[Hardware][CPU] Multi-LoRA implementation for the CPU backend

.buildkite/run-cpu-test.sh

@@ -75,6 +75,12 @@ function cpu_tests() {
       --num-prompts 20 \
       --endpoint /v1/completions \
       --tokenizer facebook/opt-125m"
+
+  # Run multi-lora tests
+  docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" bash -c "
+    set -e
+    pytest -s -v \
+    tests/lora/test_qwen2vl.py"
 }
 
 # All of CPU tests are expected to be finished less than 25 mins.

docs/source/features/compatibility_matrix.md

@@ -359,7 +359,7 @@ Check the '✗' with links to see tracking issue for unsupported feature/hardwar
      - ✅
      - ✅
      - ✅
-     - [✗](gh-pr:4830)
+     - ✅
      - ✅
    * - <abbr title="Prompt Adapter">prmpt adptr</abbr>
      - ✅

tests/lora/conftest.py

@@ -21,6 +21,7 @@
 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.platforms import current_platform
 
 
 class ContextIDInfo(TypedDict):
@@ -65,13 +66,16 @@ def cleanup_fixture(should_do_global_cleanup_after_test: bool):
 @pytest.fixture
 def dist_init():
     temp_file = tempfile.mkstemp()[1]
-    init_distributed_environment(
-        world_size=1,
-        rank=0,
-        distributed_init_method=f"file://{temp_file}",
-        local_rank=0,
-        backend="nccl",
-    )
+
+    backend = "nccl"
+    if current_platform.is_cpu():
+        backend = "gloo"
+
+    init_distributed_environment(world_size=1,
+                                 rank=0,
+                                 distributed_init_method=f"file://{temp_file}",
+                                 local_rank=0,
+                                 backend=backend)
     initialize_model_parallel(1, 1)
     yield
     cleanup_dist_env_and_memory(shutdown_ray=True)
@@ -81,13 +85,15 @@ def dist_init():
 def dist_init_torch_only():
     if torch.distributed.is_initialized():
         return
+    backend = "nccl"
+    if current_platform.is_cpu():
+        backend = "gloo"
+
     temp_file = tempfile.mkstemp()[1]
-    torch.distributed.init_process_group(
-        backend="nccl",
-        world_size=1,
-        rank=0,
-        init_method=f"file://{temp_file}",
-    )
+    torch.distributed.init_process_group(world_size=1,
+                                         rank=0,
+                                         init_method=f"file://{temp_file}",
+                                         backend=backend)
 
 
 @pytest.fixture

tests/lora/test_layers.py

@@ -48,10 +48,14 @@
     torch.float32: (5e-3, 5e-3),
     torch.bfloat16: (3e-2, 2e-2),
 }
-# TODO: Modify this based on platform
-DEVICES = [
+
+pytestmark = pytest.mark.skipif(
+    not (current_platform.is_cuda_alike() or current_platform.is_cpu()),
+    reason="Backend not supported")
+
+DEVICES = ([
     f"cuda:{i}" for i in range(1 if torch.cuda.device_count() == 1 else 2)
-]
+] if current_platform.is_cuda_alike() else ["cpu"])
 
 #For GPU, we will launch different triton kernels between the prefill and decode
 # stages, so we need to verify this. prefill stage(True) or decode stage(False)
@@ -198,6 +202,10 @@ def check_punica_wrapper(punica_wrapper) -> bool:
         from vllm.lora.punica_wrapper.punica_gpu import PunicaWrapperGPU
 
         return type(punica_wrapper) is PunicaWrapperGPU
+    elif current_platform.is_cpu():
+        from vllm.lora.punica_wrapper.punica_cpu import PunicaWrapperCPU
+
+        return type(punica_wrapper) is PunicaWrapperCPU
     else:
         return False
 
@@ -211,7 +219,8 @@ def test_embeddings(dist_init, num_loras, device, vocab_size, stage) -> None:
     # For multi-GPU testing of Triton kernel, we must explicitly set the CUDA
     # device, see: https://github.com/triton-lang/triton/issues/2925
     # Same below.
-    torch.cuda.set_device(device)
+    if current_platform.is_cuda_alike():
+        torch.cuda.set_device(device)
 
     torch.set_default_device(device)
     max_loras = 8
@@ -313,7 +322,9 @@ def create_random_embedding_layer():
 def test_embeddings_with_new_embeddings(dist_init, num_loras, device,
                                         vocab_size, stage) -> None:
 
-    torch.cuda.set_device(device)
+    if current_platform.is_cuda_alike():
+        torch.cuda.set_device(device)
+
     torch.set_default_device(device)
     max_loras = 8
     punica_wrapper = get_punica_wrapper(8192, 256, device)
@@ -450,7 +461,9 @@ def create_random_embedding_layer():
 def test_lm_head_logits_processor(dist_init, num_loras, device, vocab_size,
                                   stage) -> None:
 
-    torch.cuda.set_device(device)
+    if current_platform.is_cuda_alike():
+        torch.cuda.set_device(device)
+
     torch.set_default_device(device)
     max_loras = 8
     punica_wrapper = get_punica_wrapper(8192, 256, device)
@@ -582,7 +595,9 @@ def _pretest():
 def test_linear_replicated(dist_init, num_loras, device, stage,
                            bias_enabled) -> None:
 
-    torch.cuda.set_device(device)
+    if current_platform.is_cuda_alike():
+        torch.cuda.set_device(device)
+
     torch.set_default_device(device)
     punica_wrapper = get_punica_wrapper(8192, 256, device)
     assert check_punica_wrapper(punica_wrapper)
@@ -695,7 +710,9 @@ def create_random_linear_replicated_layer():
 def test_linear_parallel(dist_init, num_loras, orientation, fully_shard,
                          device, stage, bias_enabled) -> None:
 
-    torch.cuda.set_device(device)
+    if current_platform.is_cuda_alike():
+        torch.cuda.set_device(device)
+
     torch.set_default_device(device)
     punica_wrapper = get_punica_wrapper(8192, 256, device)
     assert check_punica_wrapper(punica_wrapper)
@@ -818,7 +835,9 @@ def create_random_linear_parallel_layer():
 def test_column_parallel_packed(dist_init, num_loras, repeats, fully_shard,
                                 device, stage, bias_enabled) -> None:
 
-    torch.cuda.set_device(device)
+    if current_platform.is_cuda_alike():
+        torch.cuda.set_device(device)
+
     torch.set_default_device(device)
     punica_wrapper = get_punica_wrapper(8192, 256, device)
     assert check_punica_wrapper(punica_wrapper)
@@ -971,6 +990,8 @@ class FakeConfig:
 @pytest.mark.parametrize("rotary_dim", [None, 32])
 @pytest.mark.parametrize("head_size", [32, 108])
 @pytest.mark.parametrize("seq_len", [11, 1024])
+@pytest.mark.skipif(not current_platform.is_cuda_alike(),
+                    reason="Only CUDA backends are supported")
 def test_rotary_embedding_long_context(dist_init, num_loras, device,
                                        scaling_factors, max_position,
                                        is_neox_style, rotary_dim, head_size,

tests/lora/test_lora_manager.py

@@ -20,6 +20,7 @@
 from vllm.lora.worker_manager import (LRUCacheWorkerLoRAManager,
                                       WorkerLoRAManager)
 from vllm.model_executor.layers.linear import RowParallelLinear
+from vllm.platforms import current_platform
 
 EMBEDDING_MODULES = {
     "embed_tokens": "input_embeddings",
@@ -28,9 +29,9 @@
 
 EMBEDDING_PADDING_MODULES = ["lm_head"]
 
-CUDA_DEVICES = [
+DEVICES = ([
     f"cuda:{i}" for i in range(1 if torch.cuda.device_count() == 1 else 2)
-]
+] if current_platform.is_cuda_alike() else ["cpu"])
 
 
 def test_peft_helper(sql_lora_files):
@@ -83,7 +84,7 @@ def test_peft_helper(sql_lora_files):
         PEFTHelper.from_dict(config)
 
