Updated examples to new interface
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Alejandro Saucedo
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6 changed files with 410 additions and 607 deletions
173
README.md
173
README.md
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@ -48,7 +48,8 @@ Below you can find a GPU multiplication example using the C++ and Python Kompute
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The C++ interface provides low level access to the native components of Kompute and Vulkan, enabling for [advanced optimizations](https://kompute.cc/overview/async-parallel.html) as well as [extension of components](https://kompute.cc/overview/reference.html).
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```c++
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int main() {
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void kompute(const std::string& shader) {
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// 1. Create Kompute Manager with default settings (device 0 and first compute compatible queue)
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kp::Manager mgr;
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@ -62,6 +63,42 @@ int main() {
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std::vector<std::shared_ptr<kp::Tensor>> params = {tensorInA, tensorInB, tensorOutA, tensorOutB};
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// 3. Create algorithm based on shader (supports buffers & push/spec constants)
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kp::Workgroup workgroup({3, 1, 1});
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kp::Constants specConsts({ 2 });
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kp::Constants pushConstsA({ 2.0 });
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kp::Constants pushConstsB({ 3.0 });
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auto algorithm = mgr.algorithm(params,
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kp::Shader::compile_source(shader),
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workgroup,
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specConsts);
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// 4. Run operation synchronously using sequence
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mgr.sequence()
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->record<kp::OpTensorSyncDevice>(params)
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->record<kp::OpAlgoDispatch>(algorithm, pushConstsA)
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->record<kp::OpAlgoDispatch>(algorithm, pushConstsB)
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->eval();
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// 5. Sync results from the GPU asynchronously
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sq = mgr.sequence()
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sq->evalAsync<kp::OpTensorSyncLocal>(params);
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// ... Do other work asynchronously whilst GPU finishes
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sq->evalAwait();
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// Prints the first output which is: { 4, 8, 12 }
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for (const float& elem : tensorOutA->data()) std::cout << elem << " ";
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// Prints the second output which is: { 10, 10, 10 }
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for (const float& elem : tensorOutB->data()) std::cout << elem << " ";
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} // Manages / releases all CPU and GPU memory resources
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int main() {
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// Define a raw string shader (or use the Kompute tools to compile to SPIRV / C++ header
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// files). This shader shows some of the main components including constants, buffers, etc
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std::string shader = (R"(
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#version 450
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@ -88,33 +125,8 @@ int main() {
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}
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)");
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kp::Workgroup workgroup({3, 1, 1});
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kp::Constants specConsts({ 2 });
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auto algorithm = mgr.algorithm(params, kp::Shader::compile_source(shader), workgroup, specConsts);
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kp::Constants pushConstsA({ 2.0 });
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kp::Constants pushConstsB({ 3.0 });
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// 4. Run operation synchronously using sequence
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mgr.sequence()
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->record<kp::OpTensorSyncDevice>(params)
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->record<kp::OpAlgoDispatch>(algorithm, pushConstsA)
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->record<kp::OpAlgoDispatch>(algorithm, pushConstsB)
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->eval();
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// 5. Sync results from the GPU asynchronously
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sq = mgr.sequence()
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sq->evalAsync<kp::OpTensorSyncLocal>(params);
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// ... Do other work asynchronously whilst GPU finishes
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sq->evalAwait();
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// Prints the first output which is: { 4, 8, 12 }
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for (const float& elem : tensorOutA->data()) std::cout << elem << " ";
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// Prints the second output which is: { 10, 10, 10 }
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for (const float& elem : tensorOutB->data()) std::cout << elem << " ";
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// Run the function declared above with our raw string shader
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kompute(shader);
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}
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```
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@ -125,70 +137,77 @@ The [Python package](https://kompute.cc/overview/python-package.html) provides a
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```python
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# 1. Create Kompute Manager with default settings (device 0 and first compute compatible queue)
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mgr = kp.Manager()
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def kompute(shader):
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# 1. Create Kompute Manager with default settings (device 0 and first compute compatible queue)
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mgr = kp.Manager()
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# 2. Create and initialise Kompute Tensors through manager
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tensor_in_a = mgr.tensor([2, 2, 2])
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tensor_in_b = mgr.tensor([1, 2, 3])
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tensor_out_a = mgr.tensor([0, 0, 0])
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tensor_out_b = mgr.tensor([0, 0, 0])
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# 2. Create and initialise Kompute Tensors through manager
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tensor_in_a = mgr.tensor([2, 2, 2])
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tensor_in_b = mgr.tensor([1, 2, 3])
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tensor_out_a = mgr.tensor([0, 0, 0])
