feat: Add merged vLLM rollout weights

dev/run_qwen3_5_localbackend_yes_no_maybe.py

@@ -51,6 +51,11 @@ def _format_int_list(values: list[int]) -> str:
 parser.add_argument(
     "--enable-thinking", action=argparse.BooleanOptionalAction, default=False
 )
+parser.add_argument(
+    "--rollout-weights-mode",
+    choices=("lora", "merged"),
+    default=None,
+)
 parser.add_argument("--trainer-gpu-ids", type=int, nargs="+")
 parser.add_argument("--inference-gpu-ids", type=int, nargs="+")
 args = parser.parse_args()
@@ -98,6 +103,8 @@ def _format_int_list(values: list[int]) -> str:
             f"INFERENCE_GPU_IDS={_format_int_list(args.inference_gpu_ids)}",
         ]
     )
+if args.rollout_weights_mode is not None:
+    env.append(f"ROLLOUT_WEIGHTS_MODE={args.rollout_weights_mode}")
 env_block = " \\\n    ".join(env)
 
 run_script = textwrap.dedent(
@@ -143,6 +150,7 @@ def _format_int_list(values: list[int]) -> str:
 print(f"  load_in_4bit: {args.load_in_4bit}")
 print(f"  load_in_16bit: {args.load_in_16bit}")
 print(f"  enable_thinking: {args.enable_thinking}")
+print(f"  rollout_weights_mode: {args.rollout_weights_mode}")
 print(f"  trainer_gpu_ids: {args.trainer_gpu_ids}")
 print(f"  inference_gpu_ids: {args.inference_gpu_ids}")
 

dev/yes-no-maybe-metrics.py

@@ -223,6 +223,12 @@ def build_internal_config() -> art.dev.InternalModelConfig:
         result["trainer_gpu_ids"] = trainer_gpu_ids
         result["inference_gpu_ids"] = inference_gpu_ids
 
+    rollout_weights_mode = os.environ.get("ROLLOUT_WEIGHTS_MODE")
+    if rollout_weights_mode is not None:
+        if rollout_weights_mode not in {"lora", "merged"}:
+            raise ValueError("ROLLOUT_WEIGHTS_MODE must be either 'lora' or 'merged'")
+        result["rollout_weights_mode"] = rollout_weights_mode
+
     return result
 
 

pyproject.toml

@@ -38,6 +38,7 @@ backend = [
     "pytest>=8.4.1",
     "nbmake>=1.5.5",
     "gql<4",
+    "nvidia-cudnn-frontend<1.21 ; sys_platform == 'linux'",
     "vllm @ https://github.com/vivekkalyan/vllm/releases/download/v0.17.0-art1/vllm-0.17.0%2Bart1-cp38-abi3-manylinux_2_31_x86_64.whl ; sys_platform == 'linux'",
 ]
 megatron = [

src/art/dev/engine.py

@@ -3,6 +3,10 @@
 from typing_extensions import TypedDict
 
 
+class WeightTransferConfig(TypedDict):
+    backend: Literal["nccl"]
+
+
 class EngineArgs(TypedDict, total=False):
     model: str
     served_model_name: str | list[str] | None
@@ -124,6 +128,7 @@ class EngineArgs(TypedDict, total=False):
     calculate_kv_scales: bool | None
 
     additional_config: dict[str, Any] | None
+    weight_transfer_config: WeightTransferConfig | None
 
     disable_log_requests: (
         bool  # Deprecated in vLLM 0.13+, use enable_log_requests instead

src/art/dev/get_model_config.py

@@ -1,6 +1,31 @@
 from .engine import EngineArgs
 from .model import InitArgs, InternalModelConfig, PeftArgs, TrainerArgs
-from .validate import is_dedicated_mode
+from .validate import QWEN3_5_MOE_MODELS, is_dedicated_mode
+
+
+def default_target_modules(base_model: str) -> list[str]:
+    if base_model in QWEN3_5_MOE_MODELS:
+        return [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+            "o_proj",
+            "in_proj_qkv",
+            "in_proj_z",
+            "out_proj",
+            "gate_proj",
+            "up_proj",
+            "down_proj",
+        ]
+    return [
+        "q_proj",
+        "k_proj",
+        "v_proj",
+        "o_proj",
+        "gate_proj",
+        "up_proj",
+        "down_proj",
+    ]
 
 
 def get_model_config(
@@ -14,6 +39,7 @@ def get_model_config(
         config = InternalModelConfig()
 
     dedicated = is_dedicated_mode(config)
+    rollout_weights_mode = config.get("rollout_weights_mode", "lora")
 
     if dedicated:
         enable_sleep_mode = False
@@ -43,15 +69,7 @@ def get_model_config(
         lora_alpha=16,
         r=8,
         random_state=3407,
-        target_modules=[
-            "q_proj",
-            "k_proj",
-            "v_proj",
-            "o_proj",
-            "gate_proj",
-            "up_proj",
-            "down_proj",
-        ],
+        target_modules=default_target_modules(base_model),
         use_gradient_checkpointing="unsloth",
     )
     peft_args.update(config.get("peft_args", {}))
@@ -78,6 +96,7 @@ def get_model_config(
         init_args=init_args,
         engine_args=engine_args,
         peft_args=peft_args,
+        rollout_weights_mode=rollout_weights_mode,
         tinker_args=config.get("tinker_args"),
         trainer_args=trainer_args,
     )

