Updated examples in readme
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Alejandro Saucedo
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3 changed files with 221 additions and 36 deletions
140
README.md
140
README.md
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@ -56,35 +56,65 @@ int main() {
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// 2. Create and initialise Kompute Tensors through manager
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auto tensorInA = mgr.tensor({ 2., 2., 2. });
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auto tensorInB = mgr.tensor({ 1., 2., 3. });
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auto tensorOut = mgr.tensor({ 0., 0., 0. });
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auto tensorOutA = mgr.tensor({ 0., 0., 0. });
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auto tensorOutB = mgr.tensor({ 0., 0., 0. });
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// 3. Specify "multiply shader" code (can also be raw string, spir-v bytes or file path)
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std::string shaderString = (R"(
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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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std::string shader = (R"(
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#version 450
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layout (local_size_x = 1) in;
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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 bina { float tina[]; };
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layout(set = 0, binding = 1) buffer binb { float tinb[]; };
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layout(set = 0, binding = 2) buffer bout { float tout[]; };
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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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// 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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// 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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tout[index] = tina[index] * tinb[index];
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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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// 3. Run operation with string shader synchronously
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mgr.evalOpDefault<kp::OpAlgoBase>(
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{ tensorInA, tensorInB, tensorOut },
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kp::Shader::compile_source(shaderString));
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kp::Workgroup workgroup({3, 1, 1});
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kp::Constants specConsts({ 2 });
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// 4. Map results back from GPU memory to print the results
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mgr.evalOpDefault<kp::OpTensorSyncLocal>({ tensorInA, tensorInB, tensorOut });
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auto algorithm = mgr.algorithm(params, kp::Shader::compile_source(shader), workgroup, specConsts);
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// Prints the output which is Output: { 2, 4, 6 }
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for (const float& elem : tensorOut->data()) std::cout << elem << " ";
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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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}
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```
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@ -94,34 +124,72 @@ int main() {
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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.
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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 = Manager()
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mgr = kp.Manager()
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# 2. Create and initialise Kompute Tensors (can be initialized with List[] or np.Array)
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tensor_in_a = Tensor([2, 2, 2])
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tensor_in_b = Tensor([1, 2, 3])
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tensor_out = 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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mgr.eval_tensor_create_def([tensor_in_a, tensor_in_b, tensor_out])
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params = [tensor_in_a, tensor_in_b, tensor_out_a, tensor_out_b]
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# 3. Specify "multiply shader" code (can also be raw string, spir-v bytes or file path)
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@python2shader
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def compute_shader_multiply(index=("input", "GlobalInvocationId", ivec3),
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data1=("buffer", 0, Array(f32)),
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data2=("buffer", 1, Array(f32)),
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data3=("buffer", 2, Array(f32))):
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i = index.x
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data3[i] = data1[i] * data2[i]
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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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# 4. Run multiplication operation synchronously
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mgr.eval_algo_data_def(
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[tensor_in_a, tensor_in_b, tensor_out], compute_shader_multiply.to_spirv())
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layout (local_size_x = 1) in;
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# 5. Map results back from GPU memory to print the results
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mgr.eval_tensor_sync_local_def([tensor_out])
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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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// 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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// 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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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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algo = mgr.algorithm(params, kp.Shader.compile_source(shader), workgroup, spec_consts)
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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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# 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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# ... Do other work asynchronously whilst GPU finishes
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sq.eval_await()
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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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# Prints [2.0, 4.0, 6.0]
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print(tensor_out.data())
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```
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### Interactive Notebooks & Hands on Videos
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@ -30,6 +30,63 @@ kp_log = logging.getLogger("kp")
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#
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# assert tensor_out.data() == [2.0, 4.0, 6.0]
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def test_end_to_end():
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mgr = kp.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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shader = """
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#version 450
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layout (local_size_x = 1) in;
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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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// 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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// 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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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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algo = mgr.algorithm(params, kp.Shader.compile_source(shader), workgroup, spec_consts)
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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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sq = mgr.sequence()
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sq.eval_async(kp.OpTensorSyncLocal(params))
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sq.eval_await()
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assert tensor_out_a.data().tolist() == [4, 8, 12]
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assert tensor_out_b.data().tolist() == [10, 10, 10]
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def test_shader_str():
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@ -3,6 +3,66 @@
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#include "kompute/Kompute.hpp"
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TEST(TestMultipleAlgoExecutions, TestEndToEndFunctionality) {
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kp::Manager mgr;
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auto tensorInA = mgr.tensor({ 2., 2., 2. });
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auto tensorInB = mgr.tensor({ 1., 2., 3. });
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auto tensorOutA = mgr.tensor({ 0., 0., 0. });
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auto tensorOutB = mgr.tensor({ 0., 0., 0. });
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std::string shader = (R"(
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#version 450
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layout (local_size_x = 1) in;
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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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// 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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// 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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std::vector<std::shared_ptr<kp::Tensor>> params = {tensorInA, tensorInB, tensorOutA, tensorOutB};
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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, kp::Shader::compile_source(shader), workgroup, specConsts);
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// 3. Run operation with string shader synchronously
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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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auto sq = mgr.sequence();
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sq->evalAsync<kp::OpTensorSyncLocal>(params);
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sq->evalAwait();
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EXPECT_EQ(tensorOutA->data(), std::vector<float>({ 4, 8, 12 }));
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EXPECT_EQ(tensorOutB->data(), std::vector<float>({ 10, 10, 10 }));
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}
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TEST(TestMultipleAlgoExecutions, SingleSequenceRecord)
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{
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