31 lines
881 B
Python
31 lines
881 B
Python
|
import torch
|
||
|
|
import torch.nn.functional as F
|
||
|
|
|
||
|
|
from cacheflow import activation_ops
|
||
|
|
|
||
|
|
|
||
|
|
def ref_silu_and_mul(x: torch.Tensor) -> torch.Tensor:
|
||
|
|
x1, x2 = x.chunk(chunks=2, dim=1)
|
||
|
|
return F.silu(x1) * x2
|
||
|
|
|
||
|
|
|
||
|
|
@torch.inference_mode()
|
||
|
|
def test_silu_and_mul(
|
||
|
|
num_tokens: int,
|
||
|
|
d: int,
|
||
|
|
dtype: torch.dtype,
|
||
|
|
) -> None:
|
||
|
|
x = torch.randn(num_tokens, 2 * d, dtype=dtype, device='cuda')
|
||
|
|
out = torch.empty(num_tokens, d, dtype=dtype, device='cuda')
|
||
|
|
activation_ops.silu_and_mul(out, x)
|
||
|
|
ref_out = ref_silu_and_mul(x)
|
||
|
|
assert torch.allclose(out, ref_out, atol=1e-5, rtol=1e-5)
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == '__main__':
|
||
|
|
for dtype in [torch.half, torch.float]:
|
||
|
|
for num_tokens in [7, 83, 2048]:
|
||
|
|
for d in [512, 4096, 13824]:
|
||
|
|
print(f'Testing dtype={dtype}, num_tokens={num_tokens}, d={d}')
|
||
|
|
test_silu_and_mul(num_tokens, d, dtype)
|