[V0 Deprecation] Remove V0 LoRA test (#23418)

Signed-off-by: Jee Jee Li <pandaleefree@gmail.com>

tests/lora/conftest.py

@@ -3,15 +3,13 @@
 
 import tempfile
 from collections import OrderedDict
-from unittest.mock import MagicMock, patch
+from unittest.mock import MagicMock
 
 import pytest
 import torch
 import torch.nn as nn
 from huggingface_hub import snapshot_download
 
-import vllm
-from vllm.config import LoRAConfig
 from vllm.distributed import (cleanup_dist_env_and_memory,
                               init_distributed_environment,
                               initialize_model_parallel)
@@ -21,7 +19,6 @@ from vllm.model_executor.layers.linear import (ColumnParallelLinear,
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
-from vllm.model_executor.model_loader import get_model
 from vllm.model_executor.models.interfaces import SupportsLoRA
 from vllm.platforms import current_platform
 
@@ -104,6 +101,7 @@ def dummy_model() -> nn.Module:
         ]))
     model.config = MagicMock()
     model.embedding_modules = {"lm_head": "lm_head"}
+    model.unpadded_vocab_size = 32000
     return model
 
 
@@ -137,6 +135,8 @@ def dummy_model_gate_up() -> nn.Module:
         ],
     }
     model.embedding_modules = {"lm_head": "lm_head"}
+    model.unpadded_vocab_size = 32000
+
     return model
 
 
@@ -221,29 +221,6 @@ def phi2_lora_files():
     return snapshot_download(repo_id="isotr0py/phi-2-test-sql-lora")
 
 
-@pytest.fixture
-def llama_2_7b_engine_extra_embeddings():
-    cleanup_dist_env_and_memory(shutdown_ray=True)
-    get_model_old = get_model
-
-    def get_model_patched(**kwargs):
-        kwargs["vllm_config"].lora_config = LoRAConfig(max_loras=4,
-                                                       max_lora_rank=8)
-        return get_model_old(**kwargs)
-
-    with patch("vllm.worker.model_runner.get_model", get_model_patched):
-        engine = vllm.LLM("meta-llama/Llama-2-7b-hf", enable_lora=False)
-    yield engine.llm_engine
-    del engine
-    cleanup_dist_env_and_memory(shutdown_ray=True)
-
-
-@pytest.fixture
-def llama_2_7b_model_extra_embeddings(llama_2_7b_engine_extra_embeddings):
-    yield (llama_2_7b_engine_extra_embeddings.model_executor.driver_worker.
-           model_runner.model)
-
-
 @pytest.fixture
 def reset_default_device():
     """

tests/lora/test_add_lora.py

@@ -5,7 +5,6 @@ import time
 
 import pytest
 
-import vllm.envs as env
 from vllm.engine.arg_utils import AsyncEngineArgs
 from vllm.entrypoints.openai.api_server import (
     build_async_engine_client_from_engine_args)
@@ -98,12 +97,10 @@ async def test_add_lora(chatglm3_lora_files):
         # Run with warmup
         add_lora_tasks = [llm.add_lora(lr) for lr in warmup_run_requests]
         add_lora_results = await asyncio.gather(*add_lora_tasks)
-        if env.VLLM_USE_V1:
+
         # Test that all all_lora calls are successful.
         assert all(add_lora_results)
-        else:
-            # No way to check V0 engine results as the calls just return None.
-            pass
+
         time_with_add_lora = await requests_processing_time(
             llm, warmup_run_requests)
 

tests/lora/test_llama_tp.py

@@ -113,8 +113,7 @@ def test_llama_lora(sql_lora_files):
         enable_lora=True,
         # also test odd max_num_seqs
         max_num_seqs=13,
-        max_loras=4,
-        enable_chunked_prefill=True)
+        max_loras=4)
     generate_and_test(llm, sql_lora_files)
 
 
@@ -128,7 +127,6 @@ def test_llama_lora_tp4(sql_lora_files):
         max_num_seqs=16,
         max_loras=4,
         tensor_parallel_size=4,
-        enable_chunked_prefill=True,
     )
     generate_and_test(llm, sql_lora_files)
 
@@ -144,7 +142,6 @@ def test_llama_lora_tp4_fully_sharded_loras(sql_lora_files):
         max_loras=4,
         tensor_parallel_size=4,
         fully_sharded_loras=True,
-        enable_chunked_prefill=True,
     )
     generate_and_test(llm, sql_lora_files)
 

tests/lora/test_lora_manager.py

@@ -21,6 +21,8 @@ from vllm.lora.worker_manager import (LRUCacheWorkerLoRAManager,
                                       WorkerLoRAManager)
 from vllm.platforms import current_platform
 
