Simplify TokenizerGroup (vllm-project#16790)

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

Author: hmellor

tests/conftest.py

@@ -23,7 +23,7 @@
 from vllm import LLM, SamplingParams
 from vllm.assets.image import ImageAsset
 from vllm.assets.video import VideoAsset
-from vllm.config import TaskOption, TokenizerPoolConfig, _get_and_verify_dtype
+from vllm.config import TaskOption, _get_and_verify_dtype
 from vllm.connections import global_http_connection
 from vllm.distributed import (cleanup_dist_env_and_memory,
                               init_distributed_environment,
@@ -1010,20 +1010,6 @@ def vllm_runner():
     return VllmRunner
 
 
-def get_tokenizer_pool_config(tokenizer_group_type):
-    if tokenizer_group_type is None:
-        return None
-    if tokenizer_group_type == "ray":
-        return TokenizerPoolConfig(pool_size=1,
-                                   pool_type="ray",
-                                   extra_config={})
-    if isinstance(tokenizer_group_type, type):
-        return TokenizerPoolConfig(pool_size=1,
-                                   pool_type=tokenizer_group_type,
-                                   extra_config={})
-    raise ValueError(f"Unknown tokenizer_group_type: {tokenizer_group_type}")
-
-
 @pytest.fixture()
 def temporary_enable_log_propagate():
     import logging

tests/lora/test_tokenizer_group.py

@@ -5,17 +5,14 @@
 
 from vllm.lora.request import LoRARequest
 from vllm.transformers_utils.tokenizer import get_lora_tokenizer
-from vllm.transformers_utils.tokenizer_group import get_tokenizer_group
-
-from ..conftest import get_tokenizer_pool_config
+from vllm.transformers_utils.tokenizer_group import TokenizerGroup
 
 
 @pytest.mark.asyncio
 @pytest.mark.parametrize("tokenizer_group_type", [None, "ray"])
 async def test_tokenizer_group_lora(sql_lora_files, tokenizer_group_type):
     reference_tokenizer = AutoTokenizer.from_pretrained(sql_lora_files)
-    tokenizer_group = get_tokenizer_group(
-        get_tokenizer_pool_config(tokenizer_group_type),
+    tokenizer_group = TokenizerGroup(
         tokenizer_id="gpt2",
         enable_lora=True,
         max_num_seqs=1,
@@ -60,8 +57,7 @@ def test_get_lora_tokenizer(sql_lora_files, tmp_path):
 @pytest.mark.parametrize("max_num_seqs", [1, 2])
 @pytest.mark.parametrize("max_loras", [1, 2])
 def test_lora_tokenizers(enable_lora, max_num_seqs, max_loras):
-    tokenizer_group = get_tokenizer_group(
-        get_tokenizer_pool_config(None),
+    tokenizer_group = TokenizerGroup(
         tokenizer_id="gpt2",
         enable_lora=enable_lora,
         max_num_seqs=max_num_seqs,

tests/tokenization/test_detokenize.py

@@ -10,7 +10,7 @@
 from vllm.inputs import token_inputs
 from vllm.sequence import Logprob, SamplingParams, Sequence, SequenceGroup
 from vllm.transformers_utils.detokenizer import Detokenizer
-from vllm.transformers_utils.tokenizer_group import get_tokenizer_group
+from vllm.transformers_utils.tokenizer_group import TokenizerGroup
 from vllm.transformers_utils.tokenizers.mistral import MistralTokenizer
 from vllm.v1.engine import EngineCoreRequest
 from vllm.v1.engine.detokenizer import (FastIncrementalDetokenizer,
@@ -212,7 +212,7 @@ def test_oov_decode(tokenizer, fast):
 
 @pytest.fixture
 def detokenizer(tokenizer_name: str) -> Detokenizer:
-    init_kwargs = dict(
+    tokenizer_group = TokenizerGroup(
         tokenizer_id=tokenizer_name,
         enable_lora=False,
         max_num_seqs=100,
@@ -222,11 +222,6 @@ def detokenizer(tokenizer_name: str) -> Detokenizer:
         revision=None,
     )
 
-    tokenizer_group = get_tokenizer_group(
-        None,
-        **init_kwargs,
-    )
-
     return Detokenizer(tokenizer_group)
 
 

tests/tokenization/test_tokenizer_group.py

@@ -1,40 +1,15 @@
 # SPDX-License-Identifier: Apache-2.0
 
-import asyncio
-import os
-import sys
-from typing import Optional
-from unittest.mock import patch
-
 import pytest
 from transformers import AutoTokenizer, PreTrainedTokenizerBase
 
