Merge branch 'master' of github.com:KomputeFoundation/kompute

README.md

@@ -250,7 +250,7 @@ You are able to try out the interactive Colab Notebooks which allow you to use a
 <tr>
 
 <td width="50%">
-<h5>Try the interactive <a href="https://colab.research.google.com/drive/1l3hNSq2AcJ5j2E3YIw__jKy5n6M615GP?authuser=1#scrollTo=1BipBsO-fQRD">C++ Colab</a> from <a href="https://towardsdatascience.com/machine-learning-and-data-processing-in-the-gpu-with-vulkan-kompute-c9350e5e5d3a">Blog Post</a></h5>
+<h5>Try the interactive <a href="https://colab.research.google.com/drive/1l3hNSq2AcJ5j2E3YIw__jKy5n6M615GP?usp=sharing">C++ Colab</a> from <a href="https://towardsdatascience.com/machine-learning-and-data-processing-in-the-gpu-with-vulkan-kompute-c9350e5e5d3a">Blog Post</a></h5>
 </td>
 
 <td>

examples/python_naive_matmul/README.md

@@ -0,0 +1,9 @@
+# Naive Matmul Implementation
+
+This demonstrate a basic matmul implementation using Python and vulkan-kompute. Many thanks for the very helpful [SGEMM in WebGL2-compute](https://www.ibiblio.org/e-notes/webgl/gpu/mul/sgemm.htm) article on the public library [ibiblio.org](https://www.ibiblio.org/).
+
+To test the implementation simply run the `matmul.py` script :
+
+```
+python matmul.py
+```

examples/python_naive_matmul/benchmark.py

@@ -0,0 +1,56 @@
+import time
+
+import kp
+import numpy as np
+from imp1_naive import MatMulOp as MatMulOp1
+from imp2_tiled import MatMulOp as MatMulOp2
+from imp3_better_tiling import MatMulOp as MatMulOp3
+
+
+def main():
+    mgr = kp.Manager()
+    for tensor_size, experiment_count in [(512, 1000), (4096, 5)]:
+        tensor_shape = [tensor_size, tensor_size]
+        tensor_shape = [tensor_size, tensor_size]
+        mat_1 = np.triu(np.ones(tensor_shape))
+        mat_2 = np.triu(np.ones(tensor_shape))
+
+        tensor_in_1 = mgr.tensor(mat_1)
+        tensor_in_2 = mgr.tensor(mat_2)
+        tensor_out = mgr.tensor(np.zeros(tensor_shape))
+        if tensor_size <= 512:
+            mat_result = mat_1 @ mat_2
+        else:
+            MatMulOp1(mgr)(tensor_shape, tensor_in_1, tensor_in_2, tensor_out)
+            mat_result = tensor_out.data().reshape(tensor_shape)  # CPU is too slow for big sizes
+
+        print(f'{tensor_shape} input tensors:\n'
+              f'{mat_1}\n'
+              f'{mat_2}\n')
+        print(f'Output :\n{mat_result}')
+
+        for MatMulOp in [MatMulOp1, MatMulOp2, MatMulOp3]:
+            tensor_out.data()[:] = 0
+            mgr.sequence().record(kp.OpTensorSyncDevice([tensor_out]))
+            matmul_op = MatMulOp(mgr)
+            matmul_op(tensor_shape, tensor_in_1, tensor_in_2, tensor_out)
+
+            start_time = time.time()
+            for _ in range(experiment_count):
+                matmul_op(tensor_shape, tensor_in_1, tensor_in_2, tensor_out)
+            end_time = time.time()
+            experiment_time = end_time - start_time
+            op_count = tensor_shape[0] * tensor_shape[1] * ((tensor_shape[1] * 2) - 1)
+
+            # print(tensor_out.data().reshape(tensor_shape))
+            if (tensor_out.data().reshape(tensor_shape) == mat_result).all():
+                print(f'From {MatMulOp.__module__} : {experiment_count} matmul time : '
+                      f'{experiment_time * 1000:0.2f}ms => '
+                      f'{experiment_count / experiment_time:0.2f}op/s or '
+                      f'{experiment_count * op_count / (1e9 * experiment_time):0.2f} GFLOPS')
+            else:
+                print(f'Test failed => output tensor is wrong :\n{tensor_out.data().reshape(tensor_shape)}')
+
+
+if __name__ == '__main__':
+    main()

examples/python_naive_matmul/first_example.py

@@ -0,0 +1,60 @@
+import kp
+import numpy as np
+
+
+def main():
+    mgr = kp.Manager()
+
+    tensor_size = 4
+    tensor_shape = [tensor_size, tensor_size]
+    tensor_in_1 = mgr.tensor(np.triu(np.ones(tensor_shape)))
+    tensor_in_2 = mgr.tensor(np.triu(np.ones(tensor_shape)))
+    tensor_out = mgr.tensor(np.zeros(tensor_shape))
+
+    print(f'Input tensors:\n'
+          f'{tensor_in_1.data().reshape(tensor_shape)}\n'
+          f'{tensor_in_2.data().reshape(tensor_shape)}\n')
+
+    params = [tensor_in_1, tensor_in_2, tensor_out]
+
+    matmul_shader = kp.Shader.compile_source('''
+#version 450
+
+layout (local_size_x = 1, local_size_y = 1) in;
+
+layout (set = 0, binding = 0) readonly buffer buf_in_tensor_1 { float in_tensor_1[]; };
+layout (set = 0, binding = 1) readonly buffer buf_in_tensor_2 { float in_tensor_2[]; };
+layout (set = 0, binding = 2) writeonly buffer buf_out_tensor { float out_tensor[]; };
+
+layout (constant_id = 0) const float tensor_size_f = 0;
+
+
+void main()
+{
+    uint globalRow = gl_GlobalInvocationID.x;
+    uint globalCol = gl_GlobalInvocationID.y;
+    uint tensor_size = uint(tensor_size_f);
+    float acc = 0.0;
+    for(uint k = 0u; k < tensor_size; k++)
+        acc += in_tensor_1[(k * tensor_size) + globalRow] * in_tensor_2[(globalCol * tensor_size) + k];
+    out_tensor[(globalCol * tensor_size) + globalRow] = acc;
+}''')
+
+    algo = mgr.algorithm(
+        params,  # params
+        matmul_shader,  # spirv
+        (*tensor_shape, 1),  # workgroup
+        [float(tensor_size)],  # spec_consts
+        [])  # push_consts
+
+    (mgr.sequence()
+     .record(kp.OpTensorSyncDevice(params))
+     .record(kp.OpAlgoDispatch(algo))
+     .record(kp.OpTensorSyncLocal(params))
+     .eval())
+
+    print(f'Output :\n{tensor_out.data().reshape(tensor_shape)}')
+
+
+if __name__ == '__main__':
+    main()

examples/python_naive_matmul/imp1_naive.py

@@ -0,0 +1,133 @@
+import time
+
+import kp
+import numpy as np
+
+
+class MatMulOp:
+    def __init__(self, manager: kp.Manager, local_size_x: int = -1, local_size_y: int = -1):
+        self.mgr = manager
+
+        props = self.mgr.get_device_properties()
+        max_workgroup_invocation = props['max_work_group_invocations']
+        max_workgroup_size = props['max_work_group_size']
+        if local_size_x < 1:
+            if local_size_y > 0:
+                local_size_x = 1
+                while (2 * local_size_x * local_size_y <= max_workgroup_invocation
+                       and 2 * local_size_x <= max_workgroup_size[0]):
+                    local_size_x *= 2
+            else:
+                local_size_x = 1
+                local_size_y = 1
+                while 2 * local_size_x * local_size_y <= max_workgroup_invocation:
+                    if 2 * local_size_x <= max_workgroup_size[0]:
+                        local_size_x *= 2
+                    if 2 * local_size_y <= max_workgroup_size[1]:
+                        local_size_y *= 2
+                    elif 2 * local_size_x > max_workgroup_size[0]:  # stop if neither x nor y can be double
+                        break
+        elif local_size_y < 0:
+            local_size_y = 1
+            while (2 * local_size_x * local_size_y <= max_workgroup_invocation
+                   and 2 * local_size_x <= max_workgroup_size[0]):
+                local_size_y *= 2
+
+        assert local_size_x > 0
+        assert local_size_y > 0
+        assert local_size_x * local_size_y <= max_workgroup_invocation
+        assert local_size_x <= max_workgroup_size[0]
+        assert local_size_y <= max_workgroup_size[1]
+        self.local_size_x = local_size_x
+        self.local_size_y = local_size_y
+
+        self.shader = '''
+#version 450
+
+layout (local_size_x = {local_size_x}, local_size_y = {local_size_y}) in;
+
+layout (set = 0, binding = 0) readonly buffer buf_in_tensor_1 {{ float in_tensor_1[]; }};
+layout (set = 0, binding = 1) readonly buffer buf_in_tensor_2 {{ float in_tensor_2[]; }};
+layout (set = 0, binding = 2) writeonly buffer buf_out_tensor {{ float out_tensor[]; }};
+
+layout (constant_id = 0) const float tensor_size_f = 0;
+
+
+void main()
+{{
+    uint globalRow = gl_GlobalInvocationID.x;
+    uint globalCol = gl_GlobalInvocationID.y;
+    uint tensor_size = uint(tensor_size_f);
+    float acc = 0.0;
+    for(uint k = 0u; k < tensor_size; k++)
+        acc += in_tensor_1[(k * tensor_size) + globalRow] * in_tensor_2[(globalCol * tensor_size) + k];
+    out_tensor[(globalCol * tensor_size) + globalRow] = acc;
+}}'''
+        self.compiled_shader = kp.Shader.compile_source(self.shader.format(