+from .utils import create_peft_lora
+
 EMBEDDING_MODULES = {
     "embed_tokens": "input_embeddings",
     "lm_head": "output_embeddings",
@@ -35,17 +37,6 @@ DEVICES = ([
 DEFAULT_DTYPE = torch.get_default_dtype()
 
 
-@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(
@@ -326,7 +317,6 @@ def test_lru_lora_model_manager(dist_init, dummy_model, device):
                                                   max_loras=2,
                                                   lora_dtype=DEFAULT_DTYPE),
                                        device=device)
-
     assert all(x is None for x in manager.lora_index_to_id)
 
     # Add up to capacity
@@ -430,32 +420,40 @@ def test_lru_lora_model_manager(dist_init, dummy_model, device):
 
 
 @pytest.mark.parametrize("device", DEVICES)
-def test_lru_cache_worker_adapter_manager(llama_2_7b_model_extra_embeddings,
-                                          sql_lora_files, device):
+def test_lru_cache_worker_adapter_manager(dist_init, dummy_model, device,
+                                          tmp_path):
     lora_config = LoRAConfig(max_lora_rank=8,
                              max_cpu_loras=4,
                              max_loras=4,
                              lora_dtype=DEFAULT_DTYPE)
+
+    dummy_lora_files = f"{tmp_path}/lora_adapter"
+    os.makedirs(dummy_lora_files, exist_ok=True)
+    create_peft_lora(
+        dummy_model,
+        save_dir=dummy_lora_files,
+        target_modules=["layer1.dense1", "dense2"],
+        lora_dtype=DEFAULT_DTYPE,
+    )
     worker_adapter_manager = LRUCacheWorkerLoRAManager(
-        4, 2, llama_2_7b_model_extra_embeddings.unpadded_vocab_size -
-        lora_config.lora_extra_vocab_size, lora_config, device,
-        EMBEDDING_MODULES, EMBEDDING_PADDING_MODULES)
-    worker_adapter_manager.create_lora_manager(
-        llama_2_7b_model_extra_embeddings)
+        4, 2,
+        dummy_model.unpadded_vocab_size - lora_config.lora_extra_vocab_size,
+        lora_config, device, EMBEDDING_MODULES, EMBEDDING_PADDING_MODULES)
+    worker_adapter_manager.create_lora_manager(dummy_model)
 
     mapping = LoRAMapping([], [])
     worker_adapter_manager.set_active_adapters([
-        LoRARequest("1", 1, sql_lora_files),
-        LoRARequest("2", 2, sql_lora_files)
+        LoRARequest("1", 1, dummy_lora_files),
+        LoRARequest("2", 2, dummy_lora_files)
     ], mapping)
     assert worker_adapter_manager.list_adapters() == {1, 2}
     assert worker_adapter_manager._adapter_manager.lora_index_to_id[0] == 1
     assert worker_adapter_manager._adapter_manager.lora_index_to_id[1] == 2
 
     worker_adapter_manager.set_active_adapters([
-        LoRARequest("1", 1, sql_lora_files),
-        LoRARequest("3", 3, sql_lora_files),
-        LoRARequest("4", 4, sql_lora_files)
+        LoRARequest("1", 1, dummy_lora_files),
+        LoRARequest("3", 3, dummy_lora_files),
+        LoRARequest("4", 4, dummy_lora_files)
     ], mapping)
     assert worker_adapter_manager.list_adapters() == {1, 2, 3, 4}
     assert worker_adapter_manager._adapter_manager.lora_index_to_id[0] == 1
@@ -464,9 +462,9 @@ def test_lru_cache_worker_adapter_manager(llama_2_7b_model_extra_embeddings,
     assert worker_adapter_manager._adapter_manager.lora_index_to_id[3] == 4
 
     worker_adapter_manager.set_active_adapters([
-        LoRARequest("1", 1, sql_lora_files),
-        LoRARequest("2", 2, sql_lora_files),
-        LoRARequest("5", 5, sql_lora_files)
+        LoRARequest("1", 1, dummy_lora_files),
+        LoRARequest("2", 2, dummy_lora_files),
+        LoRARequest("5", 5, dummy_lora_files)
     ], mapping)
     assert worker_adapter_manager.list_adapters() == {1, 2, 4, 5}
     assert worker_adapter_manager._adapter_manager.lora_index_to_id[0] == 1
@@ -475,9 +473,9 @@ def test_lru_cache_worker_adapter_manager(llama_2_7b_model_extra_embeddings,
     assert worker_adapter_manager._adapter_manager.lora_index_to_id[3] == 4
 
