Initial commit

verl/single_controller/ray/base.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import logging
+import time
+from typing import Dict, List, Any, Tuple, Optional
+
+import ray
+from ray.util import list_named_actors
+from ray.util.placement_group import placement_group, PlacementGroup
+from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy, NodeAffinitySchedulingStrategy
+from ray.experimental.state.api import get_actor
+
+from verl.single_controller.base import WorkerGroup, ResourcePool, ClassWithInitArgs, Worker
+
+__all__ = ['Worker']
+
+
+def get_random_string(length: int) -> str:
+    import random
+    import string
+    letters_digits = string.ascii_letters + string.digits
+    return ''.join(random.choice(letters_digits) for _ in range(length))
+
+
+def func_generator(self, method_name, dispatch_fn, collect_fn, execute_fn, blocking):
+
+    def func(*args, **kwargs):
+        args, kwargs = dispatch_fn(self, *args, **kwargs)
+        output = execute_fn(method_name, *args, **kwargs)
+        if blocking:
+            output = ray.get(output)
+        output = collect_fn(self, output)
+        return output
+
+    return func
+
+
+def sort_placement_group_by_node_ip(pgs: List[PlacementGroup]) -> List[PlacementGroup]:
+    """
+    Sort the placement groups by node ip, all bundles in a single placement group should be on the same node.
+
+    FSDPCheckpointManager saves sharded model states and optimizer states in local storage, which requires RANK
+    to be consistent across nodes when resume from checkpoint.
+
+    With this function, if there's only one resource pool and there's no node change, RANK should be consistent
+    across nodes in multiple ray jobs, even if the whole ray cluster is restarted.
+    """
+    node_ip = {node["NodeID"]: node["NodeManagerAddress"] for node in ray.nodes()}
+    pg_ip = {}
+    for pg in pgs:
+        specs = ray._private.state.state.placement_group_table(pg.id)
+        # all bunles should be on the same node
+        node_id = specs["bundles_to_node_id"][0]
+        pg_ip[pg.id] = node_ip[node_id]
+    return sorted(pgs, key=lambda pg: pg_ip[pg.id])
+
+
+class RayResourcePool(ResourcePool):
+
+    def __init__(self,
+                 process_on_nodes: Optional[List[int]] = None,
+                 use_gpu: bool = True,
+                 name_prefix: str = "",
+                 max_colocate_count: int = 10,
+                 detached=False) -> None:
+        super().__init__(process_on_nodes, max_colocate_count)
+        self.use_gpu = use_gpu
+        # print(f"in RayProcessDispatchConfiguration: name_prefix = {name_prefix}")
+        self.name_prefix = name_prefix
+        self.pgs = None
+        self.detached = detached
+
+    def get_placement_groups(self, strategy="STRICT_PACK", name=None):
+        if self.pgs is not None:
+            return self.pgs
+
+        pg_name_prefix = name if name else \
+            f"{self.name_prefix}verl_group_{'_'.join([str(count) for count in self._store])}:"
+        # print(f"pg_name_prefix = {pg_name_prefix}")
+        pg_scheme = [[{
+            "CPU": self.max_colocate_count,
+            "GPU": 1
+        } if self.use_gpu else {
+            "CPU": self.max_colocate_count
+        } for _ in range(process_count)] for process_count in self._store]
+
+        lifetime = 'detached' if self.detached else None
+
+        pgs = [
+            placement_group(bundles=bundles, strategy=strategy, name=pg_name_prefix + str(idx), lifetime=lifetime)
+            for idx, bundles in enumerate(pg_scheme)
+        ]
+
+        ray.get([pg.ready() for pg in pgs])
+
+        self.pgs = pgs
+        return pgs
+
+
+def extract_pg_from_exist(resource_pools: Dict[str, RayResourcePool], src_role_names: List[str],
+                          resource_pool: RayResourcePool) -> List:
+
+    src_pgs = [
+        pg for role_name, resource_pool in resource_pools.items() for pg in resource_pool.get_placement_groups()
+        if role_name in src_role_names
+    ]
+
+    sorted_src_pgs = sorted(src_pgs, key=lambda pg: pg.bundle_count, reverse=True)
+    sorted_process_on_nodes = sorted([(val, idx) for idx, val in enumerate(resource_pool.store)], reverse=True)
+
+    unsorted_pgs: List[Tuple[int, PlacementGroup]] = []
+    searching_idx = 0
+    for request_process, original_idx in sorted_process_on_nodes:
+        assert searching_idx < len(sorted_src_pgs), f"no enough nodes for request: searching {searching_idx} th node"
+        assert request_process <= sorted_src_pgs[searching_idx].bundle_count, \
+            f"requesting {request_process} processes, bundle count cannot satisfy"
+        unsorted_pgs.append((original_idx, sorted_src_pgs[searching_idx]))
+        searching_idx += 1
+
+    return [pg for _, pg in sorted(unsorted_pgs)]
+
+
+def merge_resource_pool(rp1: RayResourcePool, rp2: RayResourcePool) -> RayResourcePool:
+    assert rp1.use_gpu == rp2.use_gpu, 'Both RayResourcePool must either use_gpu or not'
+    assert rp1.max_colocate_count == rp2.max_colocate_count, 'Both RayResourcePool must has the same max_colocate_count'
+    assert rp1.n_gpus_per_node == rp2.n_gpus_per_node, 'Both RayResourcePool must has the same n_gpus_per_node'
+    assert rp1.detached == rp2.detached, 'Detached ResourcePool cannot be merged with non-detached ResourcePool'
+
+    new_store = rp1.store + rp2.store
+
+    merged = RayResourcePool(new_store, rp1.use_gpu, f"{rp1.name_prefix}_{rp2.name_prefix}")
+    merged.pgs = rp1.get_placement_groups() + rp2.get_placement_groups()
+
+    return merged
+
+
+class RayClassWithInitArgs(ClassWithInitArgs):
+
+    def __init__(self, cls, *args, **kwargs) -> None:
+        # self._options = kwargs.pop('options', dict())
+        super().__init__(cls, *args, **kwargs)
+        self._options = {}
+        self._additional_resource = {}
+
+    def set_additional_resource(self, additional_resource):
+        self._additional_resource = additional_resource
+
+    def update_options(self, options: Dict):
+        self._options.update(options)
+
+    def __call__(self,
+                 placement_group,
+                 placement_group_bundle_idx,
+                 use_gpu: bool = True,
+                 num_gpus=1,
+                 sharing_with=None) -> Any:
+        if sharing_with is not None:
+            target_node_id = ray.get(sharing_with.get_node_id.remote())
+            cuda_visible_devices = ray.get(sharing_with.get_cuda_visible_devices.remote())
+            options = {"scheduling_strategy": NodeAffinitySchedulingStrategy(node_id=target_node_id, soft=False)}
+            return self.cls.options(**options).remote(*self.args,
+                                                      cuda_visible_devices=cuda_visible_devices,
+                                                      **self.kwargs)
+
+        options = {
+            "scheduling_strategy":
+                PlacementGroupSchedulingStrategy(placement_group=placement_group,
+                                                 placement_group_bundle_index=placement_group_bundle_idx)
+        }
+        options.update(self._options)
+
+        if use_gpu:
+            options["num_gpus"] = num_gpus
+
+        if len(self._additional_resource) > 1:
+            for k, v in self._additional_resource.items():
+                options[k] = v
+
+        # print("cls:", self.cls)
+        # print("args: ", self.args)
+        # print("kwargs: ", self.kwargs)
+        return self.cls.options(**options).remote(*self.args, **self.kwargs)
+
+
+class RayWorkerGroup(WorkerGroup):
+
+    def __init__(self,
+                 resource_pool: RayResourcePool = None,
+                 ray_cls_with_init: RayClassWithInitArgs = None,
+                 bin_pack: bool = True,
+                 name_prefix: str = None,
+                 detached=False,
+                 worker_names=None,
+                 ray_wait_register_center_timeout: int = 300,
+                 **kwargs) -> None:
+        super().__init__(resource_pool=resource_pool, **kwargs)
+        self.ray_cls_with_init = ray_cls_with_init
+        self.name_prefix = get_random_string(length=6) if name_prefix is None else name_prefix
+        self._ray_wait_register_center_timeout = ray_wait_register_center_timeout
+
+        if worker_names is not None:
+            assert self._is_init_with_detached_workers
+            self._worker_names = worker_names
+
+        if self._is_init_with_detached_workers:
+            self._init_with_detached_workers(worker_names=worker_names)
+        else:
+            self._init_with_resource_pool(resource_pool=resource_pool,
+                                          ray_cls_with_init=ray_cls_with_init,
+                                          bin_pack=bin_pack,
+                                          detached=detached)
+
+        if ray_cls_with_init is not None:
+            self._bind_worker_method(self.ray_cls_with_init.cls, func_generator)
+
+    def _is_worker_alive(self, worker: ray.actor.ActorHandle):
+        worker_state_dict = get_actor(worker._actor_id.hex())
+        return worker_state_dict.get("state", "undefined") == "ALIVE" if worker_state_dict is not None else False
+
+    def _init_with_detached_workers(self, worker_names):
+        workers = [ray.get_actor(name=name) for name in worker_names]
+        self._workers = workers
+        self._world_size = len(worker_names)
+
+    def _init_with_resource_pool(self, resource_pool, ray_cls_with_init, bin_pack, detached):
+        use_gpu = resource_pool.use_gpu
+
+        strategy = "PACK"
+        if bin_pack:
+            strategy = "STRICT_PACK"
+        pgs = resource_pool.get_placement_groups(strategy=strategy)
+        world_size = resource_pool.world_size
+        self._world_size = world_size
+        # cia.add_kwarg("_world_size", world_size)
+        num_gpus = 1 / resource_pool.max_colocate_count
+
+        rank = -1
+        local_world_size = resource_pool.store[0]
+        for pg_idx, pg in enumerate(sort_placement_group_by_node_ip(pgs)):
+            assert local_world_size <= pg.bundle_count, \
+                f"when generating for {self.name_prefix}, for the "
+            for local_rank in range(local_world_size):
+                rank += 1
+
+                # we pass in environment variable at option so that Worker can use environment variable to set
+                env_vars = {
+                    'WORLD_SIZE': str(world_size),
+                    'RANK': str(rank),
+                    'WG_PREFIX': self.name_prefix,
+                    'WG_BACKEND': 'ray',
+                    'RAY_LOCAL_WORLD_SIZE': str(local_world_size),
+                    'RAY_LOCAL_RANK': str(local_rank),
+                }
+                if rank != 0:
+                    env_vars['MASTER_ADDR'] = self._master_addr
+                    env_vars['MASTER_PORT'] = self._master_port
+
+                import re
+                cia_name = type(ray_cls_with_init.cls).__name__
+                match = re.search(r"ActorClass\(([^)]+)\)", cia_name)  # ray.remote(Obj) -> "ActorClass(Obj)"
+                cia_name = match.group(1) if match else cia_name  # "ActorClass(Obj)" -> "Obj"
+                name = f"{self.name_prefix}{cia_name}_{pg_idx}:{local_rank}"  # e.g. Worker_2:5
+
+                ray_cls_with_init.update_options({'runtime_env': {'env_vars': env_vars}, 'name': name})
+
+                if detached:
+                    ray_cls_with_init.update_options({'lifetime': 'detached'})
+
+                # create a worker
+                worker = ray_cls_with_init(placement_group=pg,
+                                           placement_group_bundle_idx=local_rank,
+                                           use_gpu=use_gpu,
+                                           num_gpus=num_gpus)
+                self._workers.append(worker)
+                self._worker_names.append(name)
+
+                if rank == 0:
+                    register_center_actor = None
+                    actor_name = f"{self.name_prefix}_register_center"
+                    start_time = time.time()
+
+                    while time.time() - start_time < self._ray_wait_register_center_timeout:
+                        if actor_name in list_named_actors():
+                            register_center_actor = ray.get_actor(actor_name)
+                            break
+
+                        elapsed = int(time.time() - start_time)
+                        if elapsed % 30 == 0:
+                            logging.warning(
+                                f"Waiting for register center actor {actor_name} to be ready. "
+                                f"Elapsed time: {elapsed} seconds out of {self._ray_wait_register_center_timeout} seconds."
+                            )
+                        time.sleep(1)
+
+                    if register_center_actor is None:
+                        raise TimeoutError(
+                            f"Failed to get register_center_actor {actor_name} in {list_named_actors(all_namespaces=True)} "
+                            f"for {self._ray_wait_register_center_timeout} seconds. "
+                            "Ensure that any lingering Ray resources from previous runs are cleaned up (e.g., by restarting the Ray cluster), "
+                            "or adjust the waiting time by modifying the config `trainer.ray_wait_register_center_timeout`."
+                        )
+
+                    rank_zero_info = ray.get(register_center_actor.get_rank_zero_info.remote())
+                    self._master_addr, self._master_port = rank_zero_info['MASTER_ADDR'], rank_zero_info['MASTER_PORT']
+                    # print(f"rank_zero_info: {rank_zero_info}")
+                    # print(f"master_addr: {self._master_addr}, master_port: {self._master_port}")
+
+    @property
+    def worker_names(self):
+        return self._worker_names
+
+    @classmethod
+    def from_detached(cls, worker_names=None, ray_cls_with_init=None):
+        worker_group = cls(resource_pool=None,
+                           ray_cls_with_init=ray_cls_with_init,
+                           name_prefix=None,
+                           worker_names=worker_names)
+        return worker_group
+
+    def spawn(self, prefix_set):
+        """
+        spawn to a dictionary of worker groups, each with a subset of method with prefix.
+
+        """
+
+        def _rebind_actor_methods(worker_group, actor_name):
+            """
+            bind the method with actor_prefix to its original name
+            """
+            prefix: str = actor_name + '_'
+            for method_name in dir(worker_group):
+                if method_name.startswith(prefix):
+                    # only valid when Python >= 3.9
+                    original_method_name = method_name.removeprefix(prefix)
+                    method = getattr(worker_group, method_name)
+                    setattr(worker_group, original_method_name, method)
+
+        new_worker_group_dict = {}
+        for prefix in prefix_set:
+            new_worker_group = self.from_detached(worker_names=self._worker_names,
+                                                  ray_cls_with_init=self.ray_cls_with_init)
+
+            _rebind_actor_methods(new_worker_group, prefix)
+            new_worker_group_dict[prefix] = new_worker_group
+        return new_worker_group_dict
+
+    def execute_rank_zero_sync(self, method_name: str, *args, **kwargs):
+        return ray.get(self.execute_rank_zero_async(method_name, *args, **kwargs))
+
+    def execute_rank_zero_async(self, method_name: str, *args, **kwargs):
+        remote_call = getattr(self._workers[0], method_name)
+        return remote_call.remote(*args, **kwargs)
+
+    def execute_rank_zero(self, method_name: str, *args, **kwargs):
+        return self.execute_rank_zero_async(method_name, *args, **kwargs)
+
+    def execute_all(self, method_name: str, *args, **kwargs):
+        return self.execute_all_async(method_name, *args, **kwargs)
+
+    def execute_all_sync(self, method_name: str, *args, **kwargs):
+        return ray.get(self.execute_all_async(method_name, *args, **kwargs))
+
+    def execute_all_async(self, method_name: str, *args, **kwargs):
+        # Here, we assume that if all arguments in args and kwargs are lists, and their lengths match len(self._workers),
+        # we'll distribute each element in these lists to the corresponding worker
+        # print(f"execute_all_async: method {method_name}({args}, {kwargs})")
+        length = len(self._workers)
+        if all(isinstance(arg, list) for arg in args) and all(isinstance(kwarg, list) for kwarg in kwargs.values()):
+            if all(len(arg) == length for arg in args) and all(len(kwarg) == length for kwarg in kwargs.values()):
+                # print(f"splitting args and kwargs into {length} shards")
+                result = []
+                for i in range(length):
+                    sliced_args = tuple(arg[i] for arg in args)
+                    sliced_kwargs = {k: v[i] for k, v in kwargs.items()}
+                    remote_call = getattr(self._workers[i], method_name)
+                    result.append(remote_call.remote(*sliced_args, **sliced_kwargs))
+                return result
+
+        return [getattr(worker, method_name).remote(*args, **kwargs) for worker in self._workers]
+
+    @property
+    def master_address(self):
+        return self._master_addr
+
+    @property
+    def master_port(self):
+        return self._master_port
+
+    @property
+    def workers(self):
+        return self._workers
+
+    @property
+    def world_size(self):
+        return self._world_size
+
+
+"""
+Utilities that enables creating workers inside the same ray.Actor, 
+with code written in separate ray.Actors.
+"""
+
+from unittest.mock import patch
+from verl.single_controller.base.decorator import MAGIC_ATTR
+import os
+
+
+def _bind_workers_method_to_parent(cls, key, user_defined_cls):
+    """
+    Binds the methods of each worker to the WorkerDict. 
+    Note that we only bind public methods that are decorated by register
+    """
+    for method_name in dir(user_defined_cls):
+        try:
+            method = getattr(user_defined_cls, method_name)
+            assert callable(method), f"{method_name} in {user_defined_cls} is not callable"
+        except Exception as e:
+            # if it is a property, it will fail because Class doesn't have instance property
+            continue
+
+        if hasattr(method, MAGIC_ATTR):
+
+            def generate_function(name):
+
+                def func(self, *args, **kwargs):
+                    # dispatch to the actual worker
+                    return getattr(self.worker_dict[key], name)(*args, **kwargs)
+
+                return func
+
+            func = generate_function(method_name)
+            # pass MAGIC_ATTR for outer worker group
+            setattr(func, MAGIC_ATTR, getattr(method, MAGIC_ATTR))
+            try:
+                method_name_with_prefix = key + '_' + method_name
+                setattr(cls, method_name_with_prefix, func)
+                # print(f'Binding {method_name_with_prefix}')
+            except Exception as e:
+                raise ValueError(f'Fail to set method_name {method_name}')
+
+
+def _unwrap_ray_remote(cls):
+    if hasattr(cls, '__ray_actor_class__'):
+        cls = cls.__ray_actor_class__
+    return cls
+
+
+def create_colocated_worker_cls(class_dict: dict[str, RayClassWithInitArgs]):
+    """
+    This function should return a class instance that delegates the calls to every 
+    cls in cls_dict
+    """
+    cls_dict = {}
+    init_args_dict = {}
+    worker_cls = None
+    for key, cls in class_dict.items():
+        if worker_cls == None:
+            worker_cls = cls.cls.__ray_actor_class__.__base__
+        else:
+            assert worker_cls == cls.cls.__ray_actor_class__.__base__, \
+                'the worker class should be the same when share the same process'
+        cls_dict[key] = cls.cls
+        init_args_dict[key] = {'args': cls.args, 'kwargs': cls.kwargs}
+
+    assert cls_dict.keys() == init_args_dict.keys()
+
+    # TODO: create a class with customizable name
+    class WorkerDict(worker_cls):
+
+        def __init__(self):
+            super().__init__()
+            self.worker_dict = {}
+            for key, user_defined_cls in cls_dict.items():
+                user_defined_cls = _unwrap_ray_remote(user_defined_cls)
+                # directly instantiate the class without remote
+                # in worker class, e.g. <verl.single_controller.base.worker.Worker> when DISABLE_WORKER_INIT == 1 it will return immediately
+                with patch.dict(os.environ, {'DISABLE_WORKER_INIT': '1'}):
+                    self.worker_dict[key] = user_defined_cls(*init_args_dict[key].get('args', ()),
+                                                             **init_args_dict[key].get('kwargs', {}))
+
+    # now monkey-patch the methods from inner class to WorkerDict
+    for key, user_defined_cls in cls_dict.items():
+        user_defined_cls = _unwrap_ray_remote(user_defined_cls)
+        _bind_workers_method_to_parent(WorkerDict, key, user_defined_cls)
+
+    remote_cls = ray.remote(WorkerDict)
+    remote_cls = RayClassWithInitArgs(cls=remote_cls)
+    return remote_cls