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tensor_out_b = mgr.tensor([0, 0, 0])
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params = [tensor_in_a, tensor_in_b, tensor_out_a, tensor_out_b]
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params = [tensor_in_a, tensor_in_b, tensor_out_a, tensor_out_b]
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# 3. Create algorithm based on shader (supports buffers & push/spec constants)
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shader = """
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#version 450
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# 3. Create algorithm based on shader (supports buffers & push/spec constants)
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workgroup = (3, 1, 1)
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spec_consts = [2]
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push_consts_a = [2]
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push_consts_b = [3]
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layout (local_size_x = 1) in;
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algo = mgr.algorithm(params, kp.Shader.compile_source(shader), workgroup, spec_consts)
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// The input tensors bind index is relative to index in parameter passed
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layout(set = 0, binding = 0) buffer buf_in_a { float in_a[]; };
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layout(set = 0, binding = 1) buffer buf_in_b { float in_b[]; };
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layout(set = 0, binding = 2) buffer buf_out_a { float out_a[]; };
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layout(set = 0, binding = 3) buffer buf_out_b { float out_b[]; };
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# 4. Run operation synchronously using sequence
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(mgr.sequence()
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.record(kp.OpTensorSyncDevice(params))
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.record(kp.OpAlgoDispatch(algo, push_consts_a))
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.record(kp.OpAlgoDispatch(algo, push_consts_b))
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.eval())
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// Kompute supports push constants updated on dispatch
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layout(push_constant) uniform PushConstants {
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float val;
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} push_const;
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# 5. Sync results from the GPU asynchronously
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sq = mgr.sequence()
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sq.eval_async(kp.OpTensorSyncLocal(params))
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// Kompute also supports spec constants on initalization
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layout(constant_id = 0) const float const_one = 0;
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# ... Do other work asynchronously whilst GPU finishes
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void main() {
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uint index = gl_GlobalInvocationID.x;
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out_a[index] += in_a[index] * in_b[index];
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out_b[index] += const_one * push_const.val;
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}
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"""
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sq.eval_await()
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workgroup = (3, 1, 1)
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spec_consts = [2]
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push_consts_a = [2]
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push_consts_b = [3]
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# Prints the first output which is: { 4, 8, 12 }
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print(tensor_out_a)
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# Prints the first output which is: { 10, 10, 10 }
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print(tensor_out_b)
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algo = mgr.algorithm(params, kp.Shader.compile_source(shader), workgroup, spec_consts)
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if __name__ == "__main__":
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# 4. Run operation synchronously using sequence
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(mgr.sequence()
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.record(kp.OpTensorSyncDevice(params))
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.record(kp.OpAlgoDispatch(algo, push_consts_a))
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.record(kp.OpAlgoDispatch(algo, push_consts_b))
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.eval())
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# Define a raw string shader (or use the Kompute tools to compile to SPIRV / C++ header
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# files). This shader shows some of the main components including constants, buffers, etc
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shader = """
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#version 450
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# 5. Sync results from the GPU asynchronously
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sq = mgr.sequence()
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sq.eval_async(kp.OpTensorSyncLocal(params))
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layout (local_size_x = 1) in;
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# ... Do other work asynchronously whilst GPU finishes
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// The input tensors bind index is relative to index in parameter passed
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layout(set = 0, binding = 0) buffer buf_in_a { float in_a[]; };
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layout(set = 0, binding = 1) buffer buf_in_b { float in_b[]; };
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layout(set = 0, binding = 2) buffer buf_out_a { float out_a[]; };
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layout(set = 0, binding = 3) buffer buf_out_b { float out_b[]; };
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sq.eval_await()
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// Kompute supports push constants updated on dispatch
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layout(push_constant) uniform PushConstants {
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float val;
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} push_const;
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# Prints the first output which is: { 4, 8, 12 }
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print(tensor_out_a)
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# Prints the first output which is: { 10, 10, 10 }
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print(tensor_out_b)
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// Kompute also supports spec constants on initalization
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layout(constant_id = 0) const float const_one = 0;
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void main() {
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uint index = gl_GlobalInvocationID.x;
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out_a[index] += in_a[index] * in_b[index];
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out_b[index] += const_one * push_const.val;
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}
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"""
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kompute(shader)
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```
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