     worker_adapter_manager.set_active_adapters([
-        LoRARequest("1", 1, sql_lora_files),
-        LoRARequest("1", 1, sql_lora_files),
-        LoRARequest("1", 1, sql_lora_files)
+        LoRARequest("1", 1, dummy_lora_files),
+        LoRARequest("1", 1, dummy_lora_files),
+        LoRARequest("1", 1, dummy_lora_files)
     ], mapping)
     assert worker_adapter_manager.list_adapters() == {1, 2, 4, 5}
     assert worker_adapter_manager._adapter_manager.lora_index_to_id[0] == 1
@@ -486,9 +484,9 @@ def test_lru_cache_worker_adapter_manager(llama_2_7b_model_extra_embeddings,
     assert worker_adapter_manager._adapter_manager.lora_index_to_id[3] == 4
 
     worker_adapter_manager.set_active_adapters([
-        LoRARequest("6", 6, sql_lora_files),
-        LoRARequest("7", 7, sql_lora_files),
-        LoRARequest("8", 8, sql_lora_files)
+        LoRARequest("6", 6, dummy_lora_files),
+        LoRARequest("7", 7, dummy_lora_files),
+        LoRARequest("8", 8, dummy_lora_files)
     ], mapping)
     assert worker_adapter_manager.list_adapters() == {1, 6, 7, 8}
     assert worker_adapter_manager._adapter_manager.lora_index_to_id[0] == 1
@@ -499,11 +497,11 @@ def test_lru_cache_worker_adapter_manager(llama_2_7b_model_extra_embeddings,
     # Over capacity
     with pytest.raises(RuntimeError):
         worker_adapter_manager.set_active_adapters([
-            LoRARequest("10", 10, sql_lora_files),
-            LoRARequest("11", 11, sql_lora_files),
-            LoRARequest("12", 12, sql_lora_files),
-            LoRARequest("13", 13, sql_lora_files),
-            LoRARequest("14", 14, sql_lora_files)
+            LoRARequest("10", 10, dummy_lora_files),
+            LoRARequest("11", 11, dummy_lora_files),
+            LoRARequest("12", 12, dummy_lora_files),
+            LoRARequest("13", 13, dummy_lora_files),
+            LoRARequest("14", 14, dummy_lora_files)
         ], mapping)
 
     assert worker_adapter_manager.device == device
@@ -512,33 +510,41 @@ def test_lru_cache_worker_adapter_manager(llama_2_7b_model_extra_embeddings,
 
 
 @pytest.mark.parametrize("device", DEVICES)
-def test_worker_adapter_manager(llama_2_7b_model_extra_embeddings,
-                                sql_lora_files, device):
+def test_worker_adapter_manager(dist_init, dummy_model_gate_up, device,
+                                tmp_path):
     # Should remove every LoRA not specified in the request.
     lora_config = LoRAConfig(max_lora_rank=8,
                              max_cpu_loras=4,
                              max_loras=4,
                              lora_dtype=DEFAULT_DTYPE)
     worker_adapter_manager = WorkerLoRAManager(
-        4, 2, llama_2_7b_model_extra_embeddings.unpadded_vocab_size -
+        4, 2, dummy_model_gate_up.unpadded_vocab_size -
         lora_config.lora_extra_vocab_size, lora_config, device,
         EMBEDDING_MODULES, EMBEDDING_PADDING_MODULES)
-    worker_adapter_manager.create_lora_manager(
-        llama_2_7b_model_extra_embeddings)
+    worker_adapter_manager.create_lora_manager(dummy_model_gate_up)
+
+    dummy_lora_files = f"{tmp_path}/lora_adapter"
+    os.makedirs(dummy_lora_files, exist_ok=True)
+    create_peft_lora(
+        dummy_model_gate_up,
+        save_dir=dummy_lora_files,
+        target_modules=["layer1.dense1", "dense2"],
+        lora_dtype=DEFAULT_DTYPE,
+    )
 
     mapping = LoRAMapping([], [])
     worker_adapter_manager.set_active_adapters([
-        LoRARequest("1", 1, sql_lora_files),
-        LoRARequest("2", 2, sql_lora_files)
+        LoRARequest("1", 1, dummy_lora_files),
+        LoRARequest("2", 2, dummy_lora_files)
     ], mapping)
     assert worker_adapter_manager.list_adapters() == {1, 2}
     assert worker_adapter_manager._adapter_manager.lora_index_to_id[0] == 1
     assert worker_adapter_manager._adapter_manager.lora_index_to_id[1] == 2
 
