
-        this->mManagedTensors.insert(tensor);
-    }
-
-    if (syncDataToGPU) {
-        this->evalOpDefault<OpTensorSyncDevice>({ tensor });
-    }
 }
 
-void
-Manager::destroy(std::shared_ptr<kp::Tensor> tensor)
-{
-    KP_LOG_DEBUG("Kompute Manager rebuild Tensor triggered");
-
-    if (tensor->isInit()) {
-        tensor->freeMemoryDestroyGPUResources();
-    }
-
-    // TODO: Confirm not limiting destroying tensors owned by this manager allowed
-    std::set<std::shared_ptr<Tensor>>::iterator it =
-      this->mManagedTensors.find(tensor);
-
-    if (it != this->mManagedTensors.end()) {
-        this->mManagedTensors.erase(tensor);
-    }
-}
-
-void
-Manager::destroy(std::vector<std::shared_ptr<kp::Tensor>> tensors)
-{
-    KP_LOG_DEBUG("Kompute Manager rebuild Tensor triggered");
-
-    for (std::shared_ptr<Tensor> tensor : tensors) {
-        this->destroy(tensor);
-    }
-}
-
-void
-Manager::destroy(std::vector<std::shared_ptr<kp::Sequence>> sequences)
-{
-    KP_LOG_DEBUG("Kompute Manager rebuild Sequence triggered");
-
-    for (std::shared_ptr<kp::Sequence> sequence : sequences) {
-        this->destroy(sequence);
-    }
-}
-
-void
-Manager::destroy(std::shared_ptr<kp::Sequence> sequence)
-{
-    KP_LOG_DEBUG("Kompute Manager rebuild Sequence triggered");
-
-    // Inefficient but required to delete by value
-    // Depending on the amount of named sequences created may be worth creating
-    // a set to ensure efficient delete.
-    for (std::unordered_map<std::string, std::shared_ptr<Sequence>>::iterator it = this->mManagedSequences.begin(); it != this->mManagedSequences.end(); it++) {
-        if (it->second == sequence) {
-            this->mManagedSequences.erase(it);
-            break;
-        }
-    }
-
-    if (sequence->isInit()) {
-        sequence->freeMemoryDestroyGPUResources();
-    }
-}
-
-void
-Manager::destroy(const std::string& sequenceName)
-{
-    KP_LOG_DEBUG("Kompute Manager rebuild Sequence triggered");
-
-    std::unordered_map<std::string, std::shared_ptr<Sequence>>::iterator
-      found = this->mManagedSequences.find(sequenceName);
-
-    if (found != this->mManagedSequences.end()) {
-        // We don't call destroy(sequence) as erasing sequence by name more efficient
-        if (found->second->isInit()) {
-            found->second->freeMemoryDestroyGPUResources();
-        }
-        this->mManagedSequences.erase(sequenceName);
-    }
-}
-
-void
-Manager::destroy(const std::vector<std::string>& sequenceNames)
-{
-    KP_LOG_DEBUG("Kompute Manager rebuild Sequence triggered");
-
-    for (const std::string& sequenceName : sequenceNames) {
-        this->destroy(sequenceName);
-    }
-}
-
-
 }

src/OpAlgoBase.cpp

@@ -1,176 +0,0 @@
-#pragma once
-
-#include "kompute/operations/OpAlgoBase.hpp"
-
-namespace kp {
-
-OpAlgoBase::OpAlgoBase()
-{
-    KP_LOG_DEBUG("Kompute OpAlgoBase constructor base");
-}
-
-OpAlgoBase::OpAlgoBase(std::shared_ptr<vk::PhysicalDevice> physicalDevice,
-                       std::shared_ptr<vk::Device> device,
-                       std::shared_ptr<vk::CommandBuffer> commandBuffer,
-                       std::vector<std::shared_ptr<Tensor>>& tensors,
-                       const Workgroup& komputeWorkgroup,
-                       const Constants& specializationConstants)
-  : OpBase(physicalDevice, device, commandBuffer, tensors)
-{
-    KP_LOG_DEBUG("Kompute OpAlgoBase constructor with params numTensors: {}",
-                 tensors.size());
-
-    // The dispatch size is set up based on either explicitly provided template
-    // parameters or by default it would take the shape and size of the tensors
-    if (komputeWorkgroup[0] > 0) {
-        // If at least the x value is provided we use mainly the parameters
-        // provided
-        this->mKomputeWorkgroup = {
-            komputeWorkgroup[0],
-            komputeWorkgroup[1] > 0 ? komputeWorkgroup[1] : 1,
-            komputeWorkgroup[2] > 0 ? komputeWorkgroup[2] : 1
-        };
-    } else {
-        this->mKomputeWorkgroup = { tensors[0]->size(), 1, 1 };
-    }
-    KP_LOG_INFO("Kompute OpAlgoBase dispatch size X: {}, Y: {}, Z: {}",
-                this->mKomputeWorkgroup[0],
-                this->mKomputeWorkgroup[1],
-                this->mKomputeWorkgroup[2]);
-
-    this->mAlgorithm = std::make_shared<Algorithm>(device, commandBuffer, specializationConstants);
-}
-
-OpAlgoBase::OpAlgoBase(std::shared_ptr<vk::PhysicalDevice> physicalDevice,
-                       std::shared_ptr<vk::Device> device,
-                       std::shared_ptr<vk::CommandBuffer> commandBuffer,
-                       std::vector<std::shared_ptr<Tensor>>& tensors,
-                       std::string shaderFilePath,
-                       const Workgroup& komputeWorkgroup,
-                       const Constants& specializationConstants)
-  : OpAlgoBase(physicalDevice, device, commandBuffer, tensors, komputeWorkgroup, specializationConstants)
-{
-    KP_LOG_DEBUG(
-      "Kompute OpAlgoBase shaderFilePath constructo with shaderfile path: {}",
-      shaderFilePath);
-
-    this->mShaderFilePath = shaderFilePath;
-}
-
-OpAlgoBase::OpAlgoBase(std::shared_ptr<vk::PhysicalDevice> physicalDevice,
-                       std::shared_ptr<vk::Device> device,
-                       std::shared_ptr<vk::CommandBuffer> commandBuffer,
-                       std::vector<std::shared_ptr<Tensor>>& tensors,
-                       const std::vector<uint32_t>& shaderDataRaw,
-                       const Workgroup& komputeWorkgroup,
-                       const Constants& specializationConstants)
-  : OpAlgoBase(physicalDevice, device, commandBuffer, tensors, komputeWorkgroup, specializationConstants)
-{
-    KP_LOG_DEBUG("Kompute OpAlgoBase shaderFilePath constructo with shader raw "
-                 "data length: {}",
-                 shaderDataRaw.size());
-
-    this->mShaderDataRaw = shaderDataRaw;
-}
-
-OpAlgoBase::~OpAlgoBase()
-{
-    KP_LOG_DEBUG("Kompute OpAlgoBase destructor started");
-}
-
-void
-OpAlgoBase::init()
-{
-    KP_LOG_DEBUG("Kompute OpAlgoBase init called");
-
-    if (this->mTensors.size() < 1) {
-        throw std::runtime_error(
-          "Kompute OpAlgoBase called with less than 1 tensor");
-    }
-
-    for (std::shared_ptr<Tensor> tensor : this->mTensors) {
-        if (!tensor->isInit()) {
-            throw std::runtime_error(
-              "Kompute OpAlgoBase validation failed; all tensor parameters "
-              "must be initialised.");
-        }
-    }
-
-    KP_LOG_DEBUG("Kompute OpAlgoBase fetching spirv data");
-
-    std::vector<uint32_t> shaderFileData = this->fetchSpirvBinaryData();
-
-    KP_LOG_DEBUG("Kompute OpAlgoBase Initialising algorithm component");
-
-    this->mAlgorithm->init(shaderFileData, this->mTensors);
-}
-
-void
-OpAlgoBase::record()
-{
-    KP_LOG_DEBUG("Kompute OpAlgoBase record called");
-
-    // Barrier to ensure the data is finished writing to buffer memory
-    for (std::shared_ptr<Tensor> tensor : this->mTensors) {
-        tensor->recordBufferMemoryBarrier(
-          this->mCommandBuffer,
-          vk::AccessFlagBits::eHostWrite,
-          vk::AccessFlagBits::eShaderRead,
-          vk::PipelineStageFlagBits::eHost,
-          vk::PipelineStageFlagBits::eComputeShader);
-    }
-
-    this->mAlgorithm->recordDispatch(this->mKomputeWorkgroup[0],
-                                     this->mKomputeWorkgroup[1],
-                                     this->mKomputeWorkgroup[2]);
-}
-
-void
-OpAlgoBase::preEval()
-{
-    KP_LOG_DEBUG("Kompute OpAlgoBase preEval called");
-}
-
-void
-OpAlgoBase::postEval()
-{
-    KP_LOG_DEBUG("Kompute OpAlgoBase postSubmit called");
-}
-
-std::vector<uint32_t>
-OpAlgoBase::fetchSpirvBinaryData()
-{
-    KP_LOG_DEBUG("Kompute OpAlgoBase Running fetchSpirvBinaryData");
-
-    if (this->mShaderFilePath.size()) {
-        KP_LOG_DEBUG("Kompute OpAlgoBase Reading data from file path");
-
-        std::ifstream fileStream(this->mShaderFilePath,
-                                 std::ios::binary | std::ios::in |
-                                   std::ios::ate);
-
-        if (!fileStream.good()) {
-            throw std::runtime_error("Error reading file: " +
-                                     this->mShaderFilePath);
-        }
-
-        size_t shaderFileSize = fileStream.tellg();
-        fileStream.seekg(0, std::ios::beg);
-        char* shaderDataRaw = new char[shaderFileSize];
-        fileStream.read(shaderDataRaw, shaderFileSize);
-        fileStream.close();
-
-        KP_LOG_WARN("Kompute OpAlgoBase fetched {} bytes", shaderFileSize);
-
-        return std::vector<uint32_t>((uint32_t*)shaderDataRaw, (uint32_t*)(shaderDataRaw + shaderFileSize));
-    } else if (this->mShaderDataRaw.size()) {
-        KP_LOG_DEBUG("Kompute OpAlgoBase Reading data from data provided");
-        return this->mShaderDataRaw;
-    } else {
-        throw std::runtime_error(
-          "Kompute OpAlgoBase Error reached fetchSpirvBinaryData but neither "
-          "filepath nor data provided");
-    }
-}
-
-}

src/OpAlgoDispatch.cpp

@@ -0,0 +1,54 @@
+#pragma once
+
+#include "kompute/operations/OpAlgoDispatch.hpp"
+
+namespace kp {
+
+OpAlgoDispatch::OpAlgoDispatch(const std::shared_ptr<kp::Algorithm>& algorithm,
+                               const kp::Constants& pushConstants)
+{
+    KP_LOG_DEBUG("Kompute OpAlgoDispatch constructor");
+
+    this->mAlgorithm = algorithm;
+    this->mPushConstants = pushConstants;
+}
+
+OpAlgoDispatch::~OpAlgoDispatch()
+{
+    KP_LOG_DEBUG("Kompute OpAlgoDispatch destructor started");
+}
+
+void
+OpAlgoDispatch::record(const vk::CommandBuffer& commandBuffer)
+{
+    KP_LOG_DEBUG("Kompute OpAlgoDispatch record called");
+
+    // Barrier to ensure the data is finished writing to buffer memory
+    for (const std::shared_ptr<Tensor>& tensor :
+         this->mAlgorithm->getTensors()) {
+        tensor->recordBufferMemoryBarrier(
+          commandBuffer,
+          vk::AccessFlagBits::eHostWrite,
+          vk::AccessFlagBits::eShaderRead,
+          vk::PipelineStageFlagBits::eHost,
+          vk::PipelineStageFlagBits::eComputeShader);
+    }
+
+    this->mAlgorithm->bindCore(commandBuffer);
+    this->mAlgorithm->bindPush(commandBuffer, this->mPushConstants);
+    this->mAlgorithm->recordDispatch(commandBuffer);
+}
+
+void
+OpAlgoDispatch::preEval(const vk::CommandBuffer& commandBuffer)
+{
+    KP_LOG_DEBUG("Kompute OpAlgoDispatch preEval called");
+}
+
+void
+OpAlgoDispatch::postEval(const vk::CommandBuffer& commandBuffer)
+{
+    KP_LOG_DEBUG("Kompute OpAlgoDispatch postSubmit called");
+}
+
+}

src/OpAlgoLhsRhsOut.cpp

@@ -1,122 +0,0 @@
-#pragma once
-
-#include "kompute/operations/OpAlgoLhsRhsOut.hpp"
-
-namespace kp {
-
-OpAlgoLhsRhsOut::OpAlgoLhsRhsOut()
-{
-    KP_LOG_DEBUG("Kompute OpAlgoLhsRhsOut constructor base");
-}
-
-OpAlgoLhsRhsOut::OpAlgoLhsRhsOut(
-  std::shared_ptr<vk::PhysicalDevice> physicalDevice,
-  std::shared_ptr<vk::Device> device,
-  std::shared_ptr<vk::CommandBuffer> commandBuffer,
-  std::vector<std::shared_ptr<Tensor>> tensors,
-  const Workgroup& komputeWorkgroup)
-  // The inheritance is initialised with the copyOutputData to false given that
-  // this depencendant class handles the transfer of data via staging buffers in
-  // a granular way.
-  : OpAlgoBase(physicalDevice, device, commandBuffer, tensors, komputeWorkgroup)
-{
-    KP_LOG_DEBUG("Kompute OpAlgoLhsRhsOut constructor with params");
-}
-
-OpAlgoLhsRhsOut::~OpAlgoLhsRhsOut()
-{
-    KP_LOG_DEBUG("Kompute OpAlgoLhsRhsOut destructor started");
-}
-
-void
-OpAlgoLhsRhsOut::init()
-{
-    KP_LOG_DEBUG("Kompute OpAlgoLhsRhsOut init called");
-
-    if (this->mTensors.size() < 3) {
-        throw std::runtime_error(
-          "Kompute OpAlgoLhsRhsOut called with less than 1 tensor");
-    } else if (this->mTensors.size() > 3) {
-        KP_LOG_WARN(
-          "Kompute OpAlgoLhsRhsOut called with more than 3 this->mTensors");
-    }
-
-    this->mTensorLHS = this->mTensors[0];
-    this->mTensorRHS = this->mTensors[1];
-    this->mTensorOutput = this->mTensors[2];
-
-    if (!(this->mTensorLHS->isInit() && this->mTensorRHS->isInit() &&
-          this->mTensorOutput->isInit())) {
-        throw std::runtime_error(
-          "Kompute OpAlgoLhsRhsOut all tensor parameters must be initialised. "
-          "LHS: " +
-          std::to_string(this->mTensorLHS->isInit()) +
-          " RHS: " + std::to_string(this->mTensorRHS->isInit()) +
-          " Output: " + std::to_string(this->mTensorOutput->isInit()));
-    }
-
-    if (!(this->mTensorLHS->size() == this->mTensorRHS->size() &&
-          this->mTensorRHS->size() == this->mTensorOutput->size())) {
-        throw std::runtime_error(
-          "Kompute OpAlgoLhsRhsOut all tensor parameters must be the same size "
-          "LHS: " +
-          std::to_string(this->mTensorLHS->size()) +
-          " RHS: " + std::to_string(this->mTensorRHS->size()) +
-          " Output: " + std::to_string(this->mTensorOutput->size()));
-    }
-
-    KP_LOG_DEBUG("Kompute OpAlgoLhsRhsOut fetching spirv data");
-
-    std::vector<uint32_t> shaderFileData = this->fetchSpirvBinaryData();
-
-    KP_LOG_DEBUG("Kompute OpAlgoLhsRhsOut Initialising algorithm component");
-
-    this->mAlgorithm->init(shaderFileData, this->mTensors);
-}
-
-void
-OpAlgoLhsRhsOut::record()
-{
-    KP_LOG_DEBUG("Kompute OpAlgoLhsRhsOut record called");
-
-    // Barrier to ensure the data is finished writing to buffer memory
-    this->mTensorLHS->recordBufferMemoryBarrier(
-      this->mCommandBuffer,
-      vk::AccessFlagBits::eHostWrite,
-      vk::AccessFlagBits::eShaderRead,
-      vk::PipelineStageFlagBits::eHost,
-      vk::PipelineStageFlagBits::eComputeShader);
-    this->mTensorRHS->recordBufferMemoryBarrier(
-      this->mCommandBuffer,
-      vk::AccessFlagBits::eHostWrite,
-      vk::AccessFlagBits::eShaderRead,
-      vk::PipelineStageFlagBits::eHost,
-      vk::PipelineStageFlagBits::eComputeShader);
-
-    this->mAlgorithm->recordDispatch(this->mKomputeWorkgroup[0],
-                                     this->mKomputeWorkgroup[1],
-                                     this->mKomputeWorkgroup[2]);
-
-    // Barrier to ensure the shader code is executed before buffer read
-    this->mTensorOutput->recordBufferMemoryBarrier(
-      this->mCommandBuffer,
-      vk::AccessFlagBits::eShaderWrite,
-      vk::AccessFlagBits::eTransferRead,
-      vk::PipelineStageFlagBits::eComputeShader,
-      vk::PipelineStageFlagBits::eTransfer);
-
-    if (this->mTensorOutput->tensorType() == Tensor::TensorTypes::eDevice) {
-        this->mTensorOutput->recordCopyFromDeviceToStaging(this->mCommandBuffer,
-                                                           true);
-    }
-}
-
-void
-OpAlgoLhsRhsOut::postEval()
-{
-    KP_LOG_DEBUG("Kompute OpAlgoLhsRhsOut postSubmit called");
-
-    this->mTensorOutput->mapDataFromHostMemory();
-}
-
-}

src/OpTensorCopy.cpp

@@ -3,18 +3,16 @@
 
 namespace kp {
 
-OpTensorCopy::OpTensorCopy()
+OpTensorCopy::OpTensorCopy(const std::vector<std::shared_ptr<Tensor>>& tensors)
-{
-    KP_LOG_DEBUG("Kompute OpTensorCopy constructor base");
-}
-
-OpTensorCopy::OpTensorCopy(std::shared_ptr<vk::PhysicalDevice> physicalDevice,
-                           std::shared_ptr<vk::Device> device,
-                           std::shared_ptr<vk::CommandBuffer> commandBuffer,
-                           std::vector<std::shared_ptr<Tensor>> tensors)
-  : OpBase(physicalDevice, device, commandBuffer, tensors)
 {
     KP_LOG_DEBUG("Kompute OpTensorCopy constructor with params");
+
+    this->mTensors = tensors;
+
+    if (this->mTensors.size() < 2) {
+        throw std::runtime_error(
+          "Kompute OpTensorCopy called with less than 2 tensor");
+    }
 }
 
 OpTensorCopy::~OpTensorCopy()
@@ -23,48 +21,25 @@ OpTensorCopy::~OpTensorCopy()
 }
 
 void
-OpTensorCopy::init()
+OpTensorCopy::record(const vk::CommandBuffer& commandBuffer)
-{
-    KP_LOG_DEBUG("Kompute OpTensorCopy init called");
-
-    if (this->mTensors.size() < 2) {
-        throw std::runtime_error(
-          "Kompute OpTensorCopy called with less than 2 tensor");
-    }
-
-    for (std::shared_ptr<Tensor> tensor : this->mTensors) {
-        if (!tensor->isInit()) {
-            throw std::runtime_error(
-              "Kompute OpTensorCopy tensor parameter has not been initialized");
-        }
-        if (tensor->tensorType() == Tensor::TensorTypes::eStorage) {
-            throw std::runtime_error("Kompute OpTensorCopy tensor parameter is "
-                                     "of TensorTypes::eStorage and hence "
-                                     "cannot be used to receive or pass data.");
-        }
-    }
-}
-
-void
-OpTensorCopy::record()
 {
     KP_LOG_DEBUG("Kompute OpTensorCopy record called");
 
     // We iterate from the second tensor onwards and record a copy to all
     for (size_t i = 1; i < this->mTensors.size(); i++) {
         this->mTensors[i]->recordCopyFrom(
-          this->mCommandBuffer, this->mTensors[0], false);
+          commandBuffer, this->mTensors[0], false);
     }
 }
 
 void
-OpTensorCopy::preEval()
+OpTensorCopy::preEval(const vk::CommandBuffer& commandBuffer)
 {
     KP_LOG_DEBUG("Kompute OpTensorCopy preEval called");
 }
 
 void
-OpTensorCopy::postEval()
+OpTensorCopy::postEval(const vk::CommandBuffer& commandBuffer)
 {
     KP_LOG_DEBUG("Kompute OpTensorCopy postEval called");
 

src/OpTensorSyncDevice.cpp

@@ -1,69 +1,43 @@
 
-#include "kompute/Tensor.hpp"
-
 #include "kompute/operations/OpTensorSyncDevice.hpp"
 
 namespace kp {
 
-OpTensorSyncDevice::OpTensorSyncDevice()
-{
-    KP_LOG_DEBUG("Kompute OpTensorSyncDevice constructor base");
-}
-
 OpTensorSyncDevice::OpTensorSyncDevice(
-  std::shared_ptr<vk::PhysicalDevice> physicalDevice,
+  const std::vector<std::shared_ptr<Tensor>>& tensors)
-  std::shared_ptr<vk::Device> device,
-  std::shared_ptr<vk::CommandBuffer> commandBuffer,
-  std::vector<std::shared_ptr<Tensor>> tensors)
-  : OpBase(physicalDevice, device, commandBuffer, tensors)
 {
     KP_LOG_DEBUG("Kompute OpTensorSyncDevice constructor with params");
+
+    if (tensors.size() < 1) {
+        throw std::runtime_error(
+          "Kompute OpTensorSyncDevice called with less than 1 tensor");
+    }
+
+    this->mTensors = tensors;
 }
 
 OpTensorSyncDevice::~OpTensorSyncDevice()
 {
     KP_LOG_DEBUG("Kompute OpTensorSyncDevice destructor started");
+
+    this->mTensors.clear();
 }
 
 void
-OpTensorSyncDevice::init()
+OpTensorSyncDevice::record(const vk::CommandBuffer& commandBuffer)
-{
-    KP_LOG_DEBUG("Kompute OpTensorSyncDevice init called");
-
-    if (this->mTensors.size() < 1) {
-        throw std::runtime_error(
-          "Kompute OpTensorSyncDevice called with less than 1 tensor");
-    }
-
-    for (std::shared_ptr<Tensor> tensor : this->mTensors) {
-        if (!tensor->isInit()) {
-            throw std::runtime_error("Kompute OpTensorSyncDevice: Tensor param "
-                                     "has not been initialized");
-        }
-        if (tensor->tensorType() == Tensor::TensorTypes::eStorage) {
-            KP_LOG_WARN(
-              "Kompute OpTensorSyncLocal tensor parameter is of type "
-              "TensorTypes::eStorage and hence cannot be used to receive or "
-              "pass data.");
-        }
-    }
-}
-
-void
-OpTensorSyncDevice::record()
 {
     KP_LOG_DEBUG("Kompute OpTensorSyncDevice record called");
 
     for (size_t i = 0; i < this->mTensors.size(); i++) {
         if (this->mTensors[i]->tensorType() == Tensor::TensorTypes::eDevice) {
-            this->mTensors[i]->recordCopyFromStagingToDevice(
+            this->mTensors[i]->recordCopyFromStagingToDevice(commandBuffer,
-              this->mCommandBuffer, false);
+                                                             false);
         }
     }
 }
 
 void
-OpTensorSyncDevice::preEval()
+OpTensorSyncDevice::preEval(const vk::CommandBuffer& commandBuffer)
 {
     KP_LOG_DEBUG("Kompute OpTensorSyncDevice preEval called");
 
@@ -76,7 +50,7 @@ OpTensorSyncDevice::preEval()
 }
 
 void
-OpTensorSyncDevice::postEval()
+OpTensorSyncDevice::postEval(const vk::CommandBuffer& commandBuffer)
 {
     KP_LOG_DEBUG("Kompute OpTensorSyncDevice postEval called");
 }

src/OpTensorSyncLocal.cpp

@@ -5,19 +5,17 @@
 
 namespace kp {
 
-OpTensorSyncLocal::OpTensorSyncLocal()
-{
-    KP_LOG_DEBUG("Kompute OpTensorSyncLocal constructor base");
-}
-
 OpTensorSyncLocal::OpTensorSyncLocal(
-  std::shared_ptr<vk::PhysicalDevice> physicalDevice,
+  const std::vector<std::shared_ptr<Tensor>>& tensors)
-  std::shared_ptr<vk::Device> device,
-  std::shared_ptr<vk::CommandBuffer> commandBuffer,
-  std::vector<std::shared_ptr<Tensor>> tensors)
-  : OpBase(physicalDevice, device, commandBuffer, tensors)
 {
     KP_LOG_DEBUG("Kompute OpTensorSyncLocal constructor with params");
+
+    if (tensors.size() < 1) {
+        throw std::runtime_error(
+          "Kompute OpTensorSyncLocal called with less than 1 tensor");
+    }
+
+    this->mTensors = tensors;
 }
 
 OpTensorSyncLocal::~OpTensorSyncLocal()
@@ -26,50 +24,26 @@ OpTensorSyncLocal::~OpTensorSyncLocal()
 }
 
 void
-OpTensorSyncLocal::init()
+OpTensorSyncLocal::record(const vk::CommandBuffer& commandBuffer)
-{
-    KP_LOG_DEBUG("Kompute OpTensorSyncLocal init called");
-
-    if (this->mTensors.size() < 1) {
-        throw std::runtime_error(
-          "Kompute OpTensorSyncLocal called with less than 1 tensor");
-    }
-
-    for (std::shared_ptr<Tensor> tensor : this->mTensors) {
-        if (!tensor->isInit()) {
-            throw std::runtime_error(
-              "Kompute OpTensorSyncLocal: Tensor has not been initialized");
-        }
-        if (tensor->tensorType() == Tensor::TensorTypes::eStorage) {
-            KP_LOG_WARN(
-              "Kompute OpTensorSyncLocal tensor parameter is of type "
-              "TensorTypes::eStorage and hence cannot be used to receive or "
-              "pass data.");
-        }
-    }
-}
-
-void
-OpTensorSyncLocal::record()
 {
     KP_LOG_DEBUG("Kompute OpTensorSyncLocal record called");
 
     for (size_t i = 0; i < this->mTensors.size(); i++) {
         if (this->mTensors[i]->tensorType() == Tensor::TensorTypes::eDevice) {
-            this->mTensors[i]->recordCopyFromDeviceToStaging(
+            this->mTensors[i]->recordCopyFromDeviceToStaging(commandBuffer,
-              this->mCommandBuffer, true);
+                                                             true);
         }
     }
 }
 
 void
-OpTensorSyncLocal::preEval()
+OpTensorSyncLocal::preEval(const vk::CommandBuffer& commandBuffer)
 {
     KP_LOG_DEBUG("Kompute OpTensorSyncLocal preEval called");
 }
 
 void
-OpTensorSyncLocal::postEval()
+OpTensorSyncLocal::postEval(const vk::CommandBuffer& commandBuffer)
 {
     KP_LOG_DEBUG("Kompute OpTensorSyncLocal postEval called");
 

src/Sequence.cpp

@@ -3,12 +3,6 @@
 
 namespace kp {
 
-Sequence::Sequence()
-{
-    KP_LOG_DEBUG("Kompute Sequence base constructor");
-    this->mIsInit = false;
-}
-
 Sequence::Sequence(std::shared_ptr<vk::PhysicalDevice> physicalDevice,
                    std::shared_ptr<vk::Device> device,
                    std::shared_ptr<vk::Queue> computeQueue,
@@ -20,126 +14,92 @@ Sequence::Sequence(std::shared_ptr<vk::PhysicalDevice> physicalDevice,
     this->mDevice = device;
     this->mComputeQueue = computeQueue;
     this->mQueueIndex = queueIndex;
-    this->mIsInit = false;
+
+    this->createCommandPool();
+    this->createCommandBuffer();
 }
 
