support disaggregated weight update

lmdeploy/pytorch/engine/engine.py

@@ -195,6 +195,7 @@ def __init__(
         # infer sleeping from empty_init: empty_init still builds runtime
         # resources and has its own weight-update workflow.
         self._sleeping_tags = set()
+        self._weights_update_lock: asyncio.Lock | None = None
         self._multimodal_session_trim_count = max(0, _envs.multimodal_session_trim_count)
         self._multimodal_session_end_count = 0
 
@@ -496,6 +497,29 @@ def update_params(self, request: Any):
         """Update params."""
         self.executor.update_params(request)
 
+    def _get_weights_update_lock(self):
+        """Get the disaggregated weights-update lock."""
+        if self._weights_update_lock is None:
+            self._weights_update_lock = asyncio.Lock()
+        return self._weights_update_lock
+
+    async def _run_weights_update(self, func, request: Any):
+        """Run one serialized disaggregated weights-update operation."""
+        async with self._get_weights_update_lock():
+            return await asyncio.to_thread(func, request)
+
+    async def init_weights_update_group(self, request: Any):
+        """Init disaggregated weights-update process group."""
+        return await self._run_weights_update(self.executor.init_weights_update_group, request)
+
+    async def update_weights_from_distributed(self, request: Any):
+        """Receive weights through the disaggregated process group."""
+        return await self._run_weights_update(self.executor.update_weights_from_distributed, request)
+
+    async def destroy_weights_update_group(self, request: Any):
+        """Tear down a previously initialized weights-update process group."""
+        return await self._run_weights_update(self.executor.destroy_weights_update_group, request)
+
     def _block_new_inputs(self):
         """Block new inference work from engine instances."""
         logger.info('PyTorch engine is blocking new inference requests.')

lmdeploy/pytorch/engine/executor/base.py

@@ -99,6 +99,18 @@ def update_params(self, request: Any):
         """Update params."""
         raise NotImplementedError('Not Implemented.')
 
+    def init_weights_update_group(self, request: Any):
+        """Init disaggregated weights-update process group."""
+        raise NotImplementedError('Not Implemented.')
+
+    def update_weights_from_distributed(self, request: Any):
+        """Receive weights through the disaggregated process group."""
+        raise NotImplementedError('Not Implemented.')
+
+    def destroy_weights_update_group(self, request: Any):
+        """Tear down a previously initialized weights-update process group."""
+        raise NotImplementedError('Not Implemented.')
+
     def get_input_processor(self):
         """Get input processor."""
         raise NotImplementedError('Not Implemented.')

lmdeploy/pytorch/engine/executor/base_worker.py

@@ -118,6 +118,18 @@ def update_params(self, request: Any):
         """Update params."""
         self.model_agent.update_params(request)
 
+    def init_weights_update_group(self, request: Any):
+        """Init disaggregated weights-update process group."""
+        return self.model_agent.init_weights_update_group(request)
+
+    def update_weights_from_distributed(self, request: Any):
+        """Receive weights through the disaggregated process group."""
+        return self.model_agent.update_weights_from_distributed(request)
+
+    def destroy_weights_update_group(self, request: Any):
+        """Tear down a previously initialized weights-update process group."""
+        return self.model_agent.destroy_weights_update_group(request)
+
     def warmup(self):
         """warmup."""
         self.model_agent.warmup()

lmdeploy/pytorch/engine/executor/ray_executor.py

@@ -366,6 +366,29 @@ def update_params(self, request: Any):
         """Update params."""
         self.collective_rpc('update_params', (request, ))
 
+    def _reduce_worker_status(self, results: list[tuple[bool, str]], op_name: str) -> tuple[bool, str]:
+        """Reduce worker status results."""
+        successes, messages = zip(*results)
+        if all(successes):
+            return True, messages[0]
+        message = ' | '.join(f'rank{idx}: {message}' for idx, message in enumerate(messages))
+        return False, f'{op_name}: {message}'
+
+    def init_weights_update_group(self, request: Any):
+        """Init disaggregated weights-update process group."""
+        results = self.collective_rpc('init_weights_update_group', (request, ))
+        return self._reduce_worker_status(results, 'init_weights_update_group')
+
+    def update_weights_from_distributed(self, request: Any):
+        """Receive weights through the disaggregated process group."""
+        results = self.collective_rpc('update_weights_from_distributed', (request, ))
+        return self._reduce_worker_status(results, 'update_weights_from_distributed')
+
+    def destroy_weights_update_group(self, request: Any):
+        """Tear down a previously initialized weights-update process group."""
+        results = self.collective_rpc('destroy_weights_update_group', (request, ))
+        return self._reduce_worker_status(results, 'destroy_weights_update_group')
+
     def warmup(self):
         """Build cache engine."""
         self.collective_rpc('warmup')
@@ -499,8 +522,11 @@ async def forward_async(self, inputs):
 
         if self._prev_out is not None:
             try:
-                ray.get(self._prev_out)
-            except SystemExit:
+                # Await (instead of blocking ray.get) so the engine event loop is yielded while the
+                # previous forward runs. Blocking here stalls the whole loop, starving co-located
+                # async tasks such as the health probe.
+                await asyncio.gather(*self._prev_out)
+            except (SystemExit, ray.exceptions.RayActorError):
                 logger.error('Ray worker exited.')
                 raise
             finally:

lmdeploy/pytorch/engine/model_agent/agent.py

@@ -28,9 +28,14 @@
 from lmdeploy.pytorch.strategies.base.model_agent import ExtraInputs, ExtraOutputs, StoppingCriteria
 from lmdeploy.pytorch.utils import get_gpu_memory, monkey_patch_hf_modules_cache, wait_for_async_tasks
 from lmdeploy.pytorch.weight_loader.model_weight_loader import ModelWeightLoader, load_model_weights
-from lmdeploy.serve.openai.protocol import UpdateParamsRequest
+from lmdeploy.serve.openai.protocol import (
+    DestroyWeightsUpdateGroupRequest,
+    InitWeightsUpdateGroupRequest,
+    UpdateParamsRequest,
+    UpdateWeightsFromDistributedRequest,
+)
 from lmdeploy.tokenizer import Tokenizer
-from lmdeploy.utils import FlattenedTensorBucket, FlattenedTensorMetadata, get_logger
+from lmdeploy.utils import FlattenedTensorBucket, FlattenedTensorMetadata, get_logger, init_custom_process_group
 
 from .dp_utils import DistGatherScalar, DPForwardMeta, GatheredDPForwardMeta
 from .inputs_maker import build_inputs_maker
@@ -284,6 +289,9 @@ def __init__(
         self._update_params_ipc_tensor: torch.Tensor | None = None
         self._update_params_ipc_event: torch.cuda.Event | None = None
 
+        # disaggregated weight-update process groups, keyed by group_name
+        self._model_update_group: dict[str, dist.ProcessGroup] = {}
+
         # microbatch
         self.enable_microbatch = self.dist_config.enable_microbatch
         self.enable_microbatch_prefill_batchsize_threshold = \
@@ -1194,6 +1202,126 @@ def _split_main_and_draft(weights):
 
             torch.cuda.empty_cache()
 