verl/single_controller/ray/megatron.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Dict, Optional
+
+import ray
+
+from .base import RayWorkerGroup, RayResourcePool, RayClassWithInitArgs
+from verl.single_controller.base.megatron.worker import DistRankInfo, DistGlobalInfo
+from verl.single_controller.base.megatron.worker_group import MegatronWorkerGroup
+
+
+# NOTE(sgm): for open-source megatron-core
+class NVMegatronRayWorkerGroup(RayWorkerGroup, MegatronWorkerGroup):
+    """
+    MegatronWorkerGroup will query each worker of its megatron rank info and store it inside the WorkerGroup
+    so that the dispatcher can use it to dispatch data.
+    """
+
+    def __init__(self, resource_pool: RayResourcePool, ray_cls_with_init: RayClassWithInitArgs, **kwargs):
+        super().__init__(resource_pool=resource_pool, ray_cls_with_init=ray_cls_with_init, **kwargs)
+        self._megatron_rank_info: DistRankInfo = self.execute_all_sync(method_name='get_megatron_rank_info')
+        self._megatron_global_info: DistGlobalInfo = ray.get(
+            self.execute_rank_zero_async(method_name='get_megatron_global_info'))
+
+
+class MegatronRayWorkerGroup(RayWorkerGroup, MegatronWorkerGroup):
+    """
+    MegatronWorkerGroup will query each worker of its megatron rank info and store it inside the WorkerGroup
+    so that the dispatcher can use it to dispatch data.
+    """
+
+    def __init__(self,
+                 resource_pool: RayResourcePool,
+                 ray_cls_with_init: RayClassWithInitArgs,
+                 default_megatron_kwargs: Dict = None,
+                 **kwargs):
+        super().__init__(resource_pool=resource_pool,
+                         ray_cls_with_init=ray_cls_with_init,
+                         default_megatron_kwargs=default_megatron_kwargs,
+                         **kwargs)
+        self.init_megatron(default_megatron_kwargs=default_megatron_kwargs)
+        self._megatron_rank_info: DistRankInfo = self.execute_all_sync(method_name='get_megatron_rank_info')
+        self._megatron_global_info: DistGlobalInfo = ray.get(
+            self.execute_rank_zero_async(method_name='get_megatron_global_info'))
+
+    def init_megatron(self, default_megatron_kwargs: Optional[Dict] = None):
+        # after super, we will call init of each worker
+        if not self._is_init_with_detached_workers:
+            # only init_megatron if the WorkerGroup is created from scratch
+            self.execute_all_sync(method_name='init_megatron', default_megatron_kwargs=default_megatron_kwargs)

verl/third_party/__init__.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

verl/third_party/sglang/__init__.py

+# Copyright 2023-2024 SGLang Team
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
\ No newline at end of file

verl/third_party/sglang/parallel_state.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+# Copyright 2023 The SGlang team.
+# Adapted from
+# https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/parallel_state.py
+# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
+"""Model and data parallel groups."""
+import os
+from typing import Optional
+
+import torch
+import torch.distributed
+import sglang.srt.distributed.parallel_state as ps
+from sglang.srt.distributed.parallel_state import (
+    get_pp_group,
+    get_world_group,
+    init_distributed_environment,
+    init_model_parallel_group,
+)
+"""
+This version is strongly tied with Megatron to implement HybridEngine and weight sharing between vllm and Megatron.
+- We assume the Megatron tp+dp+pp world is already established before calling this function.
+
+"""
+
+# Device mesh for using DTensor
+_DEVICE_MESH = None
+
+# Tensor model parallel group that the current rank belongs to.
+_TP = None
+# Pipeline model parallel group that the current rank belongs to.
+_PP = None
+
+
+# This method is for initializing the ParallelGroup when using HybridEngine
+# NOTE(linjunrong): this function is for megatron
+def initialize_parallel_state(
+    distributed_init_method: str = "env://",
+    backend: str = "nccl",
+    tensor_model_parallel_size: int = 1,
+    num_tp_per_train_tp: int = 1,
+    pipeline_model_parallel_size: int = 1,
+):
+    # torch.distributed.all_reduce does not free the input tensor until
+    # the synchronization point. This causes the memory usage to grow
+    # as the number of all_reduce calls increases. This env var disables
+    # this behavior.
+    # Related issue:
+    # https://discuss.pytorch.org/t/cuda-allocation-lifetime-for-inputs-to-distributed-all-reduce/191573
+    os.environ["TORCH_NCCL_AVOID_RECORD_STREAMS"] = "1"
+
+    # NOTE(sgm): Modify for verl, Env vars will be set by TORCHRUN.
+    rank = int(os.getenv("RANK", "-1"))
+    local_rank = int(os.getenv("LOCAL_RANK", "0"))
+
+    # Use the world_size set by TORCHRUN
+    world_size = int(os.getenv("WORLD_SIZE", "-1"))
+    assert world_size != -1, "The world_size is set to -1, not initialized by TORCHRUN"
+    init_distributed_environment(world_size, rank, distributed_init_method, local_rank, backend)
+    if torch.distributed.get_world_size() > 1:
+        # NOTE: build a sepearate inference group with infer tp & micro dp
+        initialize_model_parallel_for_sglang(
+            tensor_model_parallel_size=tensor_model_parallel_size,
+            num_tensor_model_parallel_groups_per_train_tp=num_tp_per_train_tp,
+        )
+    else:
+        initialize_model_parallel(tensor_model_parallel_size, pipeline_model_parallel_size, backend)
+
+
+# NOTE(linjunrong): After init SGLang rollout using class EngineFragment, user should always remember to call
+# this function to sync the _TP, _PP define at the beginning of this file. Otherwise, only the conterparts
+# inside sglang.srt.distributed are init as ProcessGroup, the symbols defined in this file remain as None.
+# It could be weird to maintain two _TP and _PP, I follow the same way to maintain an extra ones for
+# veRL itself as how it was done in verl.third_party.vllm.parallel_state. Note that the process is a little
+# bit different
+def ensure_model_parallel_initialized(
+    tensor_model_parallel_size: int,
+    pipeline_model_parallel_size: int = 1,
+    backend: Optional[str] = None,
+) -> None:
+    """Helper to initialize model parallel groups if they are not initialized,
+    or ensure tensor-parallel and pipeline-parallel sizes are equal to expected
+    values if the model parallel groups are initialized.
+    """
+    # get the backend of _DEVICE_WORLD_GROUP
+    backend = backend or torch.distributed.get_backend(get_world_group().device_group)
+    if not model_parallel_is_initialized():
+        initialize_model_parallel(tensor_model_parallel_size, pipeline_model_parallel_size, backend)
+        return
+
+    assert get_tensor_model_parallel_world_size() == tensor_model_parallel_size, (
+        "tensor parallel group already initialized, but of unexpected size: "
+        f"{get_tensor_model_parallel_world_size()=} vs. "
+        f"{tensor_model_parallel_size=}")
+    pp_world_size = get_pp_group().world_size
+    assert pp_world_size == pipeline_model_parallel_size, (
+        "pipeline parallel group already initialized, but of unexpected size: "
+        f"{pp_world_size=} vs. "
+        f"{pipeline_model_parallel_size=}")
+
+
+# TODO(sgm): deviate from the v0.5.4, not pp now
+# NOTE(linjunrong): the SGLang version using _TP instead of ps._TP
+def model_parallel_is_initialized():
+    """Check if tensor and pipeline parallel groups are initialized."""
+    return _TP is not None
+    # and _PIPELINE_MODEL_PARALLEL_GROUP is not None)
+
+
+def initialize_model_parallel_for_sglang(
+    tensor_model_parallel_size: int,
+    num_tensor_model_parallel_groups_per_train_tp: int = 1,
+    pipeline_model_parallel_size: int = 1,
+) -> None:
+    pass
+
+    # Get world size and rank. Ensure some consistencies.
+    assert torch.distributed.is_initialized()
+
+    assert isinstance(tensor_model_parallel_size, int)
+
+    # assert num_tensor_model_parallel_groups_per_train_tp == 1 and not different_tp_group
+    # assert num_tensor_model_parallel_groups_per_train_tp > 1 and different_tp_group
+
+    # Build the tensor model-parallel groups.
+    assert ps._TP is None, "tensor model parallel group is already initialized"
+
+    global _TP
+
+    world_size: int = torch.distributed.get_world_size()
+
+    rank = torch.distributed.get_rank()
+
+    backend = torch.distributed.get_backend()
+
+    num_tensor_model_parallel_groups = world_size // tensor_model_parallel_size
+
+    if num_tensor_model_parallel_groups_per_train_tp == 1:
+        # if tensor_model_parallel_size == train_tensor_parallel_size:
+        # using the same tp group as Megatron/vllm
+        assert _TP is None, "tensor model parallel group is already initialized"
+        group_ranks = []
+        for i in range(num_tensor_model_parallel_groups):
+            ranks = range(i * tensor_model_parallel_size, (i + 1) * tensor_model_parallel_size)
+            group_ranks.append(ranks)
+        _TP = init_model_parallel_group(
+            group_ranks=group_ranks,
+            local_rank=get_world_group().local_rank,
+            backend=backend,
+            use_custom_allreduce=False,  # TODO: check why True is not work in Ray trainer
+            use_message_queue_broadcaster=True,
+        )
+        ps._TP = _TP
+        # _MICRO_DATA_PARALLEL_GROUP is move to hybrid engine
+    else:
+        # initialize a micro_dp group and a tp group
+        # assume training tp=4, infer tp=2, then, weight is partitioned as
+        # [1], [2], [3], [4] for training and [1,2], [1,2], [3,4], [3,4] for inference
+
+        # Build the inference tp groups
+        # train_tp = train_tensor_parallel_size
+        train_tp = num_tensor_model_parallel_groups_per_train_tp * tensor_model_parallel_size
+        # num_tensor_model_parallel_groups_per_train_tp = train_tp // tensor_model_parallel_size
+        assert _TP is None, "tensor model parallel group is already initialized"
+        group_ranks = []
+        for i in range(num_tensor_model_parallel_groups // num_tensor_model_parallel_groups_per_train_tp):
+            start = train_tp * i
+            end = train_tp * (i + 1)
+            for j in range(num_tensor_model_parallel_groups_per_train_tp):
+                ranks = list(range(start, end, num_tensor_model_parallel_groups_per_train_tp))
+                for i in range(len(ranks)):
+                    ranks[i] += j
+                group_ranks.append(ranks)
+        _TP = init_model_parallel_group(
+            group_ranks=group_ranks,
+            local_rank=get_world_group().local_rank,
+            backend=backend,
+            use_custom_allreduce=False,  # TODO: check why True is not work in Ray trainer
+            use_message_queue_broadcaster=True,
+        )
+        ps._TP = _TP
+
+    # Build the pipeline model-parallel groups.
+    # global _PIPELINE_MODEL_PARALLEL_GROUP
+    # global _PIPELINE_GLOBAL_RANKS
+    # assert ps._PIPELINE_MODEL_PARALLEL_GROUP is None, ("pipeline model parallel group is already initialized")
+
+    # ps._PIPELINE_MODEL_PARALLEL_GROUP = mpu.get_pipeline_model_parallel_group()
+    # ps._PIPELINE_GLOBAL_RANKS = mpu.get_pipeline_model_parallel_ranks()
+
+    # TODO: init using device mesh (not support hybrid engine now)
+    # Build the pipeline model-parallel groups.
+    num_pipeline_model_parallel_groups: int = world_size // pipeline_model_parallel_size
+    global _PP
+    assert _PP is None, "pipeline model parallel group is already initialized"
+    group_ranks = []
+    for i in range(num_pipeline_model_parallel_groups):
+        ranks = list(range(i, world_size, num_pipeline_model_parallel_groups))
+        group_ranks.append(ranks)
+    # pipeline parallel does not need custom allreduce
+    _PP = init_model_parallel_group(group_ranks, get_world_group().local_rank, backend, use_custom_allreduce=False)
+    ps._PP = _PP  # for verl
+
+
+def initialize_model_parallel(
+    tensor_model_parallel_size: int = 1,
+    pipeline_model_parallel_size: int = 1,
+    backend: Optional[str] = None,
+) -> None:
+    """
+    NOTE: This method is a hack from the open-sourced version without
+    asertion of world_size = tp * pp
+
+    Initialize model parallel groups.
+
+    Arguments:
+        tensor_model_parallel_size: number of GPUs used for tensor model
+            parallelism.
+        pipeline_model_parallel_size: number of GPUs used for pipeline model
+            parallelism.
+
+    Let's say we have a total of 8 GPUs denoted by g0 ... g7 and we
+    use 2 GPUs to parallelize the model tensor, and 4 GPUs to parallelize
+    the model pipeline. The present function will
+    create 4 tensor model-parallel groups and 2 pipeline model-parallel groups:
+        4 tensor model-parallel groups:
+            [g0, g1], [g2, g3], [g4, g5], [g6, g7]
+        2 pipeline model-parallel groups:
+            [g0, g2, g4, g6], [g1, g3, g5, g7]
+    Note that for efficiency, the caller should make sure adjacent ranks
+    are on the same DGX box. For example if we are using 2 DGX-1 boxes
+    with a total of 16 GPUs, rank 0 to 7 belong to the first box and
+    ranks 8 to 15 belong to the second box.
+    """
+    # Get world size and rank. Ensure some consistencies.
+    assert torch.distributed.is_initialized()
+    world_size: int = torch.distributed.get_world_size()
+    backend = backend or torch.distributed.get_backend(ps.get_world_group().device_group)
+
+    # NOTE(sgm) we don't assert world_size == tp * pp
+    # DP is not managed by vllm but by the VeRL WorkerGroup
+    # if (world_size !=
+    #         tensor_model_parallel_size * pipeline_model_parallel_size):
+    #     raise RuntimeError(
+    #         f"world_size ({world_size}) is not equal to "
+    #         f"tensor_model_parallel_size ({tensor_model_parallel_size}) x "
+    #         f"pipeline_model_parallel_size ({pipeline_model_parallel_size})")
+
+    num_tensor_model_parallel_groups: int = world_size // tensor_model_parallel_size
+
+    global _TP
+    assert _TP is None, "tensor model parallel group is already initialized"
+    group_ranks = []
+    for i in range(num_tensor_model_parallel_groups):
+        ranks = list(range(i * tensor_model_parallel_size, (i + 1) * tensor_model_parallel_size))
+        group_ranks.append(ranks)
+
+    # message queue broadcaster is only used in tensor model parallel group
+    if ps._TP is not None:
+        _TP = ps._TP
+    else:
+        _TP = init_model_parallel_group(
+            group_ranks,
+            get_world_group().local_rank,
+            backend,
+            use_custom_allreduce=False,  # TODO: check why True is not work in Ray trainer
+            use_message_queue_broadcaster=True,
+        )
+        ps._TP = _TP
+
+    # TODO: init using device mesh (not support hybrid engine now)
+    # Build the pipeline model-parallel groups.
+    num_pipeline_model_parallel_groups: int = world_size // pipeline_model_parallel_size
+    global _PP
+    assert _PP is None, "pipeline model parallel group is already initialized"
+    group_ranks = []
+    for i in range(num_pipeline_model_parallel_groups):
+        ranks = list(range(i, world_size, num_pipeline_model_parallel_groups))
+        group_ranks.append(ranks)
+    # pipeline parallel does not need custom allreduce
+    if ps._TP is not None:
+        _PP = ps._TP
+    else:
+        _PP = init_model_parallel_group(group_ranks, get_world_group().local_rank, backend, use_custom_allreduce=False)
+        ps._PP = _PP
+
+
+"""
+Device mesh utilities
+"""
+
+
+def get_device_mesh():
+    assert _DEVICE_MESH is not None, "device mesh is not initialized"
+    return _DEVICE_MESH
+
+
+"""
+Tensor model parallel utilities
+"""
+
+
+# NOTE(linjunrong): In the vllm version parallel_state.py. veRL created its own _TP and _PP as veRL want to use
+# the process group for some extra purpose. Under the hood, there is no difference between them and the original
+# one in vllm.distributed.parallel_state. However, the implementation need to hack the init process of inference
+# engine, as we do not maintain another SGLang here, I just use the original _TP and _PP directly.
+def get_tensor_model_parallel_group():
+    """Get the tensor model parallel group the caller rank belongs to."""
+
+    assert _TP is not None, "tensor model parallel group is not initialized"
+    return _TP.device_group
+
+
+def get_tensor_model_parallel_world_size():
+    """Return world size for the tensor model parallel group."""
+    return torch.distributed.get_world_size(group=get_tensor_model_parallel_group())
+
+
+def get_tensor_model_parallel_rank():
+    """Return my rank for the tensor model parallel group."""
+    return torch.distributed.get_rank(group=get_tensor_model_parallel_group())
+
+
+def get_tensor_model_parallel_src_rank():
+    """Calculate the global rank corresponding to the first local rank
+    in the tensor model parallel group."""
+    global_rank = torch.distributed.get_rank()
+    local_world_size = get_tensor_model_parallel_world_size()
+    return (global_rank // local_world_size) * local_world_size