 Sequence::~Sequence()
 {
     KP_LOG_DEBUG("Kompute Sequence Destructor started");
 
-    if (!this->mIsInit) {
+    this->destroy();
-        KP_LOG_INFO("Kompute Sequence destructor called but sequence is not "
-                    "initialized so no need to removing GPU resources.");
-        return;
-    } else {
-        this->freeMemoryDestroyGPUResources();
-    }
 }
 
 void
-Sequence::init()
-{
-    this->createCommandPool();
-    this->createCommandBuffer();
-    this->mIsInit = true;
-}
-
-bool
 Sequence::begin()
 {
     KP_LOG_DEBUG("Kompute sequence called BEGIN");
 
     if (this->isRecording()) {
-        KP_LOG_WARN("Kompute Sequence begin called when  already recording");
+        KP_LOG_DEBUG("Kompute Sequence begin called when already recording");
-        return false;
+        return;
     }
 
     if (this->isRunning()) {
-        KP_LOG_WARN(
+        throw std::runtime_error(
           "Kompute Sequence begin called when sequence still running");
-        return false;
     }
 
-    if (!this->mCommandPool) {
+    KP_LOG_INFO("Kompute Sequence command now started recording");
-        throw std::runtime_error("Kompute Sequence command pool is null");
+    this->mCommandBuffer->begin(vk::CommandBufferBeginInfo());
-    }
+    this->mRecording = true;
-
-    if (this->mOperations.size()) {
-        KP_LOG_INFO("Kompute Sequence clearing previous operations");
-        this->mOperations.clear();
-    }
-
-    if (!this->mRecording) {
-        KP_LOG_INFO("Kompute Sequence command recording BEGIN");
-        this->mCommandBuffer->begin(vk::CommandBufferBeginInfo());
-        this->mRecording = true;
-    } else {
-        KP_LOG_WARN("Kompute Sequence attempted to start command recording "
-                    "but recording already started");
-    }
-    return true;
 }
 
-bool
+void
 Sequence::end()
 {
     KP_LOG_DEBUG("Kompute Sequence calling END");
 
     if (!this->isRecording()) {
         KP_LOG_WARN("Kompute Sequence end called when not recording");
-        return false;
+        return;
-    }
+    } else {
-
-    if (!this->mCommandPool) {
-        throw std::runtime_error("Kompute Sequence command pool is null");
-    }
-
-    if (this->mRecording) {
         KP_LOG_INFO("Kompute Sequence command recording END");
         this->mCommandBuffer->end();
         this->mRecording = false;
-    } else {
-        KP_LOG_WARN("Kompute Sequence attempted to end command recording but "
-                    "recording not started");
     }
-    return true;
 }
 
-bool
+void
+Sequence::clear()
+{
+    KP_LOG_DEBUG("Kompute Sequence  calling clear");
+    this->end();
+}
+
+std::shared_ptr<Sequence>
 Sequence::eval()
 {
     KP_LOG_DEBUG("Kompute sequence EVAL BEGIN");
 
-    bool evalResult = this->evalAsync();
+    return this->evalAsync()->evalAwait();
-    if (!evalResult) {
-        KP_LOG_DEBUG("Kompute sequence EVAL FAILURE");
-        return false;
-    }
-
-    evalResult = this->evalAwait();
-
-    KP_LOG_DEBUG("Kompute sequence EVAL SUCCESS");
-
-    return evalResult;
 }
 
-bool
+std::shared_ptr<Sequence>
+Sequence::eval(std::shared_ptr<OpBase> op)
+{
+    this->clear();
+    return this->record(op)->eval();
+}
+
+std::shared_ptr<Sequence>
 Sequence::evalAsync()
 {
     if (this->isRecording()) {
-        KP_LOG_WARN("Kompute Sequence evalAsync called when still recording");
+        this->end();
-        return false;
     }
+
     if (this->mIsRunning) {
-        KP_LOG_WARN("Kompute Sequence evalAsync called when an eval async was "
+        throw std::runtime_error(
-                    "called without successful wait");
+          "Kompute Sequence evalAsync called when an eval async was "
-        return false;
+          "called without successful wait");
     }
 
     this->mIsRunning = true;
 
     for (size_t i = 0; i < this->mOperations.size(); i++) {
-        this->mOperations[i]->preEval();
+        this->mOperations[i]->preEval(*this->mCommandBuffer);
     }
 
     vk::SubmitInfo submitInfo(
@@ -152,15 +112,24 @@ Sequence::evalAsync()
 
     this->mComputeQueue->submit(1, &submitInfo, this->mFence);
 
-    return true;
+    return shared_from_this();
 }
 
-bool
+std::shared_ptr<Sequence>
+Sequence::evalAsync(std::shared_ptr<OpBase> op)
+{
+    this->clear();
+    this->record(op);
+    this->evalAsync();
+    return shared_from_this();
+}
+
+std::shared_ptr<Sequence>
 Sequence::evalAwait(uint64_t waitFor)
 {
     if (!this->mIsRunning) {
         KP_LOG_WARN("Kompute Sequence evalAwait called without existing eval");
-        return false;
+        return shared_from_this();
     }
 
     vk::Result result =
@@ -171,15 +140,16 @@ Sequence::evalAwait(uint64_t waitFor)
     this->mIsRunning = false;
 
     if (result == vk::Result::eTimeout) {
-        KP_LOG_WARN("Kompute Sequence evalAwait timed out");
+        KP_LOG_WARN("Kompute Sequence evalAwait reached timeout of {}",
-        return false;
+                    waitFor);
+        return shared_from_this();
     }
 
     for (size_t i = 0; i < this->mOperations.size(); i++) {
-        this->mOperations[i]->postEval();
+        this->mOperations[i]->postEval(*this->mCommandBuffer);
     }
 
-    return true;
+    return shared_from_this();
 }
 
 bool
@@ -197,54 +167,51 @@ Sequence::isRecording()
 bool
 Sequence::isInit()
 {
-    return this->mIsInit;
+    return this->mDevice && this->mCommandPool && this->mCommandBuffer &&
+           this->mComputeQueue;
 }
 
 void
-Sequence::freeMemoryDestroyGPUResources()
+Sequence::destroy()
 {
-    KP_LOG_DEBUG("Kompute Sequence freeMemoryDestroyGPUResources called");
+    KP_LOG_DEBUG("Kompute Sequence destroy called");
-
-    if (!this->mIsInit) {
-        KP_LOG_ERROR("Kompute Sequence freeMemoryDestroyGPUResources called "
-                     "but Sequence is not initialized so there's no relevant "
-                     "GPU resources.");
-        return;
-    }
 
     if (!this->mDevice) {
-        KP_LOG_ERROR("Kompute Sequence freeMemoryDestroyGPUResources called "
+        KP_LOG_WARN("Kompute Sequence destroy called "
-                     "with null Device pointer");
+                    "with null Device pointer");
-        this->mIsInit = false;
         return;
     }
 
     if (this->mFreeCommandBuffer) {
         KP_LOG_INFO("Freeing CommandBuffer");
         if (!this->mCommandBuffer) {
-            KP_LOG_ERROR(
+            KP_LOG_WARN("Kompute Sequence destroy called with null "
-              "Kompute Sequence freeMemoryDestroyGPUResources called with null "
+                        "CommandPool pointer");
-              "CommandPool pointer");
-            this->mIsInit = false;
             return;
         }
         this->mDevice->freeCommandBuffers(
           *this->mCommandPool, 1, this->mCommandBuffer.get());
+
+        this->mCommandBuffer = nullptr;
+        this->mFreeCommandBuffer = false;
+
         KP_LOG_DEBUG("Kompute Sequence Freed CommandBuffer");
     }
 
     if (this->mFreeCommandPool) {
         KP_LOG_INFO("Destroying CommandPool");
         if (this->mCommandPool == nullptr) {
-            KP_LOG_ERROR(
+            KP_LOG_WARN("Kompute Sequence destroy called with null "
-              "Kompute Sequence freeMemoryDestroyGPUResources called with null "
+                        "CommandPool pointer");
-              "CommandPool pointer");
-            this->mIsInit = false;
             return;
         }
         this->mDevice->destroy(
           *this->mCommandPool,
           (vk::Optional<const vk::AllocationCallbacks>)nullptr);
+
+        this->mCommandPool = nullptr;
+        this->mFreeCommandPool = false;
+
         KP_LOG_DEBUG("Kompute Sequence Destroyed CommandPool");
     }
 
@@ -253,7 +220,32 @@ Sequence::freeMemoryDestroyGPUResources()
         this->mOperations.clear();
     }
 
-    this->mIsInit = false;
+    if (this->mDevice) {
+        this->mDevice = nullptr;
+    }
+    if (this->mPhysicalDevice) {
+        this->mPhysicalDevice = nullptr;
+    }
+    if (this->mComputeQueue) {
+        this->mComputeQueue = nullptr;
+    }
+}
+
+std::shared_ptr<Sequence>
+Sequence::record(std::shared_ptr<OpBase> op)
+{
+    KP_LOG_DEBUG("Kompute Sequence record function started");
+
+    this->begin();
+
+    KP_LOG_DEBUG(
+      "Kompute Sequence running record on OpBase derived class instance");
+
+    op->record(*this->mCommandBuffer);
+
+    this->mOperations.push_back(op);
+
+    return shared_from_this();
 }
 
 void

src/Shader.cpp

@@ -5,11 +5,13 @@
 namespace kp {
 
 std::vector<uint32_t>
-Shader::compile_sources(const std::vector<std::string>& sources,
+Shader::compile_sources(
-                                   const std::vector<std::string>& files,
+  const std::vector<std::string>& sources,
-                                   const std::string& entryPoint,
+  const std::vector<std::string>& files,
-                                   std::vector<std::pair<std::string,std::string>> definitions,
+  const std::string& entryPoint,
-                                   const TBuiltInResource& resources) {
+  std::vector<std::pair<std::string, std::string>> definitions,
+  const TBuiltInResource& resources)
+{
 
     // Initialize glslang library.
     glslang::InitializeProcess();
@@ -18,27 +20,32 @@ Shader::compile_sources(const std::vector<std::string>& sources,
     const EShLanguage language = EShLangCompute;
     glslang::TShader shader(language);
 
-    std::vector<const char*> filesCStr(files.size()), sourcesCStr(sources.size());
+    std::vector<const char*> filesCStr(files.size()),
-    for (size_t i = 0; i < sources.size(); i++) sourcesCStr[i] = sources[i].c_str();
+      sourcesCStr(sources.size());
+    for (size_t i = 0; i < sources.size(); i++)
+        sourcesCStr[i] = sources[i].c_str();
 
     if (files.size() > 1) {
         assert(files.size() == sources.size());
-        for (size_t i = 0; i < files.size(); i++) filesCStr[i] = files[i].c_str();
+        for (size_t i = 0; i < files.size(); i++)
-        shader.setStringsWithLengthsAndNames(sourcesCStr.data(), nullptr, filesCStr.data(), filesCStr.size());
+            filesCStr[i] = files[i].c_str();
-    }
+        shader.setStringsWithLengthsAndNames(
-    else {
+          sourcesCStr.data(), nullptr, filesCStr.data(), filesCStr.size());
-        filesCStr = {""};
+    } else {
-        shader.setStringsWithLengthsAndNames(sourcesCStr.data(), nullptr, filesCStr.data(), sourcesCStr.size());
+        filesCStr = { "" };
+        shader.setStringsWithLengthsAndNames(
+          sourcesCStr.data(), nullptr, filesCStr.data(), sourcesCStr.size());
     }
 
     shader.setEntryPoint(entryPoint.c_str());
     shader.setSourceEntryPoint(entryPoint.c_str());
 
     std::string info_log = "";
-    const EShMessages messages = static_cast<EShMessages>(EShMsgDefault | EShMsgVulkanRules | EShMsgSpvRules);
+    const EShMessages messages = static_cast<EShMessages>(
-    if (!shader.parse(&resources, 100, false, messages))
+      EShMsgDefault | EShMsgVulkanRules | EShMsgSpvRules);
-    {
+    if (!shader.parse(&resources, 100, false, messages)) {
-        info_log = std::string(shader.getInfoLog()) + "\n" + std::string(shader.getInfoDebugLog());
+        info_log = std::string(shader.getInfoLog()) + "\n" +
+                   std::string(shader.getInfoDebugLog());
         KP_LOG_ERROR("Kompute Shader Error: {}", info_log);
         throw std::runtime_error(info_log);
     }
@@ -47,24 +54,23 @@ Shader::compile_sources(const std::vector<std::string>& sources,
     glslang::TProgram program;
     program.addShader(&shader);
     // Link program.
-    if (!program.link(messages))
+    if (!program.link(messages)) {
-    {
+        info_log = std::string(program.getInfoLog()) + "\n" +
-        info_log = std::string(program.getInfoLog()) + "\n" + std::string(program.getInfoDebugLog());
+                   std::string(program.getInfoDebugLog());
         KP_LOG_ERROR("Kompute Shader Error: {}", info_log);
         throw std::runtime_error(info_log);
     }
 
     // Save any info log that was generated.
-    if (shader.getInfoLog())
+    if (shader.getInfoLog()) {
-    {
+        info_log += std::string(shader.getInfoLog()) + "\n" +
-        info_log += std::string(shader.getInfoLog()) + "\n" + std::string(shader.getInfoDebugLog()) + "\n";
+                    std::string(shader.getInfoDebugLog()) + "\n";
         KP_LOG_INFO("Kompute Shader Information: {}", info_log);
     }
 
-    glslang::TIntermediate *intermediate = program.getIntermediate(language);
+    glslang::TIntermediate* intermediate = program.getIntermediate(language);
     // Translate to SPIRV.
-    if (!intermediate)
+    if (!intermediate) {
-    {
         info_log += "Failed to get shared intermediate code.\n";
         KP_LOG_ERROR("Kompute Shader Error: {}", info_log);
         throw std::runtime_error(info_log);
@@ -74,8 +80,7 @@ Shader::compile_sources(const std::vector<std::string>& sources,
     std::vector<std::uint32_t> spirv;
     glslang::GlslangToSpv(*intermediate, spirv, &logger);
 
-    if (shader.getInfoLog())
+    if (shader.getInfoLog()) {
-    {
         info_log += logger.getAllMessages() + "\n";
         KP_LOG_DEBUG("Kompute Shader all result messages: {}", info_log);
     }
@@ -87,11 +92,17 @@ Shader::compile_sources(const std::vector<std::string>& sources,
 }
 
 std::vector<uint32_t>
-Shader::compile_source(const std::string& source,
+Shader::compile_source(
-        const std::string& entryPoint,
+  const std::string& source,
-        std::vector<std::pair<std::string,std::string>> definitions,
+  const std::string& entryPoint,
-        const TBuiltInResource& resource) {
+  std::vector<std::pair<std::string, std::string>> definitions,
-    return compile_sources({source}, std::vector<std::string>({}), entryPoint, definitions, resource);
+  const TBuiltInResource& resource)
+{
+    return compile_sources({ source },
+                           std::vector<std::string>({}),
+                           entryPoint,
+                           definitions,
+                           resource);
 }
 
 }

src/Tensor.cpp

@@ -3,23 +3,19 @@
 
 namespace kp {
 
-Tensor::Tensor()
+Tensor::Tensor(std::shared_ptr<vk::PhysicalDevice> physicalDevice,
+               std::shared_ptr<vk::Device> device,
+               const std::vector<float>& data,
+               const TensorTypes& tensorType)
 {
-    KP_LOG_DEBUG("Kompute Tensor base constructor");
-    this->mTensorType = TensorTypes::eDevice;
-}
-
-Tensor::Tensor(const std::vector<float>& data, TensorTypes tensorType)
-{
-#if DEBUG
     KP_LOG_DEBUG("Kompute Tensor constructor data length: {}, and type: {}",
                  data.size(),
                  tensorType);
-#endif
 
-    this->mData = data;
+    this->mPhysicalDevice = physicalDevice;
-    this->mShape = { static_cast<uint32_t>(data.size()) };
+    this->mDevice = device;
-    this->mTensorType = tensorType;
+
+    this->rebuild(data, tensorType);
 }
 
 Tensor::~Tensor()
@@ -27,25 +23,24 @@ Tensor::~Tensor()
     KP_LOG_DEBUG("Kompute Tensor destructor started. Type: {}",
                  this->tensorType());
 
-    if (this->isInit()) {
+    this->destroy();
-        this->freeMemoryDestroyGPUResources();
-    }
 
     KP_LOG_DEBUG("Kompute Tensor destructor success");
 }
 
 void
-Tensor::init(std::shared_ptr<vk::PhysicalDevice> physicalDevice,
+Tensor::rebuild(const std::vector<float>& data, TensorTypes tensorType)
-             std::shared_ptr<vk::Device> device)
 {
-    KP_LOG_DEBUG("Kompute Tensor running init with Vulkan params and num data "
+    KP_LOG_DEBUG("Kompute Tensor rebuilding with size {}", data.size());
-                 "elementS: {}",
-                 this->mData.size());
 
-    this->mPhysicalDevice = physicalDevice;
+    this->mData = data;
-    this->mDevice = device;
+    this->mTensorType = tensorType;
 
-    this->mIsInit = true;
+    if (this->mPrimaryBuffer || this->mPrimaryMemory) {
+        KP_LOG_DEBUG(
+          "Kompute Tensor destroying existing resources before rebuild");
+        this->destroy();
+    }
 
     this->allocateMemoryCreateGPUResources();
 }
@@ -71,13 +66,7 @@ Tensor::memorySize()
 uint32_t
 Tensor::size()
 {
-    return this->mShape[0];
+    return static_cast<uint32_t>(this->mData.size());
-}
-
-std::array<uint32_t, KP_MAX_DIM_SIZE>
-Tensor::shape()
-{
-    return this->mShape;
 }
 
 Tensor::TensorTypes
@@ -89,7 +78,7 @@ Tensor::tensorType()
 bool
 Tensor::isInit()
 {
-    return this->mIsInit && this->mPrimaryBuffer && this->mPrimaryMemory;
+    return this->mDevice && this->mPrimaryBuffer && this->mPrimaryMemory;
 }
 
 void
@@ -103,7 +92,7 @@ Tensor::setData(const std::vector<float>& data)
 }
 
 void
-Tensor::recordCopyFrom(std::shared_ptr<vk::CommandBuffer> commandBuffer,
+Tensor::recordCopyFrom(const vk::CommandBuffer& commandBuffer,
                        std::shared_ptr<Tensor> copyFromTensor,
                        bool createBarrier)
 {
@@ -113,65 +102,58 @@ Tensor::recordCopyFrom(std::shared_ptr<vk::CommandBuffer> commandBuffer,
 
     KP_LOG_DEBUG("Kompute Tensor recordCopyFrom data size {}.", bufferSize);
 
-    this->copyBuffer(commandBuffer,
+    this->recordCopyBuffer(commandBuffer,
-                     copyFromTensor->mPrimaryBuffer,
+                           copyFromTensor->mPrimaryBuffer,
-                     this->mPrimaryBuffer,
+                           this->mPrimaryBuffer,
-                     bufferSize,
+                           bufferSize,
-                     copyRegion,
+                           copyRegion,
-                     createBarrier);
+                           createBarrier);
 }
 
 void
-Tensor::recordCopyFromStagingToDevice(
+Tensor::recordCopyFromStagingToDevice(const vk::CommandBuffer& commandBuffer,
-  std::shared_ptr<vk::CommandBuffer> commandBuffer,
+                                      bool createBarrier)
-  bool createBarrier)
 {
     vk::DeviceSize bufferSize(this->memorySize());
     vk::BufferCopy copyRegion(0, 0, bufferSize);
 
     KP_LOG_DEBUG("Kompute Tensor copying data size {}.", bufferSize);
 
-    this->copyBuffer(commandBuffer,
+    this->recordCopyBuffer(commandBuffer,
-                     this->mStagingBuffer,
+                           this->mStagingBuffer,
-                     this->mPrimaryBuffer,
+                           this->mPrimaryBuffer,
-                     bufferSize,
+                           bufferSize,
-                     copyRegion,
+                           copyRegion,
-                     createBarrier);
+                           createBarrier);
 }
 
 void
-Tensor::recordCopyFromDeviceToStaging(
+Tensor::recordCopyFromDeviceToStaging(const vk::CommandBuffer& commandBuffer,
-  std::shared_ptr<vk::CommandBuffer> commandBuffer,
+                                      bool createBarrier)
-  bool createBarrier)
 {
     vk::DeviceSize bufferSize(this->memorySize());
     vk::BufferCopy copyRegion(0, 0, bufferSize);
 
     KP_LOG_DEBUG("Kompute Tensor copying data size {}.", bufferSize);
 
-    this->copyBuffer(commandBuffer,
+    this->recordCopyBuffer(commandBuffer,
-                     this->mPrimaryBuffer,
+                           this->mPrimaryBuffer,
-                     this->mStagingBuffer,
+                           this->mStagingBuffer,
-                     bufferSize,
+                           bufferSize,
-                     copyRegion,
+                           copyRegion,
-                     createBarrier);
+                           createBarrier);
 }
 
 void
-Tensor::copyBuffer(std::shared_ptr<vk::CommandBuffer> commandBuffer,
+Tensor::recordCopyBuffer(const vk::CommandBuffer& commandBuffer,
-                   std::shared_ptr<vk::Buffer> bufferFrom,
+                         std::shared_ptr<vk::Buffer> bufferFrom,
-                   std::shared_ptr<vk::Buffer> bufferTo,
+                         std::shared_ptr<vk::Buffer> bufferTo,
-                   vk::DeviceSize bufferSize,
+                         vk::DeviceSize bufferSize,
-                   vk::BufferCopy copyRegion,
+                         vk::BufferCopy copyRegion,
-                   bool createBarrier)
+                         bool createBarrier)
 {
 
-    if (!this->mIsInit) {
+    commandBuffer.copyBuffer(*bufferFrom, *bufferTo, copyRegion);
-        throw std::runtime_error(
-          "Kompute Tensor attempted to run copyBuffer without init");
-    }
-
-    commandBuffer->copyBuffer(*bufferFrom, *bufferTo, copyRegion);
 
     if (createBarrier) {
         // Buffer to ensure wait until data is copied to staging buffer
@@ -184,12 +166,11 @@ Tensor::copyBuffer(std::shared_ptr<vk::CommandBuffer> commandBuffer,
 }
 
 void
-Tensor::recordBufferMemoryBarrier(
+Tensor::recordBufferMemoryBarrier(const vk::CommandBuffer& commandBuffer,
-  std::shared_ptr<vk::CommandBuffer> commandBuffer,
+                                  vk::AccessFlagBits srcAccessMask,
-  vk::AccessFlagBits srcAccessMask,
+                                  vk::AccessFlagBits dstAccessMask,
-  vk::AccessFlagBits dstAccessMask,
+                                  vk::PipelineStageFlagBits srcStageMask,
-  vk::PipelineStageFlagBits srcStageMask,
+                                  vk::PipelineStageFlagBits dstStageMask)
-  vk::PipelineStageFlagBits dstStageMask)
 {
     KP_LOG_DEBUG("Kompute Tensor recording buffer memory barrier");
 
@@ -203,12 +184,12 @@ Tensor::recordBufferMemoryBarrier(
     bufferMemoryBarrier.srcQueueFamilyIndex = VK_QUEUE_FAMILY_IGNORED;
     bufferMemoryBarrier.dstQueueFamilyIndex = VK_QUEUE_FAMILY_IGNORED;
 
-    commandBuffer->pipelineBarrier(srcStageMask,
+    commandBuffer.pipelineBarrier(srcStageMask,
-                                   dstStageMask,
+                                  dstStageMask,
-                                   vk::DependencyFlags(),
+                                  vk::DependencyFlags(),
-                                   nullptr,
+                                  nullptr,
-                                   bufferMemoryBarrier,
+                                  bufferMemoryBarrier,
-                                   nullptr);
+                                  nullptr);
 }
 
 vk::DescriptorBufferInfo
@@ -344,11 +325,6 @@ Tensor::allocateMemoryCreateGPUResources()
 {
     KP_LOG_DEBUG("Kompute Tensor creating buffer");
 
-    if (!this->mIsInit) {
-        throw std::runtime_error(
-          "Kompute Tensor attempted to run createBuffer without init");
-    }
-
     if (!this->mPhysicalDevice) {
         throw std::runtime_error("Kompute Tensor phyisical device is null");
     }
@@ -455,71 +431,77 @@ Tensor::allocateBindMemory(std::shared_ptr<vk::Buffer> buffer,
 }
 
 void
-Tensor::freeMemoryDestroyGPUResources()
+Tensor::destroy()
 {
-    KP_LOG_DEBUG("Kompute Tensor started freeMemoryDestroyGPUResources");
+    KP_LOG_DEBUG("Kompute Tensor started destroy()");
-
-    this->mIsInit = false;
 
     if (!this->mDevice) {
-        KP_LOG_ERROR(
+        KP_LOG_WARN(
           "Kompute Tensor destructor reached with null Device pointer");
         return;
     }
 
     if (this->mFreePrimaryBuffer) {
         if (!this->mPrimaryBuffer) {
-            KP_LOG_ERROR("Kompose Tensor expected to destroy primary buffer "
+            KP_LOG_WARN("Kompose Tensor expected to destroy primary buffer "
-                         "but got null buffer");
+                        "but got null buffer");
         } else {
             KP_LOG_DEBUG("Kompose Tensor destroying primary buffer");
             this->mDevice->destroy(
               *this->mPrimaryBuffer,
               (vk::Optional<const vk::AllocationCallbacks>)nullptr);
             this->mPrimaryBuffer = nullptr;
+            this->mFreePrimaryBuffer = false;
         }
     }
 
     if (this->mFreeStagingBuffer) {
         if (!this->mStagingBuffer) {
-            KP_LOG_ERROR("Kompose Tensor expected to destroy staging buffer "
+            KP_LOG_WARN("Kompose Tensor expected to destroy staging buffer "
-                         "but got null buffer");
+                        "but got null buffer");
         } else {
             KP_LOG_DEBUG("Kompose Tensor destroying staging buffer");
             this->mDevice->destroy(
               *this->mStagingBuffer,
               (vk::Optional<const vk::AllocationCallbacks>)nullptr);
             this->mStagingBuffer = nullptr;
+            this->mFreeStagingBuffer = false;
         }
     }
 
     if (this->mFreePrimaryMemory) {
         if (!this->mPrimaryMemory) {
-            KP_LOG_ERROR("Kompose Tensor expected to free primary memory but "
+            KP_LOG_WARN("Kompose Tensor expected to free primary memory but "
-                         "got null memory");
+                        "got null memory");
         } else {
             KP_LOG_DEBUG("Kompose Tensor freeing primary memory");
             this->mDevice->freeMemory(
               *this->mPrimaryMemory,
               (vk::Optional<const vk::AllocationCallbacks>)nullptr);
             this->mPrimaryMemory = nullptr;
+            this->mFreePrimaryMemory = false;
         }
     }
 
     if (this->mFreeStagingMemory) {
         if (!this->mStagingMemory) {
-            KP_LOG_ERROR("Kompose Tensor expected to free staging memory but "
+            KP_LOG_WARN("Kompose Tensor expected to free staging memory but "
-                         "got null memory");
+                        "got null memory");
         } else {
             KP_LOG_DEBUG("Kompose Tensor freeing staging memory");
             this->mDevice->freeMemory(
               *this->mStagingMemory,
               (vk::Optional<const vk::AllocationCallbacks>)nullptr);
             this->mStagingMemory = nullptr;
+            this->mFreeStagingMemory = false;
         }
     }
 