+            local_size_x=self.local_size_x, local_size_y=self.local_size_y))
+        self.tensor_shape: tuple[int, int] = (0, 0)
+        self.params: list[kp.Tensor] = []
+        self.algo = None
+
+    def __call__(self, tensor_shape: tuple[int, int], tensor_in_1: kp.Tensor, tensor_in_2: kp.Tensor,
+                 tensor_out: kp.Tensor):
+        params = [tensor_in_1, tensor_in_2, tensor_out]
+
+        if self.algo is None or self.tensor_shape != tensor_shape or self.params != params:
+            self.tensor_shape = tensor_shape
+            self.params = params
+            local_size_x = min(self.local_size_x, tensor_shape[0])
+            local_size_y = min(self.local_size_y, tensor_shape[1])
+            self.compiled_shader = kp.Shader.compile_source(self.shader.format(
+                local_size_x=local_size_x, local_size_y=local_size_y))
+            workgroup = (tensor_shape[0] // local_size_x, tensor_shape[1] // local_size_y, 1)
+            print(f'{workgroup=} {self.local_size_x=} {self.local_size_y=}')
+            self.algo = self.mgr.algorithm(
+                params,  # params
+                self.compiled_shader,  # spirv
+                workgroup,  # workgroup
+                [float(tensor_shape[0])],  # spec_consts
+                [])  # push_consts
+
+        (self.mgr.sequence()
+         .record(kp.OpTensorSyncDevice([tensor_in_1, tensor_in_2]))
+         .record(kp.OpAlgoDispatch(self.algo))
+         .record(kp.OpTensorSyncLocal([tensor_out]))
+         .eval())
+
+
+def main():
+    mgr = kp.Manager()
+
+    matmul_op = MatMulOp(mgr)
+
+    tensor_size = 4064
+    tensor_shape = [tensor_size, tensor_size]
+    tensor_in_1 = mgr.tensor(np.triu(np.ones(tensor_shape)))
+    tensor_in_2 = mgr.tensor(np.triu(np.ones(tensor_shape)))
+    tensor_out = mgr.tensor(np.zeros(tensor_shape))
+
+    print(f'{tensor_shape} input tensors:\n'
+          f'{tensor_in_1.data().reshape(tensor_shape)}\n'
+          f'{tensor_in_2.data().reshape(tensor_shape)}\n')
+
+    matmul_op(tensor_shape, tensor_in_1, tensor_in_2, tensor_out)
+
+    experiment_count = 8
+    start_time = time.time()
+    for _ in range(experiment_count):
+        matmul_op(tensor_shape, tensor_in_1, tensor_in_2, tensor_out)
+    end_time = time.time()
+    experiment_time = end_time - start_time
+    op_count = tensor_shape[0] * tensor_shape[1] * ((tensor_shape[1] * 2) - 1)
+
+    print(f'Output :\n{tensor_out.data().reshape(tensor_shape)}')
+
+    print(f'{experiment_count} matmul time : '
+          f'{experiment_time * 1000:0.2f}ms => '
+          f'{experiment_count / experiment_time:0.2f}op/s or '
+          f'{experiment_count * op_count / (1e9 * experiment_time):0.2f} GFLOPS')
+
+
+if __name__ == '__main__':
+    main()

