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# Copyright (c) 2024 NVIDIA CORPORATION.
# Licensed under the MIT license.
import math
import torch
import alias_free_cuda
from alias_free_cuda import activation1d
from activations import Snake, SnakeBeta
def test_load_fused_kernels():
try:
import alias_free_cuda
import torch
print("[Success] load_fused_kernels")
except ImportError as e:
print("[Fail] load_fused_kernels")
raise e
def test_anti_alias_activation():
data = torch.rand((10, 10, 50000), device='cuda')
# check activations.Snake cuda vs. torch
fused_anti_alias_activation = activation1d.Activation1d(activation=SnakeBeta(10), fused=True).cuda()
fused_activation_output = fused_anti_alias_activation(data)
torch_anti_alias_activation = activation1d.Activation1d(activation=SnakeBeta(10), fused=False).cuda()
torch_activation_output = torch_anti_alias_activation(data)
test_result = (fused_activation_output - torch_activation_output).abs()
while test_result.dim() != 1:
test_result = test_result.mean(dim=-1)
diff = test_result.mean(dim=-1)
if diff <= 1e-3:
print(
f"\n[Success] test_fused_anti_alias_activation"
f"\n > mean_difference={diff}"
f"\n > fused_values={fused_activation_output[-1][-1][-100:].tolist()}"
f"\n > torch_values={torch_activation_output[-1][-1][-100:].tolist()}"
)
else:
print(
f"\n[Fail] test_fused_anti_alias_activation"
f"\n > mean_difference={diff}, "
f"\n > fused_values={fused_activation_output[-1][-1][-30:].tolist()}, "
f"\n > torch_values={torch_activation_output[-1][-1][-30:].tolist()}"
)
if __name__ == "__main__":
from alias_free_cuda import load
load.load()
test_load_fused_kernels()
test_anti_alias_activation()
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