verl/third_party/vllm/__init__.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from importlib.metadata import version, PackageNotFoundError
+from packaging import version as vs
+from verl.utils.import_utils import is_sglang_available
+
+
+def get_version(pkg):
+    try:
+        return version(pkg)
+    except PackageNotFoundError:
+        return None
+
+
+package_name = 'vllm'
+package_version = get_version(package_name)
+vllm_version = None
+
+if package_version == '0.3.1':
+    vllm_version = '0.3.1'
+    from .vllm_v_0_3_1.llm import LLM
+    from .vllm_v_0_3_1.llm import LLMEngine
+    from .vllm_v_0_3_1 import parallel_state
+elif package_version == '0.4.2':
+    vllm_version = '0.4.2'
+    from .vllm_v_0_4_2.llm import LLM
+    from .vllm_v_0_4_2.llm import LLMEngine
+    from .vllm_v_0_4_2 import parallel_state
+elif package_version == '0.5.4':
+    vllm_version = '0.5.4'
+    from .vllm_v_0_5_4.llm import LLM
+    from .vllm_v_0_5_4.llm import LLMEngine
+    from .vllm_v_0_5_4 import parallel_state
+elif package_version == '0.6.3':
+    vllm_version = '0.6.3'
+    from .vllm_v_0_6_3.llm import LLM
+    from .vllm_v_0_6_3.llm import LLMEngine
+    from .vllm_v_0_6_3 import parallel_state
+elif package_version == '0.6.3+rocm624' or package_version == '0.6.2+das.opt3.dtk2504':
+    vllm_version = '0.6.3'
+    from .vllm_v_0_6_3.llm import LLM
+    from .vllm_v_0_6_3.llm import LLMEngine
+    from .vllm_v_0_6_3 import parallel_state
+elif vs.parse(package_version) >= vs.parse('0.7.0'):
+    # From 0.6.6.post2 on, vllm supports SPMD inference
+    # See https://github.com/vllm-project/vllm/pull/12071
+
+    from vllm import LLM
+    from vllm.distributed import parallel_state
+else:
+    if not is_sglang_available():
+        raise ValueError(
+            f'vllm version {package_version} not supported and SGLang also not Found. Currently supported vllm versions are 0.3.1, 0.4.2, 0.5.4, 0.6.3 and 0.7.0+'
+        )

verl/third_party/vllm/vllm_v_0_3_1/__init__.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

verl/third_party/vllm/vllm_v_0_3_1/arg_utils.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+# Copyright 2023 The vLLM team.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/engine/arg_utils.py
+import argparse
+import dataclasses
+from dataclasses import dataclass
+from typing import Dict, Optional, Tuple
+
+import torch.nn as nn
+from vllm.config import (CacheConfig, DeviceConfig, ModelConfig, ParallelConfig, SchedulerConfig, LoRAConfig)
+from transformers import PretrainedConfig
+from .config import ModelConfig
+
+
+@dataclass
+class EngineArgs:
+    """Arguments for vLLM engine."""
+    model_hf_config: PretrainedConfig = None
+    dtype: str = 'auto'
+    kv_cache_dtype: str = 'auto'
+    seed: int = 0
+    max_model_len: Optional[int] = None
+    worker_use_ray: bool = False
+    pipeline_parallel_size: int = 1
+    tensor_parallel_size: int = 1
+    max_parallel_loading_workers: Optional[int] = None
+    block_size: int = 16
+    swap_space: int = 4  # GiB
+    gpu_memory_utilization: float = 0.90
+    max_num_batched_tokens: Optional[int] = None
+    max_num_seqs: int = 256
+    max_paddings: int = 256
+    disable_log_stats: bool = False
+    revision: Optional[str] = None
+    tokenizer_revision: Optional[str] = None
+    quantization: Optional[str] = None
+    load_format: str = 'model'
+    enforce_eager: bool = False
+    max_context_len_to_capture: int = 8192
+    disable_custom_all_reduce: bool = False
+    enable_lora: bool = False
+    max_loras: int = 1
+    max_lora_rank: int = 16
+    lora_extra_vocab_size: int = 256
+    lora_dtype = 'auto'
+    max_cpu_loras: Optional[int] = None
+    device: str = 'cuda'
+
+    @staticmethod
+    def add_cli_args(parser: argparse.ArgumentParser) -> argparse.ArgumentParser:
+        """Shared CLI arguments for vLLM engine."""
+        # Model arguments
+        # TODO(shengguangming): delete the unused args
+        parser.add_argument('--model',
+                            type=str,
+                            default='facebook/opt-125m',
+                            help='name or path of the huggingface model to use')
+        parser.add_argument('--tokenizer',
+                            type=str,
+                            default=EngineArgs.tokenizer,
+                            help='name or path of the huggingface tokenizer to use')
+        parser.add_argument('--revision',
+                            type=str,
+                            default=None,
+                            help='the specific model version to use. It can be a branch '
+                            'name, a tag name, or a commit id. If unspecified, will use '
+                            'the default version.')
+        parser.add_argument('--tokenizer-revision',
+                            type=str,
+                            default=None,
+                            help='the specific tokenizer version to use. It can be a branch '
+                            'name, a tag name, or a commit id. If unspecified, will use '
+                            'the default version.')
+        parser.add_argument('--tokenizer-mode',
+                            type=str,
+                            default=EngineArgs.tokenizer_mode,
+                            choices=['auto', 'slow'],
+                            help='tokenizer mode. "auto" will use the fast '
+                            'tokenizer if available, and "slow" will '
+                            'always use the slow tokenizer.')
+        parser.add_argument('--trust-remote-code', action='store_true', help='trust remote code from huggingface')
+        parser.add_argument('--download-dir',
+                            type=str,
+                            default=EngineArgs.download_dir,
+                            help='directory to download and load the weights, '
+                            'default to the default cache dir of '
+                            'huggingface')
+        parser.add_argument('--load-format',
+                            type=str,
+                            default=EngineArgs.load_format,
+                            choices=['auto', 'pt', 'safetensors', 'npcache', 'dummy'],
+                            help='The format of the model weights to load. '
+                            '"auto" will try to load the weights in the safetensors format '
+                            'and fall back to the pytorch bin format if safetensors format '
+                            'is not available. '
+                            '"pt" will load the weights in the pytorch bin format. '
+                            '"safetensors" will load the weights in the safetensors format. '
+                            '"npcache" will load the weights in pytorch format and store '
+                            'a numpy cache to speed up the loading. '
+                            '"dummy" will initialize the weights with random values, '
+                            'which is mainly for profiling.')
+        parser.add_argument('--dtype',
+                            type=str,
+                            default=EngineArgs.dtype,
+                            choices=['auto', 'half', 'float16', 'bfloat16', 'float', 'float32'],
+                            help='data type for model weights and activations. '
+                            'The "auto" option will use FP16 precision '
+                            'for FP32 and FP16 models, and BF16 precision '
+                            'for BF16 models.')
+        parser.add_argument('--max-model-len',
+                            type=int,
+                            default=None,
+                            help='model context length. If unspecified, '
+                            'will be automatically derived from the model.')
+        # Parallel arguments
+        parser.add_argument('--worker-use-ray',
+                            action='store_true',
+                            help='use Ray for distributed serving, will be '
+                            'automatically set when using more than 1 GPU')
+        parser.add_argument('--pipeline-parallel-size',
+                            '-pp',
+                            type=int,
+                            default=EngineArgs.pipeline_parallel_size,
+                            help='number of pipeline stages')
+        parser.add_argument('--tensor-parallel-size',
+                            '-tp',
+                            type=int,
+                            default=EngineArgs.tensor_parallel_size,
+                            help='number of tensor parallel replicas')
+        # KV cache arguments
+        parser.add_argument('--block-size',
+                            type=int,
+                            default=EngineArgs.block_size,
+                            choices=[8, 16, 32],
+                            help='token block size')
+        # TODO(woosuk): Support fine-grained seeds (e.g., seed per request).
+        parser.add_argument('--seed', type=int, default=EngineArgs.seed, help='random seed')
+        parser.add_argument('--swap-space',
+                            type=int,
+                            default=EngineArgs.swap_space,
+                            help='CPU swap space size (GiB) per GPU')
+        parser.add_argument('--gpu-memory-utilization',
+                            type=float,
+                            default=EngineArgs.gpu_memory_utilization,
+                            help='the percentage of GPU memory to be used for'
+                            'the model executor')
+        parser.add_argument('--max-num-batched-tokens',
+                            type=int,
+                            default=EngineArgs.max_num_batched_tokens,
+                            help='maximum number of batched tokens per '
+                            'iteration')
+        parser.add_argument('--max-num-seqs',
+                            type=int,
+                            default=EngineArgs.max_num_seqs,
+                            help='maximum number of sequences per iteration')
+        parser.add_argument('--disable-log-stats', action='store_true', help='disable logging statistics')
+        # Quantization settings.
+        parser.add_argument('--quantization',
+                            '-q',
+                            type=str,
+                            choices=['awq', None],
+                            default=None,
+                            help='Method used to quantize the weights')
+        return parser
+
+    @classmethod
+    def from_cli_args(cls, args: argparse.Namespace) -> 'EngineArgs':
+        # Get the list of attributes of this dataclass.
+        attrs = [attr.name for attr in dataclasses.fields(cls)]
+        # Set the attributes from the parsed arguments.
+        engine_args = cls(**{attr: getattr(args, attr) for attr in attrs})
+        return engine_args
+
+    def create_engine_configs(
+        self,
+    ) -> Tuple[ModelConfig, CacheConfig, ParallelConfig, SchedulerConfig]:
+        device_config = DeviceConfig(self.device)
+        model_config = ModelConfig(self.model_hf_config, self.dtype, self.seed, self.load_format, self.revision,
+                                   self.tokenizer_revision, self.max_model_len, self.quantization, self.enforce_eager,
+                                   self.max_context_len_to_capture)
+        cache_config = CacheConfig(self.block_size, self.gpu_memory_utilization, self.swap_space, self.kv_cache_dtype,
+                                   model_config.get_sliding_window())
+        parallel_config = ParallelConfig(self.pipeline_parallel_size, self.tensor_parallel_size, self.worker_use_ray,
+                                         self.max_parallel_loading_workers, self.disable_custom_all_reduce)
+        scheduler_config = SchedulerConfig(self.max_num_batched_tokens, self.max_num_seqs, model_config.max_model_len,
+                                           self.max_paddings)
+        lora_config = LoRAConfig(max_lora_rank=self.max_lora_rank,
+                                 max_loras=self.max_loras,
+                                 lora_extra_vocab_size=self.lora_extra_vocab_size,
+                                 lora_dtype=self.lora_dtype,
+                                 max_cpu_loras=self.max_cpu_loras if self.max_cpu_loras and self.max_cpu_loras > 0 else
+                                 None) if self.enable_lora else None
+        return (model_config, cache_config, parallel_config, scheduler_config, device_config, lora_config)
+
+
+@dataclass
+class AsyncEngineArgs(EngineArgs):
+    """Arguments for asynchronous vLLM engine."""
+    engine_use_ray: bool = False
+    disable_log_requests: bool = False
+    max_log_len: Optional[int] = None
+
+    @staticmethod
+    def add_cli_args(parser: argparse.ArgumentParser) -> argparse.ArgumentParser:
+        parser = EngineArgs.add_cli_args(parser)
+        parser.add_argument('--engine-use-ray',
+                            action='store_true',
+                            help='use Ray to start the LLM engine in a '
+                            'separate process as the server process.')
+        parser.add_argument('--disable-log-requests', action='store_true', help='disable logging requests')
+        parser.add_argument('--max-log-len',
+                            type=int,
+                            default=None,
+                            help='max number of prompt characters or prompt '
+                            'ID numbers being printed in log. '
+                            'Default: unlimited.')
+        return parser

verl/third_party/vllm/vllm_v_0_3_1/config.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+# Copyright 2023 The vLLM team.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/config.py
+
+from typing import Optional, Union, ClassVar
+from dataclasses import dataclass
+import torch
+from transformers import PretrainedConfig
+from packaging.version import Version
+
+from vllm.logger import init_logger
+from vllm.transformers_utils.config import get_config
+from vllm.utils import get_cpu_memory, is_hip, get_nvcc_cuda_version
+
+logger = init_logger(__name__)
+
+_GB = 1 << 30
+
+
+class ModelConfig:
+    """Configuration for the model.
+
+    Args:
+        model: Name or path of the huggingface model to use.
+        tokenizer: Name or path of the huggingface tokenizer to use.
+        tokenizer_mode: Tokenizer mode. "auto" will use the fast tokenizer if
+            available, and "slow" will always use the slow tokenizer.
+        trust_remote_code: Trust remote code (e.g., from HuggingFace) when
+            downloading the model and tokenizer.
+        download_dir: Directory to download and load the weights, default to the
+            default cache directory of huggingface.
+        load_format: The format of the model weights to load:
+            "auto" will try to load the weights in the safetensors format and
+                fall back to the pytorch bin format if safetensors format is
+                not available.
+            "pt" will load the weights in the pytorch bin format.
+            "safetensors" will load the weights in the safetensors format.
+            "npcache" will load the weights in pytorch format and store
+                a numpy cache to speed up the loading.
+            "dummy" will initialize the weights with random values, which is
+                mainly for profiling.
+        dtype: Data type for model weights and activations. The "auto" option
+            will use FP16 precision for FP32 and FP16 models, and BF16 precision
+            for BF16 models.
+        seed: Random seed for reproducibility.
+        revision: The specific model version to use. It can be a branch name,
+            a tag name, or a commit id. If unspecified, will use the default
+            version.
+        tokenizer_revision: The specific tokenizer version to use. It can be a
+            branch name, a tag name, or a commit id. If unspecified, will use
+            the default version.
+        max_model_len: Maximum length of a sequence (including prompt and
+            output). If None, will be derived from the model.
+        quantization: Quantization method that was used to quantize the model
+            weights. If None, we assume the model weights are not quantized.
+        enforce_eager: Whether to enforce eager execution. If True, we will
+            disable CUDA graph and always execute the model in eager mode.
+            If False, we will use CUDA graph and eager execution in hybrid.
+        max_context_len_to_capture: Maximum context len covered by CUDA graphs.
+            When a sequence has context length larger than this, we fall back
+            to eager mode.
+    """
+
+    def __init__(
+        self,
+        hf_config: PretrainedConfig,
+        dtype: str,
+        seed: int,
+        load_format: str = 'model',
+        revision: Optional[str] = None,
+        tokenizer_revision: Optional[str] = None,
+        max_model_len: Optional[int] = None,
+        quantization: Optional[str] = None,
+        trust_remote_code: Optional[bool] = True,
+        enforce_eager: bool = False,
+        max_context_len_to_capture: Optional[int] = None,
+    ) -> None:
+        self.model = hf_config._name_or_path
+        self.tokenizer = hf_config._name_or_path
+        self.load_format = load_format
+        self.seed = seed
+        self.revision = revision
+        self.tokenizer_revision = tokenizer_revision
+        self.quantization = quantization
+        self.trust_remote_code = trust_remote_code
+        self.enforce_eager = enforce_eager
+        self.max_context_len_to_capture = max_context_len_to_capture
+
+        # self.hf_config = get_config(model, trust_remote_code, revision)
+        self.hf_config = hf_config
+        self.dtype = _get_and_verify_dtype(self.hf_config, dtype)
+        self.max_model_len = _get_and_verify_max_len(self.hf_config, max_model_len)
+        # self._verify_load_format()
+        # self._verify_tokenizer_mode()
+        self._verify_quantization()
+        self._verify_cuda_graph()
+
+    def _verify_load_format(self) -> None:
+        load_format = self.load_format.lower()
+        if load_format not in ["auto", "pt", "safetensors", "npcache", "dummy", "model"]:
+            raise ValueError(f"Unknown load format: {self.load_format}. Must be one of "
+                             "'auto', 'pt', 'safetensors', 'npcache', 'dummy' or 'model'.")
+        self.load_format = load_format
+
+    # def _verify_tokenizer_mode(self) -> None:
+    #     tokenizer_mode = self.tokenizer_mode.lower()
+    #     if tokenizer_mode not in ["auto", "slow"]:
+    #         raise ValueError(
+    #             f"Unknown tokenizer mode: {self.tokenizer_mode}. Must be "
+    #             "either 'auto' or 'slow'.")
+    #     self.tokenizer_mode = tokenizer_mode
+
+    def _verify_quantization(self) -> None:
+        supported_quantization = ["awq", "gptq", "squeezellm"]
+        rocm_not_supported_quantization = ["awq", "gptq"]
+        if self.quantization is not None:
+            self.quantization = self.quantization.lower()
+
+        # Parse quantization method from the HF model config, if available.
+        hf_quant_config = getattr(self.hf_config, "quantization_config", None)
+        if hf_quant_config is not None:
+            hf_quant_method = str(hf_quant_config["quant_method"]).lower()
+            if self.quantization is None:
+                self.quantization = hf_quant_method
+            elif self.quantization != hf_quant_method:
+                raise ValueError("Quantization method specified in the model config "
+                                 f"({hf_quant_method}) does not match the quantization "
+                                 f"method specified in the `quantization` argument "
+                                 f"({self.quantization}).")
+
+        if self.quantization is not None:
+            if self.quantization not in supported_quantization:
+                raise ValueError(f"Unknown quantization method: {self.quantization}. Must "
+                                 f"be one of {supported_quantization}.")
+            if is_hip() and self.quantization in rocm_not_supported_quantization:
+                raise ValueError(f"{self.quantization} quantization is currently not supported "
+                                 f"in ROCm.")
+            logger.warning(f"{self.quantization} quantization is not fully "
+                           "optimized yet. The speed can be slower than "
+                           "non-quantized models.")
+
+    def _verify_cuda_graph(self) -> None:
+        if self.max_context_len_to_capture is None:
+            self.max_context_len_to_capture = self.max_model_len
+        self.max_context_len_to_capture = min(self.max_context_len_to_capture, self.max_model_len)
+        if (self.quantization in ["gptq", "squeezellm"] and not self.enforce_eager):
+            # Related issue: https://github.com/vllm-project/vllm/issues/2147
+            logger.warning(f"{self.quantization} does not support CUDA graph "
+                           "yet. Disabling CUDA graph.")
+            self.enforce_eager = True
+
+    def verify_with_parallel_config(
+        self,
+        parallel_config: "ParallelConfig",
+    ) -> None:
+        total_num_attention_heads = self.hf_config.num_attention_heads
+        tensor_parallel_size = parallel_config.tensor_parallel_size
+        if total_num_attention_heads % tensor_parallel_size != 0:
+            raise ValueError(f"Total number of attention heads ({total_num_attention_heads})"
+                             " must be divisible by tensor parallel size "
+                             f"({tensor_parallel_size}).")
+
+        total_num_hidden_layers = self.hf_config.num_hidden_layers
+        pipeline_parallel_size = parallel_config.pipeline_parallel_size
+        if total_num_hidden_layers % pipeline_parallel_size != 0:
+            raise ValueError(f"Total number of hidden layers ({total_num_hidden_layers}) "
+                             "must be divisible by pipeline parallel size "
+                             f"({pipeline_parallel_size}).")
+
+    def get_sliding_window(self) -> Optional[int]:
+        return getattr(self.hf_config, "sliding_window", None)
+
+    def get_vocab_size(self) -> int:
+        return self.hf_config.vocab_size
+
+    def get_hidden_size(self) -> int:
+        return self.hf_config.hidden_size
+
+    def get_head_size(self) -> int:
+        # FIXME(woosuk): This may not be true for all models.
+        return self.hf_config.hidden_size // self.hf_config.num_attention_heads
+
+    def get_total_num_kv_heads(self) -> int:
+        """Returns the total number of KV heads."""
+        # For GPTBigCode & Falcon:
+        # NOTE: for falcon, when new_decoder_architecture is True, the
+        # multi_query flag is ignored and we use n_head_kv for the number of
+        # KV heads.
+        falcon_model_types = ["falcon", "RefinedWeb", "RefinedWebModel"]
+        new_decoder_arch_falcon = (self.hf_config.model_type in falcon_model_types and
+                                   getattr(self.hf_config, "new_decoder_architecture", False))
+        if not new_decoder_arch_falcon and getattr(self.hf_config, "multi_query", False):
+            # Multi-query attention, only one KV head.
+            # Currently, tensor parallelism is not supported in this case.
+            return 1
+
+        attributes = [
+            # For Falcon:
+            "n_head_kv",
+            "num_kv_heads",
+            # For LLaMA-2:
+            "num_key_value_heads",
+            # For ChatGLM:
+            "multi_query_group_num",
+        ]
+        for attr in attributes:
+            num_kv_heads = getattr(self.hf_config, attr, None)
+            if num_kv_heads is not None:
+                return num_kv_heads
+
+        # For non-grouped-query attention models, the number of KV heads is
+        # equal to the number of attention heads.
+        return self.hf_config.num_attention_heads
+
+    def get_num_kv_heads(self, parallel_config: "ParallelConfig") -> int:
+        """Returns the number of KV heads per GPU."""
+        total_num_kv_heads = self.get_total_num_kv_heads()
+        # If tensor parallelism is used, we divide the number of KV heads by
+        # the tensor parallel size. We will replicate the KV heads in the
+        # case where the number of KV heads is smaller than the tensor
+        # parallel size so each GPU has at least one KV head.
+        return max(1, total_num_kv_heads // parallel_config.tensor_parallel_size)
+
+    def get_num_layers(self, parallel_config: "ParallelConfig") -> int:
+        total_num_hidden_layers = self.hf_config.num_hidden_layers
+        return total_num_hidden_layers // parallel_config.pipeline_parallel_size
+
+
+class CacheConfig:
+    """Configuration for the KV cache.
+
+    Args:
+        block_size: Size of a cache block in number of tokens.
+        gpu_memory_utilization: Fraction of GPU memory to use for the
+            vLLM execution.
+        swap_space: Size of the CPU swap space per GPU (in GiB).
+        cache_dtype: Data type for kv cache storage.
+    """
+
+    def __init__(
+        self,
+        block_size: int,
+        gpu_memory_utilization: float,
+        swap_space: int,
+        cache_dtype: str,
+        sliding_window: Optional[int] = None,
+    ) -> None:
+        self.block_size = block_size
+        self.gpu_memory_utilization = gpu_memory_utilization
+        self.swap_space_bytes = swap_space * _GB
+        self.cache_dtype = cache_dtype
+        self.sliding_window = sliding_window
+        self._verify_args()
+        self._verify_cache_dtype()
+
+        # Will be set after profiling.
+        self.num_gpu_blocks = None
+        self.num_cpu_blocks = None
+
+    def _verify_args(self) -> None:
+        if self.gpu_memory_utilization > 1.0:
+            raise ValueError("GPU memory utilization must be less than 1.0. Got "
+                             f"{self.gpu_memory_utilization}.")
+
+    def _verify_cache_dtype(self) -> None:
+        if self.cache_dtype == "auto":
+            pass
+        elif self.cache_dtype == "fp8_e5m2":
+            nvcc_cuda_version = get_nvcc_cuda_version()
+            if nvcc_cuda_version < Version("11.8"):
+                raise ValueError("FP8 is not supported when cuda version is lower than 11.8.")
+            device_name = torch.cuda.get_device_name()
+            if "AMD" in device_name:
+                raise NotImplementedError("FP8_E5M2 KV Cache on AMD GPU has not been supported yet.")
+            logger.info("Using fp8_e5m2 data type to store kv cache. It reduces "
+                        "the GPU memory footprint and boosts the performance. "
+                        "But it may cause slight accuracy drop. "
+                        "Currently we only support fp8 without scaling factors and "
+                        "make e5m2 as a default format.")
+        else:
+            raise ValueError(f"Unknown kv cache dtype: {self.cache_dtype}")
+
+    def verify_with_parallel_config(
+        self,
+        parallel_config: "ParallelConfig",
+    ) -> None:
+        total_cpu_memory = get_cpu_memory()
+        # FIXME(woosuk): Here, it is assumed that the GPUs in a tensor parallel
+        # group are in the same node. However, the GPUs may span multiple nodes.
+        num_gpus_per_node = parallel_config.tensor_parallel_size
+        cpu_memory_usage = self.swap_space_bytes * num_gpus_per_node
+
+        msg = (f"{cpu_memory_usage / _GB:.2f} GiB out of "
+               f"the {total_cpu_memory / _GB:.2f} GiB total CPU memory is "
+               "allocated for the swap space.")
+        if cpu_memory_usage > 0.7 * total_cpu_memory:
+            raise ValueError("Too large swap space. " + msg)
+        elif cpu_memory_usage > 0.4 * total_cpu_memory:
+            logger.warning("Possibly too large swap space. " + msg)
+
+
+class ParallelConfig:
+    """Configuration for the distributed execution.
+
+    Args:
+        pipeline_parallel_size: Number of pipeline parallel groups.
+        tensor_parallel_size: Number of tensor parallel groups.
+        worker_use_ray: Whether to use Ray for model workers. Will be set to
+            True if either pipeline_parallel_size or tensor_parallel_size is
+            greater than 1.
+        max_parallel_loading_workers: Maximum number of multiple batches
+            when load model sequentially. To avoid RAM OOM when using tensor
+            parallel and large models.
+        disable_custom_all_reduce: Disable the custom all-reduce kernel and
+            fall back to NCCL.
+    """
+
+    def __init__(
+        self,
+        pipeline_parallel_size: int,
+        tensor_parallel_size: int,
+        worker_use_ray: bool,
+        max_parallel_loading_workers: Optional[int] = None,
+        disable_custom_all_reduce: bool = False,
+    ) -> None:
+        self.pipeline_parallel_size = pipeline_parallel_size
+        self.tensor_parallel_size = tensor_parallel_size
+        self.worker_use_ray = worker_use_ray
+        self.max_parallel_loading_workers = max_parallel_loading_workers
+        self.disable_custom_all_reduce = disable_custom_all_reduce
+
+        self.world_size = pipeline_parallel_size * tensor_parallel_size
+        if self.world_size > 1:
+            self.worker_use_ray = True
+        self._verify_args()
+
+    def _verify_args(self) -> None:
+        if self.pipeline_parallel_size > 1:
+            raise NotImplementedError("Pipeline parallelism is not supported yet.")
+        if not self.disable_custom_all_reduce and self.world_size > 1:
+            if is_hip():
+                self.disable_custom_all_reduce = True
+                logger.info("Disabled the custom all-reduce kernel because it is not "
+                            "supported on AMD GPUs.")
+            elif self.pipeline_parallel_size > 1:
+                self.disable_custom_all_reduce = True
+                logger.info("Disabled the custom all-reduce kernel because it is not "
+                            "supported with pipeline parallelism.")
+
+        # FIXME(woosuk): Fix the stability issues and re-enable the custom
+        # all-reduce kernel.
+        if not self.disable_custom_all_reduce and self.world_size > 1:
+            self.disable_custom_all_reduce = True
+            logger.info("Custom all-reduce kernels are temporarily disabled due to "
+                        "stability issues. We will re-enable them once the issues are "
+                        "resolved.")
+
+
+class SchedulerConfig:
+    """Scheduler configuration.
+
+    Args:
+        max_num_batched_tokens: Maximum number of tokens to be processed in
+            a single iteration.
+        max_num_seqs: Maximum number of sequences to be processed in a single
+            iteration.
+        max_model_len: Maximum length of a sequence (including prompt
+            and generated text).
+        max_paddings: Maximum number of paddings to be added to a batch.
+    """
+
+    def __init__(
+        self,
+        max_num_batched_tokens: Optional[int],
+        max_num_seqs: int,
+        max_model_len: int,
+        max_paddings: int,
+    ) -> None:
+        if max_num_batched_tokens is not None:
+            self.max_num_batched_tokens = max_num_batched_tokens
+        else:
+            # If max_model_len is too short, use 2048 as the default value for
+            # higher throughput.
+            self.max_num_batched_tokens = max(max_model_len, 2048)
+        self.max_num_seqs = max_num_seqs
+        self.max_model_len = max_model_len
+        self.max_paddings = max_paddings
+        self._verify_args()
+
+    def _verify_args(self) -> None:
+        if self.max_num_batched_tokens < self.max_model_len:
+            raise ValueError(f"max_num_batched_tokens ({self.max_num_batched_tokens}) is "
+                             f"smaller than max_model_len ({self.max_model_len}). "
+                             "This effectively limits the maximum sequence length to "
+                             "max_num_batched_tokens and makes vLLM reject longer "
+                             "sequences. Please increase max_num_batched_tokens or "
+                             "decrease max_model_len.")
+        if self.max_num_batched_tokens < self.max_num_seqs:
+            raise ValueError(f"max_num_batched_tokens ({self.max_num_batched_tokens}) must "
+                             "be greater than or equal to max_num_seqs "
+                             f"({self.max_num_seqs}).")
+
+
+class DeviceConfig:
+
+    def __init__(self, device: str = "cuda") -> None:
+        self.device = torch.device(device)
+
+
+@dataclass
+class LoRAConfig:
+    max_lora_rank: int
+    max_loras: int
+    max_cpu_loras: Optional[int] = None
+    lora_dtype: Optional[torch.dtype] = None
+    lora_extra_vocab_size: int = 256
+    # This is a constant.
+    lora_vocab_padding_size: ClassVar[int] = 256
+
+    def __post_init__(self):
+        # Keep this in sync with csrc/punica/bgmv/bgmv_config.h
+        possible_max_ranks = (8, 16, 32, 64)
+        possible_lora_extra_vocab_size = (0, 256, 512)
+        if self.max_lora_rank not in possible_max_ranks:
+            raise ValueError(f"max_lora_rank ({self.max_lora_rank}) must be one of "
+                             f"{possible_max_ranks}.")
+        if self.lora_extra_vocab_size not in possible_lora_extra_vocab_size:
+            raise ValueError(f"lora_extra_vocab_size ({self.lora_extra_vocab_size}) "
+                             f"must be one of {possible_lora_extra_vocab_size}.")
+        if self.max_loras < 1:
+            raise ValueError(f"max_loras ({self.max_loras}) must be >= 1.")
+        if self.max_cpu_loras is None:
+            self.max_cpu_loras = self.max_loras
+        elif self.max_cpu_loras < self.max_loras:
+            raise ValueError(f"max_cpu_loras ({self.max_cpu_loras}) must be >= "
+                             f"max_loras ({self.max_loras})")
+
+    def verify_with_model_config(self, model_config: ModelConfig):
+        if self.lora_dtype in (None, "auto"):
+            self.lora_dtype = model_config.dtype
+        elif isinstance(self.lora_dtype, str):
+            self.lora_dtype = getattr(torch, self.lora_dtype)
+        if model_config.quantization is not None:
+            raise ValueError("LoRA is not supported with quantized models yet.")
+
+    def verify_with_scheduler_config(self, scheduler_config: SchedulerConfig):
+        if scheduler_config.max_num_batched_tokens > 65528:
+            raise ValueError("Due to limitations of the custom LoRA CUDA kernel, "
+                             "max_num_batched_tokens must be <= 65528 when "
+                             "LoRA is enabled.")
+
+
+_STR_DTYPE_TO_TORCH_DTYPE = {
+    "half": torch.float16,
+    "float16": torch.float16,
+    "float": torch.float32,
+    "float32": torch.float32,
+    "bfloat16": torch.bfloat16,
+}
+
+_ROCM_NOT_SUPPORTED_DTYPE = ["float", "float32"]
+
+
+def _get_and_verify_dtype(
+    config: PretrainedConfig,
+    dtype: Union[str, torch.dtype],
+) -> torch.dtype:
+    # NOTE: getattr(config, "torch_dtype", torch.float32) is not correct
+    # because config.torch_dtype can be None.
+    config_dtype = getattr(config, "torch_dtype", None)
+    if config_dtype is None:
+        config_dtype = torch.float32
+
+    if isinstance(dtype, str):
+        dtype = dtype.lower()
+        if dtype == "auto":
+            if config_dtype == torch.float32:
+                # Following the common practice, we use float16 for float32
+                # models.
+                torch_dtype = torch.float16
+            else:
+                torch_dtype = config_dtype
+        else:
+            if dtype not in _STR_DTYPE_TO_TORCH_DTYPE:
+                raise ValueError(f"Unknown dtype: {dtype}")
+            torch_dtype = _STR_DTYPE_TO_TORCH_DTYPE[dtype]
+    elif isinstance(dtype, torch.dtype):
+        torch_dtype = dtype
+    else:
+        raise ValueError(f"Unknown dtype: {dtype}")
+
+    if is_hip() and torch_dtype == torch.float32:
+        rocm_supported_dtypes = [
+            k for k, v in _STR_DTYPE_TO_TORCH_DTYPE.items() if (k not in _ROCM_NOT_SUPPORTED_DTYPE)
+        ]
+        raise ValueError(f"dtype \'{dtype}\' is not supported in ROCm. "
+                         f"Supported dtypes are {rocm_supported_dtypes}")
+
+    # Verify the dtype.
+    if torch_dtype != config_dtype:
+        if torch_dtype == torch.float32:
+            # Upcasting to float32 is allowed.
+            pass
+        elif config_dtype == torch.float32:
+            # Downcasting from float32 to float16 or bfloat16 is allowed.
+            pass
+        else:
+            # Casting between float16 and bfloat16 is allowed with a warning.
+            logger.warning(f"Casting {config_dtype} to {torch_dtype}.")
+
+    return torch_dtype
+
+
+def _get_and_verify_max_len(
+    hf_config: PretrainedConfig,
+    max_model_len: Optional[int],
+) -> int:
+    """Get and verify the model's maximum length."""
+    derived_max_model_len = float("inf")
+    possible_keys = [
+        # OPT
+        "max_position_embeddings",
+        # GPT-2
+        "n_positions",
+        # MPT
+        "max_seq_len",
+        # ChatGLM2
+        "seq_length",
+        # Others
+        "max_sequence_length",
+        "max_seq_length",
+        "seq_len",
+    ]
+    for key in possible_keys:
+        max_len_key = getattr(hf_config, key, None)
+        if max_len_key is not None:
+            derived_max_model_len = min(derived_max_model_len, max_len_key)
+    if derived_max_model_len == float("inf"):
+        if max_model_len is not None:
+            # If max_model_len is specified, we use it.
+            return max_model_len
+
+        default_max_len = 2048
+        logger.warning("The model's config.json does not contain any of the following "
+                       "keys to determine the original maximum length of the model: "
+                       f"{possible_keys}. Assuming the model's maximum length is "
+                       f"{default_max_len}.")
+        derived_max_model_len = default_max_len
+
+    rope_scaling = getattr(hf_config, "rope_scaling", None)
+    if rope_scaling is not None:
+        assert "factor" in rope_scaling
+        scaling_factor = rope_scaling["factor"]
+        if rope_scaling["type"] == "yarn":
+            derived_max_model_len = rope_scaling["original_max_position_embeddings"]
+        derived_max_model_len *= scaling_factor
+
+    if max_model_len is None:
+        max_model_len = derived_max_model_len
+    elif max_model_len > derived_max_model_len:
+        raise ValueError(f"User-specified max_model_len ({max_model_len}) is greater than "
+                         f"the derived max_model_len ({max_len_key}={derived_max_model_len}"
+                         " in model's config.json). This may lead to incorrect model "
+                         "outputs or CUDA errors. Make sure the value is correct and "
+                         "within the model context size.")
+    return int(max_model_len)