+    def init_weights_update_group(self, request: InitWeightsUpdateGroupRequest):
+        """Create a NCCL process group with an external trainer for the
+        disaggregated weight-update path.
+
+        rank 0 is the trainer; this engine's local TP ranks fill `rank_offset .. rank_offset + tp - 1`.
+        """
+        with self.all_context():
+            group_name = request.group_name
+            if not group_name:
+                return False, 'group_name cannot be empty'
+            if group_name in self._model_update_group:
+                return False, f'group {group_name!r} already initialized'
+
+            local_rank = self.dist_ctx.tp_group.rank
+            rank = request.rank_offset + local_rank
+            init_method = f'tcp://{request.master_address}:{request.master_port}'
+            logger.info(f'init weights update group: master={request.master_address}:{request.master_port}, '
+                        f'rank_offset={request.rank_offset}, rank={rank}, world_size={request.world_size}, '
+                        f'group_name={group_name}, backend={request.backend}')
+            try:
+                pg = init_custom_process_group(
+                    backend=request.backend,
+                    init_method=init_method,
+                    world_size=request.world_size,
+                    rank=rank,
+                    group_name=group_name,
+                )
+                self._model_update_group[group_name] = pg
+                return True, 'Succeeded to initialize weights update group.'
+            except Exception as e:
+                msg = f'Failed to initialize weights update group: {e}'
+                logger.exception(msg)
+                return False, msg
+
+    @torch.inference_mode()
+    def update_weights_from_distributed(self, request: UpdateWeightsFromDistributedRequest):
+        """Receive a bucket of weights through the previously initialized NCCL
+        group and load them into the running model."""
+        with self.all_context():
+            group_name = request.group_name
+            pg = self._model_update_group.get(group_name)
+            if pg is None:
+                return False, (f'group {group_name!r} not initialized. '
+                               'Call init_weights_update_group first.')
+
+            device = torch.cuda.current_device()
+            try:
+                if request.names:
+                    named_tensors = []
+                    for name, dtype_str, shape in zip(request.names, request.dtypes, request.shapes):
+                        target_dtype = getattr(torch, dtype_str) if isinstance(dtype_str, str) else dtype_str
+                        named_tensors.append((name, torch.empty(shape, dtype=target_dtype, device=device)))
+
+                    if request.load_format == 'flattened_bucket':
+                        bucket = FlattenedTensorBucket(named_tensors=named_tensors)
+                        flattened_tensor = bucket.get_flattened_tensor()
+                        dist.broadcast(flattened_tensor, src=0, group=pg)
+                        weights = list(bucket.reconstruct_tensors())
+                    else:
+                        handles = []
+                        for _, tensor in named_tensors:
+                            handles.append(dist.broadcast(tensor, src=0, group=pg, async_op=True))
+                        for handle in handles:
+                            handle.wait()
+                        weights = named_tensors
+                else:
+                    weights = []
+
+                model = self.patched_model.get_model() if self.patched_model is not None else None
+                spec_model = self.spec_agent.get_model()
+                # Same draft-split rule as update_params (currently only qwen3_5_mtp).
+                if self.spec_agent.is_enabled() and self.spec_agent.method == 'qwen3_5_mtp':
+                    main_weights = [(n, w) for n, w in weights if not n.startswith('mtp.')]
+                    draft_weights = [(n, w) for n, w in weights if n.startswith('mtp.')]
+                else:
+                    main_weights, draft_weights = weights, []
+
+                for m, w, tag in [(model, main_weights, 'main'), (spec_model, draft_weights, 'draft')]:
+                    if m is None or not w:
+                        continue
+                    renamed = list(ModelWeightLoader._rename_weights_iterator(w, m))
+                    logger.info(f'update_weights_from_distributed: {tag}_num_tensors={len(renamed)}')
+                    m.load_weights(iter(renamed))
+
+                if request.finished:
+                    for m in filter(None, [model, spec_model]):
+                        for _, mod in m.named_modules():
+                            if hasattr(mod, 'update_weights'):
+                                mod.update_weights()
+                        torch.cuda.synchronize()
+                    # FusedMoE.update_weights() above replaces the gate_up / down
+                    # Parameter objects (LinearWeights.update_weight registers a new
+                    # nn.Parameter), so any CUDA graph captured before the update
+                    # still references the freed old pointers. Drop the captured
+                    # graphs so the next forward re-captures with the new params.
+                    self.reset_graph_runner()
+
+                torch.cuda.empty_cache()
+                return True, 'Succeeded to update parameter online.'
+            except Exception as e:
+                msg = (f'Failed to update parameter online: {e}. The model weights are partially updated; '
+                       'please discard them and reload.')
+                logger.exception(msg)
+                return False, msg
+
+    def destroy_weights_update_group(self, request: DestroyWeightsUpdateGroupRequest):
+        """Destroy a previously initialized weights-update process group."""
+        group_name = request.group_name
+        pg = self._model_update_group.get(group_name)
+        if pg is None:
+            return False, f'group {group_name!r} not initialized'
+        try:
+            dist.destroy_process_group(pg)
+            self._model_update_group.pop(group_name)
+            return True, f'Succeeded to destroy group {group_name!r}.'
+        except Exception as e:
+            msg = f'Failed to destroy weights update group {group_name!r}: {e}'
+            logger.exception(msg)
+            return False, msg
+
     @torch.inference_mode()
     async def sleep(self, level: int = 1):
         """Sleep."""

lmdeploy/pytorch/engine/mp_engine/base.py

@@ -67,6 +67,18 @@ def update_params(self, request: Any):
         """Update params."""
         return self._collective_rpc('update_params', request)
 
+    async def init_weights_update_group(self, request: Any):
+        """Init disaggregated weights-update process group."""
+        return await self._collective_rpc_async('init_weights_update_group', request)
+
+    async def update_weights_from_distributed(self, request: Any):
+        """Receive weights through the disaggregated process group."""
+        return await self._collective_rpc_async('update_weights_from_distributed', request)
+
+    async def destroy_weights_update_group(self, request: Any):
+        """Tear down a previously initialized weights-update process group."""
+        return await self._collective_rpc_async('destroy_weights_update_group', request)
+
     async def get_schedule_metrics(self):
         """Get schedule metrics."""
         return await self._collective_rpc_async('get_schedule_metrics')

lmdeploy/pytorch/engine/mp_engine/base_worker.py

@@ -116,6 +116,18 @@ def update_params(self, request: Any):
         """Update params."""
         return self.engine.update_params(request)
 
