Fix small matrices matmuls, imp3 working but slow
This commit is contained in:
Corentin
parent
a3f7793c17
commit
3962ee70af
5 changed files with 77 additions and 219 deletions
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@ -8,41 +8,48 @@ from imp3_better_tiling import MatMulOp as MatMulOp3
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def main():
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experiment_count = 1000
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tensor_size = 512
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tensor_shape = [tensor_size, tensor_size]
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mat_1 = np.triu(np.ones(tensor_shape))
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mat_2 = np.triu(np.ones(tensor_shape))
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mat_result = mat_1 @ mat_2
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tensor_shape = [tensor_size, tensor_size]
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print(f'{tensor_shape} input tensors:\n'
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f'{mat_1}\n'
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f'{mat_2}\n')
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print(f'Output :\n{mat_result}')
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mgr = kp.Manager()
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tensor_in_1 = mgr.tensor(mat_1)
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tensor_in_2 = mgr.tensor(mat_2)
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tensor_out = mgr.tensor(np.zeros(tensor_shape))
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for MatMulOp in [MatMulOp1, MatMulOp2, MatMulOp3]:
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matmul_op = MatMulOp(mgr)
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matmul_op(tensor_shape, tensor_in_1, tensor_in_2, tensor_out)
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for tensor_size, experiment_count in [(512, 1000), (4096, 5)]:
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tensor_shape = [tensor_size, tensor_size]
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tensor_shape = [tensor_size, tensor_size]
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mat_1 = np.triu(np.ones(tensor_shape))
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mat_2 = np.triu(np.ones(tensor_shape))
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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] * 2) - 1)
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if (tensor_out.data().reshape(tensor_shape) == mat_result).all():
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print(f'From {MatMulOp.__module__} : {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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tensor_in_1 = mgr.tensor(mat_1)
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tensor_in_2 = mgr.tensor(mat_2)
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tensor_out = mgr.tensor(np.zeros(tensor_shape))
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if tensor_size <= 512:
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mat_result = mat_1 @ mat_2
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else:
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print(f'Test failed => output tensor is wrong :\n{tensor_out.data().reshape(tensor_shape)}')
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MatMulOp1(mgr)(tensor_shape, tensor_in_1, tensor_in_2, tensor_out)
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mat_result = tensor_out.data().reshape(tensor_shape) # CPU is too slow for big sizes
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print(f'{tensor_shape} input tensors:\n'
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f'{mat_1}\n'
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f'{mat_2}\n')
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print(f'Output :\n{mat_result}')
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for MatMulOp in [MatMulOp1, MatMulOp2, MatMulOp3]:
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tensor_out.data()[:] = 0
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mgr.sequence().record(kp.OpTensorSyncDevice([tensor_out]))
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matmul_op = MatMulOp(mgr)
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matmul_op(tensor_shape, tensor_in_1, tensor_in_2, tensor_out)
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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] * 2) - 1)
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# print(tensor_out.data().reshape(tensor_shape))
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if (tensor_out.data().reshape(tensor_shape) == mat_result).all():
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print(f'From {MatMulOp.__module__} : {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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else:
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print(f'Test failed => output tensor is wrong :\n{tensor_out.data().reshape(tensor_shape)}')
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if __name__ == '__main__':
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@ -41,7 +41,7 @@ class MatMulOp:
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self.local_size_x = local_size_x
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self.local_size_y = local_size_y
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self.shader = f'''
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self.shader = '''
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#version 450
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layout (local_size_x = {local_size_x}, local_size_y = {local_size_y}) in;
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@ -63,7 +63,8 @@ void main()
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acc += in_tensor_1[(k * tensor_size) + globalRow] * in_tensor_2[(globalCol * tensor_size) + k];
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out_tensor[(globalCol * tensor_size) + globalRow] = acc;
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}}'''
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self.compiled_shader = kp.Shader.compile_source(self.shader)
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self.compiled_shader = kp.Shader.compile_source(self.shader.format(
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local_size_x=self.local_size_x, local_size_y=self.local_size_y))
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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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@ -75,7 +76,11 @@ void main()
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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.local_size_x, tensor_shape[1] // self.local_size_y, 1)
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local_size_x = min(self.local_size_x, tensor_shape[0])
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local_size_y = min(self.local_size_y, tensor_shape[1])
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self.compiled_shader = kp.Shader.compile_source(self.shader.format(
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local_size_x=local_size_x, local_size_y=local_size_y))
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workgroup = (tensor_shape[0] // local_size_x, tensor_shape[1] // local_size_y, 1)
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print(f'{workgroup=} {self.local_size_x=} {self.local_size_y=}')
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self.algo = self.mgr.algorithm(
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params, # params
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@ -85,7 +90,7 @@ void main()
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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.OpTensorSyncDevice([tensor_in_1, tensor_in_2]))
