[Misc] Enable V1 LoRA by default (vllm-project#15320)

Signed-off-by: Varun Sundar Rabindranath <[email protected]>
Co-authored-by: Varun Sundar Rabindranath <[email protected]>

Author: 2 people

tests/entrypoints/openai/test_chat.py

@@ -24,7 +24,23 @@
 
 
 @pytest.fixture(scope="module")
-def server(zephyr_lora_files, zephyr_lora_added_tokens_files):  # noqa: F811
+def monkeypatch_module():
+    from _pytest.monkeypatch import MonkeyPatch
+    mpatch = MonkeyPatch()
+    yield mpatch
+    mpatch.undo()
+
+
+@pytest.fixture(scope="module", params=[False, True])
+def server(
+        request,
+        monkeypatch_module,
+        zephyr_lora_files,  #noqa: F811
+        zephyr_lora_added_tokens_files):  # noqa: F811
+
+    use_v1 = request.param
+    monkeypatch_module.setenv('VLLM_USE_V1', '1' if use_v1 else '0')
+
     args = [
         # use half precision for speed and memory savings in CI environment
         "--dtype",
@@ -49,6 +65,13 @@ def server(zephyr_lora_files, zephyr_lora_added_tokens_files):  # noqa: F811
         yield remote_server
 
 
+@pytest.fixture
+def is_v1_server(server):
+    import os
+    assert os.environ['VLLM_USE_V1'] in ['0', '1']
+    return os.environ['VLLM_USE_V1'] == '1'
+
+
 @pytest_asyncio.fixture
 async def client(server):
     async with server.get_async_client() as async_client:
@@ -471,8 +494,13 @@ async def test_chat_completion_stream_options(client: openai.AsyncOpenAI,
 @pytest.mark.asyncio
 @pytest.mark.parametrize("guided_decoding_backend", GUIDED_DECODING_BACKENDS)
 async def test_guided_choice_chat(client: openai.AsyncOpenAI,
+                                  is_v1_server: bool,
                                   guided_decoding_backend: str,
                                   sample_guided_choice):
+
+    if is_v1_server and guided_decoding_backend != 'xgrammar':
+        pytest.skip("Only xgrammar backend is supported with V1")
+
     messages = [{
         "role": "system",
         "content": "you are a helpful assistant"
@@ -511,9 +539,13 @@ async def test_guided_choice_chat(client: openai.AsyncOpenAI,
 
 @pytest.mark.asyncio
 @pytest.mark.parametrize("guided_decoding_backend", GUIDED_DECODING_BACKENDS)
-async def test_guided_json_chat(client: openai.AsyncOpenAI,
+async def test_guided_json_chat(client: openai.AsyncOpenAI, is_v1_server: bool,
                                 guided_decoding_backend: str,
                                 sample_json_schema):
+
+    if is_v1_server:
+        pytest.skip("sample_json_schema has features unsupported in V1")
+
     messages = [{
         "role": "system",
         "content": "you are a helpful assistant"
@@ -559,7 +591,12 @@ async def test_guided_json_chat(client: openai.AsyncOpenAI,
 @pytest.mark.asyncio
 @pytest.mark.parametrize("guided_decoding_backend", GUIDED_DECODING_BACKENDS)
 async def test_guided_regex_chat(client: openai.AsyncOpenAI,
+                                 is_v1_server: bool,
                                  guided_decoding_backend: str, sample_regex):
+
+    if is_v1_server and guided_decoding_backend != 'xgrammar':
+        pytest.skip("Only xgrammar backend is supported with V1")
+
     messages = [{
         "role": "system",
         "content": "you are a helpful assistant"
@@ -617,8 +654,13 @@ async def test_guided_decoding_type_error(client: openai.AsyncOpenAI):
 @pytest.mark.asyncio
 @pytest.mark.parametrize("guided_decoding_backend", GUIDED_DECODING_BACKENDS)
 async def test_guided_choice_chat_logprobs(client: openai.AsyncOpenAI,
+                                           is_v1_server: bool,
                                            guided_decoding_backend: str,
                                            sample_guided_choice):
+
+    if is_v1_server and guided_decoding_backend != 'xgrammar':
+        pytest.skip("Only xgrammar backend is supported with V1")
+
     messages = [{
         "role": "system",
         "content": "you are a helpful assistant"
@@ -648,9 +690,13 @@ async def test_guided_choice_chat_logprobs(client: openai.AsyncOpenAI,
 