-    KP_LOG_DEBUG("Kompute Tensor successful freeMemoryDestroyGPUResources");
+    if (this->mDevice) {
+        this->mDevice = nullptr;
+    }
+
+    KP_LOG_DEBUG("Kompute Tensor successful destroy()");
 }
 
 }

src/include/kompute/Algorithm.hpp

@@ -12,13 +12,7 @@ namespace kp {
 */
 class Algorithm
 {
-public:
+  public:
-    /**
-        Base constructor for Algorithm. Should not be used unless explicit
-       intended.
-    */
-    Algorithm();
-
     /**
      *  Default constructor for Algorithm
      *
@@ -27,7 +21,9 @@ public:
      * shaders
      */
     Algorithm(std::shared_ptr<vk::Device> device,
-              std::shared_ptr<vk::CommandBuffer> commandBuffer,
+              const std::vector<std::shared_ptr<Tensor>>& tensors = {},
+              const std::vector<uint32_t>& spirv = {},
+              const Workgroup& workgroup = {},
               const Constants& specializationConstants = {});
 
     /**
@@ -36,11 +32,13 @@ 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
+     * @specalizationInstalces The specialization parameters to pass to the
-     * processing
+     * function processing
      */
-    void init(const std::vector<uint32_t>& shaderFileData,
+    void rebuild(const std::vector<std::shared_ptr<Tensor>>& tensors = {},
-              std::vector<std::shared_ptr<Tensor>> tensorParams);
+                 const std::vector<uint32_t>& spirv = {},
+                 const Workgroup& workgroup = {},
+                 const Constants& specializationConstants = {});
 
     /**
      * Destructor for Algorithm which is responsible for freeing and desroying
@@ -56,12 +54,27 @@ public:
      * @param y Layout Y dispatch value
      * @param z Layout Z dispatch value
      */
-    void recordDispatch(uint32_t x = 1, uint32_t y = 1, uint32_t z = 1);
+    void recordDispatch(const vk::CommandBuffer& commandBuffer);
 
-private:
+    void bindCore(const vk::CommandBuffer& commandBuffer);
+
+    void bindPush(const vk::CommandBuffer& commandBuffer,
+                  const Constants& pushConstants);
+
+    bool isInit();
+
+    void setWorkgroup(const Workgroup& workgroup, uint32_t minSize = 1);
+
+    const Workgroup& getWorkgroup();
+    const Constants& getSpecializationConstants();
+    const std::vector<std::shared_ptr<Tensor>>& getTensors();
+
+    void destroy();
+
+  private:
     // -------------- NEVER OWNED RESOURCES
     std::shared_ptr<vk::Device> mDevice;
-    std::shared_ptr<vk::CommandBuffer> mCommandBuffer;
+    std::vector<std::shared_ptr<Tensor>> mTensors;
 
     // -------------- OPTIONALLY OWNED RESOURCES
     std::shared_ptr<vk::DescriptorSetLayout> mDescriptorSetLayout;
@@ -80,15 +93,18 @@ private:
     bool mFreePipeline = false;
 
     // -------------- ALWAYS OWNED RESOURCES
+    std::vector<uint32_t> mSpirv;
     Constants mSpecializationConstants;
+    Workgroup mWorkgroup;
+
+    bool mIsInit;
 
     // Create util functions
-    void createShaderModule(const std::vector<uint32_t>& shaderFileData);
+    void createShaderModule();
     void createPipeline();
 
     // Parameters
-    void createParameters(std::vector<std::shared_ptr<Tensor>>& tensorParams);
+    void createParameters();
-    void createDescriptorPool();
 };
 
 } // End namespace kp

src/include/kompute/Core.hpp

@@ -60,12 +60,19 @@ 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(...)                                             \
+#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
-#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
 
@@ -73,12 +80,19 @@ 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(...)                                              \
+#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
-#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
 
@@ -86,12 +100,19 @@ 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(...)                                              \
+#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
-#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
 
@@ -99,12 +120,19 @@ 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(...)                                             \
+#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
-#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

src/include/kompute/Manager.hpp

@@ -7,8 +7,6 @@
 
 #include "kompute/Sequence.hpp"
 
-#include "kompute/operations/OpTensorSyncDevice.hpp"
-
 #define KP_DEFAULT_SESSION "DEFAULT"
 
 namespace kp {
@@ -30,6 +28,8 @@ class Manager
      * they would like to create the resources on.
      *
      * @param physicalDeviceIndex The index of the physical device to use
+     * @param manageResources (Optional) Whether to manage the memory of the
+     * resources created and destroy when the manager is destroyed.
      * @param familyQueueIndices (Optional) List of queue indices to add for
      * explicit allocation
      * @param totalQueues The total number of compute queues to create.
@@ -48,8 +48,7 @@ class Manager
      */
     Manager(std::shared_ptr<vk::Instance> instance,
             std::shared_ptr<vk::PhysicalDevice> physicalDevice,
-            std::shared_ptr<vk::Device> device,
+            std::shared_ptr<vk::Device> device);
-            uint32_t physicalDeviceIndex);
 
     /**
      * Manager destructor which would ensure all owned resources are destroyed
@@ -67,150 +66,7 @@ class Manager
      * @param queueIndex The queue to use from the available queues
      * @return Shared pointer to the manager owned sequence resource
      */
-    std::shared_ptr<Sequence> sequence(
+    std::shared_ptr<Sequence> sequence(uint32_t queueIndex = 0);
-            std::string sequenceName = KP_DEFAULT_SESSION,
-            uint32_t queueIndex = 0);
-
-    /**
-     * Function that evaluates operation against named sequence.
-     *
-     * @param tensors The tensors to be used in the operation recorded
-     * @param sequenceName The name of the sequence to be retrieved or created
-     * @param TArgs Template parameters that will be used to initialise
-     * Operation to allow for extensible configurations on initialisation
-     */
-    template<typename T, typename... TArgs>
-    void evalOp(std::vector<std::shared_ptr<Tensor>> tensors,
-                std::string sequenceName,
-                TArgs&&... params)
-    {
-        KP_LOG_DEBUG("Kompute Manager evalOp triggered");
-        std::shared_ptr<kp::Sequence> sq =
-          this->sequence(sequenceName);
-
-        KP_LOG_DEBUG("Kompute Manager evalOp running sequence BEGIN");
-        sq->begin();
-
-        KP_LOG_DEBUG("Kompute Manager evalOp running sequence RECORD");
-        sq->record<T>(tensors, std::forward<TArgs>(params)...);
-
-        KP_LOG_DEBUG("Kompute Manager evalOp running sequence END");
-        sq->end();
-
-        KP_LOG_DEBUG("Kompute Manager evalOp running sequence EVAL");
-        sq->eval();
-
-        KP_LOG_DEBUG("Kompute Manager evalOp running sequence SUCCESS");
-    }
-
-    /**
-     * Function that evaluates operation against a newly created sequence.
-     *
-     * @param tensors The tensors to be used in the operation recorded
-     * @param TArgs Template parameters that will be used to initialise
-     * Operation to allow for extensible configurations on initialisation
-     */
-    template<typename T, typename... TArgs>
-    void evalOpDefault(std::vector<std::shared_ptr<Tensor>> tensors,
-                       TArgs&&... params)
-    {
-        KP_LOG_DEBUG("Kompute Manager evalOp Default triggered");
-        this->mCurrentSequenceIndex++;
-        this->evalOp<T>(
-          tensors, KP_DEFAULT_SESSION, std::forward<TArgs>(params)...);
-    }
-
-    /**
-     * Function that evaluates operation against named sequence asynchronously.
-     *
-     * @param tensors The tensors to be used in the operation recorded
-     * @param sequenceName The name of the sequence to be retrieved or created
-     * @param params Template parameters that will be used to initialise
-     * Operation to allow for extensible configurations on initialisation
-     */
-    template<typename T, typename... TArgs>
-    void evalOpAsync(std::vector<std::shared_ptr<Tensor>> tensors,
-                     std::string sequenceName,
-                     TArgs&&... params)
-    {
-        KP_LOG_DEBUG("Kompute Manager evalOpAsync triggered");
-
-        std::shared_ptr<kp::Sequence> sq =
-          this->sequence(sequenceName);
-
-        KP_LOG_DEBUG("Kompute Manager evalOpAsync running sequence BEGIN");
-        sq->begin();
-
-        KP_LOG_DEBUG("Kompute Manager evalOpAsync running sequence RECORD");
-        sq->record<T>(tensors, std::forward<TArgs>(params)...);
-
-        KP_LOG_DEBUG("Kompute Manager evalOpAsync running sequence END");
-        sq->end();
-
-        KP_LOG_DEBUG("Kompute Manager evalOpAsync running sequence EVAL");
-        sq->evalAsync();
-
-        KP_LOG_DEBUG("Kompute Manager evalOpAsync running sequence SUCCESS");
-    }
-
-    /**
-     * Operation that evaluates operation against default sequence
-     * asynchronously.
-     *
-     * @param tensors The tensors to be used in the operation recorded
-     * @param params Template parameters that will be used to initialise
-     * Operation to allow for extensible configurations on initialisation
-     */
-    template<typename T, typename... TArgs>
-    void evalOpAsyncDefault(std::vector<std::shared_ptr<Tensor>> tensors,
-                            TArgs&&... params)
-    {
-        KP_LOG_DEBUG("Kompute Manager evalOpAsyncDefault triggered");
-        this->mCurrentSequenceIndex++;
-        this->evalOpAsync<T>(
-          tensors, KP_DEFAULT_SESSION, std::forward<TArgs>(params)...);
-    }
-
-    /**
-     * Operation that awaits for named sequence to finish.
-     *
-     * @param sequenceName The name of the sequence to wait for termination
-     * @param waitFor The amount of time to wait before timing out
-     */
-    void evalOpAwait(std::string sequenceName, uint64_t waitFor = UINT64_MAX)
-    {
-        KP_LOG_DEBUG("Kompute Manager evalOpAwait triggered with sequence {}",
-                     sequenceName);
-        std::unordered_map<std::string, std::shared_ptr<Sequence>>::iterator
-          found = this->mManagedSequences.find(sequenceName);
-
-        if (found != this->mManagedSequences.end()) {
-            if (std::shared_ptr<kp::Sequence> sq = found->second) {
-                KP_LOG_DEBUG("Kompute Manager evalOpAwait running sequence "
-                             "Sequence EVAL AWAIT");
-                if (sq->isRunning()) {
-                    sq->evalAwait(waitFor);
-                }
-            }
-            KP_LOG_DEBUG(
-              "Kompute Manager evalOpAwait running sequence SUCCESS");
-        } else {
-            KP_LOG_ERROR("Kompute Manager evalOpAwait Sequence not found");
-        }
-    }
-
-    /**
-     * Operation that awaits for default sequence to finish.
-     *
-     * @param tensors The tensors to be used in the operation recorded
-     * @param params Template parameters that will be used to initialise
-     * Operation to allow for extensible configurations on initialisation
-     */
-    void evalOpAwaitDefault(uint64_t waitFor = UINT64_MAX)
-    {
-        KP_LOG_DEBUG("Kompute Manager evalOpAwaitDefault triggered");
-        this->evalOpAwait(KP_DEFAULT_SESSION, waitFor);
-    }
 
     /**
      * Function that simplifies the common workflow of tensor creation and
@@ -225,102 +81,34 @@ class Manager
      */
     std::shared_ptr<Tensor> tensor(
       const std::vector<float>& data,
-      Tensor::TensorTypes tensorType = Tensor::TensorTypes::eDevice,
+      Tensor::TensorTypes tensorType = Tensor::TensorTypes::eDevice);
-      bool syncDataToGPU = true);
 
-    /**
+    std::shared_ptr<Algorithm> algorithm(
-     * Function that simplifies the common workflow of tensor initialisation. It
+      const std::vector<std::shared_ptr<Tensor>>& tensors = {},
-     * will take the constructor parameters for a Tensor and will will us it to
+      const std::vector<uint32_t>& spirv = {},
-     * create a new Tensor. The tensor memory will then be managed and owned by
+      const Workgroup& workgroup = {},
-     * the manager.
+      const Constants& specializationConstants = {});
-     *
-     * @param tensors Array of tensors to rebuild
-     * @param syncDataToGPU Whether to sync the data to GPU memory
-     */
-    void rebuild(std::vector<std::shared_ptr<kp::Tensor>> tensors,
-                        bool syncDataToGPU = true);
 
-    /**
+    void destroy();
-     * Function that simplifies the common workflow of tensor initialisation. It
+    void clear();
-     * will take the constructor parameters for a Tensor and will will us it to
-     * create a new Tensor. The tensor memory will then be managed and owned by
-     * the manager.
-     *
-     * @param tensors Single tensor to rebuild
-     * @param syncDataToGPU Whether to sync the data to GPU memory
-     */
-    void rebuild(std::shared_ptr<kp::Tensor> tensor,
-                        bool syncDataToGPU = true);
-
-    /**
-     * Destroy owned Vulkan GPU resources and free GPU memory for
-     * single tensor.
-     *
-     * @param tensors Single tensor to rebuild
-     */
-    void destroy(std::shared_ptr<kp::Tensor> tensor);
-
-    /**
-     * Destroy owned Vulkan GPU resources and free GPU memory for
-     * vector of tensors.
-     *
-     * @param tensors Single tensor to rebuild
-     */
-    void destroy(std::vector<std::shared_ptr<kp::Tensor>> tensors);
-
-    /**
-     * Destroy owned Vulkan GPU resources and free GPU memory for
-     * vector of sequences. Destroying by sequence name is more efficent
-     * and hence recommended instead of by object.
-     *
-     * @param sequences Vector for shared ptrs with sequences to destroy
-     */
-    void destroy(std::vector<std::shared_ptr<kp::Sequence>> sequences);
-
-    /**
-     * Destroy owned Vulkan GPU resources and free GPU memory for
-     * single sequence. Destroying by sequence name is more efficent
-     * and hence recommended instead of by object.
-     *
-     * @param sequences Single sequence to rebuild
-     */
-    void destroy(std::shared_ptr<kp::Sequence> sequence);
-
-    /**
-     * Destroy owned Vulkan GPU resources and free GPU memory for
-     * sequence by name.
-     *
-     * @param sequenceName Single name of named sequence to destroy
-     */
-    void destroy(const std::string& sequenceName);
-
-    /**
-     * Destroy owned Vulkan GPU resources and free GPU memory for
-     * sequences using vector of named sequence names.
-     *
-     * @param sequenceName Vector of sequence names to destroy
-     */
-    void destroy(const std::vector<std::string>& sequenceNames);
 
   private:
     // -------------- OPTIONALLY OWNED RESOURCES
     std::shared_ptr<vk::Instance> mInstance = nullptr;
     bool mFreeInstance = false;
     std::shared_ptr<vk::PhysicalDevice> mPhysicalDevice = nullptr;
-    uint32_t mPhysicalDeviceIndex = -1;
     std::shared_ptr<vk::Device> mDevice = nullptr;
     bool mFreeDevice = false;
 
     // -------------- ALWAYS OWNED RESOURCES
-    std::set<std::shared_ptr<Tensor>> mManagedTensors;
+    std::vector<std::weak_ptr<Tensor>> mManagedTensors;
-
+    std::vector<std::weak_ptr<Sequence>> mManagedSequences;
-    std::unordered_map<std::string, std::shared_ptr<Sequence>>
+    std::vector<std::weak_ptr<Algorithm>> mManagedAlgorithms;
-      mManagedSequences;
 
     std::vector<uint32_t> mComputeQueueFamilyIndices;
     std::vector<std::shared_ptr<vk::Queue>> mComputeQueues;
 
-    uint32_t mCurrentSequenceIndex = -1;
+    bool mManageResources = false;
 
 #if DEBUG
 #ifndef KOMPUTE_DISABLE_VK_DEBUG_LAYERS
@@ -331,7 +119,8 @@ class Manager
 
     // Create functions
     void createInstance();
-    void createDevice(const std::vector<uint32_t>& familyQueueIndices = {});
+    void createDevice(const std::vector<uint32_t>& familyQueueIndices = {},
+                      uint32_t hysicalDeviceIndex = 0);
 };
 
 } // End namespace kp

src/include/kompute/Sequence.hpp

@@ -9,14 +9,9 @@ namespace kp {
 /**
  *  Container of operations that can be sent to GPU as batch
  */
-class Sequence
+class Sequence : public std::enable_shared_from_this<Sequence>
 {
   public:
-    /**
-     *  Base constructor for Sequence. Should not be used unless explicit
-     * intended.
-     */
-    Sequence();
     /**
      * Main constructor for sequence which requires core vulkan components to
      * generate all dependent resources.
@@ -37,80 +32,8 @@ class Sequence
     ~Sequence();
 
     /**
-     * Initialises sequence including the creation of the command pool and the
-     * command buffer.
      */
-    void init();
+    std::shared_ptr<Sequence> record(std::shared_ptr<OpBase> op);
-
-    /**
-     * Begins recording commands for commands to be submitted into the command
-     * buffer.
-     *
-     * @return Boolean stating whether execution was successful.
-     */
-    bool begin();
-
-    /**
-     * Ends the recording and stops recording commands when the record command
-     * is sent.
-     *
-     * @return Boolean stating whether execution was successful.
-     */
-    bool end();
-
-    /**
-     * Eval sends all the recorded and stored operations in the vector of
-     * operations into the gpu as a submit job with a barrier.
-     *
-     * @return Boolean stating whether execution was successful.
-     */
-    bool eval();
-
-    /**
-     * Eval Async sends all the recorded and stored operations in the vector of
-     * operations into the gpu as a submit job with a barrier. EvalAwait() must
-     * be called after to ensure the sequence is terminated correctly.
-     *
-     * @return Boolean stating whether execution was successful.
-     */
-    bool evalAsync();
-
-    /**
-     * Eval Await waits for the fence to finish processing and then once it
-     * finishes, it runs the postEval of all operations.
-     *
-     * @param waitFor Number of milliseconds to wait before timing out.
-     * @return Boolean stating whether execution was successful.
-     */
-    bool evalAwait(uint64_t waitFor = UINT64_MAX);
-
-    /**
-     * Returns true if the sequence is currently in recording activated.
-     *
-     * @return Boolean stating if recording ongoing.
-     */
-    bool isRecording();
-
-    /**
-     * Returns true if the sequence is currently running - mostly used for async
-     * workloads.
-     *
-     * @return Boolean stating if currently running.
-     */
-    bool isRunning();
-
-    /**
-     * Returns true if the sequence has been successfully initialised.
-     *
-     * @return Boolean stating if sequence has been initialised.
-     */
-    bool isInit();
-
-    /**
-     * Destroys and frees the GPU resources which include the buffer and memory
-     * and sets the sequence as init=False.
-     */
-    void freeMemoryDestroyGPUResources();
 
     /**
      * Record function for operation to be added to the GPU queue in batch. This
@@ -123,44 +46,194 @@ class Sequence
      * which allows for extensible configurations on initialisation.
      */
     template<typename T, typename... TArgs>
-    bool 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");
+
         static_assert(std::is_base_of<OpBase, T>::value,
                       "Kompute Sequence record(...) template only valid with "
                       "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)...) };
+
+        return this->record(op);
+    }
+    template<typename T, typename... TArgs>
+    std::shared_ptr<Sequence> record(std::shared_ptr<Algorithm> algorithm,
+                                     TArgs&&... params)
+    {
         KP_LOG_DEBUG("Kompute Sequence record function started");
 
-        if (!this->isRecording()) {
+        static_assert(std::is_base_of<OpBase, T>::value,
-            KP_LOG_ERROR(
+                      "Kompute Sequence record(...) template only valid with "
-              "Kompute sequence record attempted when not record BEGIN");
+                      "OpBase derived classes");
-            return false;
-        }
 
         KP_LOG_DEBUG("Kompute Sequence creating OpBase derived class instance");
-        T* op = new T(this->mPhysicalDevice,
+        std::shared_ptr<T> op{ new T(algorithm,
-                      this->mDevice,
+                                     std::forward<TArgs>(params)...) };
-                      this->mCommandBuffer,
-                      tensors,
-                      std::forward<TArgs>(params)...);
 
-        OpBase* baseOp = dynamic_cast<OpBase*>(op);
+        return this->record(op);
-
-        std::unique_ptr<OpBase> baseOpPtr{ baseOp };
-
-        KP_LOG_DEBUG(
-          "Kompute Sequence running init on OpBase derived class instance");
-        baseOpPtr->init();
-
-        KP_LOG_DEBUG(
-          "Kompute Sequence running record on OpBase derived class instance");
-        baseOpPtr->record();
-
-        mOperations.push_back(std::move(baseOpPtr));
-
-        return true;
     }
 
+    /**
+     * Eval sends all the recorded and stored operations in the vector of
+     * operations into the gpu as a submit job with a barrier.
+     *
+     * @return shared_ptr<Sequence> of the Sequence class itself
+     */
+    std::shared_ptr<Sequence> eval();
+
+    std::shared_ptr<Sequence> eval(std::shared_ptr<OpBase> op);
+
+    /**
+     * Eval sends all the recorded and stored operations in the vector of
+     * operations into the gpu as a submit job with a barrier.
+     *
+     * @return shared_ptr<Sequence> of the Sequence class itself
+     */
+    // 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)
+    {
+        KP_LOG_DEBUG("Kompute Sequence record function started");
+
+        static_assert(std::is_base_of<OpBase, T>::value,
+                      "Kompute Sequence record(...) template only valid with "
+                      "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)...) };
+
+        // 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)
+    {
+        KP_LOG_DEBUG("Kompute Sequence record function started");
+
+        static_assert(std::is_base_of<OpBase, T>::value,
+                      "Kompute Sequence record(...) template only valid with "
+                      "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)...) };
+
+        return this->eval(op);
+    }
+
+    /**
+     * Eval Async sends all the recorded and stored operations in the vector of
+     * operations into the gpu as a submit job with a barrier. EvalAwait() must
+     * be called after to ensure the sequence is terminated correctly.
+     *
+     * @return Boolean stating whether execution was successful.
+     */
+    std::shared_ptr<Sequence> evalAsync();
+    std::shared_ptr<Sequence> evalAsync(std::shared_ptr<OpBase> op);
+
+    /**
+     * Eval sends all the recorded and stored operations in the vector of
+     * operations into the gpu as a submit job with a barrier.
+     *
+     * @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)
+    {
+        KP_LOG_DEBUG("Kompute Sequence record function started");
+
+        static_assert(std::is_base_of<OpBase, T>::value,
+                      "Kompute Sequence record(...) template only valid with "
+                      "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)...) };
+
+        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)
+    {
+        KP_LOG_DEBUG("Kompute Sequence record function started");
+
+        static_assert(std::is_base_of<OpBase, T>::value,
+                      "Kompute Sequence record(...) template only valid with "
+                      "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)...) };
+
+        return this->evalAsync(op);
+    }
+
+    /**
+     * Eval Await waits for the fence to finish processing and then once it
+     * finishes, it runs the postEval of all operations.
+     *
+     * @param waitFor Number of milliseconds to wait before timing out.
+     * @return Boolean stating whether execution was successful.
+     */
+    std::shared_ptr<Sequence> evalAwait(uint64_t waitFor = UINT64_MAX);
+
+    /**
+     * Clear function clears all operations currently recorded and starts
+     * recording again.
+     */
+    void clear();
+
+    /**
+     * Begins recording commands for commands to be submitted into the command
+     * buffer.
+     *
+     * @return Boolean stating whether execution was successful.
+     */
+    void begin();
+
+    /**
+     * Ends the recording and stops recording commands when the record command
+     * is sent.
+     *
+     * @return Boolean stating whether execution was successful.
+     */
+    void end();
+
+    /**
+     * Returns true if the sequence is currently in recording activated.
+     *
+     * @return Boolean stating if recording ongoing.
+     */
+    bool isRecording();
+
+    bool isInit();
+
+    /**
+     * Returns true if the sequence is currently running - mostly used for async
+     * workloads.
+     *
+     * @return Boolean stating if currently running.
+     */
+    bool isRunning();
+
+    /**
+     * Destroys and frees the GPU resources which include the buffer and memory
+     * and sets the sequence as init=False.
+     */
+    void destroy();
+
   private:
     // -------------- NEVER OWNED RESOURCES
     std::shared_ptr<vk::PhysicalDevice> mPhysicalDevice = nullptr;
@@ -176,10 +249,9 @@ class Sequence
 
     // -------------- ALWAYS OWNED RESOURCES
     vk::Fence mFence;
-    std::vector<std::unique_ptr<OpBase>> mOperations;
+    std::vector<std::shared_ptr<OpBase>> mOperations;
 
     // State
-    bool mIsInit = false;
     bool mRecording = false;
     bool mIsRunning = false;
 

src/include/kompute/Shader.hpp

@@ -4,9 +4,9 @@
 #include <iostream>
 #include <vector>
 
+#include <SPIRV/GlslangToSpv.h>
 #include <glslang/Include/ResourceLimits.h>
 #include <glslang/Public/ShaderLang.h>
-#include <SPIRV/GlslangToSpv.h>
 
 #include "kompute/Core.hpp"
 