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.record(kp.OpAlgoDispatch(self.algo))
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.record(kp.OpTensorSyncLocal([tensor_out]))
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.eval())
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@ -24,7 +24,7 @@ class MatMulOp:
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assert tile_size <= max_workgroup_size[1]
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self.tile_size = tile_size
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self.shader = f'''
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self.shader = '''
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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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@ -66,7 +66,7 @@ void main()
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}}
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out_tensor[tensor_size * globalCol + globalRow] = acc;
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}}'''
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self.compiled_shader = kp.Shader.compile_source(self.shader)
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self.compiled_shader = kp.Shader.compile_source(self.shader.format(tile_size=tile_size))
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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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@ -78,7 +78,9 @@ void main()
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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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tile_size = min(tensor_shape[0], tensor_shape[1], self.tile_size)
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self.compiled_shader = kp.Shader.compile_source(self.shader.format(tile_size=tile_size))
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workgroup = [tensor_shape[0] // tile_size, tensor_shape[1] // tile_size, 1]
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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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@ -87,7 +89,7 @@ void main()
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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.OpTensorSyncDevice([tensor_in_1, tensor_in_2]))
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.record(kp.OpAlgoDispatch(self.algo))
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.record(kp.OpTensorSyncLocal([tensor_out]))
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.eval())
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@ -1,156 +0,0 @@
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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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@ -5,7 +5,7 @@ import numpy as np
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class MatMulOp:
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def __init__(self, manager: kp.Manager, tile_size: int = -1, thread_work_ratio: int = 8):
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def __init__(self, manager: kp.Manager, tile_size: int = -1, thread_work_ratio: int = 16):
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self.mgr = manager
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props = self.mgr.get_device_properties()
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@ -14,9 +14,9 @@ class MatMulOp:
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if tile_size < 0:
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tile_size = 1
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local_size_y = tile_size // thread_work_ratio
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while (4 * tile_size * local_size_y <= max_workgroup_invocation
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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 * local_size_y <= max_workgroup_size[1]):
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and 2 * tile_size <= max_workgroup_size[1]):
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tile_size *= 2
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local_size_y = tile_size // thread_work_ratio
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else:
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@ -32,10 +32,10 @@ class MatMulOp:
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self.local_size_x = tile_size
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self.local_size_y = tile_size // thread_work_ratio
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self.shader = f'''
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self.shader = '''
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#version 450
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layout (local_size_x = {tile_size}, local_size_y = {self.local_size_y}) in;
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layout (local_size_x = {tile_size}, local_size_y = {local_size_y}) 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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@ -51,14 +51,13 @@ void main()
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uint row = gl_LocalInvocationID.x;
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uint col = gl_LocalInvocationID.y;
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uint globalRow = {tile_size} * gl_WorkGroupID.x + row;
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uint globalCol = {tile_size} * gl_WorkGroupID.y + 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[{thread_work_ratio}];
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for(uint w = 0u; w < {thread_work_ratio}; w++)
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acc[w] = 0.0;
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/*
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uint numTiles = tensor_size / {tile_size};
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for(uint t = 0u; t < numTiles; t++)
|
||||
{{
|
||||
|
|
@ -66,10 +65,10 @@ void main()
|
|||
{{
|
||||
uint tiledRow = {tile_size} * t + row;
|
||||
uint tiledCol = {tile_size} * t + col;
|
||||
sub_tensor_1[col + t * {self.local_size_y}][row] = in_tensor_1[
|
||||
(tiledCol + w * {self.local_size_y}) * tensor_size + globalRow];
|
||||
sub_tensor_2[col + t * {self.local_size_y}][row] = in_tensor_2[
|
||||
(globalCol + w * {self.local_size_y})* tensor_size + tiledRow];
|
||||
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();
|
||||
|
|
@ -77,17 +76,15 @@ void main()
|
|||
|
||||
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 * {self.local_size_y}][k];
|
||||
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 * {self.local_size_y}) * tensor_size + globalRow] = acc[w];
|
||||
out_tensor[(globalCol + w * {self.local_size_y}) * tensor_size + globalRow] = w;
|
||||
}}
|
||||
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)
|
||||
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
|
||||
|
|
@ -99,9 +96,12 @@ void main()
|
|||
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.local_size_x, tensor_shape[1] // self.local_size_y, 1)
|
||||
workgroup = (2, 2, 1)
|
||||
print(tensor_shape, self.local_size_x, self.local_size_y, workgroup)
|
||||
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
|
||||
|
|
@ -110,7 +110,7 @@ void main()
|
|||
[]) # push_consts
|
||||
|
||||
(self.mgr.sequence()
|
||||
.record(kp.OpTensorSyncDevice(self.params))
|
||||
.record(kp.OpTensorSyncDevice([tensor_in_1, tensor_in_2]))
|
||||
.record(kp.OpAlgoDispatch(self.algo))
|
||||
.record(kp.OpTensorSyncLocal([tensor_out]))
|
||||
.eval())
|
||||
|
|
@ -133,7 +133,7 @@ def main():
|
|||
|
||||
matmul_op(tensor_shape, tensor_in_1, tensor_in_2, tensor_out)
|
||||
|
||||
experiment_count = 8
|
||||
experiment_count = 2
|
||||
start_time = time.time()
|
||||
for _ in range(experiment_count):
|
||||
matmul_op(tensor_shape, tensor_in_1, tensor_in_2, tensor_out)
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue