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Fix nightly CI: NVFP4 cuda-graph crash, NVILA batching, CuTe paged-KV zero-size, Kimi-VL OOM (#32118)

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Co-authored-by: Brayden Zhong <brayden@radixark.ai>
This commit is contained in:
Brayden Zhong
2026-07-29 01:39:55 -07:00
committed by GitHub
co-authored by Brayden Zhong
parent cce5873513
commit 7dcebca255
5 changed files with 48 additions and 221 deletions
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test/registered/unit/models/test_kimi_vl.py
+5 -18
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@@ -14,9 +14,9 @@ from sglang.srt.layers.linear import (
)
from sglang.srt.managers.schedule_batch import Modality, MultimodalDataItem
from sglang.srt.models.kimi_vl import KimiVLForConditionalGeneration
from sglang.srt.models.kimi_vl_moonvit import MoonVitEncoderLayer, multihead_attention
from sglang.srt.models.kimi_vl_moonvit import MoonVitEncoderLayer
from sglang.srt.multimodal.mm_utils import run_dp_sharded_mrope_vision_model
from sglang.srt.runtime_context import get_parallel
from sglang.srt.runtime_context import get_context, get_parallel
from sglang.test.ci.ci_register import register_cpu_ci
from sglang.test.test_utils import CustomTestCase
@@ -80,17 +80,16 @@ class TestKimiVLEncoderParallelism(CustomTestCase):
def test_moonvit_uses_tensor_parallel_layers(self):
with get_parallel().override(
tp_size=1, tp_rank=0, attn_tp_size=1, attn_tp_rank=0
):
), get_context().override_server_args():
layer = MoonVitEncoderLayer(
num_heads=2,
hidden_dim=8,
mlp_dim=16,
prefix="vision_tower.encoder.blocks.0",
use_tensor_parallel=True,
)
self.assertIsInstance(layer.wqkv, QKVParallelLinear)
self.assertIsInstance(layer.wo, RowParallelLinear)
self.assertIsInstance(layer.attn.qkv_proj, QKVParallelLinear)
self.assertIsInstance(layer.attn.proj, RowParallelLinear)
self.assertIsInstance(layer.mlp.fc0, ColumnParallelLinear)
self.assertIsInstance(layer.mlp.fc1, RowParallelLinear)
@@ -142,18 +141,6 @@ class TestKimiVLEncoderParallelism(CustomTestCase):
self.assertIsInstance(output, torch.Tensor)
self.assertEqual(output.shape, (1, 4, 2))
def test_moonvit_attention_accepts_precomputed_max_seqlen(self):
q = torch.randn(4, 2, 4, dtype=torch.bfloat16)
cu_seqlens = torch.tensor([0, 4], dtype=torch.int32)
fake_output = torch.randn_like(q)
with patch(
"sglang.srt.models.kimi_vl_moonvit.flash_attn_varlen_func",
return_value=fake_output,
) as flash_attn:
output = multihead_attention(q, q, q, cu_seqlens, cu_seqlens, max_seqlen=4)
self.assertTrue(torch.equal(output, fake_output.flatten(start_dim=-2)))
self.assertEqual(flash_attn.call_args.args[5:7], (4, 4))
if __name__ == "__main__":
raise SystemExit(pytest.main([__file__, "-v"]))