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72 lines
1.7 KiB
Python
72 lines
1.7 KiB
Python
import timeit
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import torch
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import numpy as np
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from torchsearchsorted import searchsorted, numpy_searchsorted
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B = 5_000
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A = 300
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V = 100
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repeats = 20
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number = 100
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print(
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f'Benchmark searchsorted:',
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f'- a [{B} x {A}]',
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f'- v [{B} x {V}]',
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f'- reporting fastest time of {repeats} runs',
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f'- each run executes searchsorted {number} times',
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sep='\n',
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end='\n\n'
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)
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def get_arrays():
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a = np.sort(np.random.randn(B, A), axis=1)
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v = np.random.randn(B, V)
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out = np.empty_like(v, dtype=np.long)
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return a, v, out
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def get_tensors(device):
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a = torch.sort(torch.randn(B, A, device=device), dim=1)[0]
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v = torch.randn(B, V, device=device)
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out = torch.empty(B, V, device=device, dtype=torch.long)
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if torch.cuda.is_available():
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torch.cuda.synchronize()
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return a, v, out
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def searchsorted_synchronized(a,v,out=None,side='left'):
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out = searchsorted(a,v,out,side)
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torch.cuda.synchronize()
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return out
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numpy = timeit.repeat(
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stmt="numpy_searchsorted(a, v, side='left')",
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setup="a, v, out = get_arrays()",
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globals=globals(),
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repeat=repeats,
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number=number
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)
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print('Numpy: ', min(numpy), sep='\t')
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cpu = timeit.repeat(
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stmt="searchsorted(a, v, out, side='left')",
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setup="a, v, out = get_tensors(device='cpu')",
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globals=globals(),
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repeat=repeats,
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number=number
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)
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print('CPU: ', min(cpu), sep='\t')
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if torch.cuda.is_available():
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gpu = timeit.repeat(
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stmt="searchsorted_synchronized(a, v, out, side='left')",
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setup="a, v, out = get_tensors(device='cuda')",
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globals=globals(),
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repeat=repeats,
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number=number
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)
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print('CUDA: ', min(gpu), sep='\t')
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