[Core] Remove tokenizer group in vLLM

tests/detokenizer/test_stop_checker.py

@@ -1,10 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-from unittest.mock import MagicMock
-
 import pytest
-from transformers import PreTrainedTokenizer
 
 from vllm.engine.output_processor.stop_checker import StopChecker
 from vllm.inputs import token_inputs
@@ -54,10 +51,7 @@ def test_stop_on_eos_token(text_wo_eos: str, eos_token: str, eos_token_id: int,
     - When the EOS token should be ignored, and the sequence continues
     """
 
-    tokenizer = MagicMock(spec=PreTrainedTokenizer)
-    get_tokenizer_for_seq = MagicMock(return_value=tokenizer)
-    stop_checker = StopChecker(max_model_len=1024,
-                               get_tokenizer_for_seq=get_tokenizer_for_seq)
+    stop_checker = StopChecker(max_model_len=1024)
 
     seq = sequence_with_eos(
         text=text_wo_eos,

tests/engine/test_stop_checker.py

@@ -58,16 +58,13 @@ def deepseek_r1_qwen_tokenizer():
 
 @pytest.fixture
 def stop_checker():
-    return StopChecker(max_model_len=10,
-                       get_tokenizer_for_seq=deepseek_r1_qwen_tokenizer)
+    return StopChecker(max_model_len=10)
 
 
 @pytest.fixture
 def stop_checker_with_reasoner():
     reasoner = MockReasoningParser(deepseek_r1_qwen_tokenizer)
-    return StopChecker(max_model_len=10,
-                       get_tokenizer_for_seq=deepseek_r1_qwen_tokenizer,
-                       reasoner=reasoner)
+    return StopChecker(max_model_len=10, reasoner=reasoner)
 
 
 def test_eos_token_stopping(stop_checker):

tests/entrypoints/conftest.py

@@ -208,25 +208,3 @@ def zephyr_lora_files():
     """Download zephyr LoRA files once per test session."""
     from huggingface_hub import snapshot_download
     return snapshot_download(repo_id="typeof/zephyr-7b-beta-lora")
-
-
-@pytest.fixture(scope="session")
-def zephyr_lora_added_tokens_files(zephyr_lora_files):
-    """Create zephyr LoRA files with added tokens once per test session."""
-    import shutil
-    from tempfile import TemporaryDirectory
-
-    from transformers import AutoTokenizer
-
-    tmp_dir = TemporaryDirectory()
-    tmp_model_dir = f"{tmp_dir.name}/zephyr"
-    shutil.copytree(zephyr_lora_files, tmp_model_dir)
-    tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-beta")
-    # Copy tokenizer to adapter and add some unique tokens
-    # 32000, 32001, 32002
-    added = tokenizer.add_tokens(["vllm1", "vllm2", "vllm3"],
-                                 special_tokens=True)
-    assert added == 3
-    tokenizer.save_pretrained(tmp_model_dir)
-    yield tmp_model_dir
-    tmp_dir.cleanup()

tests/entrypoints/openai/test_chat.py

@@ -29,11 +29,7 @@ def monkeypatch_module():
 
 
 @pytest.fixture(scope="module", params=[False, True])
-def server(
-        request,
-        monkeypatch_module,
-        zephyr_lora_files,  #noqa: F811
-        zephyr_lora_added_tokens_files):  # noqa: F811
+def server(request, monkeypatch_module, zephyr_lora_files):  #noqa: F811
 
     use_v1 = request.param
     monkeypatch_module.setenv('VLLM_USE_V1', '1' if use_v1 else '0')
@@ -49,7 +45,6 @@ def server(
         "--enable-lora",
         "--lora-modules",
         f"zephyr-lora={zephyr_lora_files}",
-        f"zephyr-lora2={zephyr_lora_added_tokens_files}",
         "--max-lora-rank",
         "64",
         "--max-cpu-loras",
@@ -79,7 +74,7 @@ async def client(server):
 @pytest.mark.parametrize(
     # first test base model, then test loras
     "model_name",
-    [MODEL_NAME, "zephyr-lora", "zephyr-lora2"],
+    [MODEL_NAME, "zephyr-lora"],
 )
 async def test_no_logprobs_chat(client: openai.AsyncOpenAI, model_name: str):
     messages = [{

tests/entrypoints/openai/test_completion.py

@@ -27,7 +27,7 @@
 
 
 @pytest.fixture(scope="module")
-def default_server_args(zephyr_lora_files, zephyr_lora_added_tokens_files):
+def default_server_args(zephyr_lora_files):
     return [
         # use half precision for speed and memory savings in CI environment
         "--dtype",
@@ -41,7 +41,6 @@ def default_server_args(zephyr_lora_files, zephyr_lora_added_tokens_files):
         "--enable-lora",
         "--lora-modules",
         f"zephyr-lora={zephyr_lora_files}",
-        f"zephyr-lora2={zephyr_lora_added_tokens_files}",
         "--max-lora-rank",
         "64",
         "--max-cpu-loras",
@@ -87,7 +86,7 @@ async def client(server):
 @pytest.mark.parametrize(
     # first test base model, then test loras
     "model_name",
-    [MODEL_NAME, "zephyr-lora", "zephyr-lora2"],
+    [MODEL_NAME, "zephyr-lora"],
 )
 async def test_single_completion(client: openai.AsyncOpenAI, model_name: str):
     completion = await client.completions.create(model=model_name,
@@ -115,20 +114,6 @@ async def test_single_completion(client: openai.AsyncOpenAI, model_name: str):
     assert completion.choices[0].prompt_logprobs is None
 
 
-@pytest.mark.asyncio
-async def test_added_lora_tokens(client: openai.AsyncOpenAI):
-    # test using token IDs
-    completion = await client.completions.create(
-        model="zephyr-lora2",
-        prompt=[0, 0, 32000, 32001, 32002],
-        echo=True,
-        max_tokens=5,
-        temperature=0.0,
-    )
-    # Added tokens should appear in tokenized prompt
-    assert completion.choices[0].text.startswith("<unk><unk>vllm1vllm2vllm3")
-
-
 @pytest.mark.asyncio
 async def test_added_lora_tokens_base_model(client: openai.AsyncOpenAI):
     # test using token IDs
@@ -147,7 +132,7 @@ async def test_added_lora_tokens_base_model(client: openai.AsyncOpenAI):
 @pytest.mark.parametrize(
     # first test base model, then test loras
     "model_name",
-    [MODEL_NAME, "zephyr-lora", "zephyr-lora2"],
+    [MODEL_NAME, "zephyr-lora"],
 )
 async def test_no_logprobs(client: openai.AsyncOpenAI, model_name: str):
     # test using token IDs
@@ -713,7 +698,7 @@ async def test_guided_grammar(client: openai.AsyncOpenAI,
 @pytest.mark.parametrize(
     # first test base model, then test loras
     "model_name",
-    [MODEL_NAME, "zephyr-lora", "zephyr-lora2"],
+    [MODEL_NAME, "zephyr-lora"],
 )
 @pytest.mark.parametrize("logprobs_arg", [1, 0])
 async def test_echo_logprob_completion(client: openai.AsyncOpenAI,

tests/entrypoints/openai/test_completion_with_prompt_embeds.py

@@ -21,10 +21,7 @@
 
 
 @pytest.fixture(scope="module")
-def default_server_args(
-    zephyr_lora_files,
-    zephyr_lora_added_tokens_files,
-) -> list[str]:
+def default_server_args() -> list[str]:
     return [
         # use half precision for speed and memory savings in CI environment
         "--dtype",

tests/entrypoints/openai/test_lora_adapters.py

@@ -67,12 +67,6 @@ def server_with_lora_modules_json(request, monkeypatch_module,
         "base_model_name": MODEL_NAME
     }
 
-    lora_module_2 = {
-        "name": "zephyr-lora2",
-        "path": zephyr_lora_files,
-        "base_model_name": MODEL_NAME
-    }
-
     args = [
         # use half precision for speed and memory savings in CI environment
         "--dtype",
@@ -84,7 +78,6 @@ def server_with_lora_modules_json(request, monkeypatch_module,
         "--enable-lora",
         "--lora-modules",
         json.dumps(lora_module_1),
-        json.dumps(lora_module_2),
         "--max-lora-rank",
         "64",
         "--max-cpu-loras",
@@ -121,7 +114,6 @@ async def test_static_lora_lineage(client: openai.AsyncOpenAI,
                for lora_model in lora_models)
     assert all(lora_model.parent == MODEL_NAME for lora_model in lora_models)
     assert lora_models[0].id == "zephyr-lora"
-    assert lora_models[1].id == "zephyr-lora2"
 
 
 @pytest.mark.asyncio
@@ -209,7 +201,7 @@ async def test_dynamic_lora_badrequests(client: openai.AsyncOpenAI, tmp_path,
 @pytest.mark.asyncio
 async def test_multiple_lora_adapters(client: openai.AsyncOpenAI, tmp_path,
                                       zephyr_lora_files):
-    """Validate that many loras can be dynamically registered and inferenced 
+    """Validate that many loras can be dynamically registered and inferenced
     with concurrently"""
 