     worker_adapter_manager.set_active_adapters([
-        LoRARequest("1", 1, sql_lora_files),
-        LoRARequest("3", 3, sql_lora_files),
-        LoRARequest("4", 4, sql_lora_files)
+        LoRARequest("1", 1, dummy_lora_files),
+        LoRARequest("3", 3, dummy_lora_files),
+        LoRARequest("4", 4, dummy_lora_files)
     ], mapping)
     assert worker_adapter_manager.list_adapters() == {1, 3, 4}
     assert worker_adapter_manager._adapter_manager.lora_index_to_id[0] == 1
@@ -546,9 +552,9 @@ def test_worker_adapter_manager(llama_2_7b_model_extra_embeddings,
     assert worker_adapter_manager._adapter_manager.lora_index_to_id[2] == 4
 
     worker_adapter_manager.set_active_adapters([
-        LoRARequest("1", 1, sql_lora_files),
-        LoRARequest("2", 2, sql_lora_files),
-        LoRARequest("5", 5, sql_lora_files)
+        LoRARequest("1", 1, dummy_lora_files),
+        LoRARequest("2", 2, dummy_lora_files),
+        LoRARequest("5", 5, dummy_lora_files)
     ], mapping)
     assert worker_adapter_manager.list_adapters() == {1, 2, 5}
     assert worker_adapter_manager._adapter_manager.lora_index_to_id[0] == 1
@@ -556,9 +562,9 @@ def test_worker_adapter_manager(llama_2_7b_model_extra_embeddings,
     assert worker_adapter_manager._adapter_manager.lora_index_to_id[2] == 5
 
     worker_adapter_manager.set_active_adapters([
-        LoRARequest("1", 1, sql_lora_files),
-        LoRARequest("1", 1, sql_lora_files),
-        LoRARequest("1", 1, sql_lora_files)
+        LoRARequest("1", 1, dummy_lora_files),
+        LoRARequest("1", 1, dummy_lora_files),
+        LoRARequest("1", 1, dummy_lora_files)
     ], mapping)
     assert worker_adapter_manager.list_adapters() == {1}
     assert worker_adapter_manager._adapter_manager.lora_index_to_id[0] == 1
@@ -566,9 +572,9 @@ def test_worker_adapter_manager(llama_2_7b_model_extra_embeddings,
     assert worker_adapter_manager._adapter_manager.lora_index_to_id[2] is None
 
     worker_adapter_manager.set_active_adapters([
-        LoRARequest("6", 6, sql_lora_files),
-        LoRARequest("7", 7, sql_lora_files),
-        LoRARequest("8", 8, sql_lora_files)
+        LoRARequest("6", 6, dummy_lora_files),
+        LoRARequest("7", 7, dummy_lora_files),
+        LoRARequest("8", 8, dummy_lora_files)
     ], mapping)
     assert worker_adapter_manager.list_adapters() == {6, 7, 8}
     assert worker_adapter_manager._adapter_manager.lora_index_to_id[0] == 8
@@ -578,11 +584,11 @@ def test_worker_adapter_manager(llama_2_7b_model_extra_embeddings,
     # Over capacity
     with pytest.raises(RuntimeError):
         worker_adapter_manager.set_active_adapters([
-            LoRARequest("10", 10, sql_lora_files),
-            LoRARequest("11", 11, sql_lora_files),
-            LoRARequest("12", 12, sql_lora_files),
-            LoRARequest("13", 13, sql_lora_files),
-            LoRARequest("14", 14, sql_lora_files)
+            LoRARequest("10", 10, dummy_lora_files),
+            LoRARequest("11", 11, dummy_lora_files),
+            LoRARequest("12", 12, dummy_lora_files),
+            LoRARequest("13", 13, dummy_lora_files),
+            LoRARequest("14", 14, dummy_lora_files)
         ], mapping)
 
     assert worker_adapter_manager.device == device

tests/lora/test_mixtral.py

@@ -50,7 +50,6 @@ def test_mixtral_lora(mixtral_lora_files, tp_size):
         max_loras=4,
         distributed_executor_backend="ray",
         tensor_parallel_size=tp_size,
-        enable_chunked_prefill=True,
     )
 
     expected_lora_output = [

tests/lora/test_worker.py

@@ -4,17 +4,14 @@
 import os
 import random
 import tempfile
-from typing import Union
 from unittest.mock import patch
 