 @pytest.mark.asyncio
 @pytest.mark.parametrize("guided_decoding_backend", GUIDED_DECODING_BACKENDS)
-async def test_named_tool_use(client: openai.AsyncOpenAI,
+async def test_named_tool_use(client: openai.AsyncOpenAI, is_v1_server: bool,
                               guided_decoding_backend: str,
                               sample_json_schema):
+
+    if is_v1_server:
+        pytest.skip("sample_json_schema has features unsupported on V1")
+
     messages = [{
         "role": "system",
         "content": "you are a helpful assistant"
@@ -742,6 +788,10 @@ async def test_named_tool_use(client: openai.AsyncOpenAI,
 @pytest.mark.asyncio
 async def test_required_tool_use_not_yet_supported(client: openai.AsyncOpenAI,
                                                    sample_json_schema):
+
+    if is_v1_server:
+        pytest.skip("sample_json_schema has features unsupported on V1")
+
     messages = [{
         "role": "system",
         "content": "you are a helpful assistant"
@@ -787,6 +837,10 @@ async def test_required_tool_use_not_yet_supported(client: openai.AsyncOpenAI,
 @pytest.mark.asyncio
 async def test_inconsistent_tool_choice_and_tools(client: openai.AsyncOpenAI,
                                                   sample_json_schema):
+
+    if is_v1_server:
+        pytest.skip("sample_json_schema has features unsupported on V1")
+
     messages = [{
         "role": "system",
         "content": "you are a helpful assistant"

tests/lora/test_baichuan.py

@@ -11,6 +11,14 @@
 PROMPT_TEMPLATE = """I want you to act as a SQL terminal in front of an example database, you need only to return the sql command to me.Below is an instruction that describes a task, Write a response that appropriately completes the request.\n"\n##Instruction:\nconcert_singer contains tables such as stadium, singer, concert, singer_in_concert. Table stadium has columns such as Stadium_ID, Location, Name, Capacity, Highest, Lowest, Average. Stadium_ID is the primary key.\nTable singer has columns such as Singer_ID, Name, Country, Song_Name, Song_release_year, Age, Is_male. Singer_ID is the primary key.\nTable concert has columns such as concert_ID, concert_Name, Theme, Stadium_ID, Year. concert_ID is the primary key.\nTable singer_in_concert has columns such as concert_ID, Singer_ID. concert_ID is the primary key.\nThe Stadium_ID of concert is the foreign key of Stadium_ID of stadium.\nThe Singer_ID of singer_in_concert is the foreign key of Singer_ID of singer.\nThe concert_ID of singer_in_concert is the foreign key of concert_ID of concert.\n\n###Input:\n{query}\n\n###Response:"""  # noqa: E501
 
 
+@pytest.fixture(autouse=True)
+def v1(run_with_both_engines_lora):
+    # Simple autouse wrapper to run both engines for each test
+    # This can be promoted up to conftest.py to run for every
+    # test in a package
+    pass
+
+
 def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int) -> list[str]:
     prompts = [
         PROMPT_TEMPLATE.format(query="How many singers do we have?"),
@@ -40,14 +48,6 @@ def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int) -> list[str]:
     return generated_texts
 
 
-@pytest.fixture(autouse=True)
-def v1(run_with_both_engines_lora):
-    # Simple autouse wrapper to run both engines for each test
-    # This can be promoted up to conftest.py to run for every
-    # test in a package
-    pass
-
-
 def test_baichuan_lora(baichuan_lora_files):
     llm = vllm.LLM(MODEL_PATH,
                    max_model_len=1024,

tests/lora/test_chatglm3_tp.py

@@ -18,6 +18,14 @@
 ]
 
