minke.yu/sglang
1
0
Fork 0
Code Issues Pull Requests Actions 12 Packages Projects Releases Wiki Activity

enable ut test for xpu devices (#11712)

Browse Source 
Co-authored-by: jundu <jun.du@intel.com>
Co-authored-by: Gao, Pengfei <pengfei.gao@intel.com>
This commit is contained in:
DiweiSun
2026-02-03 11:15:14 -08:00
committed by GitHub
co-authored by jundu Gao, Pengfei
parent 0a6925639b
commit 495290aefd
20 changed files with 237 additions and 151 deletions
Download Patch File Download Diff File Expand all files Collapse all files
test/registered/moe/test_fused_moe.py
+20 -20
View File
@@ -9,9 +9,9 @@ from sglang.srt.layers.moe.topk import TopKConfig, select_experts
from sglang.srt.layers.quantization.fp8_kernel import is_fp8_fnuz
from sglang.srt.layers.quantization.fp8_utils import normalize_e4m3fn_to_e4m3fnuz
from sglang.srt.server_args import ServerArgs, set_global_server_args_for_scheduler
from sglang.srt.utils import is_hip
from sglang.srt.utils import get_device, get_device_capability, is_hip
from sglang.test.ci.ci_register import register_amd_ci, register_cuda_ci
from sglang.test.test_utils import CustomTestCase
from sglang.test.test_utils import CustomTestCase, empty_gpu_cache
register_cuda_ci(est_time=80, suite="stage-b-test-large-1-gpu")
register_amd_ci(est_time=30, suite="stage-b-test-small-1-gpu-amd")
@@ -25,8 +25,8 @@ class TestFusedMOE(CustomTestCase):
TOP_KS = [2, 6]
@staticmethod
def create_random_cuda_tensor(shape, dtype, mean=0, std=0.01):
"""Create a random CUDA tensor
def create_random_gpu_tensor(shape, dtype, mean=0, std=0.01):
"""Create a random Torch(device) tensor
Args:
shape: Tensor shape
@@ -35,9 +35,9 @@ class TestFusedMOE(CustomTestCase):
std: Standard deviation
Returns:
torch.Tensor: Randomly initialized CUDA tensor
torch.Tensor: Randomly initialized Torch(device) tensor
"""
return torch.empty(shape, dtype=dtype, device="cuda").normal_(mean, std)
return torch.empty(shape, dtype=dtype, device=get_device()).normal_(mean, std)
def get_tolerance(self, dtype):
"""Get tolerance values for different data types
@@ -109,20 +109,20 @@ class TestFusedMOE(CustomTestCase):
if use_fp8_w8a8:
# AssertionError: fp8e4nv data type is not supported on CUDA arch < 89
capability = torch.cuda.get_device_capability()
capability = get_device_capability()
if not _is_hip and not (capability[0] >= 9 or capability == (8, 9)):
return
a = self.create_random_cuda_tensor((m, k), dtype)
w1 = self.create_random_cuda_tensor((e, 2 * n, k), dtype)
w2 = self.create_random_cuda_tensor((e, k, n), dtype)
a = self.create_random_gpu_tensor((m, k), dtype)
w1 = self.create_random_gpu_tensor((e, 2 * n, k), dtype)
w2 = self.create_random_gpu_tensor((e, k, n), dtype)
w1 = w1.to(torch.float8_e4m3fn)
w2 = w2.to(torch.float8_e4m3fn)
score = self.create_random_cuda_tensor((m, e), dtype)
w1_scale = self.create_random_cuda_tensor(e, torch.float32)
w2_scale = self.create_random_cuda_tensor(e, torch.float32)
a1_scale = self.create_random_cuda_tensor(1, torch.float32)
a2_scale = self.create_random_cuda_tensor(1, torch.float32)
score = self.create_random_gpu_tensor((m, e), dtype)
w1_scale = self.create_random_gpu_tensor(e, torch.float32)
w2_scale = self.create_random_gpu_tensor(e, torch.float32)
a1_scale = self.create_random_gpu_tensor(1, torch.float32)
a2_scale = self.create_random_gpu_tensor(1, torch.float32)
# Handle HIP case: normalize float8 weights so fused kernel doesn't break
# on ROCm.
@@ -172,10 +172,10 @@ class TestFusedMOE(CustomTestCase):
sglang_output, torch_output, rtol=rtol, atol=atol
)
else:
a = self.create_random_cuda_tensor((m, k), dtype)
w1 = self.create_random_cuda_tensor((e, 2 * n, k), dtype)
w2 = self.create_random_cuda_tensor((e, k, n), dtype)
score = self.create_random_cuda_tensor((m, e), dtype)
a = self.create_random_gpu_tensor((m, k), dtype)
w1 = self.create_random_gpu_tensor((e, 2 * n, k), dtype)
w2 = self.create_random_gpu_tensor((e, k, n), dtype)
score = self.create_random_gpu_tensor((m, e), dtype)
topk_output = select_experts(
hidden_states=a,
@@ -236,7 +236,7 @@ class TestFusedMOE(CustomTestCase):
dtype,
use_fp8_w8a8=use_fp8_w8a8,
)
torch.cuda.empty_cache()
empty_gpu_cache()
pbar.update(1)