@@ -16,161 +16,162 @@ namespace kp {
 // Has been adobted by:
 // https://github.com/KhronosGroup/glslang/blob/master/StandAlone/ResourceLimits.cpp
 const TBuiltInResource defaultResource = {
-/* .MaxLights = */ 0,
+    /* .MaxLights = */ 0,
-/* .MaxClipPlanes = */ 0,
+    /* .MaxClipPlanes = */ 0,
-/* .MaxTextureUnits = */ 0,
+    /* .MaxTextureUnits = */ 0,
-/* .MaxTextureCoords = */ 0,
+    /* .MaxTextureCoords = */ 0,
-/* .MaxVertexAttribs = */ 64,
+    /* .MaxVertexAttribs = */ 64,
-/* .MaxVertexUniformComponents = */ 4096,
+    /* .MaxVertexUniformComponents = */ 4096,
-/* .MaxVaryingFloats = */ 64,
+    /* .MaxVaryingFloats = */ 64,
-/* .MaxVertexTextureImageUnits = */ 0,
+    /* .MaxVertexTextureImageUnits = */ 0,
-/* .MaxCombinedTextureImageUnits = */ 0,
+    /* .MaxCombinedTextureImageUnits = */ 0,
-/* .MaxTextureImageUnits = */ 0,
+    /* .MaxTextureImageUnits = */ 0,
-/* .MaxFragmentUniformComponents = */ 0,
+    /* .MaxFragmentUniformComponents = */ 0,
-/* .MaxDrawBuffers = */ 0,
+    /* .MaxDrawBuffers = */ 0,
-/* .MaxVertexUniformVectors = */ 128,
+    /* .MaxVertexUniformVectors = */ 128,
-/* .MaxVaryingVectors = */ 8,
+    /* .MaxVaryingVectors = */ 8,
-/* .MaxFragmentUniformVectors = */ 0,
+    /* .MaxFragmentUniformVectors = */ 0,
-/* .MaxVertexOutputVectors = */ 16,
+    /* .MaxVertexOutputVectors = */ 16,
-/* .MaxFragmentInputVectors = */ 0,
+    /* .MaxFragmentInputVectors = */ 0,
-/* .MinProgramTexelOffset = */ -8,
+    /* .MinProgramTexelOffset = */ -8,
-/* .MaxProgramTexelOffset = */ 7,
+    /* .MaxProgramTexelOffset = */ 7,
-/* .MaxClipDistances = */ 8,
+    /* .MaxClipDistances = */ 8,
-/* .MaxComputeWorkGroupCountX = */ 65535,
+    /* .MaxComputeWorkGroupCountX = */ 65535,
-/* .MaxComputeWorkGroupCountY = */ 65535,
+    /* .MaxComputeWorkGroupCountY = */ 65535,
-/* .MaxComputeWorkGroupCountZ = */ 65535,
+    /* .MaxComputeWorkGroupCountZ = */ 65535,
-/* .MaxComputeWorkGroupSizeX = */ 1024,
+    /* .MaxComputeWorkGroupSizeX = */ 1024,
-/* .MaxComputeWorkGroupSizeY = */ 1024,
+    /* .MaxComputeWorkGroupSizeY = */ 1024,
-/* .MaxComputeWorkGroupSizeZ = */ 64,
+    /* .MaxComputeWorkGroupSizeZ = */ 64,
-/* .MaxComputeUniformComponents = */ 1024,
+    /* .MaxComputeUniformComponents = */ 1024,
-/* .MaxComputeTextureImageUnits = */ 16,
+    /* .MaxComputeTextureImageUnits = */ 16,
-/* .MaxComputeImageUniforms = */ 8,
+    /* .MaxComputeImageUniforms = */ 8,
-/* .MaxComputeAtomicCounters = */ 8,
+    /* .MaxComputeAtomicCounters = */ 8,
-/* .MaxComputeAtomicCounterBuffers = */ 1,
+    /* .MaxComputeAtomicCounterBuffers = */ 1,
-/* .MaxVaryingComponents = */ 60,
+    /* .MaxVaryingComponents = */ 60,
-/* .MaxVertexOutputComponents = */ 64,
+    /* .MaxVertexOutputComponents = */ 64,
-/* .MaxGeometryInputComponents = */ 64,
+    /* .MaxGeometryInputComponents = */ 64,
-/* .MaxGeometryOutputComponents = */ 128,
+    /* .MaxGeometryOutputComponents = */ 128,
-/* .MaxFragmentInputComponents = */ 0,
+    /* .MaxFragmentInputComponents = */ 0,
-/* .MaxImageUnits = */ 0,
+    /* .MaxImageUnits = */ 0,
-/* .MaxCombinedImageUnitsAndFragmentOutputs = */ 0,
+    /* .MaxCombinedImageUnitsAndFragmentOutputs = */ 0,
-/* .MaxCombinedShaderOutputResources = */ 8,
+    /* .MaxCombinedShaderOutputResources = */ 8,
-/* .MaxImageSamples = */ 0,
+    /* .MaxImageSamples = */ 0,
-/* .MaxVertexImageUniforms = */ 0,
+    /* .MaxVertexImageUniforms = */ 0,
-/* .MaxTessControlImageUniforms = */ 0,
+    /* .MaxTessControlImageUniforms = */ 0,
-/* .MaxTessEvaluationImageUniforms = */ 0,
+    /* .MaxTessEvaluationImageUniforms = */ 0,
-/* .MaxGeometryImageUniforms = */ 0,
+    /* .MaxGeometryImageUniforms = */ 0,
-/* .MaxFragmentImageUniforms = */ 0,
+    /* .MaxFragmentImageUniforms = */ 0,
-/* .MaxCombinedImageUniforms = */ 0,
+    /* .MaxCombinedImageUniforms = */ 0,
-/* .MaxGeometryTextureImageUnits = */ 0,
+    /* .MaxGeometryTextureImageUnits = */ 0,
-/* .MaxGeometryOutputVertices = */ 256,
+    /* .MaxGeometryOutputVertices = */ 256,
-/* .MaxGeometryTotalOutputComponents = */ 1024,
+    /* .MaxGeometryTotalOutputComponents = */ 1024,
-/* .MaxGeometryUniformComponents = */ 1024,
+    /* .MaxGeometryUniformComponents = */ 1024,
-/* .MaxGeometryVaryingComponents = */ 64,
+    /* .MaxGeometryVaryingComponents = */ 64,
-/* .MaxTessControlInputComponents = */ 128,
+    /* .MaxTessControlInputComponents = */ 128,
-/* .MaxTessControlOutputComponents = */ 128,
+    /* .MaxTessControlOutputComponents = */ 128,
-/* .MaxTessControlTextureImageUnits = */ 0,
+    /* .MaxTessControlTextureImageUnits = */ 0,
-/* .MaxTessControlUniformComponents = */ 1024,
+    /* .MaxTessControlUniformComponents = */ 1024,
-/* .MaxTessControlTotalOutputComponents = */ 4096,
+    /* .MaxTessControlTotalOutputComponents = */ 4096,
-/* .MaxTessEvaluationInputComponents = */ 128,
+    /* .MaxTessEvaluationInputComponents = */ 128,
-/* .MaxTessEvaluationOutputComponents = */ 128,
+    /* .MaxTessEvaluationOutputComponents = */ 128,
-/* .MaxTessEvaluationTextureImageUnits = */ 16,
+    /* .MaxTessEvaluationTextureImageUnits = */ 16,
-/* .MaxTessEvaluationUniformComponents = */ 1024,
+    /* .MaxTessEvaluationUniformComponents = */ 1024,
-/* .MaxTessPatchComponents = */ 120,
+    /* .MaxTessPatchComponents = */ 120,
-/* .MaxPatchVertices = */ 32,
+    /* .MaxPatchVertices = */ 32,
-/* .MaxTessGenLevel = */ 64,
+    /* .MaxTessGenLevel = */ 64,
-/* .MaxViewports = */ 16,
+    /* .MaxViewports = */ 16,
-/* .MaxVertexAtomicCounters = */ 0,
+    /* .MaxVertexAtomicCounters = */ 0,
-/* .MaxTessControlAtomicCounters = */ 0,
+    /* .MaxTessControlAtomicCounters = */ 0,
-/* .MaxTessEvaluationAtomicCounters = */ 0,
+    /* .MaxTessEvaluationAtomicCounters = */ 0,
-/* .MaxGeometryAtomicCounters = */ 0,
+    /* .MaxGeometryAtomicCounters = */ 0,
-/* .MaxFragmentAtomicCounters = */ 0,
+    /* .MaxFragmentAtomicCounters = */ 0,
-/* .MaxCombinedAtomicCounters = */ 8,
+    /* .MaxCombinedAtomicCounters = */ 8,
-/* .MaxAtomicCounterBindings = */ 1,
+    /* .MaxAtomicCounterBindings = */ 1,
-/* .MaxVertexAtomicCounterBuffers = */ 0,
+    /* .MaxVertexAtomicCounterBuffers = */ 0,
-/* .MaxTessControlAtomicCounterBuffers = */ 0,
+    /* .MaxTessControlAtomicCounterBuffers = */ 0,
-/* .MaxTessEvaluationAtomicCounterBuffers = */ 0,
+    /* .MaxTessEvaluationAtomicCounterBuffers = */ 0,
-/* .MaxGeometryAtomicCounterBuffers = */ 0,
+    /* .MaxGeometryAtomicCounterBuffers = */ 0,
-/* .MaxFragmentAtomicCounterBuffers = */ 0,
+    /* .MaxFragmentAtomicCounterBuffers = */ 0,
-/* .MaxCombinedAtomicCounterBuffers = */ 1,
+    /* .MaxCombinedAtomicCounterBuffers = */ 1,
-/* .MaxAtomicCounterBufferSize = */ 16384,
+    /* .MaxAtomicCounterBufferSize = */ 16384,
-/* .MaxTransformFeedbackBuffers = */ 4,
+    /* .MaxTransformFeedbackBuffers = */ 4,
-/* .MaxTransformFeedbackInterleavedComponents = */ 64,
+    /* .MaxTransformFeedbackInterleavedComponents = */ 64,
-/* .MaxCullDistances = */ 8,
+    /* .MaxCullDistances = */ 8,
-/* .MaxCombinedClipAndCullDistances = */ 8,
+    /* .MaxCombinedClipAndCullDistances = */ 8,
-/* .MaxSamples = */ 4,
+    /* .MaxSamples = */ 4,
-/* .maxMeshOutputVerticesNV = */ 256,
+    /* .maxMeshOutputVerticesNV = */ 256,
-/* .maxMeshOutputPrimitivesNV = */ 512,
+    /* .maxMeshOutputPrimitivesNV = */ 512,
-/* .maxMeshWorkGroupSizeX_NV = */ 32,
+    /* .maxMeshWorkGroupSizeX_NV = */ 32,
-/* .maxMeshWorkGroupSizeY_NV = */ 1,
+    /* .maxMeshWorkGroupSizeY_NV = */ 1,
-/* .maxMeshWorkGroupSizeZ_NV = */ 1,
+    /* .maxMeshWorkGroupSizeZ_NV = */ 1,
-/* .maxTaskWorkGroupSizeX_NV = */ 32,
+    /* .maxTaskWorkGroupSizeX_NV = */ 32,
-/* .maxTaskWorkGroupSizeY_NV = */ 1,
+    /* .maxTaskWorkGroupSizeY_NV = */ 1,
-/* .maxTaskWorkGroupSizeZ_NV = */ 1,
+    /* .maxTaskWorkGroupSizeZ_NV = */ 1,
-/* .maxMeshViewCountNV = */ 4,
+    /* .maxMeshViewCountNV = */ 4,
-/* .maxDualSourceDrawBuffersEXT = */ 1,
+    /* .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 {
+class Shader
-public:
+{
+  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>& sources,
-            const std::vector<std::string>& files = {},
+      const std::vector<std::string>& files = {},
-            const std::string& entryPoint = "main",
+      const std::string& entryPoint = "main",
-            std::vector<std::pair<std::string,std::string>> definitions = {},
+      std::vector<std::pair<std::string, std::string>> definitions = {},
-            const TBuiltInResource& resources = defaultResource);
+      const TBuiltInResource& resources = defaultResource);
 
     /**
-     * Compile a single glslang source from string value. Currently this function
+     * Compile a single glslang source from string value. Currently this
-     * uses the glslang C++ interface which is not thread safe so this funciton
+     * function uses the glslang C++ interface which is not thread safe so this
-     * should not be called from multiple threads concurrently. If you have a
+     * funciton should not be called from multiple threads concurrently. If you
-     * online shader processing multithreading use-case that can't use offline 
+     * have a online shader processing multithreading use-case that can't use
-     * compilation please open an issue.
+     * 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& source,
-            const std::string& entryPoint = "main",
+      const std::string& entryPoint = "main",
-            std::vector<std::pair<std::string,std::string>> definitions = {},
+      std::vector<std::pair<std::string, std::string>> definitions = {},
-            const TBuiltInResource& resources = defaultResource);
+      const TBuiltInResource& resources = defaultResource);
-
 };
 
-
-
 }
 #endif // DKOMPUTE_DISABLE_SHADER_UTILS
-

src/include/kompute/Tensor.hpp

@@ -2,8 +2,6 @@
 
 #include "kompute/Core.hpp"
 
-#define KP_MAX_DIM_SIZE 1
-
 namespace kp {
 
 /**
@@ -30,11 +28,6 @@ class Tensor
         eStorage = 2, ///< Type is Device memory (only)
     };
 
-    /**
-     *  Base constructor, should not be used unless explicitly intended.
-     */
-    Tensor();
-
     /**
      *  Default constructor with data provided which would be used to create the
      * respective vulkan buffer and memory.
@@ -43,8 +36,10 @@ class Tensor
      * tensor
      *  @param tensorType Type for the tensor which is of type TensorTypes
      */
-    Tensor(const std::vector<float>& data,
+    Tensor(std::shared_ptr<vk::PhysicalDevice> physicalDevice,
-           TensorTypes tensorType = TensorTypes::eDevice);
+           std::shared_ptr<vk::Device> device,
+           const std::vector<float>& data,
+           const TensorTypes& tensorType = TensorTypes::eDevice);
 
     /**
      * Destructor which is in charge of freeing vulkan resources unless they
@@ -58,13 +53,15 @@ class Tensor
      * would only be created for the tensors of type TensorType::eDevice as
      * otherwise there is no need to copy from host memory.
      */
-    void init(std::shared_ptr<vk::PhysicalDevice> physicalDevice,
+    void rebuild(const std::vector<float>& data,
-              std::shared_ptr<vk::Device> device);
+                 TensorTypes tensorType = TensorTypes::eDevice);
 
     /**
      * Destroys and frees the GPU resources which include the buffer and memory.
      */
-    void freeMemoryDestroyGPUResources();
+    void destroy();
+
+    bool isInit();
 
     /**
      * Returns the vector of data currently contained by the Tensor. It is
@@ -91,26 +88,13 @@ class Tensor
      * @return Unsigned integer representing the total number of elements
      */
     uint32_t size();
-    /**
+
-     * Returns the shape of the tensor, which includes the number of dimensions
-     * and the size per dimension.
-     *
-     * @return Array containing the sizes for each dimension. Zero means
-     * respective dimension is not active.
-     */
-    std::array<uint32_t, KP_MAX_DIM_SIZE> shape();
     /**
      * Retrieve the tensor type of the Tensor
      *
      * @return Tensor type of tensor
      */
     TensorTypes tensorType();
-    /**
-     * Returns true if the tensor initialisation function has been carried out
-     * successful, which would mean that the buffer and memory will have been
-     * provisioned.
-     */
-    bool isInit();
 
     /**
      * Sets / resets the vector data of the tensor. This function does not
@@ -128,7 +112,7 @@ class Tensor
      * @param createBarrier Whether to create a barrier that ensures the data is
      * copied before further operations. Default is true.
      */
-    void recordCopyFrom(std::shared_ptr<vk::CommandBuffer> commandBuffer,
+    void recordCopyFrom(const vk::CommandBuffer& commandBuffer,
                         std::shared_ptr<Tensor> copyFromTensor,
                         bool createBarrier);
 
@@ -141,9 +125,8 @@ class Tensor
      * @param createBarrier Whether to create a barrier that ensures the data is
      * copied before further operations. Default is true.
      */
-    void recordCopyFromStagingToDevice(
+    void recordCopyFromStagingToDevice(const vk::CommandBuffer& commandBuffer,
-      std::shared_ptr<vk::CommandBuffer> commandBuffer,
+                                       bool createBarrier);
-      bool createBarrier);
 
     /**
      * Records a copy from the internal device memory to the staging memory
@@ -154,9 +137,8 @@ class Tensor
      * @param createBarrier Whether to create a barrier that ensures the data is
      * copied before further operations. Default is true.
      */
-    void recordCopyFromDeviceToStaging(
+    void recordCopyFromDeviceToStaging(const vk::CommandBuffer& commandBuffer,
-      std::shared_ptr<vk::CommandBuffer> commandBuffer,
+                                       bool createBarrier);
-      bool createBarrier);
 
     /**
      * Records the buffer memory barrier into the command buffer which
@@ -168,12 +150,11 @@ class Tensor
      * @param scrStageMask Pipeline stage flags for source stage mask
      * @param dstStageMask Pipeline stage flags for destination stage mask
      */
-    void recordBufferMemoryBarrier(
+    void recordBufferMemoryBarrier(const vk::CommandBuffer& commandBuffer,
-      std::shared_ptr<vk::CommandBuffer> commandBuffer,
+                                   vk::AccessFlagBits srcAccessMask,
-      vk::AccessFlagBits srcAccessMask,
+                                   vk::AccessFlagBits dstAccessMask,
-      vk::AccessFlagBits dstAccessMask,
+                                   vk::PipelineStageFlagBits srcStageMask,
-      vk::PipelineStageFlagBits srcStageMask,
+                                   vk::PipelineStageFlagBits dstStageMask);
-      vk::PipelineStageFlagBits dstStageMask);
 
     /**
      * Constructs a vulkan descriptor buffer info which can be used to specify
@@ -214,21 +195,18 @@ class Tensor
 
     TensorTypes mTensorType = TensorTypes::eDevice;
 
-    std::array<uint32_t, KP_MAX_DIM_SIZE> mShape;
-    bool mIsInit = false;
-
     void allocateMemoryCreateGPUResources(); // Creates the vulkan buffer
     void createBuffer(std::shared_ptr<vk::Buffer> buffer,
                       vk::BufferUsageFlags bufferUsageFlags);
     void allocateBindMemory(std::shared_ptr<vk::Buffer> buffer,
                             std::shared_ptr<vk::DeviceMemory> memory,
                             vk::MemoryPropertyFlags memoryPropertyFlags);
-    void copyBuffer(std::shared_ptr<vk::CommandBuffer> commandBuffer,
+    void recordCopyBuffer(const vk::CommandBuffer& commandBuffer,
-                    std::shared_ptr<vk::Buffer> bufferFrom,
+                          std::shared_ptr<vk::Buffer> bufferFrom,
-                    std::shared_ptr<vk::Buffer> bufferTo,
+                          std::shared_ptr<vk::Buffer> bufferTo,
-                    vk::DeviceSize bufferSize,
+                          vk::DeviceSize bufferSize,
-                    vk::BufferCopy copyRegion,
+                          vk::BufferCopy copyRegion,
-                    bool createBarrier);
+                          bool createBarrier);
 
     // Private util functions
     vk::BufferUsageFlags getPrimaryBufferUsageFlags();

src/include/kompute/operations/OpAlgoBase.hpp

@@ -1,144 +0,0 @@
-#pragma once
-
-#include <fstream>
-
-#include "kompute/Core.hpp"
-
-#include "kompute/shaders/shaderopmult.hpp"
-
-#include "kompute/Algorithm.hpp"
-#include "kompute/Tensor.hpp"
-
-#include "kompute/operations/OpBase.hpp"
-
-namespace kp {
-
-/**
- * Operation that provides a general abstraction that simplifies the use of 
- * algorithm and parameter components which can be used with shaders.
- * By default it enables the user to provide a dynamic number of tensors
- * which are then passed as inputs.
- */
-class OpAlgoBase : public OpBase
-{
-  public:
-
-    /**
-     *  Base constructor, should not be used unless explicitly intended.
-     */
-    OpAlgoBase();
-
-    /**
-     * Default constructor with parameters that provides the bare minimum
-     * requirements for the operations to be able to create and manage their
-     * sub-components.
-     *
-     * @param physicalDevice Vulkan physical device used to find device queues
-     * @param device Vulkan logical device for passing to Algorithm
-     * @param commandBuffer Vulkan Command Buffer to record commands into
-     * @param tensors Tensors that are to be used in this operation
-     * @param shaderFilePath Optional parameter to specify the shader to load (either in spirv or raw format)
-     * @param komputeWorkgroup Optional parameter to specify the layout for processing
-     */
-    OpAlgoBase(std::shared_ptr<vk::PhysicalDevice> physicalDevice,
-           std::shared_ptr<vk::Device> device,
-           std::shared_ptr<vk::CommandBuffer> commandBuffer,
-           std::vector<std::shared_ptr<Tensor>>& tensors,
-           const Workgroup& komputeWorkgroup = {},
-           const Constants& specializationConstants = {});
-
-    /**
-     * Constructor that enables a file to be passed to the operation with
-     * the contents of the shader. This can be either in raw format or in
-     * compiled SPIR-V binary format.
-     *
-     * @param physicalDevice Vulkan physical device used to find device queues
-     * @param device Vulkan logical device for passing to Algorithm
-     * @param commandBuffer Vulkan Command Buffer to record commands into
-     * @param tensors Tensors that are to be used in this operation
-     * @param shaderFilePath Parameter to specify the shader to load (either in spirv or raw format)
-     * @param komputeWorkgroup Optional parameter to specify the layout for processing
-     */
-    OpAlgoBase(std::shared_ptr<vk::PhysicalDevice> physicalDevice,
-           std::shared_ptr<vk::Device> device,
-           std::shared_ptr<vk::CommandBuffer> commandBuffer,
-           std::vector<std::shared_ptr<Tensor>>& tensors,
-           std::string shaderFilePath,
-           const Workgroup& komputeWorkgroup = {},
-           const Constants& specializationConstants = {});
-
-    /**
-     * Constructor that enables raw shader data to be passed to the main operation
-     * which can be either in raw shader glsl code or in compiled SPIR-V binary.
-     *
-     * @param physicalDevice Vulkan physical device used to find device queues
-     * @param device Vulkan logical device for passing to Algorithm
-     * @param commandBuffer Vulkan Command Buffer to record commands into
-     * @param tensors Tensors that are to be used in this operation
-     * @param shaderDataRaw Optional parameter to specify the shader data either in binary or raw form
-     * @param komputeWorkgroup Optional parameter to specify the layout for processing
-     */
-    OpAlgoBase(std::shared_ptr<vk::PhysicalDevice> physicalDevice,
-           std::shared_ptr<vk::Device> device,
-           std::shared_ptr<vk::CommandBuffer> commandBuffer,
-           std::vector<std::shared_ptr<Tensor>>& tensors,
-           const std::vector<uint32_t>& shaderDataRaw,
-           const Workgroup& komputeWorkgroup = {},
-           const Constants& specializationConstants = {});
-
-    /**
-     * Default destructor, which is in charge of destroying the algorithm
-     * components but does not destroy the underlying tensors
-     */
-    virtual ~OpAlgoBase() override;
-
-    /**
-     * The init function is responsible for the initialisation of the algorithm
-     * component based on the parameters specified, and allows for extensibility
-     * on the options provided. Further dependent classes can perform more 
-     * specific checks such as ensuring tensors provided are initialised, etc.
-     */
-    virtual void init() override;
-
-    /**
-     * This records the commands that are to be sent to the GPU. This includes
-     * the barriers that ensure the memory has been copied before going in and
-     * out of the shader, as well as the dispatch operation that sends the
-     * shader processing to the gpu. This function also records the GPU memory
-     * copy of the output data for the staging buffer so it can be read by the
-     * host.
-     */
-    virtual void record() override;
-
-
-    /**
-     * Does not perform any preEval commands.
-     */
-    virtual void preEval() override;
-
-    /**
-     * Executes after the recorded commands are submitted, and performs a copy
-     * of the GPU Device memory into the staging buffer so the output data can
-     * be retrieved.
-     */
-    virtual void postEval() override;
-
-  protected:
-    // -------------- NEVER OWNED RESOURCES
-
-    // -------------- OPTIONALLY OWNED RESOURCES
-    std::shared_ptr<Algorithm> mAlgorithm;
-    bool mFreeAlgorithm = false;
-
-    // -------------- ALWAYS OWNED RESOURCES
-
-    Workgroup mKomputeWorkgroup;
-
-    std::string mShaderFilePath; ///< Optional member variable which can be provided for the OpAlgoBase to find the data automatically and load for processing
-    std::vector<uint32_t> mShaderDataRaw; ///< Optional member variable which can be provided to contain either the raw shader content or the spirv binary content
-
-    virtual std::vector<uint32_t> fetchSpirvBinaryData();
-};
-
-} // End namespace kp
-

src/include/kompute/operations/OpAlgoDispatch.hpp

@@ -0,0 +1,58 @@
+#pragma once
+
+#include "kompute/Core.hpp"
+#include "kompute/Algorithm.hpp"
+#include "kompute/Tensor.hpp"
+#include "kompute/operations/OpBase.hpp"
+
+namespace kp {
+
+/**
+ * Operation that provides a general abstraction that simplifies the use of 
+ * algorithm and parameter components which can be used with shaders.
+ * By default it enables the user to provide a dynamic number of tensors
+ * which are then passed as inputs.
+ */
+class OpAlgoDispatch : public OpBase
+{
+  public:
+
+    OpAlgoDispatch(const std::shared_ptr<kp::Algorithm>& algorithm,
+            const kp::Constants& pushConstants = {});
+
+    /**
+     * Default destructor, which is in charge of destroying the algorithm
+     * components but does not destroy the underlying tensors
+     */
+    virtual ~OpAlgoDispatch() override;
+
+    /**
+     * This records the commands that are to be sent to the GPU. This includes
+     * the barriers that ensure the memory has been copied before going in and
+     * out of the shader, as well as the dispatch operation that sends the
+     * shader processing to the gpu. This function also records the GPU memory
+     * copy of the output data for the staging buffer so it can be read by the
+     * host.
+     */
+    virtual void record(const vk::CommandBuffer& commandBuffer) override;
+
+    /**
+     * Does not perform any preEval commands.
+     */
+    virtual void preEval(const vk::CommandBuffer& commandBuffer) override;
+
+    /**
+     * Executes after the recorded commands are submitted, and performs a copy
+     * of the GPU Device memory into the staging buffer so the output data can
+     * be retrieved.
+     */
+    virtual void postEval(const vk::CommandBuffer& commandBuffer) override;
+
+private:
+    // -------------- ALWAYS OWNED RESOURCES
+    std::shared_ptr<Algorithm> mAlgorithm;
+    Constants mPushConstants;
+};
+
+} // End namespace kp
+