examples/python_naive_matmul/imp2_tiled.py

@@ -0,0 +1,132 @@
+import time
+
+import kp
+import numpy as np
+
+
+class MatMulOp:
+    def __init__(self, manager: kp.Manager, tile_size: int = -1):
+        self.mgr = manager
+
+        props = self.mgr.get_device_properties()
+        max_workgroup_invocation = props['max_work_group_invocations']
+        max_workgroup_size = props['max_work_group_size']
+        if tile_size < 0:
+            tile_size = 1
+            while (4 * tile_size * tile_size <= max_workgroup_invocation
+                   and 2 * tile_size <= max_workgroup_size[0]
+                   and 2 * tile_size <= max_workgroup_size[1]):
+                tile_size *= 2
+
+        assert tile_size > 0
+        assert tile_size * tile_size <= max_workgroup_invocation
+        assert tile_size <= max_workgroup_size[0]
+        assert tile_size <= max_workgroup_size[1]
+        self.tile_size = tile_size
+
+        self.shader = '''
+#version 450
+
+layout (local_size_x = {tile_size}, local_size_y = {tile_size}) in;
+
+layout (set = 0, binding = 0) readonly buffer buf_in_tensor_1 {{ float in_tensor_1[]; }};
+layout (set = 0, binding = 1) readonly buffer buf_in_tensor_2 {{ float in_tensor_2[]; }};
+layout (set = 0, binding = 2) writeonly buffer buf_out_tensor {{ float out_tensor[]; }};
+
+layout (constant_id = 0) const float tensor_size_f = 0;
+
+shared float sub_tensor_1[{tile_size}][{tile_size}];
+shared float sub_tensor_2[{tile_size}][{tile_size}];
+
+void main()
+{{
+    uint row = gl_LocalInvocationID.x; // 0 .. tile_size
+    uint col = gl_LocalInvocationID.y; // 0 .. tile_size
+    // gl_WorkGroupID : 0 .. tensor_size / tile_size
+    uint globalRow = {tile_size} * gl_WorkGroupID.x + row;
+    uint globalCol = {tile_size} * gl_WorkGroupID.y + col;
+
+    uint tensor_size = uint(tensor_size_f);
+    float acc = 0.0;
+    uint numTiles = tensor_size / {tile_size};
+    for(uint t = 0u; t < numTiles; t++)
+    {{
+        uint tiledRow = ({tile_size} * t) + row;
+        uint tiledCol = ({tile_size} * t) + col;
+        sub_tensor_1[col][row] = in_tensor_1[(tiledCol * tensor_size) + globalRow];
+        sub_tensor_2[col][row] = in_tensor_2[(globalCol * tensor_size) + tiledRow];
+
+        memoryBarrierShared();
+        barrier();
+
+        for(uint k = 0u; k < {tile_size}; k++)
+            acc += sub_tensor_1[k][row] * sub_tensor_2[col][k];
+
+        barrier();
+    }}
+    out_tensor[tensor_size * globalCol + globalRow] = acc;
+}}'''
+        self.compiled_shader = kp.Shader.compile_source(self.shader.format(tile_size=tile_size))
+        self.tensor_shape: tuple[int, int] = (0, 0)
+        self.params: list[kp.Tensor] = []
+        self.algo = None
+
+    def __call__(self, tensor_shape: tuple[int, int], tensor_in_1: kp.Tensor, tensor_in_2: kp.Tensor,
+                 tensor_out: kp.Tensor):
+        params = [tensor_in_1, tensor_in_2, tensor_out]
+
+        if self.algo is None or self.tensor_shape != tensor_shape or self.params != params:
+            self.tensor_shape = tensor_shape
+            self.params = params
+            tile_size = min(tensor_shape[0], tensor_shape[1], self.tile_size)
+            self.compiled_shader = kp.Shader.compile_source(self.shader.format(tile_size=tile_size))
+            workgroup = [tensor_shape[0] // tile_size, tensor_shape[1] // tile_size, 1]
+            self.algo = self.mgr.algorithm(
+                params,  # params
+                self.compiled_shader,  # spirv
+                workgroup,  # workgroup
+                [float(tensor_shape[0])],  # spec_consts
+                [])  # push_consts
+
+        (self.mgr.sequence()
+         .record(kp.OpTensorSyncDevice([tensor_in_1, tensor_in_2]))
+         .record(kp.OpAlgoDispatch(self.algo))
+         .record(kp.OpTensorSyncLocal([tensor_out]))
+         .eval())
+
+
+def main():
+    mgr = kp.Manager()
+
+    matmul_op = MatMulOp(mgr)
+
+    tensor_size = 4096
+    tensor_shape = [tensor_size, tensor_size]
+    tensor_in_1 = mgr.tensor(np.triu(np.ones(tensor_shape)))
+    tensor_in_2 = mgr.tensor(np.triu(np.ones(tensor_shape)))
+    tensor_out = mgr.tensor(np.zeros(tensor_shape))
+
+    print(f'{tensor_shape} input tensors:\n'
+          f'{tensor_in_1.data().reshape(tensor_shape)}\n'
+          f'{tensor_in_2.data().reshape(tensor_shape)}\n')
+
+    matmul_op(tensor_shape, tensor_in_1, tensor_in_2, tensor_out)
+
+    experiment_count = 8
+    start_time = time.time()
+    for _ in range(experiment_count):
+        matmul_op(tensor_shape, tensor_in_1, tensor_in_2, tensor_out)
+    end_time = time.time()
+    experiment_time = end_time - start_time
+    op_count = tensor_shape[0] * tensor_shape[1] * ((tensor_shape[1] * 2) - 1)
+
+    print(f'Output :\n{tensor_out.data().reshape(tensor_shape)}')
+
+    print(f'{experiment_count} matmul time : '
+          f'{experiment_time * 1000:0.2f}ms => '
+          f'{experiment_count / experiment_time:0.2f}op/s or '
+          f'{experiment_count * op_count / (1e9 * experiment_time):0.2f} GFLOPS')
+
+
+if __name__ == '__main__':
+    main()

