Test Prompt Embeds/LoRA compatibility and Enable LoRA Support for OPT Models

docs/features/README.md

@@ -52,7 +52,7 @@ th:not(:first-child) {
 | [mm](multimodal_inputs.md) | ✅ | ✅ | [🟠](gh-pr:4194)<sup>^</sup> | ❔ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❔ | ✅ | | | |
 | best-of | ✅ | ✅ | ✅ | [❌](gh-issue:6137) | ✅ | ❌ | ✅ | ✅ | ✅ | ❔ | [❌](gh-issue:7968) | ✅ | ✅ | | |
 | beam-search | ✅ | ✅ | ✅ | [❌](gh-issue:6137) | ✅ | ❌ | ✅ | ✅ | ✅ | ❔ | [❌](gh-issue:7968) | ❔ | ✅ | ✅ | |
-| [prompt-embeds](prompt_embeds.md) | ✅ | [❌](gh-issue:25096) | ? | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ? | ? | ❌ | ? | ? | ✅ |
+| [prompt-embeds](prompt_embeds.md) | ✅ | [❌](gh-issue:25096) | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❔ | ❔ | ❌ | ❔ | ❔ | ✅ |
 
 \* Chunked prefill and prefix caching are only applicable to last-token pooling.  
 <sup>^</sup> LoRA is only applicable to the language backbone of multimodal models.

docs/models/supported_models.md

@@ -404,7 +404,7 @@ th {
 | `OLMo2ForCausalLM` | OLMo2 | `allenai/OLMo-2-0425-1B`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `OLMo3ForCausalLM` | OLMo3 | TBA | ✅︎ | ✅︎ | ✅︎ |
 | `OLMoEForCausalLM` | OLMoE | `allenai/OLMoE-1B-7B-0924`, `allenai/OLMoE-1B-7B-0924-Instruct`, etc. | | ✅︎ | ✅︎ |
-| `OPTForCausalLM` | OPT, OPT-IML | `facebook/opt-66b`, `facebook/opt-iml-max-30b`, etc. | | ✅︎ | ✅︎ |
+| `OPTForCausalLM` | OPT, OPT-IML | `facebook/opt-66b`, `facebook/opt-iml-max-30b`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `OrionForCausalLM` | Orion | `OrionStarAI/Orion-14B-Base`, `OrionStarAI/Orion-14B-Chat`, etc. | | ✅︎ | ✅︎ |
 | `PhiForCausalLM` | Phi | `microsoft/phi-1_5`, `microsoft/phi-2`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `Phi3ForCausalLM` | Phi-4, Phi-3 | `microsoft/Phi-4-mini-instruct`, `microsoft/Phi-4`, `microsoft/Phi-3-mini-4k-instruct`, `microsoft/Phi-3-mini-128k-instruct`, `microsoft/Phi-3-medium-128k-instruct`, etc. | ✅︎ | ✅︎ | ✅︎ |

tests/entrypoints/conftest.py

@@ -208,3 +208,11 @@ def zephyr_lora_files():
     """Download zephyr LoRA files once per test session."""
     from huggingface_hub import snapshot_download
     return snapshot_download(repo_id="typeof/zephyr-7b-beta-lora")
+
+
+@pytest.fixture(scope="session")
+def opt125_lora_files() -> str:
+    """Download opt-125m LoRA files once per test session."""
+    from huggingface_hub import snapshot_download
+    return snapshot_download(
+        repo_id="peft-internal-testing/opt-125m-dummy-lora")

tests/entrypoints/openai/test_completion_with_prompt_embeds.py

@@ -3,6 +3,7 @@
 
 import base64
 import io
+import json
 
 import openai  # use the official client for correctness check
 import pytest
@@ -16,13 +17,15 @@
 
 # any model with a chat template should work here
 MODEL_NAME = "facebook/opt-125m"
+LORA_SERVING_MODEL_NAME = "opt125m-lora"
 
 CONFIG = AutoConfig.from_pretrained(MODEL_NAME)
 
 
-@pytest.fixture(scope="module")
-def default_server_args() -> list[str]:
-    return [
+@pytest.fixture(scope="module", params=["use-lora"])
+def default_server_args(request: pytest.FixtureRequest,
+                        opt125_lora_files: str) -> list[str]:
+    args = [
         # use half precision for speed and memory savings in CI environment
         "--dtype",
         "bfloat16",
@@ -35,6 +38,25 @@ def default_server_args() -> list[str]:
         "--enable-prompt-embeds",
     ]
 
+    if request.param == "use-lora":
+        lora_module_1 = {
+            "name": LORA_SERVING_MODEL_NAME,
+            "path": opt125_lora_files,
+            "base_model_name": MODEL_NAME
+        }
+
+        args.extend([
+            "--enable-lora",
+            "--lora-module",
+            json.dumps(lora_module_1),
+            "--max-lora-rank",
+            "64",
+            "--max-cpu-loras",
+            "2",
+        ])
+
+    return args
+
 
 EXAMPLE_PROMPTS = [
     "Hello, my name is",
@@ -74,7 +96,7 @@ async def client_with_prompt_embeds(server_with_prompt_embeds):
 
 
 @pytest.mark.asyncio
-@pytest.mark.parametrize("model_name", [MODEL_NAME])
+@pytest.mark.parametrize("model_name", [MODEL_NAME, LORA_SERVING_MODEL_NAME])
 async def test_completions_with_prompt_embeds(
     example_prompt_embeds,
     client_with_prompt_embeds: openai.AsyncOpenAI,
@@ -179,7 +201,7 @@ async def test_completions_with_prompt_embeds(
 
 
 @pytest.mark.asyncio
-@pytest.mark.parametrize("model_name", [MODEL_NAME])
+@pytest.mark.parametrize("model_name", [MODEL_NAME, LORA_SERVING_MODEL_NAME])
 async def test_completions_errors_with_prompt_embeds(
         client_with_prompt_embeds: openai.AsyncOpenAI, model_name: str):
     # Test error case: invalid prompt_embeds
@@ -194,7 +216,7 @@ async def test_completions_errors_with_prompt_embeds(
 
 @pytest.mark.asyncio
 @pytest.mark.parametrize("logprobs_arg", [1, 0])
-@pytest.mark.parametrize("model_name", [MODEL_NAME])
+@pytest.mark.parametrize("model_name", [MODEL_NAME, LORA_SERVING_MODEL_NAME])
 async def test_completions_with_logprobs_and_prompt_embeds(
     example_prompt_embeds,
     client_with_prompt_embeds: openai.AsyncOpenAI,

vllm/model_executor/models/opt.py

@@ -43,7 +43,7 @@
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.sequence import IntermediateTensors
 
-from .interfaces import SupportsPP
+from .interfaces import SupportsLoRA, SupportsPP
 from .utils import (AutoWeightsLoader, WeightsMapper, is_pp_missing_parameter,
                     make_empty_intermediate_tensors_factory, make_layers,
                     maybe_prefix)
@@ -352,10 +352,9 @@ def load_weights(self, weights: Iterable[tuple[str,
         return loaded_params
 
 
-class OPTForCausalLM(nn.Module, SupportsPP):
+class OPTForCausalLM(nn.Module, SupportsPP, SupportsLoRA):
     packed_modules_mapping = {
         "qkv_proj": ["q_proj", "k_proj", "v_proj"],
-        "gate_up_proj": ["gate_proj", "up_proj"]
     }
 
     hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={