diff --git a/tests/v1/spec_decode/test_vocab_mapping.py b/tests/v1/spec_decode/test_vocab_mapping.py
index b8773c5a55..25c99ad049 100644
--- a/tests/v1/spec_decode/test_vocab_mapping.py
+++ b/tests/v1/spec_decode/test_vocab_mapping.py
@@ -2,9 +2,12 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import pytest
+import torch
 from transformers import AutoTokenizer
 
-from vllm.v1.spec_decode.vocab_mapping import _detect_space_prefix
+import vllm.utils.gpu_sync_debug as gsd
+from vllm.utils.gpu_sync_debug import with_gpu_sync_check
+from vllm.v1.spec_decode.vocab_mapping import VocabMapping, _detect_space_prefix
 
 
 @pytest.mark.parametrize(
@@ -46,3 +49,79 @@ def test_detect_space_prefix_empty_encode():
 
     result = _detect_space_prefix(EmptyTokenizer())
     assert result == ("Ġ", "▁")
+
+
+class _FakeTokenizer:
+    """Minimal tokenizer exposing only what ``VocabMapping`` needs."""
+
+    def __init__(self, vocab, unk_token_id=None, eos_token_id=None):
+        self._vocab = dict(vocab)
+        self.unk_token_id = unk_token_id
+        self.eos_token_id = eos_token_id
+
+    def get_vocab(self):
+        return dict(self._vocab)
+
+    def encode(self, text, **kwargs):
+        raise RuntimeError("no encode")
+
+    def convert_ids_to_tokens(self, ids):
+        raise RuntimeError("no convert")
+
+
+@pytest.fixture(params=["cpu", "cuda"])
+def vocab_mapping(request):
+    device = request.param
+    if device == "cuda" and not torch.cuda.is_available():
+        pytest.skip("requires CUDA")
+
+    # Intersection: {a, b}. x/y/z are target-only, u/v are draft-only
+    target = _FakeTokenizer(
+        vocab={"a": 0, "b": 1, "x": 2, "y": 3, "z": 4}, unk_token_id=0
+    )
+    draft = _FakeTokenizer(vocab={"a": 0, "b": 1, "u": 2, "v": 3}, unk_token_id=2)
+    return VocabMapping(target, draft, 5, 4, device)
+
+
+@pytest.mark.parametrize(
+    "target_ids,expected",
+    [
+        ([0, 1, 2, 3, 4], [0, 1, 2, 2, 2]),  # mixed
+        ([0, 1], [0, 1]),  # all present
+        ([2, 3, 4], [2, 2, 2]),  # all missing
+    ],
+)
+def test_map_target_to_draft_ids(monkeypatch, vocab_mapping, target_ids, expected):
+    device = vocab_mapping.device
+    monkeypatch.setattr(gsd, "_SYNC_CHECK_MODE", "error" if device == "cuda" else None)
+    monkeypatch.setattr(gsd, "_sync_check_enabled", True)
+
+    ids = torch.tensor(target_ids, dtype=torch.int32, device=device)
+    out = with_gpu_sync_check(lambda: vocab_mapping.map_target_to_draft_ids(ids))()
+
+    assert out.tolist() == expected
+    assert out.dtype == torch.int32, f"expected torch.int32, got {out.dtype}"
+    assert out.device.type == device, f"expected {device} device, got {out.device}"
+    assert (out != -1).all()
+
+
+@pytest.mark.parametrize(
+    "draft_ids,expected",
+    [
+        ([0, 1, 2, 3], [0, 1, 0, 0]),  # mixed
+        ([0, 1], [0, 1]),  # all present
+        ([2, 3], [0, 0]),  # all missing
+    ],
+)
+def test_map_draft_to_target_ids(monkeypatch, vocab_mapping, draft_ids, expected):
+    device = vocab_mapping.device
+    monkeypatch.setattr(gsd, "_SYNC_CHECK_MODE", "error" if device == "cuda" else None)
+    monkeypatch.setattr(gsd, "_sync_check_enabled", True)
+
+    ids = torch.tensor(draft_ids, dtype=torch.int32, device=device)
+    out = with_gpu_sync_check(lambda: vocab_mapping.map_draft_to_target_ids(ids))()
+
+    assert out.tolist() == expected
+    assert out.dtype == torch.int32, f"expected torch.int32, got {out.dtype}"
+    assert out.device.type == device, f"expected {device} device, got {out.device}"
+    assert (out != -1).all()
diff --git a/vllm/v1/spec_decode/vocab_mapping.py b/vllm/v1/spec_decode/vocab_mapping.py
index 9a6bbe7cfa..dad059fa77 100644
--- a/vllm/v1/spec_decode/vocab_mapping.py
+++ b/vllm/v1/spec_decode/vocab_mapping.py
@@ -137,16 +137,12 @@ class VocabMapping:
 
     def map_target_to_draft_ids(self, target_ids):
         draft_ids = self.target_to_draft_ids[target_ids]  # new tensor; no clone needed
-        missing = draft_ids == -1
-        if missing.any():
-            draft_ids[missing] = self.draft_unk_token_id
+        draft_ids[draft_ids == -1] = self.draft_unk_token_id
         return draft_ids.to(target_ids.dtype)
 
     def map_draft_to_target_ids(self, draft_ids):
         target_ids = self.draft_to_target_ids[draft_ids]  # new tensor; no clone needed
-        missing = target_ids == -1
-        if missing.any():
-            target_ids[missing] = self.target_unk_token_id
+        target_ids[target_ids == -1] = self.target_unk_token_id
         return target_ids.to(draft_ids.dtype)
 
     def constrain_draft_logits(self, logits):