src/include/kompute/operations/OpAlgoLhsRhsOut.hpp

@@ -1,84 +0,0 @@
-#pragma once
-
-#include <fstream>
-
-#include "kompute/Core.hpp"
-
-#include "kompute/Algorithm.hpp"
-#include "kompute/Tensor.hpp"
-
-#include "kompute/operations/OpAlgoBase.hpp"
-
-namespace kp {
-
-/**
- * Operation base class to simplify the creation of operations that require
- * right hand and left hand side datapoints together with a single output.
- * The expected data passed is two input tensors and one output tensor.
- */
-class OpAlgoLhsRhsOut : public OpAlgoBase
-{
-  public:
-    /**
-     *  Base constructor, should not be used unless explicitly intended.
-     */
-    OpAlgoLhsRhsOut();
-
-    /**
-     * Default constructor with parameters that provides the bare minimum
-     * requirements for the operations to be able to create and manage their
-     * sub-components.
-     *
-     * @param physicalDevice Vulkan physical device used to find device queues
-     * @param device Vulkan logical device for passing to Algorithm
-     * @param commandBuffer Vulkan Command Buffer to record commands into
-     * @param tensors Tensors that are to be used in this operation
-     * @param freeTensors Whether operation manages the memory of the Tensors
-     * @param komputeWorkgroup Optional parameter to specify the layout for processing
-     */
-    OpAlgoLhsRhsOut(std::shared_ptr<vk::PhysicalDevice> physicalDevice,
-           std::shared_ptr<vk::Device> device,
-           std::shared_ptr<vk::CommandBuffer> commandBuffer,
-           std::vector<std::shared_ptr<Tensor>> tensors,
-           const Workgroup& komputeWorkgroup = {});
-
-    /**
-     * Default destructor, which is in charge of destroying the algorithm
-     * components but does not destroy the underlying tensors
-     */
-    virtual ~OpAlgoLhsRhsOut() override;
-
-    /**
-     * The init function is responsible for ensuring that all of the tensors
-     * provided are aligned with requirements such as LHS, RHS and Output
-     * tensors, and  creates the algorithm component which processes the
-     * computation.
-     */
-    virtual void init() override;
-
-    /**
-     * This records the commands that are to be sent to the GPU. This includes
-     * the barriers that ensure the memory has been copied before going in and
-     * out of the shader, as well as the dispatch operation that sends the
-     * shader processing to the gpu. This function also records the GPU memory
-     * copy of the output data for the staging buffer so it can be read by the
-     * host.
-     */
-    virtual void record() override;
-
-    /**
-     * Executes after the recorded commands are submitted, and performs a copy
-     * of the GPU Device memory into the staging buffer so the output data can
-     * be retrieved.
-     */
-    virtual void postEval() override;
-
-  protected:
-    // -------------- NEVER OWNED RESOURCES
-    std::shared_ptr<Tensor> mTensorLHS; ///< Reference to the parameter used in the left hand side equation of the shader
-    std::shared_ptr<Tensor> mTensorRHS; ///< Reference to the parameter used in the right hand side equation of the shader
-    std::shared_ptr<Tensor> mTensorOutput; ///< Reference to the parameter used in the output of the shader and will be copied with a staging vector
-};
-
-} // End namespace kp
-

src/include/kompute/operations/OpBase.hpp

@@ -1,8 +1,8 @@
 #pragma once
 
 #include "kompute/Core.hpp"
-
 #include "kompute/Tensor.hpp"
+#include "kompute/Algorithm.hpp"
 
 namespace kp {
 
@@ -17,33 +17,6 @@ namespace kp {
 class OpBase
 {
   public:
-    /**
-     *  Base constructor, should not be used unless explicitly intended.
-     */
-    OpBase() { KP_LOG_DEBUG("Compute OpBase base constructor"); }
-
-    /**
-     * Default constructor with parameters that provides the bare minimum
-     * requirements for the operations to be able to create and manage their
-     * sub-components.
-     *
-     * @param physicalDevice Vulkan physical device used to find device queues
-     * @param device Vulkan logical device for passing to Algorithm
-     * @param commandBuffer Vulkan Command Buffer to record commands into
-     * @param tensors Tensors that are to be used in this operation
-     */
-    OpBase(std::shared_ptr<vk::PhysicalDevice> physicalDevice,
-           std::shared_ptr<vk::Device> device,
-           std::shared_ptr<vk::CommandBuffer> commandBuffer,
-           std::vector<std::shared_ptr<Tensor>>& tensors)
-    {
-        KP_LOG_DEBUG("Compute OpBase constructor with params");
-
-        this->mPhysicalDevice = physicalDevice;
-        this->mDevice = device;
-        this->mCommandBuffer = commandBuffer;
-        this->mTensors = tensors;
-    }
 
     /**
      * Default destructor for OpBase class. This OpBase destructor class should
@@ -53,37 +26,14 @@ class OpBase
     virtual ~OpBase()
     {
         KP_LOG_DEBUG("Kompute OpBase destructor started");
-
-        if (!this->mDevice) {
-            KP_LOG_WARN("Kompute OpBase destructor called with empty device");
-            return;
-        }
-
-        if (this->mFreeTensors) {
-            KP_LOG_DEBUG("Kompute OpBase freeing tensors");
-            for (std::shared_ptr<Tensor> tensor : this->mTensors) {
-                if (tensor && tensor->isInit()) {
-                    tensor->freeMemoryDestroyGPUResources();
-                } else {
-                    KP_LOG_WARN("Kompute OpBase expected to free "
-                                  "tensor but has already been freed.");
-                }
-            }
-        }
     }
 
-    /**
-     * The init function is responsible for setting up all the resources and
-     * should be called after the Operation has been created.
-     */
-    virtual void init() = 0;
-
     /**
      * The record function is intended to only send a record command or run
      * commands that are expected to record operations that are to be submitted
      * as a batch into the GPU.
      */
-    virtual void record() = 0;
+    virtual void record(const vk::CommandBuffer& commandBuffer) = 0;
 
     /**
      * Pre eval is called before the Sequence has called eval and submitted the commands to
@@ -93,7 +43,7 @@ class OpBase
      * resources that are created should be idempotent in case it's called multiple
      * times in a row.
      */
-    virtual void preEval() = 0;
+    virtual void preEval(const vk::CommandBuffer& commandBuffer) = 0;
 
     /**
      * Post eval is called after the Sequence has called eval and submitted the commands to
@@ -103,22 +53,7 @@ class OpBase
      * resources that are destroyed should not require a re-init unless explicitly
      * provided by the user.
      */
-    virtual void postEval() = 0;
+    virtual void postEval(const vk::CommandBuffer& commandBuffer) = 0;
-
-  protected:
-    // -------------- NEVER OWNED RESOURCES
-    std::shared_ptr<vk::PhysicalDevice>
-      mPhysicalDevice;                   ///< Vulkan Physical Device
-    std::shared_ptr<vk::Device> mDevice; ///< Vulkan Logical Device
-    std::shared_ptr<vk::CommandBuffer>
-      mCommandBuffer; ///< Vulkan Command Buffer
-
-    // -------------- OPTIONALLY OWNED RESOURCES
-    std::vector<std::shared_ptr<Tensor>>
-      mTensors; ///< Tensors referenced by operation that can be managed
-                ///< optionally by operation
-    bool mFreeTensors = false; ///< Explicit boolean that specifies whether the
-                               ///< tensors are freed (if they are managed)
 };
 
 } // End namespace kp

src/include/kompute/operations/OpMult.hpp

@@ -4,14 +4,12 @@
 
 #include "kompute/Core.hpp"
 
-#if RELEASE
 #include "kompute/shaders/shaderopmult.hpp"
-#endif
 
 #include "kompute/Algorithm.hpp"
 #include "kompute/Tensor.hpp"
 
-#include "kompute/operations/OpAlgoBase.hpp"
+#include "kompute/operations/OpAlgoDispatch.hpp"
 
 namespace kp {
 
@@ -19,15 +17,9 @@ namespace kp {
  * Operation that performs multiplication on two tensors and outpus on third
  * tensor.
  */
-class OpMult : public OpAlgoBase
+class OpMult : public OpAlgoDispatch
 {
   public:
-    /**
-     *  Base constructor, should not be used unless explicitly intended.
-     */
-    OpMult() {
-
-    }
 
     /**
      * Default constructor with parameters that provides the bare minimum
@@ -40,46 +32,30 @@ class OpMult : public OpAlgoBase
      * @param tensors Tensors that are to be used in this operation
      * @param komputeWorkgroup Optional parameter to specify the layout for processing
      */
-    OpMult(std::shared_ptr<vk::PhysicalDevice> physicalDevice,
+    OpMult(std::vector<std::shared_ptr<Tensor>> tensors, std::shared_ptr<Algorithm> algorithm)
-           std::shared_ptr<vk::Device> device,
+        : OpAlgoDispatch(algorithm)
-           std::shared_ptr<vk::CommandBuffer> commandBuffer,
-           std::vector<std::shared_ptr<Tensor>> tensors,
-           const Workgroup& komputeWorkgroup = {})
-      : OpAlgoBase(physicalDevice, device, commandBuffer, tensors, "", komputeWorkgroup)
     {
         KP_LOG_DEBUG("Kompute OpMult constructor with params");
 
-#ifndef RELEASE
+        if (tensors.size() != 3) {
-        this->mShaderFilePath = "shaders/glsl/opmult.comp.spv";
+            throw std::runtime_error("Kompute OpMult expected 3 tensors but got " + tensors.size());
-#endif
+        }
-    }
 
-#if RELEASE
+        std::vector<uint32_t> spirv(
-    /**
-     * If RELEASE=1 it will be using the static version of the shader which is 
-     * loaded using this file directly. Otherwise it should not override the function.
-     */
-    std::vector<uint32_t> fetchSpirvBinaryData() override
-    {
-        KP_LOG_WARN(
-          "Kompute OpMult Running shaders directly from header");
-
-        return std::vector<uint32_t>(
           (uint32_t*)shader_data::shaders_glsl_opmult_comp_spv,
           (uint32_t*)(shader_data::shaders_glsl_opmult_comp_spv +
             kp::shader_data::shaders_glsl_opmult_comp_spv_len));
 
+        algorithm->rebuild(tensors, spirv);
     }
-#endif
 
     /**
      * Default destructor, which is in charge of destroying the algorithm
      * components but does not destroy the underlying tensors
      */
-    ~OpMult() override {
+    virtual ~OpMult() override {
         KP_LOG_DEBUG("Kompute OpMult destructor started");
     }
-
 };
 
 } // End namespace kp

src/include/kompute/operations/OpTensorCopy.hpp

@@ -14,8 +14,6 @@ namespace kp {
 class OpTensorCopy : public OpBase
 {
   public:
-    OpTensorCopy();
-
     /**
      * Default constructor with parameters that provides the core vulkan resources and the tensors that will be used in the operation.
      *
@@ -24,37 +22,31 @@ class OpTensorCopy : public OpBase
      * @param commandBuffer Vulkan Command Buffer to record commands into
      * @param tensors Tensors that will be used to create in operation.
      */
-    OpTensorCopy(std::shared_ptr<vk::PhysicalDevice> physicalDevice,
+    OpTensorCopy(const std::vector<std::shared_ptr<Tensor>>& tensors);
-                   std::shared_ptr<vk::Device> device,
-                   std::shared_ptr<vk::CommandBuffer> commandBuffer,
-                   std::vector<std::shared_ptr<Tensor>> tensors);
 
     /**
      * Default destructor. This class does not manage memory so it won't be expecting the parent to perform a release.
      */
     ~OpTensorCopy() override;
 
-    /**
-     * Performs basic checks such as ensuring there are at least two tensors provided, that they are initialised and that they are not of type TensorTypes::eStorage.
-     */
-    void init() override;
-
     /**
      * Records the copy commands from the first tensor into all the other tensors provided. Also optionally records a barrier.
      */
-    void record() override;
+    void record(const vk::CommandBuffer& commandBuffer) override;
 
     /**
      * Does not perform any preEval commands.
      */
-    virtual void preEval() override;
+    virtual void preEval(const vk::CommandBuffer& commandBuffer) override;
 
     /**
      * Copies the local vectors for all the tensors to sync the data with the gpu.
      */
-    virtual void postEval() override;
+    virtual void postEval(const vk::CommandBuffer& commandBuffer) override;
 
   private:
+    // -------------- ALWAYS OWNED RESOURCES
+    std::vector<std::shared_ptr<Tensor>> mTensors;
 };
 
 } // End namespace kp

src/include/kompute/operations/OpTensorSyncDevice.hpp

@@ -1,9 +1,8 @@
 #pragma once
 
 #include "kompute/Core.hpp"
-
+#include "kompute/operations/OpBase.hpp"
 #include "kompute/Tensor.hpp"
-
 #include "kompute/operations/OpBase.hpp"
 
 namespace kp {
@@ -14,8 +13,6 @@ namespace kp {
 class OpTensorSyncDevice : public OpBase
 {
   public:
-    OpTensorSyncDevice();
-
     /**
      * Default constructor with parameters that provides the core vulkan resources and the tensors that will be used in the operation. The tensos provided cannot be of type TensorTypes::eStorage.
      *
@@ -24,37 +21,31 @@ class OpTensorSyncDevice : public OpBase
      * @param commandBuffer Vulkan Command Buffer to record commands into
      * @param tensors Tensors that will be used to create in operation.
      */
-    OpTensorSyncDevice(std::shared_ptr<vk::PhysicalDevice> physicalDevice,
+    OpTensorSyncDevice(const std::vector<std::shared_ptr<Tensor>>& tensors);
-                   std::shared_ptr<vk::Device> device,
-                   std::shared_ptr<vk::CommandBuffer> commandBuffer,
-                   std::vector<std::shared_ptr<Tensor>> tensors);
 
     /**
      * Default destructor. This class does not manage memory so it won't be expecting the parent to perform a release.
      */
     ~OpTensorSyncDevice() override;
 
-    /**
-     * Performs basic checks such as ensuring that there is at least one tensor provided with min memory of 1 element.
-     */
-    void init() override;
-
     /**
      * For device tensors, it records the copy command for the tensor to copy the data from its staging to device memory.
      */
-    void record() override;
+    void record(const vk::CommandBuffer& commandBuffer) override;
 
     /**
      * Does not perform any preEval commands.
      */
-    virtual void preEval() override;
+    virtual void preEval(const vk::CommandBuffer& commandBuffer) override;
 
     /**
      * Does not perform any postEval commands.
      */
-    virtual void postEval() override;
+    virtual void postEval(const vk::CommandBuffer& commandBuffer) override;
 
   private:
+    // -------------- ALWAYS OWNED RESOURCES
+    std::vector<std::shared_ptr<Tensor>> mTensors;
 };
 
 } // End namespace kp

src/include/kompute/operations/OpTensorSyncLocal.hpp

@@ -14,8 +14,6 @@ namespace kp {
 class OpTensorSyncLocal : public OpBase
 {
   public:
-    OpTensorSyncLocal();
-
     /**
      * Default constructor with parameters that provides the core vulkan resources and the tensors that will be used in the operation. The tensors provided cannot be of type TensorTypes::eStorage.
      *
@@ -24,38 +22,32 @@ class OpTensorSyncLocal : public OpBase
      * @param commandBuffer Vulkan Command Buffer to record commands into
      * @param tensors Tensors that will be used to create in operation.
      */
-    OpTensorSyncLocal(std::shared_ptr<vk::PhysicalDevice> physicalDevice,
+    OpTensorSyncLocal(const std::vector<std::shared_ptr<Tensor>>& tensors);
-                   std::shared_ptr<vk::Device> device,
-                   std::shared_ptr<vk::CommandBuffer> commandBuffer,
-                   std::vector<std::shared_ptr<Tensor>> tensors);
 
     /**
      * Default destructor. This class does not manage memory so it won't be expecting the parent to perform a release.
      */
     ~OpTensorSyncLocal() override;
 
-    /**
-     * Performs basic checks such as ensuring that there is at least one tensor provided with min memory of 1 element.
-     */
-    void init() override;
-
     /**
      * For device tensors, it records the copy command for the tensor to copy the data from its device to staging memory.
      */
-    void record() override;
+    void record(const vk::CommandBuffer& commandBuffer) override;
 
     /**
      * Does not perform any preEval commands.
      */
-    virtual void preEval() override;
+    virtual void preEval(const vk::CommandBuffer& commandBuffer) override;
 
     /**
      * For host tensors it performs the map command from the host memory into local memory.
      */
-    virtual void postEval() override;
+    virtual void postEval(const vk::CommandBuffer& commandBuffer) override;
 
 
   private:
+    // -------------- ALWAYS OWNED RESOURCES
+    std::vector<std::shared_ptr<Tensor>> mTensors;
 };
 
 } // End namespace kp

test/TestAsyncOperations.cpp

@@ -37,25 +37,32 @@ TEST(TestAsyncOperations, TestManagerParallelExecution)
         }
     )");
 
+    std::vector<uint32_t> spirv = kp::Shader::compile_source(shader);
+
     std::vector<float> data(size, 0.0);
     std::vector<float> resultSync(size, 100000000);
     std::vector<float> resultAsync(size, 100000000);
 
     kp::Manager mgr;
 
+    std::shared_ptr<kp::Sequence> sq = mgr.sequence();
+
     std::vector<std::shared_ptr<kp::Tensor>> inputsSyncB;
+    std::vector<std::shared_ptr<kp::Algorithm>> algorithms;
 
     for (uint32_t i = 0; i < numParallel; i++) {
-        inputsSyncB.push_back(std::make_shared<kp::Tensor>(kp::Tensor(data)));
+        inputsSyncB.push_back(mgr.tensor(data));
+        algorithms.push_back(mgr.algorithm({ inputsSyncB[i] }, spirv));
     }
 
-    mgr.rebuild(inputsSyncB);
+    sq->eval<kp::OpTensorSyncDevice>(inputsSyncB);
+
+    mgr.sequence()->eval<kp::OpTensorSyncDevice>(inputsSyncB);
 
     auto startSync = std::chrono::high_resolution_clock::now();
 
     for (uint32_t i = 0; i < numParallel; i++) {
-        mgr.evalOpDefault<kp::OpAlgoBase>(
+        sq->eval<kp::OpAlgoDispatch>(algorithms[i]);
-          { inputsSyncB[i] }, kp::Shader::compile_source(shader));
     }
 
     auto endSync = std::chrono::high_resolution_clock::now();
@@ -63,7 +70,7 @@ TEST(TestAsyncOperations, TestManagerParallelExecution)
       std::chrono::duration_cast<std::chrono::microseconds>(endSync - startSync)
         .count();
 
-    mgr.evalOpDefault<kp::OpTensorSyncLocal>(inputsSyncB);
+    sq->eval<kp::OpTensorSyncLocal>(inputsSyncB);
 
     for (uint32_t i = 0; i < numParallel; i++) {
         EXPECT_EQ(inputsSyncB[i]->data(), resultSync);
@@ -73,27 +80,27 @@ TEST(TestAsyncOperations, TestManagerParallelExecution)
 
     std::vector<std::shared_ptr<kp::Tensor>> inputsAsyncB;
 
+    std::vector<std::shared_ptr<kp::Algorithm>> algosAsync;
+
     for (uint32_t i = 0; i < numParallel; i++) {
-        inputsAsyncB.push_back(std::make_shared<kp::Tensor>(kp::Tensor(data)));
+        inputsAsyncB.push_back(mgr.tensor(data));
+        algosAsync.push_back(mgr.algorithm({ inputsAsyncB[i] }, spirv));
     }
 
-    mgrAsync.rebuild(inputsAsyncB);
+    std::vector<std::shared_ptr<kp::Sequence>> sqs;
 
     for (uint32_t i = 0; i < numParallel; i++) {
-        mgrAsync.sequence("async" + std::to_string(i), i);
+        sqs.push_back(mgrAsync.sequence(i));
     }
 
     auto startAsync = std::chrono::high_resolution_clock::now();
 
     for (uint32_t i = 0; i < numParallel; i++) {
-        mgrAsync.evalOpAsync<kp::OpAlgoBase>(
+        sqs[i]->evalAsync<kp::OpAlgoDispatch>(algosAsync[i]);
-          { inputsAsyncB[i] },
-          "async" + std::to_string(i),
-          kp::Shader::compile_source(shader));
     }
 
     for (uint32_t i = 0; i < numParallel; i++) {
-        mgrAsync.evalOpAwait("async" + std::to_string(i));
+        sqs[i]->evalAwait();
     }
 
     auto endAsync = std::chrono::high_resolution_clock::now();
@@ -101,7 +108,7 @@ TEST(TestAsyncOperations, TestManagerParallelExecution)
                            endAsync - startAsync)
                            .count();
 
-    mgrAsync.evalOpDefault<kp::OpTensorSyncLocal>({ inputsAsyncB });
+    sq->eval<kp::OpTensorSyncLocal>({ inputsAsyncB });
 
     for (uint32_t i = 0; i < numParallel; i++) {
         EXPECT_EQ(inputsAsyncB[i]->data(), resultAsync);
@@ -138,32 +145,32 @@ TEST(TestAsyncOperations, TestManagerAsyncExecution)
         }
     )");
 
+    std::vector<uint32_t> spirv = kp::Shader::compile_source(shader);
+
     std::vector<float> data(size, 0.0);
     std::vector<float> resultAsync(size, 100000000);
 
     kp::Manager mgr;
 
-    std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor(data) };
+    std::shared_ptr<kp::Tensor> tensorA = mgr.tensor(data);
-    std::shared_ptr<kp::Tensor> tensorB{ new kp::Tensor(data) };
+    std::shared_ptr<kp::Tensor> tensorB = mgr.tensor(data);
 
-    mgr.sequence("asyncOne");
+    std::shared_ptr<kp::Sequence> sq1 = mgr.sequence();
-    mgr.sequence("asyncTwo");
+    std::shared_ptr<kp::Sequence> sq2 = mgr.sequence();
 
-    mgr.rebuild({ tensorA, tensorB });
+    sq1->eval<kp::OpTensorSyncLocal>({ tensorA, tensorB });
 
-    std::vector<uint32_t> result = kp::Shader::compile_source(shader);
+    std::shared_ptr<kp::Algorithm> algo1 = mgr.algorithm({ tensorA }, spirv);
+    std::shared_ptr<kp::Algorithm> algo2 = mgr.algorithm({ tensorB }, spirv);
 
-    mgr.evalOpAsync<kp::OpAlgoBase>(
+    sq1->evalAsync<kp::OpAlgoDispatch>(algo1);
-      { tensorA }, "asyncOne", kp::Shader::compile_source(shader));
+    sq2->evalAsync<kp::OpAlgoDispatch>(algo2);
 
-    mgr.evalOpAsync<kp::OpAlgoBase>(
+    sq1->evalAwait();
-      { tensorB }, "asyncTwo", kp::Shader::compile_source(shader));
+    sq2->evalAwait();
 
-    mgr.evalOpAwait("asyncOne");
+    sq1->evalAsync<kp::OpTensorSyncLocal>({ tensorA, tensorB });
-    mgr.evalOpAwait("asyncTwo");
+    sq1->evalAwait();
-
-    mgr.evalOpAsyncDefault<kp::OpTensorSyncLocal>({ tensorA, tensorB });
-    mgr.evalOpAwaitDefault();
 
     EXPECT_EQ(tensorA->data(), resultAsync);
     EXPECT_EQ(tensorB->data(), resultAsync);

test/TestDestroy.cpp

@@ -5,7 +5,7 @@
 
 TEST(TestDestroy, TestDestroyTensorSingle)
 {
-    std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor({ 0, 0, 0 }) };
+    std::shared_ptr<kp::Tensor> tensorA = nullptr;
 
     std::string shader(R"(
       #version 450
@@ -16,37 +16,36 @@ TEST(TestDestroy, TestDestroyTensorSingle)
           pa[index] = pa[index] + 1;
       })");
 
+    std::vector<uint32_t> spirv = kp::Shader::compile_source(shader);
+
     {
         std::shared_ptr<kp::Sequence> sq = nullptr;
 
         {
             kp::Manager mgr;
 
-            mgr.rebuild({ tensorA });
+            tensorA = mgr.tensor({ 0, 0, 0 });
 
-            sq = mgr.sequence();
+            std::shared_ptr<kp::Algorithm> algo =
+              mgr.algorithm({ tensorA }, spirv);
 
-            sq->begin();
+            mgr.sequence()
-            sq->record<kp::OpAlgoBase>(
+              ->record<kp::OpAlgoDispatch>(algo)
-              { tensorA }, kp::Shader::compile_source(shader));
+              ->eval()
-            sq->end();
+              ->eval<kp::OpTensorSyncLocal>(algo->getTensors());
-
-            sq->eval();
-
-            mgr.evalOpDefault<kp::OpTensorSyncLocal>({ tensorA });
-
-            mgr.destroy(tensorA);
 
+            tensorA->destroy();
             EXPECT_FALSE(tensorA->isInit());
         }
+        EXPECT_FALSE(tensorA->isInit());
     }
     EXPECT_EQ(tensorA->data(), std::vector<float>({ 1, 1, 1 }));
 }
 
 TEST(TestDestroy, TestDestroyTensorVector)
 {
-    std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor({ 1, 1, 1 }) };
+    std::shared_ptr<kp::Tensor> tensorA = nullptr;
-    std::shared_ptr<kp::Tensor> tensorB{ new kp::Tensor({ 1, 1, 1 }) };
+    std::shared_ptr<kp::Tensor> tensorB = nullptr;
 
     std::string shader(R"(
       #version 450
@@ -58,6 +57,7 @@ TEST(TestDestroy, TestDestroyTensorVector)
           pa[index] = pa[index] + 1;
           pb[index] = pb[index] + 2;
       })");
+    std::vector<uint32_t> spirv = kp::Shader::compile_source(shader);
 
     {
         std::shared_ptr<kp::Sequence> sq = nullptr;
@@ -65,20 +65,20 @@ TEST(TestDestroy, TestDestroyTensorVector)
         {
             kp::Manager mgr;
 
-            mgr.rebuild({ tensorA, tensorB });
+            tensorA = mgr.tensor({ 1, 1, 1 });
+            tensorB = mgr.tensor({ 1, 1, 1 });
 
-            sq = mgr.sequence();
+            std::shared_ptr<kp::Algorithm> algo =
+              mgr.algorithm({ tensorA, tensorB }, spirv);
 
-            sq->begin();
+            mgr.sequence()
-            sq->record<kp::OpAlgoBase>(
+              ->record<kp::OpTensorSyncDevice>(algo->getTensors())
-              { tensorA, tensorB }, kp::Shader::compile_source(shader));
+              ->record<kp::OpAlgoDispatch>(algo)
-            sq->end();
+              ->record<kp::OpTensorSyncLocal>(algo->getTensors())
+              ->eval();
 
-            sq->eval();
+            tensorA->destroy();
-
+            tensorB->destroy();
-            mgr.evalOpDefault<kp::OpTensorSyncLocal>({ tensorA, tensorB });
-
-            mgr.destroy({ tensorA, tensorB });
 