verl/third_party/vllm/vllm_v_0_3_1/llm.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+# Copyright 2023 The vLLM team.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/llm.py
+
+from typing import Dict, List, Optional, Tuple, Union
+
+from tqdm import tqdm
+from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast
+from transformers import PretrainedConfig
+import torch.nn as nn
+from .arg_utils import EngineArgs
+from .llm_engine_sp import LLMEngine
+from vllm.lora.request import LoRARequest
+from vllm.outputs import RequestOutput
+from vllm.sampling_params import SamplingParams
+from vllm.utils import Counter
+import torch
+from torch.nn.utils.rnn import pad_sequence
+from verl.workers.rollout.tokenizer import HybridEngineBaseTokenizer
+
+
+class LLM:
+    """An LLM for generating texts from given prompts and sampling parameters.
+
+    This class includes a tokenizer, a language model (possibly distributed
+    across multiple GPUs), and GPU memory space allocated for intermediate
+    states (aka KV cache). Given a batch of prompts and sampling parameters,
+    this class generates texts from the model, using an intelligent batching
+    mechanism and efficient memory management.
+
+    NOTE: This class is intended to be used for offline inference. For online
+    serving, use the `AsyncLLMEngine` class instead.
+    NOTE: For the comprehensive list of arguments, see `EngineArgs`.
+
+    Args:
+        model: A HuggingFace Transformers model instance.
+        tokenizer: A HuggingFace Transformers tokenizer instance.
+        tokenizer_mode: The tokenizer mode. "auto" will use the fast tokenizer
+            if available, and "slow" will always use the slow tokenizer.
+        trust_remote_code: Trust remote code (e.g., from HuggingFace) when
+            downloading the model and tokenizer.
+        tensor_parallel_size: The number of GPUs to use for distributed
+            execution with tensor parallelism.
+        dtype: The data type for the model weights and activations. Currently,
+            we support `float32`, `float16`, and `bfloat16`. If `auto`, we use
+            the `torch_dtype` attribute specified in the model config file.
+            However, if the `torch_dtype` in the config is `float32`, we will
+            use `float16` instead.
+        quantization: The method used to quantize the model weights. Currently,
+            we support "awq". If None, we assume the model weights are not
+            quantized and use `dtype` to determine the data type of the weights.
+        revision: The specific model version to use. It can be a branch name,
+            a tag name, or a commit id.
+        tokenizer_revision: The specific tokenizer version to use. It can be a
+            branch name, a tag name, or a commit id.
+        seed: The seed to initialize the random number generator for sampling.
+        gpu_memory_utilization: The ratio (between 0 and 1) of GPU memory to
+            reserve for the model weights, activations, and KV cache. Higher
+            values will increase the KV cache size and thus improve the model's
+            throughput. However, if the value is too high, it may cause out-of-
+            memory (OOM) errors.
+        swap_space: The size (GiB) of CPU memory per GPU to use as swap space.
+            This can be used for temporarily storing the states of the requests
+            when their `best_of` sampling parameters are larger than 1. If all
+            requests will have `best_of=1`, you can safely set this to 0.
+            Otherwise, too small values may cause out-of-memory (OOM) errors.
+        enforce_eager: Whether to enforce eager execution. If True, we will
+            disable CUDA graph and always execute the model in eager mode.
+            If False, we will use CUDA graph and eager execution in hybrid.
+        max_context_len_to_capture: Maximum context len covered by CUDA graphs.
+            When a sequence has context length larger than this, we fall back
+            to eager mode.
+        disable_custom_all_reduce: See ParallelConfig
+    """
+
+    def __init__(
+        self,
+        model: Union[nn.Module, Dict], # model itself or its parameter dict
+        tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast, HybridEngineBaseTokenizer],
+        model_hf_config: PretrainedConfig,
+        tokenizer_mode: str = "auto",
+        trust_remote_code: bool = False,
+        tensor_parallel_size: int = 1,
+        dtype: str = "auto",
+        quantization: Optional[str] = None,
+        revision: Optional[str] = None,
+        tokenizer_revision: Optional[str] = None,
+        seed: int = 0,
+        gpu_memory_utilization: float = 0.9,
+        swap_space: int = 4,
+        enforce_eager: bool = False,
+        max_context_len_to_capture: int = 8192,
+        disable_custom_all_reduce: bool = False,
+        **kwargs,
+    ) -> None:
+        if "disable_log_stats" not in kwargs:
+            kwargs["disable_log_stats"] = True
+        engine_args = EngineArgs(
+            model_hf_config=model_hf_config,
+            tensor_parallel_size=tensor_parallel_size,
+            dtype=dtype,
+            quantization=quantization,
+            revision=revision,
+            tokenizer_revision=tokenizer_revision,
+            seed=seed,
+            gpu_memory_utilization=gpu_memory_utilization,
+            swap_space=swap_space,
+            enforce_eager=enforce_eager,
+            max_context_len_to_capture=max_context_len_to_capture,
+            disable_custom_all_reduce=disable_custom_all_reduce,
+            **kwargs,
+        )
+        tokenizer_cls = (PreTrainedTokenizer, PreTrainedTokenizerFast, HybridEngineBaseTokenizer)
+        if not isinstance(tokenizer, tokenizer_cls):
+            raise ValueError(
+                f"Unexpected tokenizer type: {type(tokenizer)}. Must be"
+                "one of the following: PreTrainedTokenizer, PreTrainedTokenizerFast, verl.workers.rollout.HybridEngineBaseTokenizer"
+            )
+        self.llm_engine = LLMEngine.from_engine_args(model, tokenizer, engine_args)
+        self.request_counter = Counter()
+
+    def init_cache_engine(self):
+        self.llm_engine.init_cache_engine()
+
+    def free_cache_engine(self):
+        self.llm_engine.free_cache_engine()
+
+    def get_tokenizer(self) -> Union[PreTrainedTokenizer, PreTrainedTokenizerFast]:
+        return self.llm_engine.tokenizer
+
+    def set_tokenizer(
+        self,
+        tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast],
+    ) -> None:
+        self.llm_engine.tokenizer = tokenizer
+
+    def generate(
+        self,
+        prompts: Optional[Union[str, List[str]]] = None,
+        sampling_params: Optional[SamplingParams] = None,
+        prompt_token_ids: Optional[List[List[int]]] = None,
+        prefix_pos: Optional[Union[int, List[int]]] = None,
+        use_tqdm: bool = True,
+        lora_request: Optional[LoRARequest] = None,
+    ) -> List[RequestOutput]:
+        """Generates the completions for the input prompts.
+
+        NOTE: This class automatically batches the given prompts, considering
+        the memory constraint. For the best performance, put all of your prompts
+        into a single list and pass it to this method.
+
+        Args:
+            prompts: A list of prompts to generate completions for.
+            sampling_params: The sampling parameters for text generation. If
+                None, we use the default sampling parameters.
+            prompt_token_ids: A list of token IDs for the prompts. If None, we
+                use the tokenizer to convert the prompts to token IDs.
+            use_tqdm: Whether to use tqdm to display the progress bar.
+
+        Returns:
+            A list of `RequestOutput` objects containing the generated
+            completions in the same order as the input prompts.
+        """
+        if prompts is None and prompt_token_ids is None:
+            raise ValueError("Either prompts or prompt_token_ids must be "
+                             "provided.")
+        if isinstance(prompts, str):
+            # Convert a single prompt to a list.
+            prompts = [prompts]
+        if prompts is not None and prompt_token_ids is not None:
+            if len(prompts) != len(prompt_token_ids):
+                raise ValueError("The lengths of prompts and prompt_token_ids "
+                                 "must be the same.")
+        if sampling_params is None:
+            # Use default sampling params.
+            sampling_params = SamplingParams()
+
+        # Add requests to the engine.
+        num_requests = len(prompts) if prompts is not None else len(prompt_token_ids)
+        for i in range(num_requests):
+            prompt = prompts[i] if prompts is not None else None
+            prefix_pos_i = prefix_pos[i] if prefix_pos is not None else None
+            token_ids = None if prompt_token_ids is None else prompt_token_ids[i]
+            if not isinstance(token_ids, list):
+                # NOTE(shengguangming): convert the rollout input into List[str]
+                token_ids = self._pre_process_inputs(token_ids)
+            self._add_request(prompt, sampling_params, token_ids, lora_request=lora_request, prefix_pos=prefix_pos_i)
+        return self._run_engine(use_tqdm)
+
+    def _add_request(
+        self,
+        prompt: Optional[str],
+        sampling_params: SamplingParams,
+        prompt_token_ids: Optional[List[int]],
+        lora_request: Optional[LoRARequest] = None,
+        prefix_pos: Optional[int] = None,
+    ) -> None:
+        request_id = str(next(self.request_counter))
+        self.llm_engine.add_request(request_id,
+                                    prompt,
+                                    sampling_params,
+                                    prompt_token_ids,
+                                    lora_request=lora_request,
+                                    prefix_pos=prefix_pos)
+
+    def _run_engine(self, use_tqdm: bool) -> List[RequestOutput]:
+        # Initialize tqdm.
+        if use_tqdm:
+            num_requests = self.llm_engine.get_num_unfinished_requests()
+            pbar = tqdm(total=num_requests, desc="Processed prompts")
+        # Run the engine.
+        outputs: List[RequestOutput] = []
+        while self.llm_engine.has_unfinished_requests():
+            step_outputs = self.llm_engine.step()
+            for output in step_outputs:
+                if output.finished:
+                    outputs.append(output)
+                    if use_tqdm:
+                        pbar.update(1)
+        if use_tqdm:
+            pbar.close()
+        # Sort the outputs by request ID.
+        # This is necessary because some requests may be finished earlier than
+        # its previous requests.
+        outputs = sorted(outputs, key=lambda x: int(x.request_id))
+        # TODO(shengguangming): maybe we can hack the autoregressive logics without only apply post process for better performance
+        return self._post_process_outputs(outputs)
+
+    # NOTE(shengguangming): add for verl
+    # TODO(sgm): we can optimize it by making the dataloader yield List[int] without padding.
+    def _pre_process_inputs(self, prompt_token_ids: torch.Tensor) -> List[int]:
+        # remove the left padding in the prompt token_id
+        pad_token_id = self.llm_engine.tokenizer.pad_token_id if self.llm_engine.tokenizer.pad_token_id is not None else self.llm_engine.tokenizer.eos_token_id
+        non_pad_index = torch.nonzero(prompt_token_ids != pad_token_id, as_tuple=False)[0][0]
+        token_ids = prompt_token_ids[non_pad_index:].tolist()
+        return token_ids
+
+    # NOTE(shengguangming): add for verl
+    def _post_process_outputs(self, outputs: List[RequestOutput]) -> Tuple[torch.Tensor, torch.Tensor]:
+        output_token_ids = []
+        logprobs = []
+        for output in outputs:  # List[RequestOutput]
+            output = output.outputs
+            for output in output:  # List[CompletionOutput], usually len == 1
+                output_token_ids.append(torch.tensor(output.token_ids))
+                # TODO(shengguangming): can be optimzied by rewrite the Sampler._get_logprobs() logits
+                logprobs_dicts = output.logprobs
+                if logprobs_dicts is not None:
+                    logprob = []
+                    for logprobs_dict, id in zip(logprobs_dicts, output.token_ids):
+                        logprob.append(logprobs_dict[id])
+                    logprobs.append(torch.tensor(logprob))
+
+        pad_token_id = self.llm_engine.tokenizer.pad_token_id if self.llm_engine.tokenizer.pad_token_id is not None else self.llm_engine.tokenizer.eos_token_id
+        output_token_ids = pad_sequence(output_token_ids, batch_first=True, padding_value=pad_token_id)
+        if len(logprobs) > 0:
+            logprobs = pad_sequence(logprobs, batch_first=True, padding_value=pad_token_id)
+        return output_token_ids, logprobs
+
+    def sync_model_weights(self, actor_weights: Dict[str, torch.Tensor]) -> None:
+        self.llm_engine.sync_model_weights(actor_weights=actor_weights)
+
+    def offload_model_weights(self) -> None:
+        self.llm_engine.offload_model_weights()