-from vllm.transformers_utils.tokenizer_group import (TokenizerGroup,
-                                                     get_tokenizer_group)
-from vllm.transformers_utils.tokenizer_group.ray_tokenizer_group import (
-    RayTokenizerGroupPool)
-
-from ..conftest import get_tokenizer_pool_config
-
-
-class CustomTokenizerGroup(TokenizerGroup):
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self._i = 0
-
-    def encode(self, *args, **kwargs):
-        self._i += 1
-        return super().encode(*args, **kwargs)
+from vllm.transformers_utils.tokenizer_group import TokenizerGroup
 
 
 @pytest.mark.asyncio
-@pytest.mark.parametrize("tokenizer_group_type",
-                         [None, "ray", CustomTokenizerGroup])
-async def test_tokenizer_group(tokenizer_group_type):
+async def test_tokenizer_group():
     reference_tokenizer = AutoTokenizer.from_pretrained("gpt2")
-    tokenizer_group = get_tokenizer_group(
-        get_tokenizer_pool_config(tokenizer_group_type),
+    tokenizer_group = TokenizerGroup(
         tokenizer_id="gpt2",
         enable_lora=False,
         max_num_seqs=1,
@@ -49,159 +24,3 @@ async def test_tokenizer_group(tokenizer_group_type):
                       PreTrainedTokenizerBase)
     assert tokenizer_group.get_lora_tokenizer(
         None) == await tokenizer_group.get_lora_tokenizer_async(None)
-    if tokenizer_group_type is CustomTokenizerGroup:
-        assert tokenizer_group._i > 0
-
-
-@pytest.mark.asyncio
-@pytest.mark.parametrize("tokenizer_group_type", ["ray"])
-async def test_tokenizer_group_pool(tokenizer_group_type):
-    reference_tokenizer = AutoTokenizer.from_pretrained("gpt2")
-    tokenizer_group_pool = get_tokenizer_group(
-        get_tokenizer_pool_config(tokenizer_group_type),
-        tokenizer_id="gpt2",
-        enable_lora=False,
-        max_num_seqs=1,
-        max_input_length=None,
-    )
-    # Send multiple requests to the tokenizer group pool
-    # (more than the pool size)
-    # and check that all requests are processed correctly.
-    num_requests = tokenizer_group_pool.pool_size * 5
-    requests = [
-        tokenizer_group_pool.encode_async(prompt=f"prompt {i}",
-                                          lora_request=None)
-        for i in range(num_requests)
-    ]
-    results = await asyncio.gather(*requests)
-    expected_results = [
-        reference_tokenizer.encode(f"prompt {i}") for i in range(num_requests)
-    ]
-    assert results == expected_results
-
-
-@pytest.mark.asyncio
-@pytest.mark.parametrize("tokenizer_group_type", ["ray"])
-async def test_tokenizer_group_ray_pool_env_var_propagation(
-        tokenizer_group_type):
-    """Test that env vars from caller process are propagated to
-    tokenizer Ray actors."""
-    env_var = "MY_ENV_VAR"
-
-    class EnvVarCheckerTokenizerGroup(TokenizerGroup):
-
-        def ping(self):
-            assert os.environ.get(env_var) == "1"
-            return super().ping()
-
-    class EnvVarCheckerRayTokenizerGroupPool(RayTokenizerGroupPool):
-        _worker_cls = EnvVarCheckerTokenizerGroup
-
-    tokenizer_pool_config = get_tokenizer_pool_config(tokenizer_group_type)
-    tokenizer_pool = EnvVarCheckerRayTokenizerGroupPool.from_config(
-        tokenizer_pool_config,
-        tokenizer_id="gpt2",
-        enable_lora=False,
-        max_num_seqs=1,
-        max_input_length=None)
-    with pytest.raises(AssertionError):
-        tokenizer_pool.ping()
-
-    with patch.dict(os.environ, {env_var: "1"}):
-        tokenizer_pool_config = get_tokenizer_pool_config(tokenizer_group_type)
-        tokenizer_pool = EnvVarCheckerRayTokenizerGroupPool.from_config(
-            tokenizer_pool_config,
-            tokenizer_id="gpt2",
-            enable_lora=False,
-            max_num_seqs=1,
-            max_input_length=None)
-        tokenizer_pool.ping()
-
-
-@pytest.mark.asyncio
-@pytest.mark.parametrize("tokenizer_group_type", ["ray"])
-async def test_tokenizer_group_ray_pool_fault_tolerance(tokenizer_group_type):
-    """Test that Ray tokenizer pool group can recover from failures and
-    if that's not possible, mark itself as unhealthy."""
-
-    class FailingTokenizerGroup(TokenizerGroup):
-
-        def __init__(self,
-                     *args,
-                     fail_at: Optional[list[int]] = None,
-                     **kwargs):
-            super().__init__(*args, **kwargs)
-            self.i = 0
-            self.fail_at = fail_at or []
-
-        def encode(self, *args, **kwargs):
-            self.i += 1
-            if self.i in self.fail_at:
-                sys.exit(1)
-            return super().encode(*args, **kwargs)
-
-    class FailingRayTokenizerGroupPool(RayTokenizerGroupPool):
-        _worker_cls = FailingTokenizerGroup
-
-    # Fail at first iteration
-    fail_at = [1]
-    tokenizer_pool_config = get_tokenizer_pool_config(tokenizer_group_type)
-    tokenizer_group_pool = FailingRayTokenizerGroupPool.from_config(
-        tokenizer_pool_config,
-        tokenizer_id="gpt2",
-        enable_lora=False,
-        max_num_seqs=1,
-        max_input_length=None,
-        fail_at=fail_at)
-    tokenizer_actors = tokenizer_group_pool.tokenizer_actors.copy()
-
-    # Modify fail at to not fail at all (will be re-read when actor is
-    # re-initialized).
-    fail_at[0] = 1000
-
-    # We should recover successfully.
-    await tokenizer_group_pool.encode_async(prompt="prompt", lora_request=None)
-    await tokenizer_group_pool.encode_async(prompt="prompt", lora_request=None)
-
-    # Check that we have a new actor
-    assert len(tokenizer_group_pool.tokenizer_actors) == len(tokenizer_actors)
-    assert tokenizer_group_pool.tokenizer_actors != tokenizer_actors
-
-    # Fail at first iteration
-    fail_at = [1]
-    tokenizer_group_pool = FailingRayTokenizerGroupPool.from_config(
-        tokenizer_pool_config,
-        tokenizer_id="gpt2",
-        enable_lora=False,
-        max_num_seqs=1,
-        max_input_length=None,
-        fail_at=fail_at)
-
-    # We should fail after re-initialization.
-    with pytest.raises(RuntimeError):
-        await tokenizer_group_pool.encode_async(prompt="prompt",
-                                                lora_request=None)
-
-    # check_health should raise the same thing
-    with pytest.raises(RuntimeError):
-        tokenizer_group_pool.check_health()
-
-    # Ensure that non-ActorDiedErrors are still propagated correctly and do not
-    # cause a re-initialization.
-    fail_at = []
-    tokenizer_group_pool = FailingRayTokenizerGroupPool.from_config(
-        tokenizer_pool_config,
-        tokenizer_id="gpt2",
-        enable_lora=False,
-        max_num_seqs=1,
-        max_input_length=2,
-        fail_at=fail_at)
-    tokenizer_actors = tokenizer_group_pool.tokenizer_actors.copy()
-
-    # Prompt too long error
-    with pytest.raises(ValueError):
-        await tokenizer_group_pool.encode_async(prompt="prompt" * 100,
-                                                lora_request=None)
-    await tokenizer_group_pool.encode_async(prompt="prompt", lora_request=None)
-    # Actors should stay the same.
-    assert tokenizer_group_pool.tokenizer_actors == tokenizer_actors