             EXPECT_FALSE(tensorA->isInit());
             EXPECT_FALSE(tensorB->isInit());
@@ -88,32 +88,9 @@ TEST(TestDestroy, TestDestroyTensorVector)
     EXPECT_EQ(tensorB->data(), std::vector<float>({ 3, 3, 3 }));
 }
 
-TEST(TestDestroy, TestDestroyTensorVectorUninitialised)
-{
-    std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor({ 1, 1, 1 }) };
-    std::shared_ptr<kp::Tensor> tensorB{ new kp::Tensor({ 1, 1, 1 }) };
-
-    {
-        std::shared_ptr<kp::Sequence> sq = nullptr;
-
-        {
-            kp::Manager mgr;
-
-            mgr.rebuild({ tensorA, tensorB });
-
-            mgr.destroy({ tensorA, tensorB });
-
-            EXPECT_FALSE(tensorA->isInit());
-            EXPECT_FALSE(tensorB->isInit());
-        }
-    }
-    EXPECT_EQ(tensorA->data(), std::vector<float>({ 1, 1, 1 }));
-    EXPECT_EQ(tensorA->data(), std::vector<float>({ 1, 1, 1 }));
-}
-
 TEST(TestDestroy, TestDestroySequenceSingle)
 {
-    std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor({ 0, 0, 0 }) };
+    std::shared_ptr<kp::Tensor> tensorA = nullptr;
 
     std::string shader(R"(
       #version 450
@@ -124,247 +101,27 @@ TEST(TestDestroy, TestDestroySequenceSingle)
           pa[index] = pa[index] + 1;
       })");
 
+    std::vector<uint32_t> spirv = kp::Shader::compile_source(shader);
+
     {
         std::shared_ptr<kp::Sequence> sq = nullptr;
 
         {
             kp::Manager mgr;
 
-            mgr.rebuild({ tensorA });
+            tensorA = mgr.tensor({ 0, 0, 0 });
 
-            sq = mgr.sequence();
+            sq =
+              mgr.sequence()
+                ->record<kp::OpTensorSyncDevice>({ tensorA })
+                ->record<kp::OpAlgoDispatch>(mgr.algorithm({ tensorA }, spirv))
+                ->record<kp::OpTensorSyncLocal>({ tensorA })
+                ->eval();
 
-            sq->begin();
+            sq->destroy();
-            sq->record<kp::OpAlgoBase>(
-              { tensorA }, kp::Shader::compile_source(shader));
-            sq->end();
-
-            sq->eval();
-
-            mgr.evalOpDefault<kp::OpTensorSyncLocal>({ tensorA });
-
-            mgr.destroy(sq);
 
             EXPECT_FALSE(sq->isInit());
         }
     }
     EXPECT_EQ(tensorA->data(), std::vector<float>({ 1, 1, 1 }));
 }
-
-TEST(TestDestroy, TestDestroySequenceVector)
-{
-    std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor({ 0, 0, 0 }) };
-
-    std::string shader(R"(
-      #version 450
-      layout (local_size_x = 1) in;
-      layout(set = 0, binding = 0) buffer a { float pa[]; };
-      void main() {
-          uint index = gl_GlobalInvocationID.x;
-          pa[index] = pa[index] + 1;
-      })");
-
-    {
-        std::shared_ptr<kp::Sequence> sq1 = nullptr;
-        std::shared_ptr<kp::Sequence> sq2 = nullptr;
-
-        {
-            kp::Manager mgr;
-
-            mgr.rebuild({ tensorA });
-
-            sq1 = mgr.sequence("One");
-            sq1->begin();
-            sq1->record<kp::OpAlgoBase>(
-              { tensorA }, kp::Shader::compile_source(shader));
-            sq1->end();
-            sq1->eval();
-
-            sq2 = mgr.sequence("Two");
-            sq2->begin();
-            sq2->record<kp::OpAlgoBase>(
-              { tensorA }, kp::Shader::compile_source(shader));
-            sq2->end();
-            sq2->eval();
-
-            mgr.evalOpDefault<kp::OpTensorSyncLocal>({ tensorA });
-
-            mgr.destroy({ sq1, sq2 });
-
-            EXPECT_FALSE(sq1->isInit());
-            EXPECT_FALSE(sq2->isInit());
-        }
-    }
-    EXPECT_EQ(tensorA->data(), std::vector<float>({ 2, 2, 2 }));
-}
-
-TEST(TestDestroy, TestDestroySequenceNameSingleInsideManager)
-{
-    std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor({ 0, 0, 0 }) };
-
-    std::string shader(R"(
-      #version 450
-      layout (local_size_x = 1) in;
-      layout(set = 0, binding = 0) buffer a { float pa[]; };
-      void main() {
-          uint index = gl_GlobalInvocationID.x;
-          pa[index] = pa[index] + 1;
-      })");
-
-    {
-        kp::Manager mgr;
-        {
-            mgr.rebuild({ tensorA });
-
-            mgr.evalOp<kp::OpAlgoBase>(
-              { tensorA }, "one",
-              kp::Shader::compile_source(shader));
-
-            mgr.evalOp<kp::OpAlgoBase>(
-              { tensorA }, "two",
-              kp::Shader::compile_source(shader));
-
-            mgr.evalOpDefault<kp::OpTensorSyncLocal>({ tensorA });
-
-            mgr.destroy("one");
-            mgr.destroy("two");
-        }
-    }
-    EXPECT_EQ(tensorA->data(), std::vector<float>({ 2, 2, 2 }));
-}
-
-TEST(TestDestroy, TestDestroySequenceNameSingleOutsideManager)
-{
-    std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor({ 0, 0, 0 }) };
-
-    std::string shader(R"(
-      #version 450
-      layout (local_size_x = 1) in;
-      layout(set = 0, binding = 0) buffer a { float pa[]; };
-      void main() {
-          uint index = gl_GlobalInvocationID.x;
-          pa[index] = pa[index] + 1;
-      })");
-
-    {
-        std::shared_ptr<kp::Sequence> sq1 = nullptr;
-
-        {
-            kp::Manager mgr;
-
-            mgr.rebuild({ tensorA });
-
-            sq1 = mgr.sequence("One");
-            sq1->begin();
-            sq1->record<kp::OpAlgoBase>(
-              { tensorA }, kp::Shader::compile_source(shader));
-            sq1->end();
-            sq1->eval();
-
-            mgr.evalOpDefault<kp::OpTensorSyncLocal>({ tensorA });
-
-            mgr.destroy("One");
-
-            EXPECT_FALSE(sq1->isInit());
-        }
-    }
-    EXPECT_EQ(tensorA->data(), std::vector<float>({ 1, 1, 1 }));
-}
-
-TEST(TestDestroy, TestDestroySequenceNameVectorInsideManager)
-{
-    std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor({ 0, 0, 0 }) };
-
-    std::string shader(R"(
-      #version 450
-      layout (local_size_x = 1) in;
-      layout(set = 0, binding = 0) buffer a { float pa[]; };
-      void main() {
-          uint index = gl_GlobalInvocationID.x;
-          pa[index] = pa[index] + 1;
-      })");
-
-    {
-        kp::Manager mgr;
-        {
-            mgr.rebuild({ tensorA });
-
-            mgr.evalOp<kp::OpAlgoBase>(
-              { tensorA }, "one",
-              kp::Shader::compile_source(shader));
-
-            mgr.evalOp<kp::OpAlgoBase>(
-              { tensorA }, "two",
-              kp::Shader::compile_source(shader));
-
-            mgr.evalOpDefault<kp::OpTensorSyncLocal>({ tensorA });
-
-            mgr.destroy(std::vector<std::string>({"one", "two"}));
-        }
-    }
-    EXPECT_EQ(tensorA->data(), std::vector<float>({ 2, 2, 2 }));
-}
-
-TEST(TestDestroy, TestDestroySequenceNameVectorOutsideManager)
-{
-    std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor({ 0, 0, 0 }) };
-
-    std::string shader(R"(
-      #version 450
-      layout (local_size_x = 1) in;
-      layout(set = 0, binding = 0) buffer a { float pa[]; };
-      void main() {
-          uint index = gl_GlobalInvocationID.x;
-          pa[index] = pa[index] + 1;
-      })");
-
-    {
-        kp::Manager mgr;
-        {
-            mgr.rebuild({ tensorA });
-
-            mgr.evalOp<kp::OpAlgoBase>(
-              { tensorA }, "one",
-              kp::Shader::compile_source(shader));
-
-            mgr.evalOp<kp::OpAlgoBase>(
-              { tensorA }, "two",
-              kp::Shader::compile_source(shader));
-
-            mgr.evalOpDefault<kp::OpTensorSyncLocal>({ tensorA });
-
-            mgr.destroy(std::vector<std::string>({"one", "two"}));
-        }
-    }
-    EXPECT_EQ(tensorA->data(), std::vector<float>({ 2, 2, 2 }));
-}
-
-TEST(TestDestroy, TestDestroySequenceNameDefaultOutsideManager)
-{
-    std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor({ 0, 0, 0 }) };
-
-    std::string shader(R"(
-      #version 450
-      layout (local_size_x = 1) in;
-      layout(set = 0, binding = 0) buffer a { float pa[]; };
-      void main() {
-          uint index = gl_GlobalInvocationID.x;
-          pa[index] = pa[index] + 1;
-      })");
-
-    {
-        kp::Manager mgr;
-        {
-            mgr.rebuild({ tensorA });
-
-            mgr.evalOpDefault<kp::OpAlgoBase>(
-              { tensorA },
-              kp::Shader::compile_source(shader));
-
-            mgr.evalOpDefault<kp::OpTensorSyncLocal>({ tensorA });
-
-            mgr.destroy(KP_DEFAULT_SESSION);
-        }
-    }
-    EXPECT_EQ(tensorA->data(), std::vector<float>({ 1, 1, 1 }));
-}

test/TestLogisticRegression.cpp

@@ -11,47 +11,45 @@ TEST(TestLogisticRegression, TestMainLogisticRegression)
     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 }) };
-
-    std::shared_ptr<kp::Tensor> y{ new kp::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> bIn{ new kp::Tensor({ 0 }) };
-    std::shared_ptr<kp::Tensor> bOut{ new kp::Tensor({ 0, 0, 0, 0, 0 }) };
-
-    std::shared_ptr<kp::Tensor> lOut{ new kp::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::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::Sequence> sq = mgr.sequence();
+        std::shared_ptr<kp::Tensor> y = mgr.tensor({ 0, 0, 0, 1, 1 });
 
-        // Record op algo base
+        std::shared_ptr<kp::Tensor> wIn = mgr.tensor({ 0.001, 0.001 });
-        sq->begin();
+        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 });
 
-        sq->record<kp::OpTensorSyncDevice>({ wIn, bIn });
+        std::shared_ptr<kp::Tensor> bIn = mgr.tensor({ 0 });
+        std::shared_ptr<kp::Tensor> bOut = mgr.tensor({ 0, 0, 0, 0, 0 });
 
-        sq->record<kp::OpAlgoBase>(
+        std::shared_ptr<kp::Tensor> lOut = mgr.tensor({ 0, 0, 0, 0, 0 });
-          params,
-          std::vector<uint32_t>(
-            (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::Workgroup(), kp::Constants({5.0}));
 
-        sq->record<kp::OpTensorSyncLocal>({ wOutI, wOutJ, bOut, lOut });
+        std::vector<std::shared_ptr<kp::Tensor>> params = { xI,  xJ,    y,
+                                                            wIn, wOutI, wOutJ,
+                                                            bIn, bOut,  lOut };
 
-        sq->end();
+        mgr.sequence()->eval<kp::OpTensorSyncDevice>(params);
+
+        std::vector<uint32_t> spirv = std::vector<uint32_t>(
+          (uint32_t*)kp::shader_data::
+            test_shaders_glsl_test_logistic_regression_comp_spv,
+          (uint32_t*)(kp::shader_data::
+                        test_shaders_glsl_test_logistic_regression_comp_spv +
+                      kp::shader_data::
+                        test_shaders_glsl_test_logistic_regression_comp_spv_len));
+
+        std::shared_ptr<kp::Algorithm> algorithm = mgr.algorithm(
+          params, spirv, kp::Workgroup({ 5 }), kp::Constants({ 5.0 }));
+
+        std::shared_ptr<kp::Sequence> sq =
+          mgr.sequence()
+            ->record<kp::OpTensorSyncDevice>({ wIn, bIn })
+            ->record<kp::OpAlgoDispatch>(algorithm)
+            ->record<kp::OpTensorSyncLocal>({ wOutI, wOutJ, bOut, lOut });
 
         // Iterate across all expected iterations
         for (size_t i = 0; i < ITERATIONS; i++) {
@@ -64,21 +62,21 @@ TEST(TestLogisticRegression, TestMainLogisticRegression)
                 bIn->data()[0] -= learningRate * bOut->data()[j];
             }
         }
+
+        // Based on the inputs the outputs should be at least:
+        // * wi < 0.01
+        // * wj > 1.0
+        // * b < 0
+        // TODO: Add EXPECT_DOUBLE_EQ instead
+        EXPECT_LT(wIn->data()[0], 0.01);
+        EXPECT_GT(wIn->data()[1], 1.0);
+        EXPECT_LT(bIn->data()[0], 0.0);
+
+        KP_LOG_WARN("Result wIn i: {}, wIn j: {}, bIn: {}",
+                    wIn->data()[0],
+                    wIn->data()[1],
+                    bIn->data()[0]);
     }
-
-    // Based on the inputs the outputs should be at least:
-    // * wi < 0.01
-    // * wj > 1.0
-    // * b < 0
-    // TODO: Add EXPECT_DOUBLE_EQ instead
-    EXPECT_LT(wIn->data()[0], 0.01);
-    EXPECT_GT(wIn->data()[1], 1.0);
-    EXPECT_LT(bIn->data()[0], 0.0);
-
-    KP_LOG_WARN("Result wIn i: {}, wIn j: {}, bIn: {}",
-                wIn->data()[0],
-                wIn->data()[1],
-                bIn->data()[0]);
 }
 
 TEST(TestLogisticRegression, TestMainLogisticRegressionManualCopy)
@@ -87,50 +85,46 @@ TEST(TestLogisticRegression, TestMainLogisticRegressionManualCopy)
     uint32_t ITERATIONS = 100;
     float learningRate = 0.1;
 
-    kp::Constants wInVec = { 0.001, 0.001 };
-    std::vector<float> bInVec = { 0 };
-
-    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 }) };
-
-    std::shared_ptr<kp::Tensor> y{ new kp::Tensor({ 0, 0, 0, 1, 1 }) };
-
-    std::shared_ptr<kp::Tensor> wIn{ new kp::Tensor(
-      wInVec, kp::Tensor::TensorTypes::eHost) };
-    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> bIn{ new kp::Tensor(
-      bInVec, kp::Tensor::TensorTypes::eHost) };
-    std::shared_ptr<kp::Tensor> bOut{ new kp::Tensor({ 0, 0, 0, 0, 0 }) };
-
-    std::shared_ptr<kp::Tensor> lOut{ new kp::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::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::Sequence> sq = mgr.sequence();
+        std::shared_ptr<kp::Tensor> y = mgr.tensor({ 0, 0, 0, 1, 1 });
 
-        // Record op algo base
+        std::shared_ptr<kp::Tensor> wIn =
-        sq->begin();
+          mgr.tensor({ 0.001, 0.001 }, kp::Tensor::TensorTypes::eHost);
+        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 });
 
-        sq->record<kp::OpAlgoBase>(
+        std::shared_ptr<kp::Tensor> bIn =
-          params,
+          mgr.tensor({ 0 }, kp::Tensor::TensorTypes::eHost);
-          std::vector<uint32_t>(
+        std::shared_ptr<kp::Tensor> bOut = mgr.tensor({ 0, 0, 0, 0, 0 });
-            (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::Workgroup(), kp::Constants({5.0}));
 
-        sq->record<kp::OpTensorSyncLocal>({ wOutI, wOutJ, bOut, lOut });
+        std::shared_ptr<kp::Tensor> lOut = mgr.tensor({ 0, 0, 0, 0, 0 });
 
-        sq->end();
+        std::vector<std::shared_ptr<kp::Tensor>> params = { xI,  xJ,    y,
+                                                            wIn, wOutI, wOutJ,
+                                                            bIn, bOut,  lOut };
+
+        mgr.sequence()->record<kp::OpTensorSyncDevice>(params)->eval();
+
+        std::vector<uint32_t> spirv = std::vector<uint32_t>(
+          (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));
+
+        std::shared_ptr<kp::Algorithm> algorithm =
+          mgr.algorithm(params, spirv, kp::Workgroup(), kp::Constants({ 5.0 }));
+
+        std::shared_ptr<kp::Sequence> sq =
+          mgr.sequence()
+            ->record<kp::OpTensorSyncDevice>({ wIn, bIn })
+            ->record<kp::OpAlgoDispatch>(algorithm)
+            ->record<kp::OpTensorSyncLocal>({ wOutI, wOutJ, bOut, lOut });
 
         // Iterate across all expected iterations
         for (size_t i = 0; i < ITERATIONS; i++) {
@@ -145,19 +139,19 @@ TEST(TestLogisticRegression, TestMainLogisticRegressionManualCopy)
             wIn->mapDataIntoHostMemory();
             bIn->mapDataIntoHostMemory();
         }
+
+        // Based on the inputs the outputs should be at least:
+        // * wi < 0.01
+        // * wj > 1.0
+        // * b < 0
+        // TODO: Add EXPECT_DOUBLE_EQ instead
+        EXPECT_LT(wIn->data()[0], 0.01);
+        EXPECT_GT(wIn->data()[1], 1.0);
+        EXPECT_LT(bIn->data()[0], 0.0);
+
+        KP_LOG_WARN("Result wIn i: {}, wIn j: {}, bIn: {}",
+                    wIn->data()[0],
+                    wIn->data()[1],
+                    bIn->data()[0]);
     }
-
-    // Based on the inputs the outputs should be at least:
-    // * wi < 0.01
-    // * wj > 1.0
-    // * b < 0
-    // TODO: Add EXPECT_DOUBLE_EQ instead
-    EXPECT_LT(wIn->data()[0], 0.01);
-    EXPECT_GT(wIn->data()[1], 1.0);
-    EXPECT_LT(bIn->data()[0], 0.0);
-
-    KP_LOG_WARN("Result wIn i: {}, wIn j: {}, bIn: {}",
-                wIn->data()[0],
-                wIn->data()[1],
-                bIn->data()[0]);
 }

test/TestManager.cpp

@@ -3,53 +3,43 @@
 
 #include "kompute/Kompute.hpp"
 
-TEST(TestManager, EndToEndOpMultFlow)
+TEST(TestManager, EndToEndOpMultEvalFlow)
 {
     kp::Manager mgr;
 
-    std::shared_ptr<kp::Tensor> tensorLHS{ new kp::Tensor({ 0, 1, 2 }) };
+    std::shared_ptr<kp::Tensor> tensorLHS = mgr.tensor({ 0, 1, 2 });
-    mgr.rebuild({ tensorLHS });
+    std::shared_ptr<kp::Tensor> tensorRHS = mgr.tensor({ 2, 4, 6 });
+    std::shared_ptr<kp::Tensor> tensorOutput = mgr.tensor({ 0, 0, 0 });
 
-    std::shared_ptr<kp::Tensor> tensorRHS{ new kp::Tensor({ 2, 4, 6 }) };
+    std::vector<std::shared_ptr<kp::Tensor>> params = { tensorLHS,
-    mgr.rebuild({ tensorRHS });
+                                                        tensorRHS,
+                                                        tensorOutput };
 
-    std::shared_ptr<kp::Tensor> tensorOutput{ new kp::Tensor({ 0, 0, 0 }) };
+    mgr.sequence()
-
+      ->eval<kp::OpTensorSyncDevice>(params)
-    mgr.rebuild({ tensorOutput });
+      ->eval<kp::OpMult>(params, mgr.algorithm())
-
+      ->eval<kp::OpTensorSyncLocal>(params);
-    mgr.evalOpDefault<kp::OpMult>({ tensorLHS, tensorRHS, tensorOutput });
-
-    mgr.evalOpDefault<kp::OpTensorSyncLocal>({ tensorOutput });
 
     EXPECT_EQ(tensorOutput->data(), std::vector<float>({ 0, 4, 12 }));
 }
 
-TEST(TestManager, OpMultSequenceFlow)
+TEST(TestManager, EndToEndOpMultSeqFlow)
 {
-
-    std::shared_ptr<kp::Tensor> tensorLHS{ new kp::Tensor({ 0, 1, 2 }) };
-
-    std::shared_ptr<kp::Tensor> tensorRHS{ new kp::Tensor({ 2, 4, 6 }) };
-
-    std::shared_ptr<kp::Tensor> tensorOutput{ new kp::Tensor({ 0, 0, 0 }) };
-
     kp::Manager mgr;
 
-    {
+    std::shared_ptr<kp::Tensor> tensorLHS = mgr.tensor({ 0, 1, 2 });
-        mgr.rebuild({ tensorLHS, tensorRHS, tensorOutput });
+    std::shared_ptr<kp::Tensor> tensorRHS = mgr.tensor({ 2, 4, 6 });
+    std::shared_ptr<kp::Tensor> tensorOutput = mgr.tensor({ 0, 0, 0 });
 
-        std::shared_ptr<kp::Sequence> sq =
+    std::vector<std::shared_ptr<kp::Tensor>> params = { tensorLHS,
-          mgr.sequence("newSequence");
+                                                        tensorRHS,
+                                                        tensorOutput };
 
-        sq->begin();
+    mgr.sequence()
-
+      ->record<kp::OpTensorSyncDevice>(params)
-        sq->record<kp::OpMult>({ tensorLHS, tensorRHS, tensorOutput });
+      ->record<kp::OpMult>(params, mgr.algorithm())
-
+      ->record<kp::OpTensorSyncLocal>(params)
-        sq->record<kp::OpTensorSyncLocal>({ tensorOutput });
+      ->eval();
-
-        sq->end();
-        sq->eval();
-    }
 
     EXPECT_EQ(tensorOutput->data(), std::vector<float>({ 0, 4, 12 }));
 }
@@ -58,75 +48,17 @@ TEST(TestManager, TestMultipleSequences)
 {
     kp::Manager mgr;
 
-    std::shared_ptr<kp::Sequence> sqOne =
+    std::shared_ptr<kp::Tensor> tensorLHS = mgr.tensor({ 0, 1, 2 });
-      mgr.sequence("sqOne");
+    std::shared_ptr<kp::Tensor> tensorRHS = mgr.tensor({ 2, 4, 6 });
+    std::shared_ptr<kp::Tensor> tensorOutput = mgr.tensor({ 0, 0, 0 });
 
-    std::shared_ptr<kp::Sequence> sqTwo =
+    std::vector<std::shared_ptr<kp::Tensor>> params = { tensorLHS,
-      mgr.sequence("sqTwo");
+                                                        tensorRHS,
+                                                        tensorOutput };
 
-    std::shared_ptr<kp::Sequence> sqOneRef =
+    mgr.sequence()->eval<kp::OpTensorSyncDevice>(params);
-      mgr.sequence("sqOne");
+    mgr.sequence()->eval<kp::OpMult>(params, mgr.algorithm());
-
+    mgr.sequence()->eval<kp::OpTensorSyncLocal>(params);
-    std::shared_ptr<kp::Sequence> sqTwoRef =
-      mgr.sequence("sqTwo");
-
-    EXPECT_EQ(sqOne, sqOneRef);
-    EXPECT_NE(sqTwo, sqOneRef);
-    EXPECT_EQ(sqTwo, sqTwoRef);
-    EXPECT_NE(sqOneRef, sqTwoRef);
-}
-
-TEST(TestManager, TestMultipleTensorsAtOnce)
-{
-
-    std::shared_ptr<kp::Tensor> tensorLHS{ new kp::Tensor({ 0, 1, 2 }) };
-
-    std::shared_ptr<kp::Tensor> tensorRHS{ new kp::Tensor({ 2, 4, 6 }) };
-
-    std::shared_ptr<kp::Tensor> tensorOutput{ new kp::Tensor({ 0, 0, 0 }) };
-
-    kp::Manager mgr;
-
-    std::shared_ptr<kp::Sequence> sq =
-      mgr.sequence("newSequence");
-
-    {
-        mgr.rebuild({ tensorLHS, tensorRHS, tensorOutput });
-
-        EXPECT_TRUE(tensorLHS->isInit());
-        EXPECT_TRUE(tensorRHS->isInit());
-        EXPECT_TRUE(tensorOutput->isInit());
-
-        sq->begin();
-
-        sq->record<kp::OpMult>({ tensorLHS, tensorRHS, tensorOutput });
-
-        sq->record<kp::OpTensorSyncLocal>({ tensorOutput });
-
-        sq->end();
-        sq->eval();
-    }
 
     EXPECT_EQ(tensorOutput->data(), std::vector<float>({ 0, 4, 12 }));
 }
-
-TEST(TestManager, TestCreateInitTensor)
-{
-    kp::Manager mgr;
-
-    std::shared_ptr<kp::Tensor> tensorA = mgr.tensor({ 0, 1, 2 });
-    std::shared_ptr<kp::Tensor> tensorB = mgr.tensor({ 0, 0, 0 });
-
-    mgr.evalOpDefault<kp::OpTensorCopy>({ tensorA, tensorB });
-
-    mgr.evalOpDefault<kp::OpTensorSyncLocal>({ tensorB });
-
-    EXPECT_EQ(tensorB->data(), std::vector<float>({ 0, 1, 2 }));
-
-    std::shared_ptr<kp::Tensor> tensorC =
-      mgr.tensor({ 0, 0, 0 }, kp::Tensor::TensorTypes::eHost);
-
-    mgr.evalOpDefault<kp::OpTensorCopy>({ tensorA, tensorC });
-
-    EXPECT_EQ(tensorC->data(), std::vector<float>({ 0, 1, 2 }));
-}

test/TestMultipleAlgoExecutions.cpp

@@ -3,12 +3,76 @@
 
 #include "kompute/Kompute.hpp"
 