verl/third_party/vllm/vllm_v_0_3_1/llm_engine_sp.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+# Copyright 2023 The vLLM team.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/engine/llm_engine.py
+
+import os
+import socket
+import time
+import torch
+from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple, Union
+
+from vllm.lora.request import LoRARequest
+from vllm.config import (CacheConfig, DeviceConfig, ModelConfig, ParallelConfig, SchedulerConfig, LoRAConfig)
+from vllm.core.scheduler import Scheduler, SchedulerOutputs
+from vllm.logger import init_logger
+from vllm.outputs import RequestOutput
+from vllm.sampling_params import SamplingParams
+from vllm.sequence import (SamplerOutput, Sequence, SequenceGroup, SequenceGroupMetadata, SequenceGroupOutput,
+                           SequenceOutput, SequenceStatus)
+from vllm.transformers_utils.tokenizer import detokenize_incrementally
+from vllm.engine.metrics import StatLogger, Stats
+from vllm.utils import Counter
+import torch.nn as nn
+from .arg_utils import EngineArgs
+from .tokenizer import TokenizerGroup
+
+logger = init_logger(__name__)
+_LOCAL_LOGGING_INTERVAL_SEC = 5
+
+
+class LLMEngine:
+    """An LLM engine that receives requests and generates texts.
+
+    This is the main class for the vLLM engine. It receives requests
+    from clients and generates texts from the LLM. It includes a tokenizer, a
+    language model (possibly distributed across multiple GPUs), and GPU memory
+    space allocated for intermediate states (aka KV cache). This class utilizes
+    iteration-level scheduling and efficient memory management to maximize the
+    serving throughput.
+
+    The `LLM` class wraps this class for offline batched inference and the
+    `AsyncLLMEngine` class wraps this class for online serving.
+
+    NOTE: The config arguments are derived from the `EngineArgs` class. For the
+    comprehensive list of arguments, see `EngineArgs`.
+
+    Args:
+        model_config: The configuration related to the LLM model.
+        cache_config: The configuration related to the KV cache memory
+            management.
+        parallel_config: The configuration related to distributed execution.
+        scheduler_config: The configuration related to the request scheduler.
+        distributed_init_method: The initialization method for distributed
+            execution. See `torch.distributed.init_process_group` for details.
+        placement_group: Ray placement group for distributed execution.
+            Required for distributed execution.
+        log_stats: Whether to log statistics.
+    """
+
+    def __init__(
+        self,
+        model: Union[nn.Module, Dict], # model itself or its parameter dict
+        tokenizer: nn.Module,
+        model_config: ModelConfig,
+        cache_config: CacheConfig,
+        parallel_config: ParallelConfig,
+        scheduler_config: SchedulerConfig,
+        device_config: DeviceConfig,
+        lora_config: Optional[LoRAConfig],
+        distributed_init_method: str,
+        placement_group: Optional[None],
+        log_stats: bool,
+    ) -> None:
+        logger.info("Initializing an LLM engine with config: "
+                    f"model={model_config.model!r}, "
+                    f"tokenizer={model_config.tokenizer!r}, "
+                    # f"tokenizer_mode={model_config.tokenizer_mode}, "
+                    f"revision={model_config.revision}, "
+                    f"tokenizer_revision={model_config.tokenizer_revision}, "
+                    # f"trust_remote_code={model_config.trust_remote_code}, "
+                    f"dtype={model_config.dtype}, "
+                    f"max_seq_len={model_config.max_model_len}, "
+                    # f"download_dir={model_config.download_dir!r}, "
+                    # f"load_format={model_config.load_format}, "
+                    f"disable_custom_all_reduce={parallel_config.disable_custom_all_reduce}, "
+                    f"tensor_parallel_size={parallel_config.tensor_parallel_size}, "
+                    f"quantization={model_config.quantization}, "
+                    f"seed={model_config.seed})")
+        # TODO(woosuk): Print more configs in debug mode.
+
+        self.model_config = model_config  # TODO: currently is hfconfig
+        self.cache_config = cache_config
+        self.lora_config = lora_config
+        assert self.cache_config.sliding_window == getattr(self.model_config.hf_config, "sliding_window", None)
+        self.parallel_config = parallel_config
+        self.scheduler_config = scheduler_config
+        self.device_config = device_config
+        self.log_stats = log_stats
+        self._verify_args()
+
+        # self.model = model # should not store the model, it should be deleted
+        # TODO(shengguangming): maybe we can choose init here or from arguments
+        self._init_tokenizer(tokenizer)
+
+        self.seq_counter = Counter()
+
+        # Create the parallel GPU workers.
+        self._init_workers_sp(model, distributed_init_method)
+
+        # Profile the memory usage and initialize the cache.
+        self._init_cache_sp()
+
+        # Create the scheduler.
+        # NOTE(shengguangming): each process will have independent scheduler
+        self.scheduler = Scheduler(scheduler_config, cache_config, lora_config)
+
+        # Metric Logging.
+        if self.log_stats:
+            self.stat_logger = StatLogger(local_interval=_LOCAL_LOGGING_INTERVAL_SEC)
+
+        # Logging.
+        self.last_logging_time = 0.0
+        # List of (timestamp, num_tokens)
+        self.num_prompt_tokens: List[Tuple[float, int]] = []
+        # List of (timestamp, num_tokens)
+        self.num_generation_tokens: List[Tuple[float, int]] = []
+
+    def _init_tokenizer(self, tokenizer, **tokenizer_init_kwargs):
+        init_kwargs = dict(enable_lora=bool(self.lora_config),
+                           max_num_seqs=self.scheduler_config.max_num_seqs,
+                           max_input_length=None)
+        init_kwargs.update(tokenizer_init_kwargs)
+        self.tokenizer: TokenizerGroup = TokenizerGroup(tokenizer, **init_kwargs)
+
+    # TODO: check get_lora_tokenizer func
+    def get_tokenizer_for_seq(self, sequence: Sequence):
+        return self.tokenizer.get_lora_tokenizer(sequence.lora_request)
+
+    def _init_workers_sp(self, model, distributed_init_method: str):
+        # Lazy import the Worker to avoid importing torch.cuda/xformers
+        # before CUDA_VISIBLE_DEVICES is set in the Worker
+        from .worker import Worker  # pylint: disable=import-outside-toplevel
+
+        rank = int(os.getenv("RANK"))
+
+        self.worker = Worker(
+            model,
+            self.model_config,
+            self.parallel_config,
+            self.scheduler_config,
+            self.device_config,
+            rank,
+            distributed_init_method,
+            lora_config=self.lora_config,
+            kv_cache_dtype=self.cache_config.cache_dtype,
+        )
+
+        # NOTE(shengguangming): torch.distributed.init_process_group will be called inside the init_model()
+        self.worker.init_model()
+        self.worker.load_model()
+
+    def _verify_args(self) -> None:
+        self.model_config.verify_with_parallel_config(self.parallel_config)
+        self.cache_config.verify_with_parallel_config(self.parallel_config)
+
+    def _init_cache_sp(self) -> None:
+        """Profiles the memory usage and initializes the KV cache."""
+        # Get the maximum number of blocks that can be allocated on GPU and CPU.
+        num_blocks = self.worker.profile_num_available_blocks(
+            block_size=self.cache_config.block_size,
+            gpu_memory_utilization=self.cache_config.gpu_memory_utilization,
+            cpu_swap_space=self.cache_config.swap_space_bytes,
+            cache_dtype=self.cache_config.cache_dtype,
+        )
+
+        # NOTE(shengguangming): Now we don't use a shared centralized controler but each process will
+        # have its own scheduler
+        num_gpu_blocks = num_blocks[0]
+        num_cpu_blocks = num_blocks[1]
+
+        # FIXME(woosuk): Change to debug log.
+        logger.info(f"# GPU blocks: {num_gpu_blocks}, "
+                    f"# CPU blocks: {num_cpu_blocks}")
+
+        if num_gpu_blocks <= 0:
+            raise ValueError("No available memory for the cache blocks. "
+                             "Try increasing `gpu_memory_utilization` when "
+                             "initializing the engine.")
+
+        max_seq_len = self.cache_config.block_size * num_gpu_blocks
+        if self.model_config.max_model_len > max_seq_len:
+            raise ValueError(f"The model's max seq len ({self.model_config.max_model_len}) "
+                             "is larger than the maximum number of tokens that can be "
+                             f"stored in KV cache ({max_seq_len}). Try increasing "
+                             "`gpu_memory_utilization` or decreasing `max_model_len` when "
+                             "initializing the engine.")
+
+        self.cache_config.num_gpu_blocks = num_gpu_blocks
+        self.cache_config.num_cpu_blocks = num_cpu_blocks
+
+        # Initialize the cache.
+        self.worker.init_cache_engine(cache_config=self.cache_config)
+        self.worker.warm_up_model()
+
+    def init_cache_engine(self):
+        self.worker.init_cache_engine(cache_config=self.cache_config)
+
+    def free_cache_engine(self):
+        self.worker.free_cache_engine()
+
+    @classmethod
+    def from_engine_args(cls, model, tokenizer, engine_args: EngineArgs) -> "LLMEngine":
+        """Creates an LLM engine from the engine arguments."""
+        # Create the engine configs.
+        engine_configs = engine_args.create_engine_configs()
+        parallel_config = engine_configs[2]
+        # Initialize the cluster.
+        distributed_init_method, placement_group = initialize_cluster(parallel_config)
+        # Create the LLM engine.
+        engine = cls(model,
+                     tokenizer,
+                     *engine_configs,
+                     distributed_init_method,
+                     placement_group,
+                     log_stats=not engine_args.disable_log_stats)
+        return engine
+
+    def add_request(
+        self,
+        request_id: str,
+        prompt: Optional[str],
+        sampling_params: SamplingParams,
+        prompt_token_ids: Optional[List[int]] = None,
+        arrival_time: Optional[float] = None,
+        lora_request: Optional[LoRARequest] = None,
+        prefix_pos: Optional[int] = None,
+    ) -> None:
+        """Add a request to the engine's request pool.
+
+        The request is added to the request pool and will be processed by the
+        scheduler as `engine.step()` is called. The exact scheduling policy is
+        determined by the scheduler.
+
+        Args:
+            request_id: The unique ID of the request.
+            prompt: The prompt string. Can be None if prompt_token_ids is
+                provided.
+            sampling_params: The sampling parameters for text generation.
+            prompt_token_ids: The token IDs of the prompt. If None, we
+                use the tokenizer to convert the prompts to token IDs.
+            arrival_time: The arrival time of the request. If None, we use
+                the current monotonic time.
+            prefix_pos: If not None, we use the given position as the prefix
+                position for each prompt. We will cache the prefix's KV
+                cache and reuse it for the next request with the same prefix.
+                This is an experimental feature, and may be replaced with
+                automatic prefix caching in the future.
+
+        Details:
+            - Set arrival_time to the current time if it is None.
+            - Set prompt_token_ids to the encoded prompt if it is None.
+            - Create `best_of` number of :class:`~vllm.Sequence` objects.
+            - Create a :class:`~vllm.SequenceGroup` object
+              from the list of :class:`~vllm.Sequence`.
+            - Add the :class:`~vllm.SequenceGroup` object to the scheduler.
+
+        Example:
+            >>> # initialize engine
+            >>> engine = LLMEngine.from_engine_args(engine_args)
+            >>> # set request arguments
+            >>> example_prompt = "Who is the president of the United States?"
+            >>> sampling_params = SamplingParams(temperature=0.0)
+            >>> request_id = 0
+            >>>
+            >>> # add the request to the engine
+            >>> engine.add_request(
+            >>>    str(request_id),
+            >>>    example_prompt,
+            >>>    SamplingParams(temperature=0.0))
+            >>> # continue the request processing
+            >>> ...
+        """
+        if lora_request is not None and not self.lora_config:
+            raise ValueError(f"Got lora_request {lora_request} but LoRA is "
+                             "not enabled!")
+        if arrival_time is None:
+            arrival_time = time.monotonic()
+        if prompt_token_ids is None:
+            assert prompt is not None
+            prompt_token_ids = self.tokenizer.encode(prompt)
+
+        # Create the sequences.
+        block_size = self.cache_config.block_size
+        seq_id = next(self.seq_counter)
+        seq = Sequence(seq_id, prompt, prompt_token_ids, block_size, lora_request)
+
+        # Check whether the input specifies prefix
+        prefix = self.scheduler.prefix_pool.add_or_get_prefix(prompt_token_ids[:prefix_pos], lora_request.lora_int_id if
+                                                              lora_request else 0) if prefix_pos is not None else None
+
+        # Create the sequence group.
+        seq_group = SequenceGroup(request_id, [seq], sampling_params, arrival_time, lora_request, prefix)
+
+        # Add the sequence group to the scheduler.
+        self.scheduler.add_seq_group(seq_group)
+
+    def abort_request(self, request_id: Union[str, Iterable[str]]) -> None:
+        """Aborts a request(s) with the given ID.
+
+        Args:
+            request_id: The ID(s) of the request to abort.
+
+        Details:
+            - Refer to the
+              :meth:`~vllm.core.scheduler.Scheduler.abort_seq_group`
+              from class :class:`~vllm.core.scheduler.Scheduler`.
+
+        Example:
+            >>> # initialize engine and add a request with request_id
+            >>> request_id = str(0)
+            >>> # abort the request
+            >>> engine.abort_request(request_id)
+        """
+        self.scheduler.abort_seq_group(request_id)
+
+    def get_model_config(self) -> ModelConfig:
+        """Gets the model configuration."""
+        return self.model_config
+
+    def get_num_unfinished_requests(self) -> int:
+        """Gets the number of unfinished requests."""
+        return self.scheduler.get_num_unfinished_seq_groups()
+
+    def has_unfinished_requests(self) -> bool:
+        """Returns True if there are unfinished requests."""
+        return self.scheduler.has_unfinished_seqs()
+
+    def _check_beam_search_early_stopping(
+        self,
+        early_stopping: Union[bool, str],
+        sampling_params: SamplingParams,
+        best_running_seq: Sequence,
+        current_worst_seq: Sequence,
+    ) -> bool:
+        assert sampling_params.use_beam_search
+        length_penalty = sampling_params.length_penalty
+        if early_stopping is True:
+            return True
+
+        current_worst_score = (current_worst_seq.get_beam_search_score(
+            length_penalty=length_penalty, eos_token_id=self.get_tokenizer_for_seq(current_worst_seq).eos_token_id))
+        if early_stopping is False:
+            highest_attainable_score = (best_running_seq.get_beam_search_score(
+                length_penalty=length_penalty, eos_token_id=self.get_tokenizer_for_seq(best_running_seq).eos_token_id))
+        else:
+            assert early_stopping == "never"
+            if length_penalty > 0.0:
+                # If length_penalty > 0.0, beam search will prefer longer
+                # sequences. The highest attainable score calculation is
+                # based on the longest possible sequence length in this case.
+                max_possible_length = max(best_running_seq.get_prompt_len() + sampling_params.max_tokens,
+                                          self.scheduler_config.max_model_len)
+                highest_attainable_score = (best_running_seq.get_beam_search_score(
+                    length_penalty=length_penalty,
+                    eos_token_id=self.get_tokenizer_for_seq(best_running_seq).eos_token_id,
+                    seq_len=max_possible_length))
+            else:
+                # Otherwise, beam search will prefer shorter sequences. The
+                # highest attainable score calculation is based on the current
+                # sequence length.
+                highest_attainable_score = (best_running_seq.get_beam_search_score(
+                    length_penalty=length_penalty,
+                    eos_token_id=self.get_tokenizer_for_seq(best_running_seq).eos_token_id))
+
+    def _process_sequence_group_outputs(self, seq_group: SequenceGroup, outputs: SequenceGroupOutput) -> None:
+
+        # Process prompt logprobs
+        prompt_logprobs = outputs.prompt_logprobs
+        if prompt_logprobs is not None:
+            seq_group.prompt_logprobs = prompt_logprobs
+
+        # Process samples
+        samples = outputs.samples
+        parent_seqs = seq_group.get_seqs(status=SequenceStatus.RUNNING)
+        existing_finished_seqs = seq_group.get_finished_seqs()
+        parent_child_dict = {parent_seq.seq_id: [] for parent_seq in parent_seqs}
+        for sample in samples:
+            parent_child_dict[sample.parent_seq_id].append(sample)
+        # List of (child, parent)
+        child_seqs: List[Tuple[Sequence, Sequence]] = []
+
+        # Process the child samples for each parent sequence
+        for parent in parent_seqs:
+            child_samples: List[SequenceOutput] = parent_child_dict[parent.seq_id]
+            if len(child_samples) == 0:
+                # This parent sequence has no children samples. Remove
+                # the parent sequence from the sequence group since it will
+                # not be used in the future iterations.
+                parent.status = SequenceStatus.FINISHED_ABORTED
+                seq_group.remove(parent.seq_id)
+                self.scheduler.free_seq(parent)
+                continue
+            # Fork the parent sequence if there are multiple child samples.
+            for child_sample in child_samples[:-1]:
+                new_child_seq_id = next(self.seq_counter)
+                child = parent.fork(new_child_seq_id)
+                child.append_token_id(child_sample.output_token, child_sample.logprobs)
+                child_seqs.append((child, parent))
+            # Continue the parent sequence for the last child sample.
+            # We reuse the parent sequence here to reduce redundant memory
+            # copies, especially when using non-beam search sampling methods.
+            last_child_sample = child_samples[-1]
+            parent.append_token_id(last_child_sample.output_token, last_child_sample.logprobs)
+            child_seqs.append((parent, parent))
+
+        for seq, _ in child_seqs:
+            # self._decode_sequence(seq, seq_group.sampling_params)
+            self._check_stop(seq, seq_group.sampling_params)
+
+        # Non-beam search case
+        if not seq_group.sampling_params.use_beam_search:
+            # For newly created child sequences, add them to the sequence group
+            # and fork them in block manager if they are not finished.
+            for seq, parent in child_seqs:
+                if seq is not parent:
+                    seq_group.add(seq)
+                    if not seq.is_finished():
+                        self.scheduler.fork_seq(parent, seq)
+
+            # Free the finished and selected parent sequences' memory in block
+            # manager. Keep them in the sequence group as candidate output.
+            # NOTE: we need to fork the new sequences before freeing the
+            # old sequences.
+            for seq, parent in child_seqs:
+                if seq is parent and seq.is_finished():
+                    self.scheduler.free_seq(seq)
+            return
+
+        # Beam search case
+        # Select the child sequences to keep in the sequence group.
+        selected_child_seqs = []
+        unselected_child_seqs = []
+        beam_width = seq_group.sampling_params.best_of
+        length_penalty = seq_group.sampling_params.length_penalty
+
+        # Select the newly finished sequences with the highest scores
+        # to replace existing finished sequences.
+        # Tuple of (seq, parent, is_new)
+        existing_finished_seqs = [(seq, None, False) for seq in existing_finished_seqs]
+        new_finished_seqs = [(seq, parent, True) for seq, parent in child_seqs if seq.is_finished()]
+        all_finished_seqs = existing_finished_seqs + new_finished_seqs
+        # Sort the finished sequences by their scores.
+        all_finished_seqs.sort(key=lambda x: x[0].get_beam_search_score(
+            length_penalty=length_penalty, eos_token_id=self.get_tokenizer_for_seq(x[0]).eos_token_id),
+                               reverse=True)
+        for seq, parent, is_new in all_finished_seqs[:beam_width]:
+            if is_new:
+                # A newly generated child sequence finishes and has a high
+                # score, so we will add it into the sequence group.
+                selected_child_seqs.append((seq, parent))
+        for seq, parent, is_new in all_finished_seqs[beam_width:]:
+            if is_new:
+                # A newly generated child sequence finishes but has a low
+                # score, so we will not add it into the sequence group.
+                # Additionally, if this sequence is a continuation of a
+                # parent sequence, we will need remove the parent sequence
+                # from the sequence group.
+                unselected_child_seqs.append((seq, parent))
+            else:
+                # An existing finished sequence has a low score, so we will
+                # remove it from the sequence group.
+                seq_group.remove(seq.seq_id)
+
+        # select the top beam_width sequences from the running
+        # sequences for the next iteration to continue the beam
+        # search.
+        running_child_seqs = [(seq, parent) for seq, parent in child_seqs if not seq.is_finished()]
+        # Sort the running sequences by their scores.
+        running_child_seqs.sort(key=lambda x: x[0].get_beam_search_score(
+            length_penalty=length_penalty, eos_token_id=self.get_tokenizer_for_seq(x[0]).eos_token_id),
+                                reverse=True)
+
+        # Check if we can stop the beam search.
+        if len(running_child_seqs) == 0:
+            # No running sequences, stop the beam search.
+            stop_beam_search = True
+        elif len(all_finished_seqs) < beam_width:
+            # Not enough finished sequences, continue the beam search.
+            stop_beam_search = False
+        else:
+            # Check the early stopping criteria
+            best_running_seq = running_child_seqs[0][0]
+            current_worst_seq = all_finished_seqs[beam_width - 1][0]
+            stop_beam_search = self._check_beam_search_early_stopping(seq_group.sampling_params.early_stopping,
+                                                                      seq_group.sampling_params, best_running_seq,
+                                                                      current_worst_seq)
+
+        if stop_beam_search:
+            # Stop the beam search and remove all the running sequences from
+            # the sequence group.
+            unselected_child_seqs.extend(running_child_seqs)
+        else:
+            # Continue the beam search and select the top beam_width sequences
+            # to continue the beam search.
+            selected_child_seqs.extend(running_child_seqs[:beam_width])
+            # The remaining running sequences will not be used in the next
+            # iteration. Again, if these sequences are continuations of
+            # parent sequences, we will need to remove the parent sequences
+            # from the sequence group.
+            unselected_child_seqs.extend(running_child_seqs[beam_width:])
+
+        # For newly created child sequences, add them to the sequence group
+        # and fork them in block manager if they are not finished.
+        for seq, parent in selected_child_seqs:
+            if seq is not parent:
+                seq_group.add(seq)
+                if not seq.is_finished():
+                    self.scheduler.fork_seq(parent, seq)
+
+        # Free the finished and selected parent sequences' memory in block
+        # manager. Keep them in the sequence group as candidate output.
+        for seq, parent in selected_child_seqs:
+            if seq is parent and seq.is_finished():
+                self.scheduler.free_seq(seq)
+
+        # Remove the unselected parent sequences from the sequence group and
+        # free their memory in block manager.
+        for seq, parent in unselected_child_seqs:
+            if seq is parent:
+                # Remove the parent sequence if it is not selected for next
+                # iteration
+                seq_group.remove(seq.seq_id)
+                self.scheduler.free_seq(seq)
+
+    def _process_model_outputs(self, output: SamplerOutput, scheduler_outputs: SchedulerOutputs) -> List[RequestOutput]:
+        # Update the scheduled sequence groups with the model outputs.
+        scheduled_seq_groups = scheduler_outputs.scheduled_seq_groups
+        for seq_group, outputs in zip(scheduled_seq_groups, output):
+            self._process_sequence_group_outputs(seq_group, outputs)
+
+        # Free the finished sequence groups.
+        self.scheduler.free_finished_seq_groups()
+
+        # Create the outputs.
+        request_outputs: List[RequestOutput] = []
+        for seq_group in scheduled_seq_groups:
+            request_output = RequestOutput.from_seq_group(seq_group)
+            request_outputs.append(request_output)
+        for seq_group in scheduler_outputs.ignored_seq_groups:
+            request_output = RequestOutput.from_seq_group(seq_group)
+            request_outputs.append(request_output)
+
+        # Update prefix state, now all the uncomputed prefixes are computed.
+        for seq_group in scheduled_seq_groups:
+            if (seq_group.prefix is not None and seq_group.prefix.allocated and not seq_group.prefix.computed):
+                seq_group.prefix.computed = True
+
+        # Log stats.
+        if self.log_stats:
+            self.stat_logger.log(self._get_stats(scheduler_outputs))
+
+        return request_outputs
+
+    def step(self) -> List[RequestOutput]:
+        """Performs one decoding iteration and returns newly generated results.
+
+        This function performs one decoding iteration of the engine. It first
+        schedules the sequences to be executed in the next iteration and the
+        token blocks to be swapped in/out/copy. Then, it executes the model
+        and updates the scheduler with the model outputs. Finally, it decodes
+        the sequences and returns the newly generated results.
+        """
+        seq_group_metadata_list, scheduler_outputs = self.scheduler.schedule()
+        if not scheduler_outputs.is_empty():
+            output = self.worker.execute_model(
+                        seq_group_metadata_list=seq_group_metadata_list, # TODO: check this input
+                        blocks_to_swap_in=scheduler_outputs.blocks_to_swap_in,
+                        blocks_to_swap_out=scheduler_outputs.blocks_to_swap_out,
+                        blocks_to_copy=scheduler_outputs.blocks_to_copy,)
+        else:
+            return [RequestOutput.from_seq_group(seq_group) for seq_group in scheduler_outputs.ignored_seq_groups]
+
+        return self._process_model_outputs(output, scheduler_outputs)
+
+    def do_log_stats(self) -> None:
+        """Forced log when no requests active."""
+        if self.log_stats:
+            self.stat_logger.log(self._get_stats(scheduler_outputs=None))
+
+    def _get_stats(self, scheduler_outputs: Optional[SchedulerOutputs]) -> Stats:
+        """Get Stats to be Logged to Prometheus."""
+        now = time.monotonic()
+
+        # KV Cache Usage in %.
+        num_total_gpu = self.cache_config.num_gpu_blocks
+        num_free_gpu = self.scheduler.block_manager.get_num_free_gpu_blocks()
+        gpu_cache_usage = 1.0 - (num_free_gpu / num_total_gpu)
+
+        num_total_cpu = self.cache_config.num_cpu_blocks
+        cpu_cache_usage = 0.
+        if num_total_cpu > 0:
+            num_free_cpu = self.scheduler.block_manager.get_num_free_cpu_blocks()
+            cpu_cache_usage = 1.0 - (num_free_cpu / num_total_cpu)
+
+        # Scheduler State
+        num_running = len(self.scheduler.running)
+        num_swapped = len(self.scheduler.swapped)
+        num_waiting = len(self.scheduler.waiting)
+
+        # Iteration stats if we have scheduler output.
+        num_prompt_tokens = 0
+        num_generation_tokens = 0
+        time_to_first_tokens = []
+        time_per_output_tokens = []
+        time_e2e_requests = []
+        if scheduler_outputs is not None:
+            prompt_run = scheduler_outputs.prompt_run
+
+            # Number of Tokens.
+            if prompt_run:
+                num_prompt_tokens = scheduler_outputs.num_batched_tokens
+            else:
+                num_generation_tokens = scheduler_outputs.num_batched_tokens
+
+            # Latency Timings.
+            time_last_iters = []
+            for seq_group in scheduler_outputs.scheduled_seq_groups:
+                # Time since last token. (n.b. updates seq_group.last_token_time)
+                time_last_iters.append(seq_group.get_last_latency(now))
+                # Time since arrival for all finished requests.
+                if seq_group.is_finished():
+                    time_e2e_requests.append(now - seq_group.arrival_time)
+
+            time_to_first_tokens = time_last_iters if prompt_run else []
+            time_per_output_tokens = [] if prompt_run else time_last_iters
+
+        return Stats(
+            now=now,
+            num_running=num_running,
+            num_swapped=num_swapped,
+            num_waiting=num_waiting,
+            gpu_cache_usage=gpu_cache_usage,
+            cpu_cache_usage=cpu_cache_usage,
+            num_prompt_tokens=num_prompt_tokens,
+            num_generation_tokens=num_generation_tokens,
+            time_to_first_tokens=time_to_first_tokens,
+            time_per_output_tokens=time_per_output_tokens,
+            time_e2e_requests=time_e2e_requests,
+        )
+
+    # TODO: we may not need to decode
+    def _decode_sequence(self, seq: Sequence, prms: SamplingParams) -> None:
+        """Decodes the new token for a sequence."""
+        (new_tokens, new_output_text, prefix_offset, read_offset) = detokenize_incrementally(
+            self.get_tokenizer_for_seq(seq),
+            all_input_ids=seq.get_token_ids(),
+            prev_tokens=seq.tokens,
+            prefix_offset=seq.prefix_offset,
+            read_offset=seq.read_offset,
+            skip_special_tokens=prms.skip_special_tokens,
+            spaces_between_special_tokens=prms.spaces_between_special_tokens,
+        )
+        if seq.tokens is None:
+            seq.tokens = new_tokens
+        else:
+            seq.tokens.extend(new_tokens)
+        seq.prefix_offset = prefix_offset
+        seq.read_offset = read_offset
+        seq.output_text += new_output_text
+
+    def _check_stop(self, seq: Sequence, sampling_params: SamplingParams) -> None:
+        """Stop the finished sequences."""
+        # for stop_str in sampling_params.stop:
+        #     if seq.output_text.endswith(stop_str):
+        #         self._finalize_sequence(seq, sampling_params, stop_str)
+        #         seq.status = SequenceStatus.FINISHED_STOPPED
+        #         return
+        # if seq.get_last_token_id() in sampling_params.stop_token_ids:
+        #     stop_str = self.get_tokenizer_for_seq(seq).convert_ids_to_tokens(seq.get_last_token_id())
+        #     self._finalize_sequence(seq, sampling_params, stop_str)
+        #     seq.status = SequenceStatus.FINISHED_STOPPED
+        #     return
+
+        # Check if the sequence has reached max_model_len.
+        if seq.get_len() > self.scheduler_config.max_model_len:
+            seq.status = SequenceStatus.FINISHED_LENGTH_CAPPED
+            return
+
+        # Check if the sequence has reached max_tokens.
+        if seq.get_output_len() == sampling_params.max_tokens:
+            seq.status = SequenceStatus.FINISHED_LENGTH_CAPPED
+            return
+
+        # Check if the sequence has generated the EOS token.
+        if ((not sampling_params.ignore_eos) and
+                seq.get_last_token_id() == self.get_tokenizer_for_seq(seq).eos_token_id):
+            seq.status = SequenceStatus.FINISHED_STOPPED
+            return
+
+    def _finalize_sequence(self, seq: Sequence, sampling_params: SamplingParams, stop_string: str) -> None:
+        if not sampling_params.include_stop_str_in_output and stop_string:
+            # Truncate the output text so that the stop string is
+            # not included in the output.
+            seq.output_text = seq.output_text[:-len(stop_string)]
+
+    def add_lora(self, lora_request: LoRARequest) -> bool:
+        assert lora_request.lora_int_id > 0, "lora_id must be greater than 0."
+        return self.worker.add_lora(lora_request)
+
+    def remove_lora(self, lora_id: int) -> bool:
+        assert lora_id > 0, "lora_id must be greater than 0."
+        return self.worker.remove_lora(lora_id)
+
+    def list_loras(self) -> List[int]:
+        return self.worker.list_loras()
+
+    def sync_model_weights(self, actor_weights: Dict[str, torch.Tensor]) -> None:
+        self.worker.sync_model_weights(actor_weights=actor_weights)
+
+    def offload_model_weights(self) -> None:
+        self.worker.offload_model_weights()
+
+
+def initialize_cluster(
+    parallel_config: ParallelConfig,
+    engine_use_ray: bool = False,
+    ray_address: Optional[str] = None,
+) -> Tuple[str, Optional[None]]:
+    """Initialize the distributed cluster probably with Ray.
+
+    Args:
+        parallel_config: The configurations for parallel execution.
+        engine_use_ray: Whether to use Ray for async engine.
+        ray_address: The address of the Ray cluster. If None, uses
+            the default Ray cluster address.
+
+    Returns:
+        A tuple of (`distributed_init_method`, `placement_group`). The
+        `distributed_init_method` is the address for initializing the
+        distributed backend. `placement_group` includes the specification
+        of the resources for each distributed worker.
+    """
+
+    # Initialize cluster locally.
+    port = get_open_port()
+    # We need to setup the distributed init method to make sure
+    # the distributed megatron code (e.g., get world size) works correctly.
+    distributed_init_method = f"tcp://localhost:{port}"
+    return distributed_init_method, None
+
+
+def get_open_port():
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+        s.bind(("", 0))
+        return s.getsockname()[1]