examples/python_naive_matmul/imp3_better_tiling.py

@@ -0,0 +1,153 @@
+import time
+
+import kp
+import numpy as np
+
+
+class MatMulOp:
+    def __init__(self, manager: kp.Manager, tile_size: int = -1, thread_work_ratio: int = 16):
+        self.mgr = manager
+
+        props = self.mgr.get_device_properties()
+        max_workgroup_invocation = props['max_work_group_invocations']
+        max_workgroup_size = props['max_work_group_size']
+        if tile_size < 0:
+            tile_size = 1
+            local_size_y = tile_size // thread_work_ratio
+            while (4 * tile_size * tile_size <= max_workgroup_invocation
+                   and 2 * tile_size <= max_workgroup_size[0]
+                   and 2 * tile_size <= max_workgroup_size[1]):
+                tile_size *= 2
+                local_size_y = tile_size // thread_work_ratio
+        else:
+            local_size_y = tile_size // thread_work_ratio
+
+        assert tile_size > 0
+        assert thread_work_ratio > 0
+        assert tile_size * local_size_y <= max_workgroup_invocation
+        assert tile_size <= max_workgroup_size[0]
+        assert local_size_y <= max_workgroup_size[1]
+        self.tile_size = tile_size
+        self.thread_work_ratio = thread_work_ratio
+
+        self.local_size_x = tile_size
+        self.local_size_y = tile_size // thread_work_ratio
+        self.shader = '''
+#version 450
+
+layout (local_size_x = {tile_size}, local_size_y = {local_size_y}) in;
+
+layout (set = 0, binding = 0) readonly buffer buf_in_tensor_1 {{ float in_tensor_1[]; }};
+layout (set = 0, binding = 1) readonly buffer buf_in_tensor_2 {{ float in_tensor_2[]; }};
+layout (set = 0, binding = 2) writeonly buffer buf_out_tensor {{ float out_tensor[]; }};
+
+layout (constant_id = 0) const float tensor_size_f = 0;
+
+shared float sub_tensor_1[{tile_size}][{tile_size}];
+shared float sub_tensor_2[{tile_size}][{tile_size}];
+
+void main()
+{{
+    uint row = gl_LocalInvocationID.x;
+    uint col = gl_LocalInvocationID.y;
+    uint globalRow = {tile_size} * gl_WorkGroupID.x + row;
+    uint globalCol = {tile_size} * gl_WorkGroupID.y + col;
+
+    uint tensor_size = uint(tensor_size_f);
+    float acc[{thread_work_ratio}];
+    for(uint w = 0u; w < {thread_work_ratio}; w++)
+        acc[w] = 0.0;
+
+    uint numTiles = tensor_size / {tile_size};
+    for(uint t = 0u; t < numTiles; t++)
+    {{
+        for(uint w = 0u; w < {thread_work_ratio}; w++)
+        {{
+            uint tiledRow = {tile_size} * t + row;
+            uint tiledCol = {tile_size} * t + col;
+            sub_tensor_1[col + w * {local_size_y}][row] = in_tensor_1[
+                (tiledCol + w * {local_size_y}) * tensor_size + globalRow];
+            sub_tensor_2[col + w * {local_size_y}][row] = in_tensor_2[
+                (globalCol + w * {local_size_y})* tensor_size + tiledRow];
+        }}
+
+        memoryBarrierShared();
+        barrier();
+
+        for(uint k = 0u; k < {tile_size}; k++)
+            for(uint w = 0u; w < {thread_work_ratio}; w++)
+                acc[w] += sub_tensor_1[k][row] * sub_tensor_2[col + w * {local_size_y}][k];
