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Convert formatting to use ruff instead of yapf + isort (#26247)

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Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Harry Mellor
2025-10-05 15:06:22 +01:00
committed by GitHub
parent 17edd8a807
commit d6953beb91
1508 changed files with 115244 additions and 94146 deletions
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57
tests/kernels/core/test_activation.py
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@@ -8,19 +8,23 @@ import torch
from tests.kernels.allclose_default import get_default_atol, get_default_rtol
from tests.kernels.utils import opcheck
from vllm.model_executor.layers.activation import (FastGELU, FatreluAndMul,
GeluAndMul, MulAndSilu,
NewGELU, QuickGELU,
SiluAndMul, SwigluOAIAndMul)
from vllm.model_executor.layers.activation import (
FastGELU,
FatreluAndMul,
GeluAndMul,
MulAndSilu,
NewGELU,
QuickGELU,
SiluAndMul,
SwigluOAIAndMul,
)
from vllm.platforms import current_platform
DTYPES = [torch.half, torch.bfloat16, torch.float]
NUM_TOKENS = [7, 83, 2048] # Arbitrary values for testing
D = [512, 13824] # Arbitrary values for testing
SEEDS = [0]
CUDA_DEVICES = [
f"cuda:{i}" for i in range(1 if torch.cuda.device_count() == 1 else 2)
]
CUDA_DEVICES = [f"cuda:{i}" for i in range(1 if torch.cuda.device_count() == 1 else 2)]
@pytest.mark.parametrize(
@@ -73,24 +77,19 @@ def test_act_and_mul(
out = layer(x)
ref_out = layer.forward_native(x)
if activation == "swigluoai_and_mul":
rtol = {
#For fp16, change the relative tolerance from 1e-3 to 2e-3
torch.float16:
2e-3,
torch.bfloat16:
2e-2,
torch.float:
1.3e-6
# For fp16, change the relative tolerance from 1e-3 to 2e-3
torch.float16: 2e-3,
torch.bfloat16: 2e-2,
torch.float: 1.3e-6,
}
def _get_rtol(output) -> float:
return rtol[output.dtype]
torch.testing.assert_close(out,
ref_out,
atol=get_default_atol(out),
rtol=_get_rtol(out))
torch.testing.assert_close(
out, ref_out, atol=get_default_atol(out), rtol=_get_rtol(out)
)
else:
# The SiluAndMul, MulAndSilu, GELU and FatReLU implementations are
# equivalent to the native PyTorch implementations, so we can do exact
@@ -98,7 +97,7 @@ def test_act_and_mul(
torch.testing.assert_close(out, ref_out, atol=0.0, rtol=0.0)
d = x.shape[-1] // 2
output_shape = (x.shape[:-1] + (d, ))
output_shape = x.shape[:-1] + (d,)
out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
if activation == "fatrelu":
opcheck(fn, (out, x, threshold))
@@ -108,9 +107,14 @@ def test_act_and_mul(
opcheck(fn, (out, x))
@pytest.mark.parametrize("activation", [(FastGELU, torch.ops._C.gelu_fast),
(NewGELU, torch.ops._C.gelu_new),
(QuickGELU, torch.ops._C.gelu_quick)])
@pytest.mark.parametrize(
"activation",
[
(FastGELU, torch.ops._C.gelu_fast),
(NewGELU, torch.ops._C.gelu_new),
(QuickGELU, torch.ops._C.gelu_quick),
],
)
@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
@pytest.mark.parametrize("d", D)
@pytest.mark.parametrize("dtype", DTYPES)
@@ -132,10 +136,9 @@ def test_activation(
fn = activation[1]
out = layer(x)
ref_out = layer.forward_native(x)
torch.testing.assert_close(out,
ref_out,
atol=get_default_atol(out),
rtol=get_default_rtol(out))
torch.testing.assert_close(
out, ref_out, atol=get_default_atol(out), rtol=get_default_rtol(out)
)
out = torch.empty_like(x)
opcheck(fn, (out, x))