verl/third_party/vllm/vllm_v_0_3_1/model_loader.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+# Copyright 2023 The vLLM team.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# Adapted from https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/model_loader
+"""Utilities for selecting and loading models."""
+import contextlib
+from typing import Dict, Type, Union
+
+import torch
+import torch.nn as nn
+from transformers import PretrainedConfig, PreTrainedModel
+from megatron.core.tensor_parallel.utils import VocabUtility
+
+from vllm.model_executor.models import ModelRegistry
+from vllm.model_executor.weight_utils import (get_quant_config, initialize_dummy_weights)
+
+from .config import ModelConfig
+from vllm.config import DeviceConfig, LoRAConfig
+from .weight_loaders import *
+from vllm.model_executor.sampling_metadata import SamplingMetadata, SamplingTensors
+from vllm.sequence import SamplerOutput
+from typing import Optional
+from vllm.model_executor.layers.sampler import Sampler
+from vllm.model_executor.layers.sampler import _prune_hidden_states, _apply_logits_processors, _apply_penalties, _apply_top_k_top_p, _apply_min_p, _apply_penalties, _sample, _get_logprobs, _build_sampler_output
+
+
+@contextlib.contextmanager
+def _set_default_torch_dtype(dtype: torch.dtype):
+    """Sets the default torch dtype to the given dtype."""
+    old_dtype = torch.get_default_dtype()
+    torch.set_default_dtype(dtype)
+    yield
+    torch.set_default_dtype(old_dtype)
+
+
+def _get_model_architecture(config: PretrainedConfig) -> Type[nn.Module]:
+    architectures = getattr(config, "architectures", [])
+    for arch in architectures:
+        model_cls = ModelRegistry.load_model_cls(arch)
+        if model_cls is not None:
+            return model_cls
+    raise ValueError(f"Model architectures {architectures} are not supported for now. "
+                     f"Supported architectures: {ModelRegistry.get_supported_archs()}")
+
+
+from vllm.model_executor.layers.linear import *
+from vllm.model_executor.layers.vocab_parallel_embedding import VocabParallelEmbedding, ParallelLMHead
+from vllm.model_executor.layers.activation import ScaledActivation
+
+__LAYER_WEIGHT_LOADER_REGISTRY__ = {
+    ColumnParallelLinear: parallel_weight_loader,
+    MergedColumnParallelLinear: parallel_weight_loader,
+    QKVParallelLinear: parallel_weight_loader,
+    RowParallelLinear: parallel_weight_loader,
+    VocabParallelEmbedding: parallel_weight_loader,
+    ParallelLMHead: parallel_weight_loader
+    # "ScaledActivation.weight_loader": ScaledActivation, # TODO(shengguangming): latest commit in vllm fix awq for this function and add load_weights
+    # "default_weight_loader": default_weight_loader
+}
+
+# NOTE(gmsheng): change the weight_loader function in runtime
+for layer_class, weight_loader in __LAYER_WEIGHT_LOADER_REGISTRY__.items():
+    layer_class.weight_loader = weight_loader
+
+__MODEL_WEIGHT_LOADER_REGISTRY__ = {
+    'GPT2LMHeadModel': gpt2_weight_loader,
+    'LlamaForCausalLM': llama_weight_loader,
+    'LLaMAForCausalLM': llama_weight_loader,
+    'MistralForCausalLM': mistral_weight_loader,
+}
+
+# FIXME(shengguangming): the vLLM vocab will pad to 64, which may incur out of bounds
+# so we need to rewrite the init function of vocab
+DEFAULT_VOCAB_PADDING_SIZE = 64
+
+
+def vocab_init(self,
+               num_embeddings: int,
+               embedding_dim: int,
+               params_dtype: Optional[torch.dtype] = None,
+               org_num_embeddings: Optional[int] = None,
+               padding_size: int = DEFAULT_VOCAB_PADDING_SIZE):
+    super(VocabParallelEmbedding, self).__init__()
+
+    # Keep the input dimensions.
+    # TODO (pad to be divided by 4)
+    self.num_embeddings = num_embeddings
+    self.org_vocab_size = org_num_embeddings or num_embeddings
+
+    # self.num_embeddings_padded = pad_vocab_size(num_embeddings,
+    #                                             padding_size)
+    self.embedding_dim = embedding_dim
+    if params_dtype is None:
+        params_dtype = torch.get_default_dtype()
+    self.tp_size = get_tensor_model_parallel_world_size()
+    # Divide the weight matrix along the vocaburaly dimension.
+
+    self.vocab_start_index, self.vocab_end_index = (VocabUtility.vocab_range_from_global_vocab_size(
+        self.num_embeddings, get_tensor_model_parallel_rank(), self.tp_size))
+    self.num_embeddings_per_partition = (self.vocab_end_index - self.vocab_start_index)
+    self.weight = Parameter(
+        torch.empty(
+            self.num_embeddings_per_partition,
+            self.embedding_dim,
+            # device=torch.cuda.current_device(),
+            dtype=params_dtype))
+    set_weight_attrs(self.weight, {"parallel_dim": 0, "weight_loader": self.weight_loader})
+
+
+VocabParallelEmbedding.__init__ = vocab_init
+
+
+def _get_model_weight_loader(arch: str):
+    if arch in __MODEL_WEIGHT_LOADER_REGISTRY__:
+        return __MODEL_WEIGHT_LOADER_REGISTRY__[arch]
+    raise ValueError(f"Model architectures {arch} are not supported for now. "
+                     f"Supported architectures: {ModelRegistry.get_supported_archs()}")
+
+
+def get_model(actor_model: Union[PreTrainedModel, Dict],
+              model_config: ModelConfig,
+              device_config: DeviceConfig,
+              lora_config: Optional[LoRAConfig] = None) -> nn.Module:
+    model_class = _get_model_architecture(model_config.hf_config)
+
+    # Get the quantization config.
+    linear_method = None
+    quant_config = None
+    if model_config.quantization is not None:
+        quant_config = get_quant_config(model_config.quantization, model_config.model, model_config.hf_config,
+                                        model_config.download_dir)
+        capability = torch.cuda.get_device_capability()
+        capability = capability[0] * 10 + capability[1]
+        if capability < quant_config.get_min_capability():
+            raise ValueError(f"The quantization method {model_config.quantization} is not "
+                             "supported for the current GPU. "
+                             f"Minimum capability: {quant_config.get_min_capability()}. "
+                             f"Current capability: {capability}.")
+        supported_dtypes = quant_config.get_supported_act_dtypes()
+        if model_config.dtype not in supported_dtypes:
+            raise ValueError(f"{model_config.dtype} is not supported for quantization "
+                             f"method {model_config.quantization}. Supported dtypes: "
+                             f"{supported_dtypes}")
+        linear_method = quant_config.get_linear_method()
+
+    with _set_default_torch_dtype(model_config.dtype):
+        # Create a model instance.
+        # The weights will be initialized as empty tensors.
+        # with torch.device(device_config.device):
+        # NOTE(sgm): init the model in cpu
+        model = model_class(model_config.hf_config, linear_method)
+
+        if model_config.load_format == "dummy":
+            model = model.cuda()
+            # NOTE(woosuk): For accurate performance evaluation, we assign
+            # random values to the weights.
+            initialize_dummy_weights(model)
+        elif model_config.load_format == 'model' or model_config.load_format == 'auto':
+            # NOTE(shengguangming) Load the weights from the actor model
+            if isinstance(actor_model, nn.Module):
+                load_weights(actor_weights=dict(actor_model.named_parameters(remove_duplicate=False)), vllm_model=model)
+            else:
+                load_weights(actor_weights=actor_model, vllm_model=model)
+
+        # NOTE(sgm) Some weights are point to gpu, but still need this.
+        model = model.cuda()  # NOTE (zhangchi.usc1992) We need this for vllm to profile memory usage
+    return model.eval()
+
+
+# the actor model is .state_dict()
+def load_weights(actor_weights: Dict, vllm_model: nn.Module):
+    weight_loader = _get_model_weight_loader(vllm_model.__class__.__name__)
+    weight_loader(actor_weights, vllm_model)
+    # NOTE(sgm) to reduce peak memory usage, we offload vllm model to cpu
+    # after init, and we need this after sync model weights for in first iter.
+    vllm_model = vllm_model.cuda()
+
+
+# FIXME(sgm): hack the Sampler function in vllm v0.3.1
+# as they use ray, the sampler result will only need to return to the driver node,
+# therefore gather is enough. However, we use SPMD instead of a central scheduler,
+# all_gather is required (aligned with v0.2.6)
+def _get_logits(self, hidden_states: torch.Tensor, embedding: torch.Tensor,
+                embedding_bias: Optional[torch.Tensor]) -> torch.Tensor:
+    # Get the logits for the next tokens.
+    logits = torch.matmul(hidden_states, embedding.t())
+    if embedding_bias is not None:
+        logits += embedding_bias
+    logits = tensor_model_parallel_all_gather(logits)
+    # Remove paddings in vocab (if any).
+    if logits is not None:
+        logits = logits[:, :self.org_vocab_size]
+    return logits
+
+
+def forward(
+    self,
+    embedding: torch.Tensor,
+    hidden_states: torch.Tensor,
+    sampling_metadata: SamplingMetadata,
+    embedding_bias: Optional[torch.Tensor] = None,
+) -> Optional[SamplerOutput]:
+    # Get the hidden states that we use for sampling.
+    hidden_states = _prune_hidden_states(hidden_states, sampling_metadata)
+
+    # Get the logits for the next tokens.
+    logits = self._get_logits(hidden_states, embedding, embedding_bias)
+    # save origin logprobs for sampler_output
+    origin_logprobs = torch.log_softmax(logits, dim=-1, dtype=torch.float)
+
+    # Only perform sampling in the driver worker.
+    # Note: `_get_logits` is still distributed across TP workers because
+    # the `embedding` weight is distributed across TP workers.
+    # TODO(zhuohan): Change the get_logits part to a separate stage.
+    if not sampling_metadata.perform_sampling:
+        return None
+
+    assert logits is not None
+    _, vocab_size = logits.shape
+
+    # Apply logits processors (if any).
+    logits = _apply_logits_processors(logits, sampling_metadata)
+
+    # Prepare sampling tensors with pinned memory to avoid blocking.
+    (sampling_tensors, do_penalties, do_top_p_top_k,
+     do_min_p) = SamplingTensors.from_sampling_metadata(sampling_metadata, vocab_size, logits.device, logits.dtype)
+
+    # Apply presence and frequency penalties.
+    if do_penalties:
+        logits = _apply_penalties(logits, sampling_tensors.prompt_tokens, sampling_tensors.output_tokens,
+                                  sampling_tensors.presence_penalties, sampling_tensors.frequency_penalties,
+                                  sampling_tensors.repetition_penalties)
+
+    # Apply temperature scaling.
+    # Use in-place division to avoid creating a new tensor.
+    logits.div_(sampling_tensors.temperatures.unsqueeze_(dim=1))
+
+    if do_top_p_top_k:
+        logits = _apply_top_k_top_p(logits, sampling_tensors.top_ps, sampling_tensors.top_ks)
+
+    if do_min_p:
+        logits = _apply_min_p(logits, sampling_tensors.min_ps)
+
+    # We use float32 for probabilities and log probabilities.
+    # Compute the probabilities.
+    probs = torch.softmax(logits, dim=-1, dtype=torch.float)
+    # Compute the log probabilities.
+    # Use log_softmax to ensure numerical stability.
+    logprobs = torch.log_softmax(logits, dim=-1, dtype=torch.float)
+
+    # Sample the next tokens.
+    sample_results = _sample(probs, logprobs, sampling_metadata)
+
+    # Get the logprobs query results.
+    # prompt_logprobs, sample_logprobs = _get_logprobs(
+    #     logprobs, sampling_metadata, sample_results)
+    prompt_logprobs, sample_logprobs = _get_logprobs(origin_logprobs, sampling_metadata, sample_results)
+
+    return _build_sampler_output(sample_results, sampling_metadata, prompt_logprobs, sample_logprobs)
+
+
+from vllm.model_executor.layers.sampler import Sampler
+
+Sampler._get_logits = _get_logits
+Sampler.forward = forward

