[V1] LoRA Support

tests/lora/conftest.py

@@ -306,3 +306,20 @@ def get_model_patched(**kwargs):
 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(params=[True, False])
+def run_with_both_engines_lora(request, monkeypatch):
+    # Automatically runs tests twice, once with V1 and once without
+    use_v1 = request.param
+    # Tests decorated with `@skip_v1` are only run without v1
+    skip_v1 = request.node.get_closest_marker("skip_v1")
+
+    if use_v1:
+        if skip_v1:
+            pytest.skip("Skipping test on vllm V1")
+        monkeypatch.setenv('VLLM_USE_V1', '1')
+    else:
+        monkeypatch.setenv('VLLM_USE_V1', '0')
+
+    yield

tests/lora/test_baichuan.py

@@ -42,6 +42,14 @@ 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

@@ -2,6 +2,8 @@
 
 from typing import List
 
+import pytest
+
 import vllm
 from tests.utils import fork_new_process_for_each_test
 from vllm.lora.request import LoRARequest
@@ -47,6 +49,15 @@ 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
+
+
+@pytest.mark.skip_v1
 @fork_new_process_for_each_test
 def test_chatglm3_lora(chatglm3_lora_files):
     llm = vllm.LLM(MODEL_PATH,
@@ -66,6 +77,7 @@ def test_chatglm3_lora(chatglm3_lora_files):
         assert output2[i] == EXPECTED_LORA_OUTPUT[i]
 
 
+@pytest.mark.skip_v1
 @multi_gpu_test(num_gpus=4)
 @fork_new_process_for_each_test
 def test_chatglm3_lora_tp4(chatglm3_lora_files):
@@ -87,6 +99,7 @@ def test_chatglm3_lora_tp4(chatglm3_lora_files):
         assert output2[i] == EXPECTED_LORA_OUTPUT[i]
 
 
+@pytest.mark.skip_v1
 @multi_gpu_test(num_gpus=4)
 @fork_new_process_for_each_test
 def test_chatglm3_lora_tp4_fully_sharded_loras(chatglm3_lora_files):

tests/lora/test_gemma.py

@@ -33,6 +33,14 @@ 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
+
+
 @pytest.mark.xfail(current_platform.is_rocm(),
                    reason="There can be output mismatch on ROCm")
 def test_gemma_lora(gemma_lora_files):

tests/lora/test_llama_tp.py

@@ -2,6 +2,7 @@
 
 from typing import List
 
+import pytest
 import ray
 
 import vllm
@@ -73,6 +74,14 @@ 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
+
+
 @fork_new_process_for_each_test
 def test_llama_lora(sql_lora_files):
 
@@ -85,6 +94,9 @@ def test_llama_lora(sql_lora_files):
     generate_and_test(llm, sql_lora_files)
 
 
+# Skipping for v1 as v1 doesn't have a good way to expose the num_gpu_blocks
+# used by the engine yet.
+@pytest.mark.skip_v1
 @fork_new_process_for_each_test
 def test_llama_lora_warmup(sql_lora_files):
     """Test that the LLM initialization works with a warmup LORA path and

tests/lora/test_lora_bias_e2e.py

@@ -30,6 +30,17 @@ 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
 @pytest.mark.parametrize("lora_bias", [True])
 @pytest.mark.parametrize("fully_sharded", [True, False])
 def test_lora_bias(lora_bias_files: str, lora_bias: bool, fully_sharded: bool):

tests/lora/test_phi.py

@@ -2,6 +2,8 @@
 
 from typing import List
 
+import pytest
+
 import vllm
 from vllm.lora.request import LoRARequest
 
@@ -48,6 +50,17 @@ 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
 def test_phi2_lora(phi2_lora_files):
     # We enable enforce_eager=True here to reduce VRAM usage for lora-test CI,
     # Otherwise, the lora-test will fail due to CUDA OOM.

tests/lora/test_quant_model.py

@@ -70,6 +70,14 @@ 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,

tests/v1/core/test_kv_cache_utils.py

@@ -163,7 +163,7 @@ def test_generate_block_hash_extra_keys():
 
     # Test with no overlap
     extra_keys, next_mm_idx = generate_block_hash_extra_keys(request, 6, 10, 0)
-    assert extra_keys == ()
+    assert extra_keys is None
     assert next_mm_idx == 1
 
     # Test with multiple extra keys

vllm/lora/layers.py

@@ -16,8 +16,7 @@
                               get_tensor_model_parallel_world_size,
                               split_tensor_along_last_dim,
                               tensor_model_parallel_all_gather,
-                              tensor_model_parallel_all_reduce,
-                              tensor_model_parallel_gather)
+                              tensor_model_parallel_all_reduce)
 from vllm.distributed.utils import divide
 # yapf: disable
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
@@ -1043,7 +1042,10 @@ def _get_logits(
         logits = lm_head.linear_method.apply(lm_head, hidden_states)
         if embedding_bias is not None:
             logits += embedding_bias
-        logits = tensor_model_parallel_gather(logits)
+
+        # Gather logits for TP
+        logits = self.base_layer._gather_logits(logits)
+
         if logits is None:
             return None
 

vllm/model_executor/layers/logits_processor.py

@@ -51,7 +51,6 @@ def __init__(self,
         # Soft cap the logits. Used in Gemma 2.
         self.soft_cap = soft_cap
         # Whether to use gather or all-gather to gather the logits.
-
         parallel_config = get_current_vllm_config().parallel_config
         self.use_all_gather = current_platform.is_tpu() \
             or envs.VLLM_USE_V1 \
@@ -88,6 +87,20 @@ def forward(
 
         return logits
 
+    def _gather_logits(self, logits: torch.Tensor) -> torch.Tensor:
+        """gather/all-gather the logits tensor across model parallel group."""
+        if self.use_all_gather:
+            # Gather is not supported for some devices such as TPUs.
+            # Use all-gather instead.
+            # NOTE(woosuk): Here, the outputs of every device should not be None
+            # because XLA requires strict SPMD among all devices. Every device
+            # should execute the same operations after gathering the logits.
+            logits = tensor_model_parallel_all_gather(logits)
+        else:
+            # None may be returned for rank > 0
+            logits = tensor_model_parallel_gather(logits)
+        return logits
+
     def _get_logits(
         self,
         hidden_states: torch.Tensor,
@@ -99,16 +112,9 @@ def _get_logits(
                                              hidden_states,
                                              bias=embedding_bias)
 
-        if self.use_all_gather:
-            # Gather is not supported for some devices such as TPUs.
-            # Use all-gather instead.
-            # NOTE(woosuk): Here, the outputs of every device should not be None
-            # because XLA requires strict SPMD among all devices. Every device
-            # should execute the same operations after gathering the logits.
-            logits = tensor_model_parallel_all_gather(logits)
-        else:
-            # None may be returned for rank > 0
-            logits = tensor_model_parallel_gather(logits)
+        # Gather logits for TP
+        logits = self._gather_logits(logits)
+
         # Remove paddings in vocab (if any).
         if logits is not None:
             logits = logits[..., :self.org_vocab_size]