 
-@pytest.mark.parametrize("device", CUDA_DEVICES)
+@pytest.mark.parametrize("device", DEVICES)
 def test_from_lora_tensors(sql_lora_files, device):
     tensors = load_file(
         os.path.join(sql_lora_files, "adapter_model.safetensors"))
@@ -171,7 +172,7 @@ def test_replace_submodules(dist_init, dummy_model):
     manager = LoRAModelManager(
         model, 1, 1, 1,
         LoRAConfig(max_lora_rank=8, max_cpu_loras=8, max_loras=8),
-        torch.device("cuda"))
+        torch.device(DEVICES[0]))
     model = manager.model
 
     assert isinstance(model.get_submodule("dense1"),
@@ -183,7 +184,7 @@ def test_replace_submodules(dist_init, dummy_model):
                       RowParallelLinearWithLoRA)
 
 
-@pytest.mark.parametrize("device", CUDA_DEVICES)
+@pytest.mark.parametrize("device", DEVICES)
 def test_lora_model_manager(dist_init, dummy_model, device):
     model = dummy_model
     model.supported_lora_modules = ["dense1", "dense2", "lm_head"]
@@ -244,7 +245,7 @@ def test_lora_model_manager(dist_init, dummy_model, device):
     assert manager.punica_wrapper.device == device
 
 
-@pytest.mark.parametrize("device", CUDA_DEVICES)
+@pytest.mark.parametrize("device", DEVICES)
 def test_lora_lru_cache_model_manager(dist_init, dummy_model, device):
     model = dummy_model
     model.supported_lora_modules = ["dense1", "dense2", "lm_head"]
@@ -336,7 +337,7 @@ def test_lora_lru_cache_model_manager(dist_init, dummy_model, device):
     assert manager.device == device
 
 
-@pytest.mark.parametrize("device", CUDA_DEVICES)
+@pytest.mark.parametrize("device", DEVICES)
 def test_lru_lora_model_manager(dist_init, dummy_model, device):
     # This tests just the LRU cache functionality, everything else is
     # tested in test_lora_model_manager
@@ -466,7 +467,7 @@ def test_lru_lora_model_manager(dist_init, dummy_model, device):
     assert manager.device == device
 
 
-@pytest.mark.parametrize("device", CUDA_DEVICES)
+@pytest.mark.parametrize("device", DEVICES)
 def test_lru_cache_worker_adapter_manager(llama_2_7b_model_extra_embeddings,
                                           sql_lora_files, device):
     lora_config = LoRAConfig(max_lora_rank=8, max_cpu_loras=4, max_loras=4)
@@ -545,7 +546,7 @@ def test_lru_cache_worker_adapter_manager(llama_2_7b_model_extra_embeddings,
             device)
 
 
-@pytest.mark.parametrize("device", CUDA_DEVICES)
+@pytest.mark.parametrize("device", DEVICES)
 def test_worker_adapter_manager(llama_2_7b_model_extra_embeddings,
                                 sql_lora_files, device):
     # Should remove every LoRA not specified in the request.
@@ -621,7 +622,7 @@ def test_worker_adapter_manager(llama_2_7b_model_extra_embeddings,
             device)
 
 
-@pytest.mark.parametrize("device", CUDA_DEVICES)
+@pytest.mark.parametrize("device", DEVICES)
 def test_packed_loras(dist_init, dummy_model_gate_up, device):
     model = dummy_model_gate_up
     model.supported_lora_modules = ["gate_up_proj"]

tests/lora/test_mixtral.py

@@ -5,6 +5,7 @@
 
 import vllm
 from vllm.lora.request import LoRARequest
+from vllm.platforms import current_platform
 
 MODEL_PATH = "mistralai/Mixtral-8x7B-Instruct-v0.1"
 
@@ -31,7 +32,8 @@ def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int,
 @pytest.mark.parametrize("tp_size", [4])
 def test_mixtral_lora(mixtral_lora_files, tp_size):
     """Original test, the LoRA model has the common target modules, not all"""
-    if torch.cuda.device_count() < tp_size:
+    if torch.cuda.device_count(
+    ) < tp_size and tp_size > 1 and current_platform.is_cuda_alike():
         pytest.skip(f"Not enough GPUs for tensor parallelism {tp_size}")
 
     prompts = [