+TEST(TestMultipleAlgoExecutions, TestEndToEndFunctionality)
+{
+
+    kp::Manager mgr;
+
+    auto tensorInA = mgr.tensor({ 2., 2., 2. });
+    auto tensorInB = mgr.tensor({ 1., 2., 3. });
+    auto tensorOutA = mgr.tensor({ 0., 0., 0. });
+    auto tensorOutB = mgr.tensor({ 0., 0., 0. });
+
+    std::string shader = (R"(
+        #version 450
+
+        layout (local_size_x = 1) in;
+
+        // The input tensors bind index is relative to index in parameter passed
+        layout(set = 0, binding = 0) buffer buf_in_a { float in_a[]; };
+        layout(set = 0, binding = 1) buffer buf_in_b { float in_b[]; };
+        layout(set = 0, binding = 2) buffer buf_out_a { float out_a[]; };
+        layout(set = 0, binding = 3) buffer buf_out_b { float out_b[]; };
+
+        // Kompute supports push constants updated on dispatch
+        layout(push_constant) uniform PushConstants {
+            float val;
+        } push_const;
+
+        // Kompute also supports spec constants on initalization
+        layout(constant_id = 0) const float const_one = 0;
+
+        void main() {
+            uint index = gl_GlobalInvocationID.x;
+            out_a[index] += in_a[index] * in_b[index];
+            out_b[index] += const_one * push_const.val;
+        }
+    )");
+
+    std::vector<std::shared_ptr<kp::Tensor>> params = {
+        tensorInA, tensorInB, tensorOutA, tensorOutB
+    };
+
+    kp::Workgroup workgroup({ 3, 1, 1 });
+    kp::Constants specConsts({ 2 });
+    kp::Constants pushConstsA({ 2.0 });
+    kp::Constants pushConstsB({ 3.0 });
+
+    auto algorithm = mgr.algorithm(
+      params, kp::Shader::compile_source(shader), workgroup, specConsts);
+
+    // 3. Run operation with string shader synchronously
+    mgr.sequence()
+      ->record<kp::OpTensorSyncDevice>(params)
+      ->record<kp::OpAlgoDispatch>(algorithm, pushConstsA)
+      ->record<kp::OpAlgoDispatch>(algorithm, pushConstsB)
+      ->eval();
+
+    auto sq = mgr.sequence();
+    sq->evalAsync<kp::OpTensorSyncLocal>(params);
+
+    sq->evalAwait();
+
+    EXPECT_EQ(tensorOutA->data(), std::vector<float>({ 4, 8, 12 }));
+    EXPECT_EQ(tensorOutB->data(), std::vector<float>({ 10, 10, 10 }));
+}
+
 TEST(TestMultipleAlgoExecutions, SingleSequenceRecord)
 {
 
     kp::Manager mgr;
 
-    std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor({ 0, 0, 0 }) };
+    std::shared_ptr<kp::Tensor> tensorA = mgr.tensor({ 0, 0, 0 });
 
     std::string shader(R"(
       #version 450
@@ -19,25 +83,16 @@ TEST(TestMultipleAlgoExecutions, SingleSequenceRecord)
           pa[index] = pa[index] + 1;
       })");
 
-    mgr.rebuild({ tensorA });
+    std::vector<uint32_t> spirv = kp::Shader::compile_source(shader);
-
-    std::shared_ptr<kp::Sequence> sq =
-      mgr.sequence("newSequence");
 
     {
-        sq->begin();
+        mgr.sequence()
-
+          ->record<kp::OpTensorSyncDevice>({ tensorA })
-        sq->record<kp::OpAlgoBase>(
+          ->record<kp::OpAlgoDispatch>(mgr.algorithm({ tensorA }, spirv))
-          { tensorA }, kp::Shader::compile_source(shader));
+          ->record<kp::OpAlgoDispatch>(mgr.algorithm({ tensorA }, spirv))
-        sq->record<kp::OpAlgoBase>(
+          ->record<kp::OpAlgoDispatch>(mgr.algorithm({ tensorA }, spirv))
-          { tensorA }, kp::Shader::compile_source(shader));
+          ->record<kp::OpTensorSyncLocal>({ tensorA })
-        sq->record<kp::OpAlgoBase>(
+          ->eval();
-          { tensorA }, kp::Shader::compile_source(shader));
-
-        sq->record<kp::OpTensorSyncLocal>({ tensorA });
-
-        sq->end();
-        sq->eval();
     }
 
     EXPECT_EQ(tensorA->data(), std::vector<float>({ 3, 3, 3 }));
@@ -47,7 +102,7 @@ TEST(TestMultipleAlgoExecutions, MultipleCmdBufRecords)
 {
     kp::Manager mgr;
 
-    std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor({ 0, 0, 0 }) };
+    std::shared_ptr<kp::Tensor> tensorA = mgr.tensor({ 0, 0, 0 });
 
     std::string shader(R"(
       #version 450
@@ -58,41 +113,22 @@ TEST(TestMultipleAlgoExecutions, MultipleCmdBufRecords)
           pa[index] = pa[index] + 1;
       })");
 
-    mgr.rebuild({ tensorA }, false);
+    std::vector<uint32_t> spirv = kp::Shader::compile_source(shader);
 
-    std::shared_ptr<kp::Sequence> sqTensor = mgr.sequence();
+    std::shared_ptr<kp::Algorithm> algorithm =
+      mgr.algorithm({ tensorA }, spirv);
 
     std::shared_ptr<kp::Sequence> sq = mgr.sequence();
 
-    // First create the tensor in a separate sequence
+    mgr.sequence()->record<kp::OpTensorSyncDevice>({ tensorA })->eval();
-    sqTensor->begin();
-    sqTensor->record<kp::OpTensorSyncDevice>({ tensorA });
-    sqTensor->end();
-    sqTensor->eval();
 
-    // Then perform the computations
+    mgr.sequence()->record<kp::OpAlgoDispatch>(algorithm)->eval();
-    sq->begin();
-    sq->record<kp::OpAlgoBase>({ tensorA },
-                               kp::Shader::compile_source(shader));
-    sq->end();
-    sq->eval();
 
-    sq->begin();
+    mgr.sequence()->record<kp::OpAlgoDispatch>(algorithm)->eval();
-    sq->record<kp::OpAlgoBase>({ tensorA },
-                               kp::Shader::compile_source(shader));
-    sq->end();
-    sq->eval();
 
-    sq->begin();
+    mgr.sequence()->record<kp::OpAlgoDispatch>(algorithm)->eval();
-    sq->record<kp::OpAlgoBase>({ tensorA },
-                               kp::Shader::compile_source(shader));
-    sq->end();
-    sq->eval();
 
-    sq->begin();
+    mgr.sequence()->record<kp::OpTensorSyncLocal>({ tensorA })->eval();
-    sq->record<kp::OpTensorSyncLocal>({ tensorA });
-    sq->end();
-    sq->eval();
 
     EXPECT_EQ(tensorA->data(), std::vector<float>({ 3, 3, 3 }));
 }
@@ -102,7 +138,7 @@ TEST(TestMultipleAlgoExecutions, MultipleSequences)
 
     kp::Manager mgr;
 
-    std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor({ 0, 0, 0 }) };
+    std::shared_ptr<kp::Tensor> tensorA = mgr.tensor({ 0, 0, 0 });
 
     std::string shader(R"(
       #version 450
@@ -113,68 +149,31 @@ TEST(TestMultipleAlgoExecutions, MultipleSequences)
           pa[index] = pa[index] + 1;
       })");
 
-    mgr.rebuild({ tensorA });
+    std::vector<uint32_t> spirv = kp::Shader::compile_source(shader);
 
-    {
+    std::shared_ptr<kp::Algorithm> algorithm =
-        std::shared_ptr<kp::Sequence> sq =
+      mgr.algorithm({ tensorA }, spirv);
-          mgr.sequence("newSequence");
 
-        sq->begin();
+    std::shared_ptr<kp::Sequence> sq = mgr.sequence();
 
-        sq->record<kp::OpAlgoBase>(
+    sq->record<kp::OpTensorSyncDevice>({ tensorA })->eval();
-          { tensorA }, kp::Shader::compile_source(shader));
 
-        sq->end();
+    sq->record<kp::OpAlgoDispatch>(algorithm)->eval();
-        sq->eval();
-    }
 
-    {
+    sq->record<kp::OpAlgoDispatch>(algorithm)->eval();
-        std::shared_ptr<kp::Sequence> sq =
-          mgr.sequence("newSequence2");
 
-        sq->begin();
+    sq->record<kp::OpAlgoDispatch>(algorithm)->eval();
 
-        sq->record<kp::OpAlgoBase>(
+    sq->record<kp::OpTensorSyncLocal>({ tensorA })->eval();
-          { tensorA }, kp::Shader::compile_source(shader));
-
-        sq->end();
-        sq->eval();
-    }
-
-    {
-        std::shared_ptr<kp::Sequence> sq =
-          mgr.sequence("newSequence3");
-
-        sq->begin();
-
-        sq->record<kp::OpAlgoBase>(
-          { tensorA }, kp::Shader::compile_source(shader));
-
-        sq->end();
-        sq->eval();
-    }
-
-    {
-        std::shared_ptr<kp::Sequence> sq =
-          mgr.sequence("newSequence5");
-
-        sq->begin();
-
-        sq->record<kp::OpTensorSyncLocal>({ tensorA });
-
-        sq->end();
-        sq->eval();
-    }
 
     EXPECT_EQ(tensorA->data(), std::vector<float>({ 3, 3, 3 }));
 }
 
 TEST(TestMultipleAlgoExecutions, SingleRecordMultipleEval)
 {
-
     kp::Manager mgr;
 
-    std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor({ 0, 0, 0 }) };
+    std::shared_ptr<kp::Tensor> tensorA = mgr.tensor({ 0, 0, 0 });
 
     std::string shader(R"(
       #version 450
@@ -185,169 +184,56 @@ TEST(TestMultipleAlgoExecutions, SingleRecordMultipleEval)
           pa[index] = pa[index] + 1;
       })");
 
-    mgr.rebuild({ tensorA }, false);
+    std::vector<uint32_t> spirv = kp::Shader::compile_source(shader);
 
-    {
+    std::shared_ptr<kp::Algorithm> algorithm =
-        std::shared_ptr<kp::Sequence> sq =
+      mgr.algorithm({ tensorA }, spirv);
-          mgr.sequence("newSequence");
 
-        sq->begin();
+    std::shared_ptr<kp::Sequence> sq = mgr.sequence();
 
-        sq->record<kp::OpTensorSyncDevice>({ tensorA });
+    sq->record<kp::OpTensorSyncDevice>({ tensorA })->eval();
 
-        sq->end();
+    sq->record<kp::OpAlgoDispatch>(algorithm)->eval()->eval()->eval();
-        sq->eval();
-    }
 
-    {
+    sq->record<kp::OpTensorSyncLocal>({ tensorA })->eval();
-        std::shared_ptr<kp::Sequence> sq =
-          mgr.sequence("newSequence2");
-
-        sq->begin();
-
-        sq->record<kp::OpAlgoBase>(
-          { tensorA }, kp::Shader::compile_source(shader));
-
-        sq->end();
-
-        sq->eval();
-        sq->eval();
-        sq->eval();
-    }
-
-    {
-        std::shared_ptr<kp::Sequence> sq =
-          mgr.sequence("newSequence3");
-
-        sq->begin();
-
-        sq->record<kp::OpTensorSyncLocal>({ tensorA });
-
-        sq->end();
-
-        sq->eval();
-        sq->eval();
-        sq->eval();
-    }
 
     EXPECT_EQ(tensorA->data(), std::vector<float>({ 3, 3, 3 }));
 }
 
-TEST(TestMultipleAlgoExecutions, ManagerEvalMultSourceStrOpCreate)
-{
-
-    kp::Manager mgr;
-
-    std::shared_ptr<kp::Tensor> tensorInA{ new kp::Tensor({ 2.0, 4.0, 6.0 }) };
-    std::shared_ptr<kp::Tensor> tensorInB{ new kp::Tensor({ 0.0, 1.0, 2.0 }) };
-    std::shared_ptr<kp::Tensor> tensorOut{ new kp::Tensor({ 0.0, 0.0, 0.0 }) };
-
-    mgr.rebuild({ tensorInA, tensorInB, tensorOut });
-
-    std::string shader(R"(
-        // The version to use 
-        #version 450
-
-        // The execution structure
-        layout (local_size_x = 1) in;
-
-        // The buffers are provided via the tensors
-        layout(binding = 0) buffer bufA { float a[]; };
-        layout(binding = 1) buffer bufB { float b[]; };
-        layout(binding = 2) buffer bufOut { float o[]; };
-
-        void main() {
-            uint index = gl_GlobalInvocationID.x;
-
-            o[index] = a[index] * b[index];
-        }
-      )");
-
-    mgr.evalOpDefault<kp::OpAlgoBase>(
-      { tensorInA, tensorInB, tensorOut },
-      kp::Shader::compile_source(shader));
-
-    mgr.evalOpDefault<kp::OpTensorSyncLocal>({ tensorOut });
-
-    EXPECT_EQ(tensorOut->data(), std::vector<float>({ 0.0, 4.0, 12.0 }));
-}
-
-TEST(TestMultipleAlgoExecutions, ManagerEvalMultSourceStrMgrCreate)
-{
-
-    kp::Manager mgr;
-
-    auto tensorInA = mgr.tensor(
-      { 2.0, 4.0, 6.0 }, kp::Tensor::TensorTypes::eDevice, false);
-    auto tensorInB = mgr.tensor(
-      { 0.0, 1.0, 2.0 }, kp::Tensor::TensorTypes::eDevice, false);
-    auto tensorOut = mgr.tensor(
-      { 0.0, 0.0, 0.0 }, kp::Tensor::TensorTypes::eDevice, false);
-
-    std::string shader(R"(
-        // The version to use 
-        #version 450
-
-        // The execution structure
-        layout (local_size_x = 1) in;
-
-        // The buffers are provided via the tensors
-        layout(binding = 0) buffer bufA { float a[]; };
-        layout(binding = 1) buffer bufB { float b[]; };
-        layout(binding = 2) buffer bufOut { float o[]; };
-
-        void main() {
-            uint index = gl_GlobalInvocationID.x;
-
-            o[index] = a[index] * b[index];
-        }
-      )");
-
-    mgr.evalOpDefault<kp::OpTensorSyncDevice>(
-      { tensorInA, tensorInB, tensorOut });
-
-    mgr.evalOpDefault<kp::OpAlgoBase>(
-      { tensorInA, tensorInB, tensorOut },
-      kp::Shader::compile_source(shader));
-
-    mgr.evalOpDefault<kp::OpTensorSyncLocal>({ tensorOut });
-
-    EXPECT_EQ(tensorOut->data(), std::vector<float>({ 0.0, 4.0, 12.0 }));
-}
-
 TEST(TestMultipleAlgoExecutions, SequenceAlgoDestroyOutsideManagerScope)
 {
-    std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor({ 0, 0, 0 }) };
+    std::shared_ptr<kp::Tensor> tensorA = nullptr;
-
-    std::string shader(R"(
-      #version 450
-      layout (local_size_x = 1) in;
-      layout(set = 0, binding = 0) buffer a { float pa[]; };
-      void main() {
-          uint index = gl_GlobalInvocationID.x;
-          pa[index] = pa[index] + 1;
-      })");
 
     {
         std::shared_ptr<kp::Sequence> sq = nullptr;
-
         {
             kp::Manager mgr;
 
-            mgr.rebuild({ tensorA });
+            tensorA = mgr.tensor({ 0, 0, 0 });
+
+            std::string shader(R"(
+              #version 450
+              layout (local_size_x = 1) in;
+              layout(set = 0, binding = 0) buffer a { float pa[]; };
+              void main() {
+                  uint index = gl_GlobalInvocationID.x;
+                  pa[index] = pa[index] + 1;
+              })");
+
+            std::vector<uint32_t> spirv = kp::Shader::compile_source(shader);
+
+            std::shared_ptr<kp::Algorithm> algorithm =
+              mgr.algorithm({ tensorA }, spirv);
 
             sq = mgr.sequence();
 
-            sq->begin();
+            sq->record<kp::OpTensorSyncDevice>({ tensorA })->eval();
-            sq->record<kp::OpAlgoBase>(
-              { tensorA }, kp::Shader::compile_source(shader));
-            sq->end();
 
-            sq->eval();
+            sq->record<kp::OpAlgoDispatch>(algorithm)->eval()->eval()->eval();
 
-            mgr.evalOpDefault<kp::OpTensorSyncLocal>({ tensorA });
+            sq->record<kp::OpTensorSyncLocal>({ tensorA })->eval();
         }
     }
-    EXPECT_EQ(tensorA->data(), std::vector<float>({ 1, 1, 1 }));
-}
 
+    EXPECT_EQ(tensorA->data(), std::vector<float>({ 3, 3, 3 }));
+}

test/TestOpAlgoLoopsPassingData.cpp

@@ -1,80 +0,0 @@
-
-#include "gtest/gtest.h"
-
-#include "kompute/Kompute.hpp"
-
-TEST(TestProcessingIterations, IterateThroughMultipleSumAndCopies)
-{
-    kp::Manager mgr;
-
-    float TOTAL_ITER = 10;
-
-    std::vector<float> testExpectedOutVec = { TOTAL_ITER,
-                                              TOTAL_ITER,
-                                              TOTAL_ITER };
-
-    std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor({ 0, 0, 0 }) };
-    std::shared_ptr<kp::Tensor> tensorB{ new kp::Tensor({ 0, 0, 0 }) };
-
-    std::string shader(R"(
-        #version 450
-
-        layout (local_size_x = 1) in;
-
-        layout(set = 0, binding = 0) buffer a { float pa[]; };
-        layout(set = 0, binding = 1) buffer b { float pb[]; };
-
-        void main() {
-            uint index = gl_GlobalInvocationID.x;
-            pb[index] = pa[index] + 1;
-        }
-    )");
-
-    mgr.rebuild({ tensorA, tensorB }, false);
-
-    {
-        std::shared_ptr<kp::Sequence> sq =
-          mgr.sequence("default");
-
-        sq->begin();
-
-        sq->record<kp::OpTensorSyncDevice>({ tensorA, tensorB });
-
-        sq->end();
-
-        sq->eval();
-    }
-
-    {
-        std::shared_ptr<kp::Sequence> sq =
-          mgr.sequence("run");
-
-        sq->begin();
-
-        sq->record<kp::OpAlgoBase>(
-          { tensorA, tensorB },
-          kp::Shader::compile_source(shader));
-
-        sq->record<kp::OpTensorCopy>({ tensorB, tensorA });
-        sq->end();
-
-        for (size_t i = 0; i < TOTAL_ITER; i++) {
-            sq->eval();
-        }
-    }
-
-    {
-        std::shared_ptr<kp::Sequence> sq =
-          mgr.sequence("export");
-
-        sq->begin();
-
-        sq->record<kp::OpTensorSyncLocal>({ tensorA, tensorB });
-
-        sq->end();
-
-        sq->eval();
-    }
-
-    EXPECT_EQ(tensorA->data(), testExpectedOutVec);
-}

test/TestOpShadersFromStringAndFile.cpp

@@ -5,13 +5,12 @@
 
 #include "kompute_test/shaders/shadertest_op_custom_shader.hpp"
 
-TEST(TestOpAlgoBase, ShaderRawDataFromConstructor)
+TEST(TestOpAlgoCreate, ShaderRawDataFromConstructor)
 {
     kp::Manager mgr;
 
-    std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor({ 3, 4, 5 }) };
+    std::shared_ptr<kp::Tensor> tensorA = mgr.tensor({ 3, 4, 5 });
-    std::shared_ptr<kp::Tensor> tensorB{ new kp::Tensor({ 0, 0, 0 }) };
+    std::shared_ptr<kp::Tensor> tensorB = mgr.tensor({ 0, 0, 0 });
-    mgr.rebuild({ tensorA, tensorB });
 
     std::string shader(R"(
         #version 450
@@ -28,50 +27,60 @@ TEST(TestOpAlgoBase, ShaderRawDataFromConstructor)
         }
     )");
 
-    mgr.evalOpDefault<kp::OpAlgoBase>(
+    std::vector<uint32_t> spirv = kp::Shader::compile_source(shader);
-      { tensorA, tensorB }, kp::Shader::compile_source(shader));
 
-    mgr.evalOpDefault<kp::OpTensorSyncLocal>({ tensorA, tensorB });
+    std::vector<std::shared_ptr<kp::Tensor>> params = { tensorA, tensorB };
+
+    mgr.sequence()
+      ->eval<kp::OpTensorSyncDevice>(params)
+      ->eval<kp::OpAlgoDispatch>(mgr.algorithm(params, spirv))
+      ->eval<kp::OpTensorSyncLocal>(params);
 
     EXPECT_EQ(tensorA->data(), std::vector<float>({ 0, 1, 2 }));
     EXPECT_EQ(tensorB->data(), std::vector<float>({ 3, 4, 5 }));
 }
 
-TEST(TestOpAlgoBase, ShaderCompiledDataFromConstructor)
+TEST(TestOpAlgoCreate, ShaderCompiledDataFromConstructor)
 {
     kp::Manager mgr;
 
-    std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor({ 3, 4, 5 }) };
+    std::shared_ptr<kp::Tensor> tensorA = mgr.tensor({ 3, 4, 5 });
-    std::shared_ptr<kp::Tensor> tensorB{ new kp::Tensor({ 0, 0, 0 }) };
+    std::shared_ptr<kp::Tensor> tensorB = mgr.tensor({ 0, 0, 0 });
-    mgr.rebuild({ tensorA, tensorB });
 
-    mgr.evalOpDefault<kp::OpAlgoBase>(
+    std::vector<uint32_t> spirv = std::vector<uint32_t>(
-      { tensorA, tensorB },
+      (uint32_t*)
-      std::vector<uint32_t>(
+        kp::shader_data::test_shaders_glsl_test_op_custom_shader_comp_spv,
-        (uint32_t*)kp::shader_data::test_shaders_glsl_test_op_custom_shader_comp_spv,
+      (uint32_t*)(kp::shader_data::
-        (uint32_t*)(kp::shader_data::test_shaders_glsl_test_op_custom_shader_comp_spv +
+                    test_shaders_glsl_test_op_custom_shader_comp_spv +
-          kp::shader_data::
+                  kp::shader_data::
-            test_shaders_glsl_test_op_custom_shader_comp_spv_len)));
+                    test_shaders_glsl_test_op_custom_shader_comp_spv_len));
 
-    mgr.evalOpDefault<kp::OpTensorSyncLocal>({ tensorA, tensorB });
+    std::vector<std::shared_ptr<kp::Tensor>> params = { tensorA, tensorB };
+
+    mgr.sequence()
+      ->eval<kp::OpTensorSyncDevice>(params)
+      ->eval<kp::OpAlgoDispatch>(mgr.algorithm(params, spirv))
+      ->eval<kp::OpTensorSyncLocal>(params);
 
     EXPECT_EQ(tensorA->data(), std::vector<float>({ 0, 1, 2 }));
     EXPECT_EQ(tensorB->data(), std::vector<float>({ 3, 4, 5 }));
 }
 
-TEST(TestOpAlgoBase, ShaderCompiledDataFromFile)
+// TODO: Add support to read from file for shader
-{
+// TEST(TestOpAlgoCreate, ShaderCompiledDataFromFile)
-    kp::Manager mgr;
+//{
-
+//    kp::Manager mgr;
-    std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor({ 3, 4, 5 }) };
+//
-    std::shared_ptr<kp::Tensor> tensorB{ new kp::Tensor({ 0, 0, 0 }) };
+//    std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor({ 3, 4, 5 }) };
-    mgr.rebuild({ tensorA, tensorB });
+//    std::shared_ptr<kp::Tensor> tensorB{ new kp::Tensor({ 0, 0, 0 }) };
-
+//    mgr.rebuild({ tensorA, tensorB });
-    mgr.evalOpDefault<kp::OpAlgoBase>(
+//
-      { tensorA, tensorB }, "test/shaders/glsl/test_op_custom_shader.comp.spv");
+//    mgr.evalOpDefault<kp::OpAlgoCreate>(
-
+//      { tensorA, tensorB },
-    mgr.evalOpDefault<kp::OpTensorSyncLocal>({ tensorA, tensorB });
+//      "test/shaders/glsl/test_op_custom_shader.comp.spv");
-
+//
-    EXPECT_EQ(tensorA->data(), std::vector<float>({ 0, 1, 2 }));
+//    mgr.evalOpDefault<kp::OpTensorSyncLocal>({ tensorA, tensorB });
-    EXPECT_EQ(tensorB->data(), std::vector<float>({ 3, 4, 5 }));
+//
-}
+//    EXPECT_EQ(tensorA->data(), std::vector<float>({ 0, 1, 2 }));
+//    EXPECT_EQ(tensorB->data(), std::vector<float>({ 3, 4, 5 }));
+//}

test/TestOpTensorCopy.cpp

@@ -11,20 +11,18 @@ TEST(TestOpTensorCopy, CopyDeviceToDeviceTensor)
     std::vector<float> testVecA{ 1, 2, 3 };
     std::vector<float> testVecB{ 0, 0, 0 };
 
-    std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor(testVecA) };
+    std::shared_ptr<kp::Tensor> tensorA = mgr.tensor(testVecA);
-    std::shared_ptr<kp::Tensor> tensorB{ new kp::Tensor(testVecB) };
+    std::shared_ptr<kp::Tensor> tensorB = mgr.tensor(testVecB);
-
-    mgr.rebuild({ tensorA, tensorB });
 
     EXPECT_TRUE(tensorA->isInit());
     EXPECT_TRUE(tensorB->isInit());
 
-    mgr.evalOpDefault<kp::OpTensorCopy>({ tensorA, tensorB });
+    mgr.sequence()
-
+      ->eval<kp::OpTensorSyncDevice>({ tensorA, tensorB })
-    EXPECT_EQ(tensorA->data(), tensorB->data());
+      ->eval<kp::OpTensorCopy>({ tensorA, tensorB })
+      ->eval<kp::OpTensorSyncLocal>({ tensorA, tensorB });
 
     // Making sure the GPU holds the same data
-    mgr.evalOpDefault<kp::OpTensorSyncLocal>({ tensorB });
     EXPECT_EQ(tensorA->data(), tensorB->data());
 }
 
@@ -37,23 +35,24 @@ TEST(TestOpTensorCopy, CopyDeviceToDeviceTensorMulti)
     std::vector<float> testVecB{ 0, 0, 0 };
     std::vector<float> testVecC{ 0, 0, 0 };
 
-    std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor(testVecA) };
+    std::shared_ptr<kp::Tensor> tensorA = mgr.tensor(testVecA);
-    std::shared_ptr<kp::Tensor> tensorB{ new kp::Tensor(testVecB) };
+    std::shared_ptr<kp::Tensor> tensorB = mgr.tensor(testVecB);
-    std::shared_ptr<kp::Tensor> tensorC{ new kp::Tensor(testVecC) };
+    std::shared_ptr<kp::Tensor> tensorC = mgr.tensor(testVecC);
-
-    mgr.rebuild({ tensorA, tensorB, tensorC });
 
     EXPECT_TRUE(tensorA->isInit());
     EXPECT_TRUE(tensorB->isInit());
     EXPECT_TRUE(tensorC->isInit());
 
-    mgr.evalOpDefault<kp::OpTensorCopy>({ tensorA, tensorB, tensorC });
+    mgr.sequence()
+      ->eval<kp::OpTensorSyncLocal>({ tensorA, tensorB, tensorC })
+      ->eval<kp::OpTensorCopy>({ tensorA, tensorB, tensorC });
 
     EXPECT_EQ(tensorA->data(), tensorB->data());
     EXPECT_EQ(tensorA->data(), tensorC->data());
 