 
+@pytest.fixture(autouse=True)
+def v1(run_with_both_engines_lora):
+    # Simple autouse wrapper to run both engines for each test
+    # This can be promoted up to conftest.py to run for every
+    # test in a package
+    pass
+
+
 def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int) -> list[str]:
     prompts = [
         PROMPT_TEMPLATE.format(query="How many singers do we have?"),
@@ -46,14 +54,6 @@ def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int) -> list[str]:
     return generated_texts
 
 
-@pytest.fixture(autouse=True)
-def v1(run_with_both_engines_lora):
-    # Simple autouse wrapper to run both engines for each test
-    # This can be promoted up to conftest.py to run for every
-    # test in a package
-    pass
-
-
 @create_new_process_for_each_test()
 def test_chatglm3_lora(chatglm3_lora_files):
     llm = vllm.LLM(MODEL_PATH,

tests/lora/test_gemma.py

@@ -9,6 +9,14 @@
 MODEL_PATH = "google/gemma-7b"
 
 
+@pytest.fixture(autouse=True)
+def v1(run_with_both_engines_lora):
+    # Simple autouse wrapper to run both engines for each test
+    # This can be promoted up to conftest.py to run for every
+    # test in a package
+    pass
+
+
 def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int) -> list[str]:
     prompts = [
         "Quote: Imagination is",
@@ -31,14 +39,6 @@ def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int) -> list[str]:
     return generated_texts
 
 
-@pytest.fixture(autouse=True)
-def v1(run_with_both_engines_lora):
-    # Simple autouse wrapper to run both engines for each test
-    # This can be promoted up to conftest.py to run for every
-    # test in a package
-    pass
-
-
 # The V1 lora test for this model requires more than 24GB.
 @pytest.mark.skip_v1
 @pytest.mark.xfail(current_platform.is_rocm(),

tests/lora/test_layers.py

@@ -1,6 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
 
-import importlib
 import random
 from copy import deepcopy
 from dataclasses import dataclass
@@ -82,10 +81,6 @@ def v1(run_with_both_engines_lora):
     # This can be promoted up to conftest.py to run for every
     # test in a package
 
-    # Reload punica_gpu as the kernels used are tied to engine type.
-    from vllm.lora.punica_wrapper import punica_gpu
-    importlib.reload(punica_gpu)
-
     # Release any memory we might be holding on to. CI runs OOMs otherwise.
     from vllm.lora.ops.triton_ops.utils import (_LORA_A_PTR_DICT,
                                                 _LORA_B_PTR_DICT)

tests/lora/test_llama_tp.py

@@ -28,6 +28,14 @@
 ]
 
 
+@pytest.fixture(autouse=True)
+def v1(run_with_both_engines_lora):
+    # Simple autouse wrapper to run both engines for each test
+    # This can be promoted up to conftest.py to run for every
+    # test in a package
+    pass
+
+
 def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int) -> list[str]:
     prompts = [
         "[user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_name_74 (icao VARCHAR, airport VARCHAR)\n\n question: Name the ICAO for lilongwe international airport [/user] [assistant]",  # noqa: E501
@@ -71,16 +79,6 @@ def generate_and_test(llm, sql_lora_files):
     print("removing lora")
 
 
-@pytest.fixture(autouse=True)
-def v1(run_with_both_engines_lora):
-    # Simple autouse wrapper to run both engines for each test
-    # This can be promoted up to conftest.py to run for every
-    # test in a package
-    pass
-
-
-# V1 Test: Failing due to numerics on V1.
-@pytest.mark.skip_v1
 @create_new_process_for_each_test()
 def test_llama_lora(sql_lora_files):
 
@@ -126,8 +124,6 @@ def get_num_gpu_blocks_no_lora():
         "less when using lora than when not using lora")
 