tests/v1/engine/conftest.py

@@ -47,7 +47,7 @@ def _build_test_vectors_no_logprobs() -> DummyOutputProcessorTestVectors:
         tokenizer=tokenizer,
         tokenizer_group=init_tokenizer_from_configs(
             vllm_config.model_config, vllm_config.scheduler_config,
-            vllm_config.parallel_config, vllm_config.lora_config),
+            vllm_config.lora_config),
         vllm_config=vllm_config,
         full_tokens=[tokenizer(text).input_ids for text in FULL_STRINGS],
         prompt_tokens=prompt_tokens,

tests/v1/engine/utils.py

@@ -8,8 +8,7 @@
 from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast
 
 from vllm.engine.arg_utils import EngineArgs
-from vllm.transformers_utils.tokenizer_group.base_tokenizer_group import (
-    BaseTokenizerGroup)
+from vllm.transformers_utils.tokenizer_group import TokenizerGroup
 from vllm.v1.engine import EngineCoreOutput, FinishReason
 from vllm.v1.outputs import LogprobsLists, LogprobsTensors
 
@@ -296,7 +295,7 @@ def generate_dummy_prompt_logprobs_tensors(
 class DummyOutputProcessorTestVectors:
     """Dummy test vectors for output processor tests"""
     tokenizer: GeneralTokenizerType
-    tokenizer_group: BaseTokenizerGroup
+    tokenizer_group: TokenizerGroup
     vllm_config: EngineArgs
     full_tokens: list[list[int]]  # Prompt + generated tokens
     prompt_tokens: list[list[int]]