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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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21
tests/kernels/quantization/test_block_int8.py
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@@ -10,12 +10,12 @@ import torch
from tests.kernels.quant_utils import native_w8a8_block_matmul
from vllm.config import VllmConfig
from vllm.model_executor.layers.quantization.utils.int8_utils import (
w8a8_block_int8_matmul)
w8a8_block_int8_matmul,
)
from vllm.platforms import current_platform
if current_platform.get_device_capability() < (7, 0):
pytest.skip("INT8 Triton requires CUDA 7.0 or higher",
allow_module_level=True)
pytest.skip("INT8 Triton requires CUDA 7.0 or higher", allow_module_level=True)
vllm_config = VllmConfig()
vllm_config.scheduler_config.max_num_seqs = 128
@@ -36,8 +36,10 @@ def setup_cuda():
torch.set_default_device("cuda")
@pytest.mark.parametrize("M,N,K,block_size,out_dtype,seed",
itertools.product(M, N, K, BLOCK_SIZE, DTYPES, SEEDS))
@pytest.mark.parametrize(
"M,N,K,block_size,out_dtype,seed",
itertools.product(M, N, K, BLOCK_SIZE, DTYPES, SEEDS),
)
@torch.inference_mode()
def test_w8a8_block_int8_matmul(M, N, K, block_size, out_dtype, seed):
torch.manual_seed(seed)
@@ -58,11 +60,10 @@ def test_w8a8_block_int8_matmul(M, N, K, block_size, out_dtype, seed):
As = torch.rand(M, k_tiles, dtype=torch.float32) * factor_for_scale
Bs = torch.rand(n_tiles, k_tiles, dtype=torch.float32) * factor_for_scale
ref_out = native_w8a8_block_matmul(A_fp8, B_fp8, As, Bs, block_size,
out_dtype)
ref_out = native_w8a8_block_matmul(A_fp8, B_fp8, As, Bs, block_size, out_dtype)
out = w8a8_block_int8_matmul(A_fp8, B_fp8, As, Bs, block_size, out_dtype)
rel_diff = (torch.mean(
torch.abs(out.to(torch.float32) - ref_out.to(torch.float32))) /
torch.mean(torch.abs(ref_out.to(torch.float32))))
rel_diff = torch.mean(
torch.abs(out.to(torch.float32) - ref_out.to(torch.float32))
) / torch.mean(torch.abs(ref_out.to(torch.float32)))
assert rel_diff < 0.001