     // Making sure the GPU holds the same data
-    mgr.evalOpDefault<kp::OpTensorSyncLocal>({ tensorB, tensorC });
+    mgr.sequence()->eval<kp::OpTensorSyncLocal>({ tensorB, tensorC });
+
     EXPECT_EQ(tensorA->data(), tensorB->data());
     EXPECT_EQ(tensorA->data(), tensorC->data());
 }
@@ -66,24 +65,22 @@ TEST(TestOpTensorCopy, CopyDeviceToHostTensor)
     std::vector<float> testVecA{ 3, 4, 5 };
     std::vector<float> testVecB{ 0, 0, 0 };
 
-    std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor(testVecA) };
+    std::shared_ptr<kp::Tensor> tensorA = mgr.tensor(testVecA);
-    std::shared_ptr<kp::Tensor> tensorB{ new kp::Tensor(
+    std::shared_ptr<kp::Tensor> tensorB =
-      testVecB, kp::Tensor::TensorTypes::eHost) };
+      mgr.tensor(testVecB, kp::Tensor::TensorTypes::eHost);
-
-    mgr.rebuild({ tensorA, tensorB }, false);
 
     //  Only calling sync on device type tensor
-    mgr.evalOpDefault<kp::OpTensorSyncDevice>({ tensorA });
+    mgr.sequence()->eval<kp::OpTensorSyncDevice>({ tensorA });
 
     EXPECT_TRUE(tensorA->isInit());
     EXPECT_TRUE(tensorB->isInit());
 
-    mgr.evalOpDefault<kp::OpTensorCopy>({ tensorA, tensorB });
+    mgr.sequence()->eval<kp::OpTensorCopy>({ tensorA, tensorB });
 
     EXPECT_EQ(tensorA->data(), tensorB->data());
 
     // Making sure the GPU holds the same data
-    mgr.evalOpDefault<kp::OpTensorSyncLocal>({ tensorB });
+    mgr.sequence()->eval<kp::OpTensorSyncLocal>({ tensorB });
     EXPECT_EQ(tensorA->data(), tensorB->data());
 }
 
@@ -95,27 +92,22 @@ TEST(TestOpTensorCopy, CopyHostToDeviceTensor)
     std::vector<float> testVecA{ 4, 5, 6 };
     std::vector<float> testVecB{ 0, 0, 0 };
 
-    std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor(
+    std::shared_ptr<kp::Tensor> tensorA =
-      testVecA, kp::Tensor::TensorTypes::eHost) };
+      mgr.tensor(testVecA, kp::Tensor::TensorTypes::eHost);
-    std::shared_ptr<kp::Tensor> tensorB{ new kp::Tensor(testVecB) };
+    std::shared_ptr<kp::Tensor> tensorB = mgr.tensor(testVecB);
-
-    mgr.rebuild({ tensorA, tensorB }, false);
-
-    // Manually copy data into host memory of Tensor
-    tensorA->mapDataIntoHostMemory();
 
     //  Only calling sync on device type tensor
-    mgr.evalOpDefault<kp::OpTensorSyncDevice>({ tensorB });
+    mgr.sequence()->eval<kp::OpTensorSyncDevice>({ tensorA, tensorB });
 
     EXPECT_TRUE(tensorA->isInit());
     EXPECT_TRUE(tensorB->isInit());
 
-    mgr.evalOpDefault<kp::OpTensorCopy>({ tensorA, tensorB });
+    mgr.sequence()->eval<kp::OpTensorCopy>({ tensorA, tensorB });
 
     EXPECT_EQ(tensorA->data(), tensorB->data());
 
     // Making sure the GPU holds the same data
-    mgr.evalOpDefault<kp::OpTensorSyncLocal>({ tensorB });
+    mgr.sequence()->eval<kp::OpTensorSyncLocal>({ tensorB });
     EXPECT_EQ(tensorA->data(), tensorB->data());
 }
 
@@ -127,22 +119,22 @@ TEST(TestOpTensorCopy, CopyHostToHostTensor)
     std::vector<float> testVecA{ 5, 6, 7 };
     std::vector<float> testVecB{ 0, 0, 0 };
 
-    std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor(
+    std::shared_ptr<kp::Tensor> tensorA =
-      testVecA, kp::Tensor::TensorTypes::eHost) };
+      mgr.tensor(testVecA, kp::Tensor::TensorTypes::eHost);
-    std::shared_ptr<kp::Tensor> tensorB{ new kp::Tensor(
+    std::shared_ptr<kp::Tensor> tensorB =
-      testVecB, kp::Tensor::TensorTypes::eHost) };
+      mgr.tensor(testVecB, kp::Tensor::TensorTypes::eHost);
-
-    mgr.rebuild({ tensorA, tensorB });
 
     EXPECT_TRUE(tensorA->isInit());
     EXPECT_TRUE(tensorB->isInit());
 
-    mgr.evalOpDefault<kp::OpTensorCopy>({ tensorA, tensorB });
+    mgr.sequence()
+      ->eval<kp::OpTensorSyncDevice>({ tensorA })
+      ->eval<kp::OpTensorCopy>({ tensorA, tensorB });
 
     EXPECT_EQ(tensorA->data(), tensorB->data());
 
     // Making sure the GPU holds the same data
-    mgr.evalOpDefault<kp::OpTensorSyncLocal>({ tensorB });
+    mgr.sequence()->eval<kp::OpTensorSyncLocal>({ tensorB });
     EXPECT_EQ(tensorA->data(), tensorB->data());
 }
 
@@ -153,13 +145,11 @@ TEST(TestOpTensorCopy, SingleTensorShouldFail)
 
     std::vector<float> testVecA{ 6, 7, 8 };
 
-    std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor(
+    std::shared_ptr<kp::Tensor> tensorA =
-      testVecA, kp::Tensor::TensorTypes::eHost) };
+      mgr.tensor(testVecA, kp::Tensor::TensorTypes::eHost);
-
-    mgr.rebuild({ tensorA }, false);
 
     EXPECT_TRUE(tensorA->isInit());
 
-    EXPECT_THROW(mgr.evalOpDefault<kp::OpTensorCopy>({ tensorA }),
+    EXPECT_THROW(mgr.sequence()->eval<kp::OpTensorCopy>({ tensorA }),
                  std::runtime_error);
 }

test/TestOpTensorCreate.cpp

@@ -6,12 +6,12 @@
 TEST(TestOpTensorCreate, CreateSingleTensorSingleOp)
 {
     std::vector<float> testVecA{ 9, 8, 7 };
-    std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor(testVecA) };
+    std::shared_ptr<kp::Tensor> tensorA = nullptr;
 
     {
         kp::Manager mgr;
 
-        mgr.rebuild({ tensorA });
+        tensorA = mgr.tensor(testVecA);
 
         EXPECT_TRUE(tensorA->isInit());
 
@@ -21,120 +21,23 @@ TEST(TestOpTensorCreate, CreateSingleTensorSingleOp)
     EXPECT_FALSE(tensorA->isInit());
 }
 
-TEST(TestOpTensorCreate, CreateMultipleTensorSingleOp)
-{
-
-    kp::Manager mgr;
-
-    std::vector<float> testVecA{ 9, 8, 7 };
-    std::vector<float> testVecB{ 6, 5, 4 };
-
-    std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor(testVecA) };
-    std::shared_ptr<kp::Tensor> tensorB{ new kp::Tensor(testVecB) };
-
-    mgr.rebuild({ tensorA, tensorB });
-
-    EXPECT_TRUE(tensorA->isInit());
-    EXPECT_TRUE(tensorB->isInit());
-
-    EXPECT_EQ(tensorA->data(), testVecA);
-    EXPECT_EQ(tensorB->data(), testVecB);
-}
-
-TEST(TestOpTensorCreate, CreateMultipleTensorMultipleOp)
-{
-
-    kp::Manager mgr;
-
-    std::vector<float> testVecA{ 9, 8, 7 };
-    std::vector<float> testVecB{ 6, 5, 4 };
-
-    std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor(testVecA) };
-    std::shared_ptr<kp::Tensor> tensorB{ new kp::Tensor(testVecB) };
-
-    mgr.rebuild({ tensorA });
-    mgr.rebuild({ tensorB });
-
-    EXPECT_TRUE(tensorA->isInit());
-    EXPECT_TRUE(tensorB->isInit());
-
-    EXPECT_EQ(tensorA->data(), testVecA);
-    EXPECT_EQ(tensorB->data(), testVecB);
-}
-
-TEST(TestOpTensorCreate, TestTensorMemoryManagedByManagerDestroyed)
-{
-
-    std::vector<float> testVecA{ 9, 8, 7 };
-    std::vector<float> testVecB{ 6, 5, 4 };
-
-    std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor(testVecA) };
-    std::shared_ptr<kp::Tensor> tensorB{ new kp::Tensor(testVecB) };
-
-    {
-        kp::Manager mgr;
-        mgr.rebuild({ tensorA });
-        mgr.rebuild({ tensorB });
-
-        EXPECT_TRUE(tensorA->isInit());
-        EXPECT_TRUE(tensorB->isInit());
-
-        EXPECT_EQ(tensorA->data(), testVecA);
-        EXPECT_EQ(tensorB->data(), testVecB);
-    }
-
-    EXPECT_FALSE(tensorA->isInit());
-    EXPECT_FALSE(tensorB->isInit());
-}
-
-TEST(TestOpTensorCreate, TestTensorMemoryManagedByManagerNOTDestroyed)
-{
-
-    std::vector<float> testVecA{ 9, 8, 7 };
-    std::vector<float> testVecB{ 6, 5, 4 };
-
-    std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor(testVecA) };
-    std::shared_ptr<kp::Tensor> tensorB{ new kp::Tensor(testVecB) };
-
-    kp::Manager mgr;
-
-    {
-        mgr.rebuild({ tensorA });
-        mgr.rebuild({ tensorB });
-
-        EXPECT_TRUE(tensorA->isInit());
-        EXPECT_TRUE(tensorB->isInit());
-
-        EXPECT_EQ(tensorA->data(), testVecA);
-        EXPECT_EQ(tensorB->data(), testVecB);
-    }
-
-    EXPECT_TRUE(tensorA->isInit());
-    EXPECT_TRUE(tensorB->isInit());
-}
-
 TEST(TestOpTensorCreate, NoErrorIfTensorFreedBefore)
 {
 
     std::vector<float> testVecA{ 9, 8, 7 };
     std::vector<float> testVecB{ 6, 5, 4 };
 
-    std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor(testVecA) };
-    std::shared_ptr<kp::Tensor> tensorB{ new kp::Tensor(testVecB) };
-
     kp::Manager mgr;
 
-    mgr.rebuild({ tensorA });
+    std::shared_ptr<kp::Tensor> tensorA = mgr.tensor(testVecA);
-    mgr.rebuild({ tensorB });
+    std::shared_ptr<kp::Tensor> tensorB = mgr.tensor(testVecB);
-
-    EXPECT_TRUE(tensorA->isInit());
-    EXPECT_TRUE(tensorB->isInit());
 
     EXPECT_EQ(tensorA->data(), testVecA);
     EXPECT_EQ(tensorB->data(), testVecB);
 
-    tensorA->freeMemoryDestroyGPUResources();
+    tensorA->destroy();
-    tensorB->freeMemoryDestroyGPUResources();
+    tensorB->destroy();
+
     EXPECT_FALSE(tensorA->isInit());
     EXPECT_FALSE(tensorB->isInit());
 }
@@ -143,12 +46,10 @@ TEST(TestOpTensorCreate, ExceptionOnZeroSizeTensor)
 {
     std::vector<float> testVecA;
 
-    std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor(testVecA) };
-
     kp::Manager mgr;
 
     try {
-        mgr.rebuild({ tensorA });
+        std::shared_ptr<kp::Tensor> tensorA = mgr.tensor(testVecA);
     } catch (const std::runtime_error& err) {
         // check exception
         ASSERT_TRUE(std::string(err.what()).find("zero-sized") !=

test/TestOpTensorSync.cpp

@@ -11,17 +11,15 @@ TEST(TestOpTensorSync, SyncToDeviceMemorySingleTensor)
     std::vector<float> testVecPreA{ 0, 0, 0 };
     std::vector<float> testVecPostA{ 9, 8, 7 };
 
-    std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor(testVecPreA) };
+    std::shared_ptr<kp::Tensor> tensorA = mgr.tensor(testVecPreA);
-
-    mgr.rebuild({ tensorA }, false);
 
     EXPECT_TRUE(tensorA->isInit());
 
     tensorA->setData(testVecPostA);
 
-    mgr.evalOpDefault<kp::OpTensorSyncDevice>({ tensorA });
+    mgr.sequence()->eval<kp::OpTensorSyncDevice>({ tensorA });
 
-    mgr.evalOpDefault<kp::OpTensorSyncLocal>({ tensorA });
+    mgr.sequence()->eval<kp::OpTensorSyncLocal>({ tensorA });
 
     EXPECT_EQ(tensorA->data(), testVecPostA);
 }
@@ -33,11 +31,9 @@ TEST(TestOpTensorSync, SyncToDeviceMemoryMultiTensor)
 
     std::vector<float> testVec{ 9, 8, 7 };
 
-    std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor({ 0, 0, 0 }) };
+    std::shared_ptr<kp::Tensor> tensorA = mgr.tensor({ 0, 0, 0 });
-    std::shared_ptr<kp::Tensor> tensorB{ new kp::Tensor({ 0, 0, 0 }) };
+    std::shared_ptr<kp::Tensor> tensorB = mgr.tensor({ 0, 0, 0 });
-    std::shared_ptr<kp::Tensor> tensorC{ new kp::Tensor({ 0, 0, 0 }) };
+    std::shared_ptr<kp::Tensor> tensorC = mgr.tensor({ 0, 0, 0 });
-
-    mgr.rebuild({ tensorA, tensorB, tensorC }, false);
 
     EXPECT_TRUE(tensorA->isInit());
     EXPECT_TRUE(tensorB->isInit());
@@ -45,11 +41,11 @@ TEST(TestOpTensorSync, SyncToDeviceMemoryMultiTensor)
 
     tensorA->setData(testVec);
 
-    mgr.evalOpDefault<kp::OpTensorSyncDevice>({ tensorA });
+    mgr.sequence()->eval<kp::OpTensorSyncDevice>({ tensorA });
 
-    mgr.evalOpDefault<kp::OpTensorCopy>({ tensorA, tensorB, tensorC });
+    mgr.sequence()->eval<kp::OpTensorCopy>({ tensorA, tensorB, tensorC });
 
-    mgr.evalOpDefault<kp::OpTensorSyncLocal>({ tensorA, tensorB, tensorC });
+    mgr.sequence()->eval<kp::OpTensorSyncLocal>({ tensorA, tensorB, tensorC });
 
     EXPECT_EQ(tensorA->data(), testVec);
     EXPECT_EQ(tensorB->data(), testVec);

test/TestPushConstant.cpp

@@ -0,0 +1,49 @@
+#include "gtest/gtest.h"
+
+#include "kompute/Kompute.hpp"
+
+#include "fmt/ranges.h"
+
+TEST(TestPushConstants, TestTwoConstants)
+{
+    {
+        std::string shader(R"(
+          #version 450
+          layout(push_constant) uniform PushConstants {
+            float x;
+            float y;
+            float z;
+          } pcs;
+          layout (local_size_x = 1) in;
+          layout(set = 0, binding = 0) buffer a { float pa[]; };
+          void main() {
+              pa[0] += pcs.x;
+              pa[1] += pcs.y;
+              pa[2] += pcs.z;
+          })");
+
+        std::vector<uint32_t> spirv = kp::Shader::compile_source(shader);
+
+        std::shared_ptr<kp::Sequence> sq = nullptr;
+
+        {
+            kp::Manager mgr;
+
+            std::shared_ptr<kp::Tensor> tensor = mgr.tensor({ 0, 0, 0 });
+
+            std::shared_ptr<kp::Algorithm> algo =
+              mgr.algorithm({ tensor }, spirv, kp::Workgroup({ 1 }));
+
+            sq = mgr.sequence()
+                   ->record<kp::OpTensorSyncDevice>({ tensor })
+                   ->record<kp::OpAlgoDispatch>(algo,
+                                                kp::Constants{ 0.1, 0.2, 0.3 })
+                   ->record<kp::OpAlgoDispatch>(algo,
+                                                kp::Constants{ 0.3, 0.2, 0.1 })
+                   ->record<kp::OpTensorSyncLocal>({ tensor })
+                   ->eval();
+
+            EXPECT_EQ(tensor->data(), kp::Constants({ 0.4, 0.4, 0.4 }));
+        }
+    }
+}

test/TestSequence.cpp

@@ -3,28 +3,6 @@
 
 #include "kompute/Kompute.hpp"
 
-TEST(TestSequence, CmdBufSequenceBeginEnd)
-{
-    kp::Manager mgr;
-
-    {
-        std::shared_ptr<kp::Sequence> sq =
-          mgr.sequence("newSequence");
-
-        EXPECT_TRUE(sq->eval());
-        EXPECT_TRUE(!sq->isRecording());
-        EXPECT_TRUE(sq->begin());
-        EXPECT_TRUE(sq->isRecording());
-        EXPECT_TRUE(!sq->begin());
-        EXPECT_TRUE(sq->isRecording());
-        EXPECT_TRUE(sq->end());
-        EXPECT_TRUE(!sq->isRecording());
-        EXPECT_TRUE(!sq->end());
-        EXPECT_TRUE(!sq->isRecording());
-        EXPECT_TRUE(sq->eval());
-    }
-}
-
 TEST(TestSequence, SequenceDestructorViaManager)
 {
     std::shared_ptr<kp::Sequence> sq = nullptr;
@@ -32,11 +10,10 @@ TEST(TestSequence, SequenceDestructorViaManager)
     {
         kp::Manager mgr;
 
-        sq = mgr.sequence("newSequence");
+        sq = mgr.sequence();
 
         EXPECT_TRUE(sq->isInit());
     }
 
     EXPECT_FALSE(sq->isInit());
 }
-

test/TestSpecializationConstant.cpp

@@ -4,46 +4,46 @@
 
 TEST(TestSpecializationConstants, TestTwoConstants)
 {
-    std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor({ 0, 0, 0 }) };
-    std::shared_ptr<kp::Tensor> tensorB{ new kp::Tensor({ 0, 0, 0 }) };
-
-    std::string shader(R"(
-      #version 450
-      layout (constant_id = 0) const float cOne = 1;
-      layout (constant_id = 1) const float cTwo = 1;
-      layout (local_size_x = 1) in;
-      layout(set = 0, binding = 0) buffer a { float pa[]; };
-      layout(set = 0, binding = 1) buffer b { float pb[]; };
-      void main() {
-          uint index = gl_GlobalInvocationID.x;
-          pa[index] = cOne;
-          pb[index] = cTwo;
-      })");
-
     {
+        std::string shader(R"(
+          #version 450
+          layout (constant_id = 0) const float cOne = 1;
+          layout (constant_id = 1) const float cTwo = 1;
+          layout (local_size_x = 1) in;
+          layout(set = 0, binding = 0) buffer a { float pa[]; };
+          layout(set = 0, binding = 1) buffer b { float pb[]; };
+          void main() {
+              uint index = gl_GlobalInvocationID.x;
+              pa[index] = cOne;
+              pb[index] = cTwo;
+          })");
+
+        std::vector<uint32_t> spirv = kp::Shader::compile_source(shader);
+
         std::shared_ptr<kp::Sequence> sq = nullptr;
 
         {
             kp::Manager mgr;
 
-            mgr.rebuild({ tensorA, tensorB });
+            std::shared_ptr<kp::Tensor> tensorA = mgr.tensor({ 0, 0, 0 });
+            std::shared_ptr<kp::Tensor> tensorB = mgr.tensor({ 0, 0, 0 });
 
-            sq = mgr.sequence();
+            std::vector<std::shared_ptr<kp::Tensor>> params = { tensorA,
+                                                                tensorB };
 
-            auto spec = kp::Constants({5.0, 0.3});
+            kp::Constants spec = kp::Constants({ 5.0, 0.3 });
 
-            sq->begin();
+            std::shared_ptr<kp::Algorithm> algo =
-            sq->record<kp::OpAlgoBase>(
+              mgr.algorithm(params, spirv, {}, spec);
-              { tensorA, tensorB },
-              kp::Shader::compile_source(shader),
-              kp::Workgroup(), spec);
-            sq->end();
 
-            sq->eval();
+            sq = mgr.sequence()
+                   ->record<kp::OpTensorSyncDevice>(params)
+                   ->record<kp::OpAlgoDispatch>(algo)
+                   ->record<kp::OpTensorSyncLocal>(params)
+                   ->eval();
 
-            mgr.evalOpDefault<kp::OpTensorSyncLocal>({ tensorA, tensorB });
+            EXPECT_EQ(tensorA->data(), std::vector<float>({ 5, 5, 5 }));
+            EXPECT_EQ(tensorB->data(), std::vector<float>({ 0.3, 0.3, 0.3 }));
         }
     }
-    EXPECT_EQ(tensorA->data(), std::vector<float>({ 5, 5, 5 }));
-    EXPECT_EQ(tensorB->data(), std::vector<float>({ 0.3, 0.3, 0.3 }));
 }

test/TestTensor.cpp

@@ -5,36 +5,9 @@
 
 TEST(TestTensor, ConstructorData)
 {
-    std::vector<float> vec{ 0, 1, 2 };
-    kp::Tensor tensor(vec);
-    EXPECT_EQ(tensor.size(), vec.size());
-    EXPECT_EQ(tensor.data(), vec);
-}
-
-TEST(TestTensor, CopyFromHostData)
-{
-    std::vector<float> vecA{ 0, 1, 2 };
-    std::vector<float> vecB{ 0, 0, 0 };
-
-    std::shared_ptr<kp::Tensor> tensorA =
-      std::make_shared<kp::Tensor>(vecA, kp::Tensor::TensorTypes::eHost);
-    std::shared_ptr<kp::Tensor> tensorB =
-      std::make_shared<kp::Tensor>(vecB, kp::Tensor::TensorTypes::eHost);
-
     kp::Manager mgr;
-
+    std::vector<float> vec{ 0, 1, 2 };
-    mgr.rebuild({ tensorA, tensorB });
+    std::shared_ptr<kp::Tensor> tensor = mgr.tensor(vec);
-
+    EXPECT_EQ(tensor->size(), vec.size());
-    if (std::shared_ptr<kp::Sequence> sq =
+    EXPECT_EQ(tensor->data(), vec);
-          mgr.sequence("new")) {
-        sq->begin();
-
-        sq->record<kp::OpTensorCopy>({ tensorA, tensorB });
-
-        sq->end();
-
-        sq->eval();
-    }
-
-    EXPECT_EQ(tensorA->data(), tensorB->data());
 }

test/TestWorkgroup.cpp

@@ -5,44 +5,63 @@
 
 #include "kompute_test/shaders/shadertest_workgroup.hpp"
 
-
 TEST(TestWorkgroup, TestSimpleWorkgroup)
 {
-    std::shared_ptr<kp::Tensor> tensorA{ new kp::Tensor(std::vector<float>(16 * 8)) };
+    std::shared_ptr<kp::Tensor> tensorA = nullptr;
-    std::shared_ptr<kp::Tensor> tensorB{ new kp::Tensor(std::vector<float>(16 * 8)) };
+    std::shared_ptr<kp::Tensor> tensorB = nullptr;
-
     {
         std::shared_ptr<kp::Sequence> sq = nullptr;
 
         {
             kp::Manager mgr;
 
-            mgr.rebuild({ tensorA, tensorB });
+            tensorA = mgr.tensor(std::vector<float>(16 * 8));
+            tensorB = mgr.tensor(std::vector<float>(16 * 8));
 
-            kp::Workgroup workgroup = {16, 8, 1};
+            std::vector<std::shared_ptr<kp::Tensor>> params = { tensorA,
+                                                                tensorB };
+
+            std::vector<uint32_t> spirv(
+              (uint32_t*)
+                kp::shader_data::test_shaders_glsl_test_workgroup_comp_spv,
+              (uint32_t*)(kp::shader_data::
+                            test_shaders_glsl_test_workgroup_comp_spv +
+                          kp::shader_data::
+                            test_shaders_glsl_test_workgroup_comp_spv_len));
+
+            kp::Workgroup workgroup = { 16, 8, 1 };
+
+            std::shared_ptr<kp::Algorithm> algorithm =
+              mgr.algorithm(params, spirv, workgroup);
 
             sq = mgr.sequence();
-            sq->begin();
+            sq->record<kp::OpTensorSyncDevice>(params);
-            sq->record<kp::OpAlgoBase>(
+            sq->record<kp::OpAlgoDispatch>(algorithm);
-              { tensorA, tensorB },
+            sq->record<kp::OpTensorSyncLocal>(params);
-              std::vector<uint32_t>(
-                (uint32_t*)kp::shader_data::test_shaders_glsl_test_workgroup_comp_spv,
-                (uint32_t*)(kp::shader_data::test_shaders_glsl_test_workgroup_comp_spv +
-                  kp::shader_data::test_shaders_glsl_test_workgroup_comp_spv_len)),
-                workgroup);
-            sq->end();
-
             sq->eval();
-
-            mgr.evalOpDefault<kp::OpTensorSyncLocal>({ tensorA, tensorB });
         }
     }
 
-    std::vector<float> expectedA = { 0,  0,  0,  0,  0,  0,  0,  0,  1,  1,  1,  1,  1,  1,  1,  1,  2,  2,  2,  2,  2,  2,  2,  2,  3,  3,  3,  3,  3,  3,  3,  3,  4,  4,  4,  4,  4,  4,  4,  4,  5,  5,  5,  5,  5,  5,  5,  5,  6,  6,  6,  6,  6,  6,  6,  6,  7,  7,  7,  7,  7,  7,  7,  7,  8,  8,  8,  8,  8,  8,  8,  8,  9,  9,  9,  9,  9,  9,  9,  9, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 15};
+    std::vector<float> expectedA = {
+        0,  0,  0,  0,  0,  0,  0,  0,  1,  1,  1,  1,  1,  1,  1,  1,
+        2,  2,  2,  2,  2,  2,  2,  2,  3,  3,  3,  3,  3,  3,  3,  3,
+        4,  4,  4,  4,  4,  4,  4,  4,  5,  5,  5,  5,  5,  5,  5,  5,
+        6,  6,  6,  6,  6,  6,  6,  6,  7,  7,  7,  7,  7,  7,  7,  7,
+        8,  8,  8,  8,  8,  8,  8,  8,  9,  9,  9,  9,  9,  9,  9,  9,
+        10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11,
+        12, 12, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 13, 13,
+        14, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 15
+    };
 
-    std::vector<float> expectedB = { 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7 };
+    std::vector<float> expectedB = {
+        0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5,
+        6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3,
+        4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1,
+        2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7,
+        0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5,
+        6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7
+    };
 
     EXPECT_EQ(tensorA->data(), expectedA);
     EXPECT_EQ(tensorB->data(), expectedB);
 }
-