-import vllm.envs as envs
 from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig,
                          ModelConfig, ParallelConfig, SchedulerConfig,
                          VllmConfig)
 from vllm.lora.models import LoRAMapping
 from vllm.lora.request import LoRARequest
-from vllm.v1.worker.gpu_worker import Worker as V1Worker
-from vllm.worker.worker import Worker
+from vllm.v1.worker.gpu_worker import Worker
 
 NUM_LORAS = 16
 
@@ -22,19 +19,12 @@ NUM_LORAS = 16
 @patch.dict(os.environ, {"RANK": "0"})
 def test_worker_apply_lora(sql_lora_files):
 
-    def set_active_loras(worker: Union[Worker, V1Worker],
-                         lora_requests: list[LoRARequest]):
+    def set_active_loras(worker: Worker, lora_requests: list[LoRARequest]):
         lora_mapping = LoRAMapping([], [])
-        if isinstance(worker, Worker):
-            # v0 case
-            worker.model_runner.set_active_loras(lora_requests, lora_mapping)
-        else:
-            # v1 case
+
         worker.model_runner.lora_manager.set_active_adapters(
             lora_requests, lora_mapping)
 
-    worker_cls = V1Worker if envs.VLLM_USE_V1 else Worker
-
     vllm_config = VllmConfig(
         model_config=ModelConfig(
             "meta-llama/Llama-2-7b-hf",
@@ -62,7 +52,7 @@ def test_worker_apply_lora(sql_lora_files):
                                max_cpu_loras=NUM_LORAS,
                                max_loras=NUM_LORAS),
     )
-    worker = worker_cls(
+    worker = Worker(
         vllm_config=vllm_config,
         local_rank=0,
         rank=0,

tests/lora/utils.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import json
+import os
 from dataclasses import dataclass
 from typing import Optional, Union
 
 import torch
+from safetensors.torch import save_file
 
 from vllm.lora.lora import LoRALayerWeights, PackedLoRALayerWeights
 
@@ -340,3 +343,76 @@ def generate_data_for_nslices(
         seq_len_tensor,
         indices,
     )
+
+
+def create_peft_lora(
+    model: torch.nn.Module,
+    save_dir: str,
+    target_modules: list[str],
+    rank: int = 8,
+    alpha: int = 16,
+    dropout: float = 0.1,
+    lora_dtype: torch.dtype = torch.float16,
+) -> dict[str, torch.Tensor]:
+    lora_weights = {}
+    adapter_config = {
+        "peft_type": "LORA",
+        "auto_mapping": None,
+        "base_model_name_or_path": "dummy_model",
+        "revision": None,
+        "task_type": "CAUSAL_LM",
+        "inference_mode": False,
+        "r": rank,
+        "lora_alpha": alpha,
+        "lora_dropout": dropout,
+        "fan_in_fan_out": False,
+        "bias": "none",
+        "modules_to_save": None,
+        "init_lora_weights": True,
+        "layers_to_transform": None,
+        "layers_pattern": None,
+        "target_modules": target_modules,
+        "exclude_modules": None,
+        "use_rslora": False,
+        "use_dora": False,
+        "loftq_config": None,
+    }
+
+    for module_name in target_modules:
+
+        module = model
+        for attr in module_name.split("."):
+            module = getattr(module, attr)
+
+        if hasattr(module, "input_size") and hasattr(module, "output_size"):
+
+            in_features = module.input_size
+            out_features = module.output_size
+
+        elif hasattr(module, "embedding_dim") and hasattr(
+                module, "num_embeddings"):
+            # ParallelLMHead
+            in_features = module.embedding_dim
+            out_features = module.num_embeddings
+        else:
+            raise ValueError(
+                f"Unable to determine dimensions for module {module_name}")
+
+        lora_A = torch.randn(rank, in_features, dtype=lora_dtype)
+
+        torch.nn.init.kaiming_uniform_(lora_A, a=5**0.5)
+
+        lora_B = torch.zeros(out_features, rank, dtype=lora_dtype)
+
+        # PEFT style
+        lora_weights[f"base_model.model.{module_name}.lora_A.weight"] = lora_A
+        lora_weights[f"base_model.model.{module_name}.lora_B.weight"] = lora_B
+
+    config_path = os.path.join(save_dir, "adapter_config.json")
+    with open(config_path, "w", encoding="utf-8") as f:
+        json.dump(adapter_config, f, indent=2, ensure_ascii=False)
+
+    weights_path = os.path.join(save_dir, "adapter_model.safetensors")
+    save_file(lora_weights, weights_path)
+
+    return lora_weights