Making large AI models cheaper, faster and more accessible
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import pytest
import torch
from packaging import version
from torch import nn
try:
from colossalai.kernel.triton.softmax import softmax
HAS_TRITON = True
except ImportError:
HAS_TRITON = False
print("please install triton from https://github.com/openai/triton")
TRITON_CUDA_SUPPORT = version.parse(torch.version.cuda) > version.parse("11.4")
@pytest.mark.skipif(
not TRITON_CUDA_SUPPORT or not HAS_TRITON, reason="triton requires cuda version to be higher than 11.4"
)
def test_softmax_op():
data_samples = [
torch.randn((3, 4, 5, 32), device="cuda", dtype=torch.float32),
torch.randn((320, 320, 78), device="cuda", dtype=torch.float32),
torch.randn((2345, 4, 5, 64), device="cuda", dtype=torch.float16),
]
for data in data_samples:
module = nn.Softmax(dim=-1)
data_torch_out = module(data)
data_triton_out = softmax(data)
check = torch.allclose(data_torch_out.cpu(), data_triton_out.cpu(), rtol=1e-3, atol=1e-3)
assert check is True, "softmax outputs from triton and torch are not matched"
if __name__ == "__main__":
test_softmax_op()