Fix second implementation, add benchmark script

* Third implementation is broken (WIP)

examples/python_naive_matmul/imp2_tiled_debug.py

@@ -0,0 +1,156 @@
+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
+
+        print(f'{tile_size=}')
+        self.shader = f'''
+#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 (set = 0, binding = 3) writeonly buffer buf_test1_tensor {{ float test1_tensor[]; }};
+layout (set = 0, binding = 4) writeonly buffer buf_test2_tensor {{ float test2_tensor[]; }};
+layout (set = 0, binding = 5) writeonly buffer buf_test3_tensor {{ float test3_tensor[]; }};
+layout (set = 0, binding = 6) writeonly buffer buf_test4_tensor {{ float test4_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();
+    }}
+    uint globalIndex = (tensor_size * globalCol) + globalRow;
+    out_tensor[globalIndex] = acc;
+    test1_tensor[globalIndex] = row;
+    test2_tensor[globalIndex] = col;
+    test3_tensor[globalIndex] = gl_WorkGroupID.x;
+    test4_tensor[globalIndex] = gl_WorkGroupID.y;
+}}'''
+        print(self.shader)
+        self.compiled_shader = kp.Shader.compile_source(self.shader)
+        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, tensor_test_1: kp.Tensor, tensor_test_2: kp.Tensor,
+                 tensor_test_3: kp.Tensor, tensor_test_4: kp.Tensor):
+        # params = [tensor_in_1, tensor_in_2, tensor_out]
+        params = [tensor_in_1, tensor_in_2, tensor_out, tensor_test_1, tensor_test_2, tensor_test_3, tensor_test_4]
+
+        if self.algo is None or self.tensor_shape != tensor_shape or self.params != params:
+            self.tensor_shape = tensor_shape
+            self.params = params
+            workgroup = (tensor_shape[0] // self.tile_size, tensor_shape[1] // self.tile_size, 1)
+            # workgroup = (2, 2, 1)
+            print(f'{float(tensor_shape[0])=} {self.tile_size=} {workgroup=}')
+            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(self.params))
+         .record(kp.OpAlgoDispatch(self.algo))
+         .record(kp.OpTensorSyncLocal(self.params[2:]))
+         # .record(kp.OpTensorSyncLocal([tensor_out]))
+         .eval())
+
+
+def main():
+    mgr = kp.Manager()
+
+    matmul_op = MatMulOp(mgr, 4)
+
+    tensor_size = 8
+    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))
+    tensor_test_1 = mgr.tensor(np.zeros(tensor_shape))
+    tensor_test_2 = mgr.tensor(np.zeros(tensor_shape))
+    tensor_test_3 = mgr.tensor(np.zeros(tensor_shape))
+    tensor_test_4 = 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)
+    matmul_op(tensor_shape, tensor_in_1, tensor_in_2,
+              tensor_out, tensor_test_1, tensor_test_2, tensor_test_3, tensor_test_4)
+
+    # experiment_count = 10
+    # 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] - 1)
+
+    print(f'Output :\n{tensor_out.data().reshape(tensor_shape)}')
+    print(f'test_1 :\n{tensor_test_1.data().reshape(tensor_shape)}')
+    print(f'test_2 :\n{tensor_test_2.data().reshape(tensor_shape)}')
+    print(f'test_3 :\n{tensor_test_3.data().reshape(tensor_shape)}')
+    print(f'test_4 :\n{tensor_test_4.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()