+    async def init_weights_update_group(self, request: Any):
+        """Init disaggregated weights-update process group."""
+        return await self.engine.init_weights_update_group(request)
+
+    async def update_weights_from_distributed(self, request: Any):
+        """Receive weights through the disaggregated process group."""
+        return await self.engine.update_weights_from_distributed(request)
+
+    async def destroy_weights_update_group(self, request: Any):
+        """Tear down a previously initialized weights-update process group."""
+        return await self.engine.destroy_weights_update_group(request)
+
     def close(self) -> None:
         """Close engine worker."""
         self.engine.close()

lmdeploy/serve/openai/api_server.py

@@ -63,13 +63,15 @@
     CompletionResponseStreamChoice,
     CompletionStreamResponse,
     DeltaMessage,
+    DestroyWeightsUpdateGroupRequest,
     EmbeddingsRequest,
     EncodeRequest,
     EncodeResponse,
     ErrorResponse,
     GenerateReqInput,
     GenerateReqMetaOutput,
     GenerateReqOutput,
+    InitWeightsUpdateGroupRequest,
     LogProbs,
     ModelCard,
     ModelList,
@@ -78,6 +80,7 @@
     PoolingResponse,
     TopLogprob,
     UpdateParamsRequest,
+    UpdateWeightsFromDistributedRequest,
     UsageInfo,
 )
 from lmdeploy.serve.utils.request_cleanup import with_request_cleanup
@@ -1201,6 +1204,52 @@ def update_params(request: UpdateParamsRequest, raw_request: Request = None):
     return JSONResponse(content=None)
 
 
+def _check_pytorch_backend_for_disagg_weight_update():
+    """Disaggregated weight-update endpoints are PyTorch-backend only for
+    now."""
+    backend = getattr(VariableInterface.async_engine, 'backend', None)
+    if backend != 'pytorch':
+        return create_error_response(
+            HTTPStatus.NOT_IMPLEMENTED,
+            f'Disaggregated weight-update endpoints require backend="pytorch", got {backend!r}.')
+    return None
+
+
+@router.post('/init_weights_update_group', dependencies=[Depends(validate_json_request)])
+async def init_weights_update_group(request: InitWeightsUpdateGroupRequest, raw_request: Request = None):
+    """Initialize the torch.distributed process group used by an external
+    trainer to broadcast weights into this rollout engine."""
+    err = _check_pytorch_backend_for_disagg_weight_update()
+    if err is not None:
+        return err
+    success, message = await VariableInterface.async_engine.engine.init_weights_update_group(request)
+    content = {'success': success, 'message': message}
+    return JSONResponse(content=content, status_code=200 if success else HTTPStatus.BAD_REQUEST)
+
+
+@router.post('/update_weights_from_distributed', dependencies=[Depends(validate_json_request)])
+async def update_weights_from_distributed(request: UpdateWeightsFromDistributedRequest, raw_request: Request = None):
+    """Receive a bucket of weights through a previously initialized weights-
+    update group and load them into the running model."""
+    err = _check_pytorch_backend_for_disagg_weight_update()
+    if err is not None:
+        return err
+    success, message = await VariableInterface.async_engine.engine.update_weights_from_distributed(request)
+    content = {'success': success, 'message': message}
+    return JSONResponse(content=content, status_code=200 if success else HTTPStatus.BAD_REQUEST)
+
+
+@router.post('/destroy_weights_update_group', dependencies=[Depends(validate_json_request)])
+async def destroy_weights_update_group(request: DestroyWeightsUpdateGroupRequest, raw_request: Request = None):
+    """Tear down a previously initialized weights-update group."""
+    err = _check_pytorch_backend_for_disagg_weight_update()
+    if err is not None:
+        return err
+    success, message = await VariableInterface.async_engine.engine.destroy_weights_update_group(request)
+    content = {'success': success, 'message': message}
+    return JSONResponse(content=content, status_code=200 if success else HTTPStatus.BAD_REQUEST)
+
+
 @router.post('/sleep', dependencies=[Depends(validate_json_request)])
 async def sleep(raw_request: Request = None):
     level = raw_request.query_params.get('level', '1')