+
+        barrier();
+    }}
+    for(uint w = 0u; w < {thread_work_ratio}; w++)
+        out_tensor[(globalCol + w * {local_size_y}) * tensor_size + globalRow] = acc[w];
+}}'''
+        self.compiled_shader = kp.Shader.compile_source(self.shader.format(
+            tile_size=tile_size, thread_work_ratio=thread_work_ratio, local_size_y=local_size_y))
+        self.tensor_shape: tuple[int, int] = (0, 0)
+        self.params: list[kp.Tensor] = []
+        self.algo = None
+
+    def __call__(self, tensor_shape: tuple[int, int], tensor_in_1: kp.Tensor, tensor_in_2: kp.Tensor,
+                 tensor_out: kp.Tensor):
+        params = [tensor_in_1, tensor_in_2, tensor_out]
+
+        if self.algo is None or self.tensor_shape != tensor_shape or self.params != params:
+            self.tensor_shape = tensor_shape
+            self.params = params
+            tile_size = min(self.tensor_shape[0], self.tile_size)
+            thread_work_ratio = min(self.tensor_shape[1] // self.tile_size, self.thread_work_ratio)
+            local_size_y = tile_size // thread_work_ratio
+            self.compiled_shader = kp.Shader.compile_source(self.shader.format(
+                tile_size=tile_size, thread_work_ratio=thread_work_ratio, local_size_y=local_size_y))
+            workgroup = (tensor_shape[0] // self.local_size_x, tensor_shape[1] // self.local_size_y, 1)
+            self.algo = self.mgr.algorithm(
+                params,  # params
+                self.compiled_shader,  # spirv
+                workgroup,  # workgroup
+                [float(tensor_shape[0])],  # spec_consts
+                [])  # push_consts
+
+        (self.mgr.sequence()
+         .record(kp.OpTensorSyncDevice([tensor_in_1, tensor_in_2]))
+         .record(kp.OpAlgoDispatch(self.algo))
+         .record(kp.OpTensorSyncLocal([tensor_out]))
+         .eval())
+
+
+def main():
+    mgr = kp.Manager()
+
+    matmul_op = MatMulOp(mgr)
+
+    tensor_size = 4096
+    tensor_shape = [tensor_size, tensor_size]
+    tensor_in_1 = mgr.tensor(np.triu(np.ones(tensor_shape)))
+    tensor_in_2 = mgr.tensor(np.triu(np.ones(tensor_shape)))
+    tensor_out = mgr.tensor(np.zeros(tensor_shape))
+
+    print(f'{tensor_shape} input tensors:\n'
+          f'{tensor_in_1.data().reshape(tensor_shape)}\n'
+          f'{tensor_in_2.data().reshape(tensor_shape)}\n')
+
+    matmul_op(tensor_shape, tensor_in_1, tensor_in_2, tensor_out)
+
+    experiment_count = 2
+    start_time = time.time()
+    for _ in range(experiment_count):
+        matmul_op(tensor_shape, tensor_in_1, tensor_in_2, tensor_out)
+    end_time = time.time()
+    experiment_time = end_time - start_time
+    op_count = tensor_shape[0] * tensor_shape[1] * ((tensor_shape[1] * 2) - 1)
+
+    print(f'Output :\n{tensor_out.data().reshape(tensor_shape)}')
+
+    print(f'{experiment_count} matmul time : '
+          f'{experiment_time * 1000:0.2f}ms => '
+          f'{experiment_count / experiment_time:0.2f}op/s or '
+          f'{experiment_count * op_count / (1e9 * experiment_time):0.2f} GFLOPS')
+
+
+if __name__ == '__main__':
+    main()

examples/python_naive_matmul/matmul_plot.py

@@ -0,0 +1,97 @@
+from argparse import ArgumentParser
+
+import cv2
+import numpy as np
+
+
+def plot_tensor(window_name: str, tensor: np.ndarray, coord_highlight: tuple[int, int] = None):
+    font_size = 48
+    image = np.zeros((tensor.shape[1] * font_size, tensor.shape[0] * font_size, 3), dtype=np.uint8)
+
+    for y in range(tensor.shape[1]):
+        for x in range(tensor.shape[0]):
+            if coord_highlight and x == coord_highlight[1] and y == coord_highlight[0]:
+                cv2.putText(
+                    image, str(int(tensor[y, x])), (x * font_size, int((y + 0.8) * font_size)),
+                    cv2.FONT_HERSHEY_TRIPLEX, 1., (127, 127, 255))
+            else:
+                cv2.putText(
+                    image, str(int(tensor[y, x])), (x * font_size, int((y + 0.8) * font_size)),
+                    cv2.FONT_HERSHEY_TRIPLEX, 1., (255, 255, 255))
+
+    cv2.imshow(window_name, image)
+
+
+def main():
+    parser = ArgumentParser()
+    parser.add_argument('tensor_size', type=int, help='Size of the square tensors')
+    parser.add_argument('tile_size', type=int)
+    parser.add_argument('local_size', type=int, nargs=2)
+    parser.add_argument('workgroup', type=int, nargs=2)
+    arguments = parser.parse_args()
+
+    tensor_size: int = arguments.tensor_size
+    tile_size: int = arguments.tile_size
+    local_size: tuple[int, int, int] = tuple(arguments.local_size)
+    workgroup: tuple[int, int, int] = tuple(arguments.workgroup)
+
+    tensor_shape = (tensor_size, tensor_size)
+    tensor_1 = np.triu(np.ones(tensor_shape))
+    tensor_2 = np.triu(np.ones(tensor_shape))
+    tensor_out = np.zeros(tensor_shape)
+    tensor_test_1 = np.zeros(tensor_shape)
+    tensor_test_2 = np.zeros(tensor_shape)
+    tensor_test_3 = np.zeros(tensor_shape)
+    tensor_test_4 = np.zeros(tensor_shape)
+    tensor_test_5 = np.zeros(tensor_shape)
+
+    plot_tensor('tensor_1', tensor_1)
+    plot_tensor('tensor_2', tensor_2)
+    plot_tensor('tensor_out', tensor_out)
+    plot_tensor('tensor_test_1', tensor_test_1)
+    plot_tensor('tensor_test_2', tensor_test_2)
+    plot_tensor('tensor_test_3', tensor_test_3)
+    plot_tensor('tensor_test_4', tensor_test_4)
+    plot_tensor('tensor_test_5', tensor_test_5)
+    cv2.waitKey(-1)
+
+    print(f'{workgroup=} {local_size=}')
+    for workgroup_x in range(workgroup[0]):
+        for workgroup_y in range(workgroup[1]):
+            for invocation_x in range(workgroup_x * local_size[0], (workgroup_x + 1) * local_size[0]):
+                for invocation_y in range(workgroup_y * local_size[1], (workgroup_y + 1) * local_size[1]):
+                    row = invocation_x
+                    col = invocation_y
+                    globalRow = (tile_size * workgroup_x) + row
+                    globalCol = (tile_size * workgroup_y) + col
+                    try:
+                        tensor_out[row, col] = row
+                        tensor_test_1[row, col] = col
+                        tensor_test_2[row, col] = workgroup_x
+                        tensor_test_3[row, col] = workgroup_y
+                        tensor_test_4[row, col] = globalRow
+                        tensor_test_5[row, col] = globalCol
+                        plot_tensor('tensor_out', tensor_out, (row, col))
+                        plot_tensor('tensor_test_1', tensor_test_1, (row, col))
+                        plot_tensor('tensor_test_2', tensor_test_2, (row, col))
+                        plot_tensor('tensor_test_3', tensor_test_3, (row, col))
+                        plot_tensor('tensor_test_4', tensor_test_4, (row, col))
+                        plot_tensor('tensor_test_5', tensor_test_5, (row, col))
+                        cv2.waitKey(-1)
+                    except IndexError as error:
+                        print(f'{workgroup_x=} {workgroup_y=} {row=} {col=}')
+                        raise error
+
+    plot_tensor('tensor_1', tensor_1)
+    plot_tensor('tensor_2', tensor_2)
+    plot_tensor('tensor_out', tensor_out)
+    plot_tensor('tensor_test_1', tensor_test_1)
+    plot_tensor('tensor_test_2', tensor_test_2)
+    plot_tensor('tensor_test_3', tensor_test_3)
+    plot_tensor('tensor_test_4', tensor_test_4)
+    plot_tensor('tensor_test_5', tensor_test_5)
+    cv2.waitKey(-1)
+
+
+if __name__ == '__main__':
+    main()