src/art/dev/model.py

@@ -1,9 +1,12 @@
 from enum import Enum
+from typing import Literal
 
 from typing_extensions import Required, TypedDict
 
 from .engine import EngineArgs
 
+RolloutWeightsMode = Literal["lora", "merged"]
+
 
 # Vendored from transformers.training_args.OptimizerNames
 class OptimizerNames(str, Enum):
@@ -120,6 +123,10 @@ class InternalModelConfig(TypedDict, total=False):
             inference run on separate GPUs.
         inference_gpu_ids: GPU IDs for vLLM inference (e.g., [1]). When set
             with trainer_gpu_ids, enables dedicated mode.
+        rollout_weights_mode: How inference weights are applied in vLLM.
+            - "lora": load LoRA adapters into vLLM directly
+            - "merged": keep training LoRA adapters, but push merged weights
+              into vLLM for inference
     """
 
     init_args: "InitArgs"
@@ -130,6 +137,7 @@ class InternalModelConfig(TypedDict, total=False):
     trainer_args: "TrainerArgs"
     trainer_gpu_ids: list[int]
     inference_gpu_ids: list[int]
+    rollout_weights_mode: "RolloutWeightsMode"
 
 
 class TinkerArgs(TypedDict, total=False):

src/art/dev/validate.py

@@ -1,13 +1,30 @@
 """Validation functions for model configuration."""
 
-from .model import InternalModelConfig
+from .model import InternalModelConfig, RolloutWeightsMode
+
+QWEN3_5_MOE_MODELS = {
+    "Qwen/Qwen3.5-35B-A3B",
+    "Qwen/Qwen3.5-397B-A17B",
+}
 
 
 def is_dedicated_mode(config: InternalModelConfig) -> bool:
     """Return True if the config specifies dedicated mode (separate training and inference GPUs)."""
     return "trainer_gpu_ids" in config and "inference_gpu_ids" in config
 
 
+def _rollout_weights_mode(config: InternalModelConfig) -> RolloutWeightsMode:
+    mode = config.get("rollout_weights_mode", "lora")
+    if mode in {"lora", "merged"}:
+        return mode
+    raise ValueError("rollout_weights_mode must be either 'lora' or 'merged'")
+
+
+def _is_qwen3_5_moe_model(config: InternalModelConfig) -> bool:
+    model_name = config.get("engine_args", {}).get("model")
+    return model_name in QWEN3_5_MOE_MODELS
+
+
 def validate_dedicated_config(config: InternalModelConfig) -> None:
     """Validate dedicated mode GPU configuration.
 
@@ -16,12 +33,19 @@ def validate_dedicated_config(config: InternalModelConfig) -> None:
     """
     has_trainer = "trainer_gpu_ids" in config
     has_inference = "inference_gpu_ids" in config
+    rollout_weights_mode = _rollout_weights_mode(config)
 
     if has_trainer != has_inference:
         raise ValueError(
             "trainer_gpu_ids and inference_gpu_ids must both be set or both unset"
         )
 
+    if rollout_weights_mode == "merged" and not has_trainer:
+        raise ValueError(
+            "rollout_weights_mode='merged' requires dedicated mode "
+            "(set both trainer_gpu_ids and inference_gpu_ids)"
+        )
+
     if not has_trainer:
         return
 
@@ -65,3 +89,9 @@ def validate_dedicated_config(config: InternalModelConfig) -> None:
             "enable_sleep_mode is incompatible with dedicated mode "
             "(dedicated mode runs vLLM on a separate GPU, sleep/wake is not needed)"
         )
+
+    if _is_qwen3_5_moe_model(config) and rollout_weights_mode == "lora":
+        raise ValueError(
+            "Qwen3.5-MoE models require rollout_weights_mode='merged' with the "
+            "current vLLM version because direct LoRA inference is currently broken"
+        )

src/art/megatron/service.py

@@ -19,6 +19,7 @@
 from vllm.v1.engine.async_llm import AsyncLLM
 
 from .. import dev, types
+from ..dev.get_model_config import default_target_modules
 from ..local.checkpoints import get_last_checkpoint_dir
 from ..preprocessing.pack import DiskPackedTensors
 from ..preprocessing.tokenize import SFTBatch
@@ -66,15 +67,7 @@ def _default_lora_adapter_config(self) -> LoraConfig:
         return LoraConfig(
             r=1,
             lora_alpha=32,
-            target_modules=[
-                "q_proj",
-                "k_proj",
-                "v_proj",
-                "o_proj",
-                "gate_proj",
-                "up_proj",
-                "down_proj",
-            ],
+            target_modules=default_target_modules(self.base_model),
             bias="none",
         )