 
-# V1 Test: Failing due to numerics on V1.
-@pytest.mark.skip_v1
 @multi_gpu_test(num_gpus=4)
 @create_new_process_for_each_test()
 def test_llama_lora_tp4(sql_lora_files):

tests/lora/test_lora_manager.py

@@ -7,7 +7,6 @@
 from safetensors.torch import load_file
 from torch import nn
 
-from vllm import envs
 from vllm.config import LoRAConfig
 from vllm.lora.layers import (ColumnParallelLinearWithLoRA,
                               MergedColumnParallelLinearWithLoRA,
@@ -33,6 +32,17 @@
 ] if current_platform.is_cuda_alike() else ["cpu"])
 
 
+@pytest.fixture(scope="function", autouse=True)
+def use_v0_only(monkeypatch: pytest.MonkeyPatch):
+    """
+    Some tests depend on V0 internals. Since both V0 and V1 use the same
+    LoRAModelManager it is okay to just test V0.
+    """
+    with monkeypatch.context() as m:
+        m.setenv('VLLM_USE_V1', '0')
+        yield
+
+
 @pytest.mark.parametrize("device", DEVICES)
 def test_from_lora_tensors(sql_lora_files, device):
     tensors = load_file(
@@ -411,7 +421,6 @@ def test_lru_lora_model_manager(dist_init, dummy_model, device):
     assert manager.device == device
 
 
-@pytest.mark.skipif(envs.VLLM_USE_V1, reason="Test leverages V0 internals.")
 @pytest.mark.parametrize("device", DEVICES)
 def test_lru_cache_worker_adapter_manager(llama_2_7b_model_extra_embeddings,
                                           sql_lora_files, device):
@@ -491,7 +500,6 @@ def test_lru_cache_worker_adapter_manager(llama_2_7b_model_extra_embeddings,
             device)
 
 
-@pytest.mark.skipif(envs.VLLM_USE_V1, reason="Test leverages V0 internals.")
 @pytest.mark.parametrize("device", DEVICES)
 def test_worker_adapter_manager(llama_2_7b_model_extra_embeddings,
                                 sql_lora_files, device):

tests/lora/test_phi.py

@@ -10,6 +10,14 @@
 PROMPT_TEMPLATE = "### Instruct: {sql_prompt}\n\n### Context: {context}\n\n### Output:"  # noqa: E501
 
 
+@pytest.fixture(autouse=True)
+def v1(run_with_both_engines_lora):
+    # Simple autouse wrapper to run both engines for each test
+    # This can be promoted up to conftest.py to run for every
+    # test in a package
+    pass
+
+
 def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int) -> list[str]:
     prompts = [
         PROMPT_TEMPLATE.format(
@@ -48,14 +56,6 @@ def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int) -> list[str]:
     return generated_texts
 
 
-@pytest.fixture(autouse=True)
-def v1(run_with_both_engines_lora):
-    # Simple autouse wrapper to run both engines for each test
-    # This can be promoted up to conftest.py to run for every
-    # test in a package
-    pass
-
-
 # Skipping for V1 for now as we are hitting,
 # "Head size 80 is not supported by FlashAttention." error.
 @pytest.mark.skip_v1

tests/lora/test_quant_model.py

@@ -37,6 +37,14 @@ class ModelWithQuantization:
     ]
 
 
+@pytest.fixture(autouse=True)
+def v1(run_with_both_engines_lora):
+    # Simple autouse wrapper to run both engines for each test
+    # This can be promoted up to conftest.py to run for every
+    # test in a package
+    pass
+
+
 def do_sample(llm: vllm.LLM,
               lora_path: str,
               lora_id: int,
@@ -69,14 +77,6 @@ def format_prompt_tuples(prompt):
     return generated_texts
 
 
-@pytest.fixture(autouse=True)
-def v1(run_with_both_engines_lora):
-    # Simple autouse wrapper to run both engines for each test
-    # This can be promoted up to conftest.py to run for every
-    # test in a package
-    pass
-
-
 @pytest.mark.parametrize("model", MODELS)
 @pytest.mark.parametrize("tp_size", [1])
 def test_quant_model_lora(tinyllama_lora_files, num_gpus_available, model,