     # This test file configures the server with --max-cpu-loras=2 and this test

tests/entrypoints/openai/test_models.py

@@ -26,7 +26,6 @@ def server(zephyr_lora_files):
         "--enable-lora",
         "--lora-modules",
         f"zephyr-lora={zephyr_lora_files}",
-        f"zephyr-lora2={zephyr_lora_files}",
         "--max-lora-rank",
         "64",
         "--max-cpu-loras",
@@ -56,4 +55,3 @@ async def test_check_models(client: openai.AsyncOpenAI, zephyr_lora_files):
     assert all(lora_model.root == zephyr_lora_files
                for lora_model in lora_models)
     assert lora_models[0].id == "zephyr-lora"
-    assert lora_models[1].id == "zephyr-lora2"

tests/entrypoints/openai/test_tokenization.py

@@ -14,7 +14,7 @@
 
 
 @pytest.fixture(scope="module")
-def server(zephyr_lora_added_tokens_files: str):  # noqa: F811
+def server():
     args = [
         # use half precision for speed and memory savings in CI environment
         "--dtype",
@@ -24,12 +24,6 @@ def server(zephyr_lora_added_tokens_files: str):  # noqa: F811
         "--enforce-eager",
         "--max-num-seqs",
         "128",
-        # lora config
-        "--enable-lora",
-        "--lora-modules",
-        f"zephyr-lora2={zephyr_lora_added_tokens_files}",
-        "--max-lora-rank",
-        "64",
         "--enable-tokenizer-info-endpoint",
     ]
 
@@ -38,10 +32,8 @@ def server(zephyr_lora_added_tokens_files: str):  # noqa: F811
 
 
 @pytest.fixture(scope="module")
-def tokenizer_name(model_name: str,
-                   zephyr_lora_added_tokens_files: str):  # noqa: F811
-    return zephyr_lora_added_tokens_files if (
-        model_name == "zephyr-lora2") else model_name
+def tokenizer_name(model_name: str):
+    return model_name
 
 
 @pytest_asyncio.fixture
@@ -53,7 +45,7 @@ async def client(server):
 @pytest.mark.asyncio
 @pytest.mark.parametrize(
     "model_name,tokenizer_name",
-    [(MODEL_NAME, MODEL_NAME), ("zephyr-lora2", "zephyr-lora2")],
+    [(MODEL_NAME, MODEL_NAME)],
     indirect=["tokenizer_name"],
 )
 async def test_tokenize_completions(
@@ -86,7 +78,7 @@ async def test_tokenize_completions(
 @pytest.mark.asyncio
 @pytest.mark.parametrize(
     "model_name,tokenizer_name",
-    [(MODEL_NAME, MODEL_NAME), ("zephyr-lora2", "zephyr-lora2")],
+    [(MODEL_NAME, MODEL_NAME)],
     indirect=["tokenizer_name"],
 )
 async def test_tokenize_chat(
@@ -148,7 +140,7 @@ async def test_tokenize_chat(
 @pytest.mark.asyncio
 @pytest.mark.parametrize(
     "model_name,tokenizer_name",
-    [(MODEL_NAME, MODEL_NAME), ("zephyr-lora2", "zephyr-lora2")],
+    [(MODEL_NAME, MODEL_NAME)],
     indirect=["tokenizer_name"],
 )
 async def test_tokenize_chat_with_tools(
@@ -225,7 +217,7 @@ async def test_tokenize_chat_with_tools(
 @pytest.mark.asyncio
 @pytest.mark.parametrize(
     "model_name, tokenizer_name",
-    [(MODEL_NAME, MODEL_NAME), ("zephyr-lora2", "zephyr-lora2")],
+    [(MODEL_NAME, MODEL_NAME)],
     indirect=["tokenizer_name"],
 )
 async def test_tokenize_with_return_token_strs(
@@ -260,7 +252,7 @@ async def test_tokenize_with_return_token_strs(
 @pytest.mark.asyncio
 @pytest.mark.parametrize(
     "model_name,tokenizer_name",
-    [(MODEL_NAME, MODEL_NAME), ("zephyr-lora2", "zephyr-lora2")],
+    [(MODEL_NAME, MODEL_NAME)],
     indirect=["tokenizer_name"],
 )
 async def test_detokenize(
@@ -287,7 +279,7 @@ async def test_detokenize(
 @pytest.mark.asyncio
 @pytest.mark.parametrize(
     "model_name,tokenizer_name",
-    [(MODEL_NAME, MODEL_NAME), ("zephyr-lora2", "zephyr-lora2")],
+    [(MODEL_NAME, MODEL_NAME)],
     indirect=["tokenizer_name"],
 )
 async def test_tokenizer_info_basic(
@@ -384,4 +376,4 @@ async def test_tokenizer_info_chat_template(server: RemoteOpenAIServer):
     if chat_template:
         assert isinstance(chat_template,
                           str), ("Chat template should be a string")
-        assert chat_template.strip(), "Chat template should not be empty"
+        assert chat_template.strip(), "Chat template should not be empty"

tests/entrypoints/openai/tool_parsers/test_hermes_tool_parser.py

@@ -18,6 +18,8 @@
     "--enable-lora",
     "--lora-modules",
     f"{LORA_MODEL}={LORA_MODEL}",
+    "--tokenizer",
+    f"{LORA_MODEL}",
 ]
 
 TOOLS = [{