[Model][ROCm] Enable DeepSeek V4 Vision

.buildkite/test_areas/models_basic.yaml

@@ -48,8 +48,9 @@ steps:
   - tests/models/test_terratorch.py
   - tests/models/transformers/test_backend.py
   - tests/models/test_registry.py
+  - tests/models/test_deepseek_v4_vl_rocm.py
   commands:
-    - pytest -v -s models/test_terratorch.py models/transformers/test_backend.py models/test_registry.py
+    - pytest -v -s models/test_terratorch.py models/transformers/test_backend.py models/test_registry.py models/test_deepseek_v4_vl_rocm.py
   mirror:
     amd:
       label: ":amd: (MI300) Basic Models (Other)"
@@ -59,9 +60,10 @@ steps:
       depends_on:
       - image-build-amd
       source_file_dependencies:
+      - tests/models/test_deepseek_v4_vl_rocm.py
       - tests/models/test_hyv4_rocm.py
       commands:
-      - pytest -v -s models/test_terratorch.py models/transformers/test_backend.py models/test_registry.py
+      - pytest -v -s models/test_terratorch.py models/transformers/test_backend.py models/test_registry.py models/test_deepseek_v4_vl_rocm.py
       - VLLM_ROCM_USE_AITER=1 pytest -v -s models/test_hyv4_rocm.py -m 'not distributed'
 
 - label: ":nvidia: (B200) Inkling"

tests/kernels/attention/test_rocm_triton_attn_dsv4.py

@@ -336,6 +336,101 @@ def test_compute_global_topk_ragged_indices_and_indptr() -> None:
     torch.testing.assert_close(actual_lens, expected_lens)
 
 
+@torch.inference_mode()
+def test_combine_topk_swa_indices_adds_image_visibility() -> None:
+    from vllm.models.deepseek_v4.amd.rocm import combine_topk_swa_indices
+
+    device = torch.device("cuda")
+    num_tokens = 8
+    topk_indices = torch.full((num_tokens, 1), -1, dtype=torch.int32, device=device)
+    query_start_loc = torch.tensor([0, num_tokens], dtype=torch.int32, device=device)
+    seq_lens = torch.tensor([num_tokens], dtype=torch.int32, device=device)
+    gather_lens = seq_lens.clone()
+    left_visible = torch.tensor(
+        [0, 0, 0, 1, 2, 3, 4, 0], dtype=torch.int32, device=device
+    )
+    right_visible = torch.tensor(
+        [0, 0, 4, 3, 2, 1, 0, 0], dtype=torch.int32, device=device
+    )
+
+    indices, lens = combine_topk_swa_indices(
+        topk_indices,
+        query_start_loc,
+        seq_lens,
+        gather_lens,
+        window_size=4,
+        compress_ratio=1,
+        topk=0,
+        M=16,
+        N=0,
+        max_image_tokens=5,
+        left_visible=left_visible,
+        right_visible=right_visible,
+    )
+
+    expected_rows = [
+        [0],
+        [0, 1],
+        [0, 1, 2, 3, 4, 5, 6],
+        [0, 1, 2, 3, 4, 5, 6],
+        [1, 2, 3, 4, 5, 6],
+        [2, 3, 4, 5, 6],
+        [2, 3, 4, 5, 6],
+        [4, 5, 6, 7],
+    ]
+    for token_idx, expected in enumerate(expected_rows):
+        actual = indices[token_idx, : lens[token_idx]].cpu().tolist()
+        assert actual == expected
+
+
+@torch.inference_mode()
+def test_combine_topk_swa_indices_apc_hit_inside_image() -> None:
+    from vllm.models.deepseek_v4.amd.rocm import combine_topk_swa_indices
+
+    device = torch.device("cuda")
+    indices, lens = combine_topk_swa_indices(
+        torch.full((2, 1), -1, dtype=torch.int32, device=device),
+        torch.tensor([0, 2], dtype=torch.int32, device=device),
+        torch.tensor([10], dtype=torch.int32, device=device),
+        # Only positions [5, 10) exist in the gathered SWA workspace.
+        torch.tensor([5], dtype=torch.int32, device=device),
+        window_size=4,
+        compress_ratio=1,
+        topk=0,
+        M=10,
+        N=0,
+        max_image_tokens=10,
+        left_visible=torch.tensor([8, 9], dtype=torch.int32, device=device),
+        # Deliberately extends beyond seq_len to exercise the upper clamp too.
+        right_visible=torch.tensor([5, 5], dtype=torch.int32, device=device),
+    )
+
+    assert lens.cpu().tolist() == [5, 5]
+    assert indices[:, :5].cpu().tolist() == [list(range(5)), list(range(5))]
+
+
+@torch.inference_mode()
+def test_combine_topk_swa_indices_keeps_vision_row_width_without_images() -> None:
+    from vllm.models.deepseek_v4.amd.rocm import combine_topk_swa_indices
+
+    device = torch.device("cuda")
+    indices, lens = combine_topk_swa_indices(
+        torch.full((1, 1), -1, dtype=torch.int32, device=device),
+        torch.tensor([0, 1], dtype=torch.int32, device=device),
+        torch.tensor([1], dtype=torch.int32, device=device),
+        torch.tensor([1], dtype=torch.int32, device=device),
+        window_size=120,
+        compress_ratio=1,
+        topk=0,
+        M=256,
+        N=0,
+        max_image_tokens=16,
+    )
+
+    assert indices.shape == (1, 256)
+    assert lens.item() == 1
+
+
 def test_extra_cache_nan_free_provenance_gate(monkeypatch) -> None:
     from vllm.models.deepseek_v4.amd import rocm as mod
 

tests/models/multimodal/processing/test_tensor_schema.py

@@ -164,10 +164,11 @@ def test_model_tensor_schema(model_id: str):
             "Kimi-K2.5's offline inference has issues about vision chunks. Fix later."
         )
 
-    if model_id == "deepseek-ai/DeepSeek-V4-Flash-Vision-Exp" and not (
-        current_platform.is_cuda()
+    if (
+        model_id == "deepseek-ai/DeepSeek-V4-Flash-Vision-Exp"
+        and not current_platform.is_cuda_alike()
     ):
-        pytest.skip("Deepseek V4 is only supported on CUDA")
+        pytest.skip("Deepseek V4 vision is only supported on CUDA and ROCm")
 
     if model_id == "zai-org/GLM-5.3-Flash" and (current_platform.is_xpu()):
         pytest.skip("GLM-5.3-Flash is not supported on XPU")