feat(minimax-h3): enable 2x RTX 4090/5090 with DLO (#5764)

Signed-off-by: lishunyang12 <lishunyang03@gmail.com>
Signed-off-by: lishunyang12 <lishunyang12@163.com>
Co-authored-by: lishunyang12 <lishunyang03@gmail.com>

tests/diffusion/attention/test_cudnn_attn.py

@@ -0,0 +1,64 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from contextlib import nullcontext
+
+import pytest
+import torch
+
+from vllm_omni.diffusion.attention.backends.abstract import AttentionMetadata
+from vllm_omni.diffusion.attention.backends.cudnn_attn import CuDNNAttentionImpl
+
+pytestmark = [pytest.mark.diffusion, pytest.mark.cpu, pytest.mark.core_model]
+
+
+def test_cudnn_slices_valid_kv_prefix_without_padding_mask(monkeypatch):
+    observed = {}
+
+    def fake_sdpa(query, key, value, **kwargs):
+        observed.update(query=query, key=key, value=value, kwargs=kwargs)
+        return query
+
+    monkeypatch.setattr(
+        "vllm_omni.diffusion.attention.backends.cudnn_attn.sdpa_kernel",
+        lambda _backends: nullcontext(),
+    )
+    monkeypatch.setattr(torch.nn.functional, "scaled_dot_product_attention", fake_sdpa)
+    impl = CuDNNAttentionImpl(
+        num_heads=2,
+        head_size=4,
+        softmax_scale=0.5,
+    )
+    query = torch.randn(1, 8, 2, 4)
+    key = torch.randn_like(query)
+    value = torch.randn_like(query)
+
+    output = impl.forward_cuda(
+        query,
+        key,
+        value,
+        AttentionMetadata(extra={"valid_kv_length": 5}),
+    )
+
+    assert output.shape == query.shape
+    assert observed["query"].shape == (1, 2, 8, 4)
+    assert observed["key"].shape == (1, 2, 5, 4)
+    assert observed["value"].shape == (1, 2, 5, 4)
+    assert observed["kwargs"]["attn_mask"] is None
+
+
+def test_cudnn_rejects_invalid_valid_kv_length():
+    impl = CuDNNAttentionImpl(
+        num_heads=2,
+        head_size=4,
+        softmax_scale=0.5,
+    )
+    query = torch.randn(1, 8, 2, 4)
+
+    with pytest.raises(ValueError, match="valid_kv_length"):
+        impl.forward_cuda(
+            query,
+            query,
+            query,
+            AttentionMetadata(extra={"valid_kv_length": 9}),
+        )