76 lines
2.3 KiB
Python
76 lines
2.3 KiB
Python
import pytest
|
|
import torch
|
|
import torch.nn.functional as F
|
|
from transformers.activations import get_activation
|
|
|
|
from vllm import activation_ops
|
|
|
|
DTYPES = [torch.half, torch.bfloat16, torch.float]
|
|
NUM_TOKENS = [7, 83, 2048] # Arbitrary values for testing
|
|
D = [512, 4096, 5120, 13824] # Arbitrary values for testing
|
|
SEEDS = [0]
|
|
|
|
|
|
def ref_silu_and_mul(x: torch.Tensor) -> torch.Tensor:
|
|
x1, x2 = x.chunk(chunks=2, dim=1)
|
|
return F.silu(x1) * x2
|
|
|
|
|
|
@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
|
|
@pytest.mark.parametrize("d", D)
|
|
@pytest.mark.parametrize("dtype", DTYPES)
|
|
@pytest.mark.parametrize("seed", SEEDS)
|
|
@torch.inference_mode()
|
|
def test_silu_and_mul(
|
|
num_tokens: int,
|
|
d: int,
|
|
dtype: torch.dtype,
|
|
seed: int,
|
|
) -> None:
|
|
torch.random.manual_seed(seed)
|
|
torch.cuda.manual_seed(seed)
|
|
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)
|
|
|
|
|
|
@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
|
|
@pytest.mark.parametrize("d", D)
|
|
@pytest.mark.parametrize("dtype", DTYPES)
|
|
@pytest.mark.parametrize("seed", SEEDS)
|
|
@torch.inference_mode()
|
|
def test_gelu_new(
|
|
num_tokens: int,
|
|
d: int,
|
|
dtype: torch.dtype,
|
|
seed: int,
|
|
) -> None:
|
|
torch.random.manual_seed(seed)
|
|
torch.cuda.manual_seed(seed)
|
|
x = torch.randn(num_tokens, d, dtype=dtype, device="cuda")
|
|
out = torch.empty(num_tokens, d, dtype=dtype, device="cuda")
|
|
activation_ops.gelu_new(out, x)
|
|
ref_out = get_activation("gelu_new")(x)
|
|
assert torch.allclose(out, ref_out, atol=1e-5, rtol=1e-5)
|
|
|
|
|
|
@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
|
|
@pytest.mark.parametrize("d", D)
|
|
@pytest.mark.parametrize("dtype", DTYPES)
|
|
@pytest.mark.parametrize("seed", SEEDS)
|
|
def test_gelu_fast(
|
|
num_tokens: int,
|
|
d: int,
|
|
dtype: torch.dtype,
|
|
seed: int,
|
|
) -> None:
|
|
torch.random.manual_seed(seed)
|
|
torch.cuda.manual_seed(seed)
|
|
x = torch.randn(num_tokens, d, dtype=dtype, device="cuda")
|
|
out = torch.empty(num_tokens, d, dtype=dtype, device="cuda")
|
|
activation_ops.gelu_fast(out, x)
|
|
ref_out = get_activation("gelu_fast")(x)
|
|
assert torch.allclose(out, ref_out, atol=1e-5, rtol=1e-5)
|