ColossalAI/tests/test_infer_ops/triton/test_layernorm_triton.py

45 lines
1.4 KiB
Python

import pytest
import torch
from packaging import version
from colossalai.kernel.triton import layer_norm
from colossalai.testing.utils import parameterize
try:
import triton
import triton.language as tl
from colossalai.kernel.triton.fused_layernorm import _layer_norm_fwd_fused
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")
@parameterize('M', [2, 4, 8, 16])
@parameterize('N', [64, 128])
def test_layer_norm(M, N):
dtype = torch.float16
eps = 1e-5
x_shape = (M, N)
w_shape = (x_shape[-1],)
weight = torch.rand(w_shape, dtype=dtype, device='cuda')
bias = torch.rand(w_shape, dtype=dtype, device='cuda')
x = -2.3 + 0.5 * torch.randn(x_shape, dtype=dtype, device='cuda')
y_triton = layer_norm(x, weight, bias, eps)
y_torch = torch.nn.functional.layer_norm(x, w_shape, weight, bias, eps).to(dtype)
assert y_triton.shape == y_torch.shape
assert y_triton.dtype == y_torch.dtype
print("max delta: ", torch.max(torch.abs(y_triton - y_torch)))
assert torch.allclose(y_triton, y_torch, atol=1e-2, rtol=0)
if __name__ == "__main__":
test_layer_norm()