156 lines
6.3 KiB
Python
156 lines
6.3 KiB
Python
import time
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import kp
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import numpy as np
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class MatMulOp:
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def __init__(self, manager: kp.Manager, tile_size: int = -1):
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self.mgr = manager
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props = self.mgr.get_device_properties()
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max_workgroup_invocation = props['max_work_group_invocations']
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max_workgroup_size = props['max_work_group_size']
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if tile_size < 0:
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tile_size = 1
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while (4 * tile_size * tile_size <= max_workgroup_invocation
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and 2 * tile_size <= max_workgroup_size[0]
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and 2 * tile_size <= max_workgroup_size[1]):
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tile_size *= 2
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assert tile_size > 0
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assert tile_size * tile_size <= max_workgroup_invocation
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assert tile_size <= max_workgroup_size[0]
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assert tile_size <= max_workgroup_size[1]
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self.tile_size = tile_size
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print(f'{tile_size=}')
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self.shader = f'''
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#version 450
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layout (local_size_x = {tile_size}, local_size_y = {tile_size}) in;
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layout (set = 0, binding = 0) readonly buffer buf_in_tensor_1 {{ float in_tensor_1[]; }};
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layout (set = 0, binding = 1) readonly buffer buf_in_tensor_2 {{ float in_tensor_2[]; }};
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layout (set = 0, binding = 2) writeonly buffer buf_out_tensor {{ float out_tensor[]; }};
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layout (set = 0, binding = 3) writeonly buffer buf_test1_tensor {{ float test1_tensor[]; }};
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layout (set = 0, binding = 4) writeonly buffer buf_test2_tensor {{ float test2_tensor[]; }};
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layout (set = 0, binding = 5) writeonly buffer buf_test3_tensor {{ float test3_tensor[]; }};
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layout (set = 0, binding = 6) writeonly buffer buf_test4_tensor {{ float test4_tensor[]; }};
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layout (constant_id = 0) const float tensor_size_f = 0;
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shared float sub_tensor_1[{tile_size}][{tile_size}];
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shared float sub_tensor_2[{tile_size}][{tile_size}];
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void main()
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{{
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uint row = gl_LocalInvocationID.x; // 0 .. tile_size
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uint col = gl_LocalInvocationID.y; // 0 .. tile_size
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// gl_WorkGroupID : 0 .. tensor_size / tile_size
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uint globalRow = ({tile_size} * gl_WorkGroupID.x) + row;
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uint globalCol = ({tile_size} * gl_WorkGroupID.y) + col;
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uint tensor_size = uint(tensor_size_f);
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float acc = 0.0;
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uint numTiles = tensor_size / {tile_size};
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for(uint t = 0u; t < numTiles; t++)
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{{
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uint tiledRow = ({tile_size} * t) + row;
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uint tiledCol = ({tile_size} * t) + col;
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sub_tensor_1[col][row] = in_tensor_1[(tiledCol * tensor_size) + globalRow];
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sub_tensor_2[col][row] = in_tensor_2[(globalCol * tensor_size) + tiledRow];
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memoryBarrierShared();
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barrier();
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for(uint k = 0u; k < {tile_size}; k++)
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acc += sub_tensor_1[k][row] * sub_tensor_2[col][k];
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barrier();
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}}
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uint globalIndex = (tensor_size * globalCol) + globalRow;
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out_tensor[globalIndex] = acc;
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test1_tensor[globalIndex] = row;
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test2_tensor[globalIndex] = col;
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test3_tensor[globalIndex] = gl_WorkGroupID.x;
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test4_tensor[globalIndex] = gl_WorkGroupID.y;
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}}'''
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print(self.shader)
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self.compiled_shader = kp.Shader.compile_source(self.shader)
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self.tensor_shape: tuple[int, int] = (0, 0)
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self.params: list[kp.Tensor] = []
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self.algo = None
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def __call__(self, tensor_shape: tuple[int, int], tensor_in_1: kp.Tensor, tensor_in_2: kp.Tensor,
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tensor_out: kp.Tensor, tensor_test_1: kp.Tensor, tensor_test_2: kp.Tensor,
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tensor_test_3: kp.Tensor, tensor_test_4: kp.Tensor):
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# params = [tensor_in_1, tensor_in_2, tensor_out]
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params = [tensor_in_1, tensor_in_2, tensor_out, tensor_test_1, tensor_test_2, tensor_test_3, tensor_test_4]
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if self.algo is None or self.tensor_shape != tensor_shape or self.params != params:
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self.tensor_shape = tensor_shape
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self.params = params
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workgroup = (tensor_shape[0] // self.tile_size, tensor_shape[1] // self.tile_size, 1)
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# workgroup = (2, 2, 1)
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print(f'{float(tensor_shape[0])=} {self.tile_size=} {workgroup=}')
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self.algo = self.mgr.algorithm(
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params, # params
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self.compiled_shader, # spirv
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workgroup, # workgroup
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[float(tensor_shape[0])], # spec_consts
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[]) # push_consts
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(self.mgr.sequence()
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.record(kp.OpTensorSyncDevice(self.params))
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.record(kp.OpAlgoDispatch(self.algo))
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.record(kp.OpTensorSyncLocal(self.params[2:]))
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# .record(kp.OpTensorSyncLocal([tensor_out]))
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.eval())
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def main():
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mgr = kp.Manager()
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matmul_op = MatMulOp(mgr, 4)
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tensor_size = 8
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tensor_shape = [tensor_size, tensor_size]
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tensor_in_1 = mgr.tensor(np.triu(np.ones(tensor_shape)))
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tensor_in_2 = mgr.tensor(np.triu(np.ones(tensor_shape)))
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tensor_out = mgr.tensor(np.zeros(tensor_shape))
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tensor_test_1 = mgr.tensor(np.zeros(tensor_shape))
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tensor_test_2 = mgr.tensor(np.zeros(tensor_shape))
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tensor_test_3 = mgr.tensor(np.zeros(tensor_shape))
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tensor_test_4 = mgr.tensor(np.zeros(tensor_shape))
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print(f'{tensor_shape} input tensors:\n'
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f'{tensor_in_1.data().reshape(tensor_shape)}\n'
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f'{tensor_in_2.data().reshape(tensor_shape)}\n')
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# matmul_op(tensor_shape, tensor_in_1, tensor_in_2, tensor_out)
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matmul_op(tensor_shape, tensor_in_1, tensor_in_2,
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tensor_out, tensor_test_1, tensor_test_2, tensor_test_3, tensor_test_4)
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# experiment_count = 10
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# start_time = time.time()
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# for _ in range(experiment_count):
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# matmul_op(tensor_shape, tensor_in_1, tensor_in_2, tensor_out)
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# end_time = time.time()
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# experiment_time = end_time - start_time
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# op_count = tensor_shape[0] * tensor_shape[1] * (tensor_shape[1] - 1)
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print(f'Output :\n{tensor_out.data().reshape(tensor_shape)}')
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print(f'test_1 :\n{tensor_test_1.data().reshape(tensor_shape)}')
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print(f'test_2 :\n{tensor_test_2.data().reshape(tensor_shape)}')
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print(f'test_3 :\n{tensor_test_3.data().reshape(tensor_shape)}')
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print(f'test_4 :\n{tensor_test_4.data().reshape(tensor_shape)}')
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# print(f'{experiment_count} matmul time : '
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# f'{experiment_time * 1000:0.2f}ms => '
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# f'{experiment_count / experiment_time:0.2f}op/s or '
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# f'{experiment_count * op_count / (1e9 * experiment_time):0.2f}GFLOPS')
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if __name__ == '__main__':
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main()
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