verl/third_party/vllm/vllm_v_0_3_1/model_runner.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+# Copyright 2023 The vLLM team.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/worker/model_runner.py
+
+from typing import Dict, List, Optional, Tuple, Set, Union
+import contextlib
+import time
+import numpy as np
+import torch
+import torch.nn as nn
+
+from vllm.config import (DeviceConfig, ModelConfig, LoRAConfig, ParallelConfig, SchedulerConfig)
+from vllm.logger import init_logger
+from vllm.model_executor import InputMetadata, SamplingMetadata
+from vllm.sampling_params import SamplingParams, SamplingType
+from vllm.sequence import SamplerOutput, SequenceData, SequenceGroupMetadata
+from vllm.lora.worker_manager import LRUCacheWorkerLoRAManager
+from vllm.lora.layers import LoRAMapping
+from vllm.lora.request import LoRARequest
+from vllm.utils import in_wsl
+from vllm.worker.model_runner import ModelRunner, CUDAGraphRunner, _async_h2d
+
+from .model_loader import get_model
+
+logger = init_logger(__name__)
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+_PAD_SLOT_ID = -1
+LORA_WARMUP_RANK = 8
+# Capture graphs for batch size 1, 2, 4, 8, 16, 24, 32, 40, ..., 256.
+# NOTE: _get_graph_batch_size needs to be updated if this list is changed.
+_BATCH_SIZES_TO_CAPTURE = [1, 2, 4] + [8 * i for i in range(1, 33)]
+
+
+class ModelRunner(ModelRunner):
+
+    def __init__(
+        self,
+        model: Union[nn.Module, Dict], # model itself or its parameter dict
+        model_config: ModelConfig,
+        parallel_config: ParallelConfig,
+        scheduler_config: SchedulerConfig,
+        device_config: DeviceConfig,
+        lora_config: Optional[LoRAConfig],
+        kv_cache_dtype: Optional[str] = "auto",
+    ):
+        self.model_config = model_config
+        self.parallel_config = parallel_config
+        self.scheduler_config = scheduler_config
+        self.lora_config = lora_config
+
+        # model_config can be None in tests/samplers/test_sampler.py.
+        # FIXME(woosuk): This is a hack to make the tests work. Refactor this.
+        self.sliding_window = (model_config.get_sliding_window() if model_config is not None else None)
+
+        self.device_config = (device_config if device_config is not None else DeviceConfig())
+        self.device = self.device_config.device
+
+        self.model = model  # this will be replaced by get_model()
+        self.block_size = None  # Set after initial profiling.
+        self.lora_manager = None
+
+        self.graph_runners: Dict[int, CUDAGraphRunner] = {}
+        self.graph_memory_pool = None  # Set during graph capture.
+
+        self.max_context_len_to_capture = (self.model_config.max_context_len_to_capture
+                                           if self.model_config is not None else 0)
+        # When using CUDA graph, the input block tables must be padded to
+        # max_context_len_to_capture. However, creating the block table in
+        # Python can be expensive. To optimize this, we cache the block table
+        # in numpy and only copy the actual input content at every iteration.
+        # The shape of the cached block table will be
+        # (max batch size to capture, max context len to capture / block size).
+        self.graph_block_tables = None  # Set after initial profiling.
+        # cache in_wsl result
+        self.in_wsl = in_wsl()
+        self.kv_cache_dtype = kv_cache_dtype
+
+    def load_model(self) -> None:
+        self.model = get_model(actor_model=self.model,
+                               model_config=self.model_config,
+                               device_config=self.device_config,
+                               lora_config=self.lora_config)
+        vocab_size = self.model.config.vocab_size
+
+        if self.lora_config:
+            assert hasattr(
+                self.model,
+                "supported_lora_modules") and self.model.supported_lora_modules, "Model does not support LoRA"
+            assert hasattr(self.model, "embedding_modules"), "Model does not have embedding_modules"
+            assert hasattr(self.model, "embedding_padding_modules"), "Model does not have embedding_padding_modules"
+            self.lora_manager = LRUCacheWorkerLoRAManager(
+                self.scheduler_config.max_num_seqs,
+                self.scheduler_config.max_num_batched_tokens + self.scheduler_config.max_paddings, vocab_size,
+                self.lora_config, self.device, self.model.embedding_modules, self.model.embedding_padding_modules)
+            self.model = self.lora_manager.create_lora_manager(self.model)
+
+    def _prepare_sample(
+        self,
+        seq_group_metadata_list: List[SequenceGroupMetadata],
+        prompt_lens: List[int],
+        subquery_lens: Optional[List[int]],
+    ) -> SamplingMetadata:
+        seq_groups: List[Tuple[List[int], SamplingParams]] = []
+        selected_token_indices: List[int] = []
+        selected_token_start_idx = 0
+        categorized_sample_indices = {t: [] for t in SamplingType}
+        categorized_sample_indices_start_idx = 0
+
+        max_subquery_len = max(subquery_lens) if subquery_lens else 1
+        for i, seq_group_metadata in enumerate(seq_group_metadata_list):
+            seq_ids = list(seq_group_metadata.seq_data.keys())
+            sampling_params = seq_group_metadata.sampling_params
+            seq_groups.append((seq_ids, sampling_params))
+
+            if seq_group_metadata.is_prompt:
+                assert len(seq_ids) == 1
+                assert subquery_lens is not None
+                subquery_len = subquery_lens[i]
+                if sampling_params.prompt_logprobs is not None:
+                    # NOTE: prompt token positions do not need sample, skip
+                    categorized_sample_indices_start_idx += subquery_len - 1
+
+                categorized_sample_indices[sampling_params.sampling_type].append(categorized_sample_indices_start_idx)
+                categorized_sample_indices_start_idx += 1
+
+                if sampling_params.prompt_logprobs is not None:
+                    selected_token_indices.extend(
+                        range(selected_token_start_idx, selected_token_start_idx + subquery_len - 1))
+                selected_token_indices.append(selected_token_start_idx + subquery_len - 1)
+                selected_token_start_idx += max_subquery_len
+            else:
+                num_seqs = len(seq_ids)
+                selected_token_indices.extend(range(selected_token_start_idx, selected_token_start_idx + num_seqs))
+                selected_token_start_idx += num_seqs
+
+                categorized_sample_indices[sampling_params.sampling_type].extend(
+                    range(categorized_sample_indices_start_idx, categorized_sample_indices_start_idx + num_seqs))
+                categorized_sample_indices_start_idx += num_seqs
+
+        selected_token_indices = _async_h2d(selected_token_indices,
+                                            dtype=torch.long,
+                                            target_device=self.device,
+                                            pin_memory=not self.in_wsl)
+        categorized_sample_indices = {
+            t: _async_h2d(seq_ids, dtype=torch.int, target_device=self.device, pin_memory=not self.in_wsl)
+            for t, seq_ids in categorized_sample_indices.items()
+        }
+
+        seq_data: Dict[int, SequenceData] = {}
+        for seq_group_metadata in seq_group_metadata_list:
+            seq_data.update(seq_group_metadata.seq_data)
+
+        sampling_metadata = SamplingMetadata(
+            seq_groups=seq_groups,
+            seq_data=seq_data,
+            prompt_lens=prompt_lens,
+            selected_token_indices=selected_token_indices,
+            categorized_sample_indices=categorized_sample_indices,
+        )
+        return sampling_metadata
+
+    def prepare_input_tensors(
+        self,
+        seq_group_metadata_list: Optional[List[SequenceGroupMetadata]],
+    ) -> Tuple[torch.Tensor, torch.Tensor, InputMetadata, SamplingMetadata, Set[int], LoRAMapping]:
+        # NOTE: We assume that all sequences in the group are all prompts or
+        # all decodes.
+        is_prompt = seq_group_metadata_list[0].is_prompt
+        # Prepare input tensors.
+        if is_prompt:
+            (input_tokens, input_positions, input_metadata, prompt_lens, subquery_lens, lora_index_mapping,
+             lora_prompt_mapping, lora_requests) = self._prepare_prompt(seq_group_metadata_list)
+        else:
+            (input_tokens, input_positions, input_metadata, lora_index_mapping, lora_prompt_mapping,
+             lora_requests) = self._prepare_decode(seq_group_metadata_list)
+            prompt_lens = []
+            subquery_lens = None
+        sampling_metadata = self._prepare_sample(seq_group_metadata_list, prompt_lens, subquery_lens)
+        if self.lora_config:
+            flat_lora_index_mapping = [item for sublist in lora_index_mapping for item in sublist]
+            lora_mapping = LoRAMapping(
+                flat_lora_index_mapping,
+                lora_prompt_mapping,
+            )
+        else:
+            lora_mapping = None
+
+        return (input_tokens, input_positions, input_metadata, sampling_metadata, lora_requests, lora_mapping)
+
+    @torch.inference_mode()
+    def execute_model(
+        self,
+        seq_group_metadata_list: Optional[List[SequenceGroupMetadata]],
+        kv_caches: List[Tuple[torch.Tensor, torch.Tensor]],
+    ) -> Optional[SamplerOutput]:
+        (input_tokens, input_positions, input_metadata, sampling_metadata, lora_requests,
+         lora_mapping) = self.prepare_input_tensors(seq_group_metadata_list)
+
+        if self.lora_config:
+            self.set_active_loras(lora_requests, lora_mapping)
+
+        # Execute the model.
+        if input_metadata.use_cuda_graph:
+            graph_batch_size = input_tokens.shape[0]
+            model_executable = self.graph_runners[graph_batch_size]
+        else:
+            model_executable = self.model
+        hidden_states = model_executable(
+            input_ids=input_tokens,
+            positions=input_positions,
+            kv_caches=kv_caches,
+            input_metadata=input_metadata,
+        )
+
+        # Sample the next token.
+        output = self.model.sample(
+            hidden_states=hidden_states,
+            sampling_metadata=sampling_metadata,
+        )
+        return output
+
+    @torch.inference_mode()
+    def profile_run(self) -> None:
+        # Enable top-k sampling to reflect the accurate memory usage.
+        vocab_size = self.model_config.get_vocab_size()
+        # FIXME(sgm): this sampling params will call cumsum(), causing the
+        # deterministic cumsum throw error
+        sampling_params = SamplingParams(top_p=0.99, top_k=vocab_size - 1)
+        max_num_batched_tokens = self.scheduler_config.max_num_batched_tokens
+        max_num_seqs = self.scheduler_config.max_num_seqs
+
+        # This represents the maximum number of different requests
+        # that will have unique loras, an therefore the max amount of memory
+        # consumption create dummy lora request copies from the lora request
+        # passed in, which contains a lora from the lora warmup path.
+        dummy_lora_requests = []
+        dummy_lora_requests_per_seq = []
+        if self.lora_config:
+            for idx in range(self.lora_config.max_loras):
+                lora_id = idx + 1
+                dummy_lora_request = LoRARequest(
+                    lora_name=f"warmup_{lora_id}",
+                    lora_int_id=lora_id,
+                    lora_local_path="/not/a/real/path",
+                )
+                self.lora_manager.add_dummy_lora(dummy_lora_request, rank=LORA_WARMUP_RANK)
+                dummy_lora_requests.append(dummy_lora_request)
+            dummy_lora_requests_per_seq = [
+                dummy_lora_requests[idx % len(dummy_lora_requests)] for idx in range(max_num_seqs)
+            ]
+
+        # Profile memory usage with max_num_sequences sequences and the total
+        # number of tokens equal to max_num_batched_tokens.
+        seqs: List[SequenceGroupMetadata] = []
+        for group_id in range(max_num_seqs):
+            seq_len = (max_num_batched_tokens // max_num_seqs + (group_id < max_num_batched_tokens % max_num_seqs))
+            seq_data = SequenceData([0] * seq_len)
+            seq = SequenceGroupMetadata(
+                request_id=str(group_id),
+                is_prompt=True,
+                seq_data={group_id: seq_data},
+                sampling_params=sampling_params,
+                block_tables=None,
+                lora_request=dummy_lora_requests_per_seq[group_id] if dummy_lora_requests_per_seq else None,
+            )
+            seqs.append(seq)
+
+        # Run the model with the dummy inputs.
+        num_layers = self.model_config.get_num_layers(self.parallel_config)
+        kv_caches = [(None, None)] * num_layers
+        self.execute_model(seqs, kv_caches)
+        torch.cuda.synchronize()
+        return

verl/third_party/vllm/vllm_v_0_3_1/parallel_state.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+# Copyright 2023 The vLLM team.
+# Adapted from
+# https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/parallel_state.py
+# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
+"""Model and data parallel groups."""
+
+import torch
+import torch.distributed
+
+import vllm.model_executor.parallel_utils.parallel_state as ps
+"""
+This version is strongly tied with Megatron to implement HybridEngine and weight sharing between vllm and Megatron.
+- We assume the Megatron tp+dp+pp world is already established before calling this function.
+
+"""
+
+# Tensor model parallel group that the current rank belongs to.
+_TENSOR_MODEL_PARALLEL_GROUP = None
+
+# Micro Data parallel group. Micro data parallel group is additional dp group that origins from splitting training tp
+# into infer_tp and micro_tp. By default, we use order micro_dp - tp
+_MICRO_DATA_PARALLEL_GROUP = None
+
+
+def initialize_model_parallel_from_megatron(
+        tensor_model_parallel_size=None  # we set None for backward compatibility to set infer_tp = train_tp
+) -> None:
+    from megatron.core import parallel_state as mpu
+    from megatron.distributed import new_group
+    # Get world size and rank. Ensure some consistencies.
+    assert torch.distributed.is_initialized()
+
+    if tensor_model_parallel_size is None:
+        tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size()
+    else:
+        assert isinstance(tensor_model_parallel_size, int)
+
+    # Build the tensor model-parallel groups.
+    assert ps._TENSOR_MODEL_PARALLEL_GROUP is None, ("tensor model parallel group is already initialized")
+
+    assert tensor_model_parallel_size <= mpu.get_tensor_model_parallel_world_size(
+    ), 'Not implemented for infer_tp > train_tp'
+
+    global _TENSOR_MODEL_PARALLEL_GROUP
+    global _MICRO_DATA_PARALLEL_GROUP
+
+    assert mpu.get_tensor_model_parallel_world_size() % tensor_model_parallel_size == 0
+
+    micro_dp_size = mpu.get_tensor_model_parallel_world_size() // tensor_model_parallel_size
+
+    world_size: int = torch.distributed.get_world_size()
+
+    num_micro_dp_groups = world_size // micro_dp_size
+
+    rank = torch.distributed.get_rank()
+
+    # Build the micro dp groups.
+    assert _MICRO_DATA_PARALLEL_GROUP is None, ("micro data parallel group is already initialized")
+    for i in range(num_micro_dp_groups):
+        ranks = range(i * micro_dp_size, (i + 1) * micro_dp_size)
+        group = new_group(rank=rank, ranks=ranks, group_type='micro_dp')
+        if rank in ranks:
+            _MICRO_DATA_PARALLEL_GROUP = group
+
+    if tensor_model_parallel_size == mpu.get_tensor_model_parallel_world_size():
+        # using the same tp group as Megatron
+        ps._TENSOR_MODEL_PARALLEL_GROUP = mpu.get_tensor_model_parallel_group()
+
+        _TENSOR_MODEL_PARALLEL_GROUP = mpu.get_tensor_model_parallel_group()
+        # no _MICRO_DATA_PARALLEL_GROUP
+    else:
+        # initialize a micro_dp group and a tp group
+        # assume training tp=4, infer tp=2, then, weight is partitioned as
+        # [1], [2], [3], [4] for training and [1,2], [1,2], [3,4], [3,4] for inference
+
+        # Build the inference tp groups
+        train_tp = mpu.get_tensor_model_parallel_world_size()
+        num_tensor_model_parallel_groups_per_train_tp = train_tp // tensor_model_parallel_size
+        num_tensor_model_parallel_groups = world_size // tensor_model_parallel_size
+        assert _TENSOR_MODEL_PARALLEL_GROUP is None, ("tensor model parallel group is already initialized")
+        for i in range(num_tensor_model_parallel_groups // num_tensor_model_parallel_groups_per_train_tp):
+            start = train_tp * i
+            end = train_tp * (i + 1)
+            for j in range(num_tensor_model_parallel_groups_per_train_tp):
+                ranks = list(range(start, end, num_tensor_model_parallel_groups_per_train_tp))
+                for i in range(len(ranks)):
+                    ranks[i] += j
+                # group = torch.distributed.new_group(ranks)
+                group = new_group(rank=rank, ranks=ranks, group_type='infer_tp')
+                if rank in ranks:
+                    _TENSOR_MODEL_PARALLEL_GROUP = group
+                    ps._TENSOR_MODEL_PARALLEL_GROUP = _TENSOR_MODEL_PARALLEL_GROUP
+    # Build the pipeline model-parallel groups.
+    # global _PIPELINE_MODEL_PARALLEL_GROUP
+    # global _PIPELINE_GLOBAL_RANKS
+    # assert ps._PIPELINE_MODEL_PARALLEL_GROUP is None, ("pipeline model parallel group is already initialized")
+
+    # ps._PIPELINE_MODEL_PARALLEL_GROUP = mpu.get_pipeline_model_parallel_group()
+    # ps._PIPELINE_GLOBAL_RANKS = mpu.get_pipeline_model_parallel_ranks()
+
+
+"""
+Tensor model parallel utilities
+"""
+
+
+def get_tensor_model_parallel_group():
+    """Get the tensor model parallel group the caller rank belongs to."""
+    assert _TENSOR_MODEL_PARALLEL_GROUP is not None, ("tensor model parallel group is not initialized")
+    return _TENSOR_MODEL_PARALLEL_GROUP
+
+
+def get_tensor_model_parallel_world_size():
+    """Return world size for the tensor model parallel group."""
+    return torch.distributed.get_world_size(group=get_tensor_model_parallel_group())
+
+
+def get_tensor_model_parallel_rank():
+    """Return my rank for the tensor model parallel group."""
+    return torch.distributed.get_rank(group=get_tensor_model_parallel_group())
+
+
+def get_tensor_model_parallel_src_rank():
+    """Calculate the global rank corresponding to the first local rank
+    in the tensor model parallel group."""
+    global_rank = torch.distributed.get_rank()
+    local_world_size = get_tensor_model_parallel_world_size()
+    return (global_rank // local_world_size) * local_world_size
+
+
+"""
+Micro Data parallel group
+"""
+
+
+def get_micro_data_parallel_group():
+    assert _MICRO_DATA_PARALLEL_GROUP is not None
+    return _MICRO_DATA_PARALLEL_GROUP
+
+
+def get_micro_data_parallel_world_size():
+    return torch.distributed.get_world_size(group=get_micro_data_parallel_group())
+
+
+def get_micro_data_parallel_rank():
+    return torch.distributed.get_rank(group=get_micro_data_parallel_group())

verl/third_party/vllm/vllm_v_0_3_1/tokenizer.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+# Copyright 2023 The vLLM team.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/transformers_utils/tokenizer_group/tokenizer_group.py
+
+from typing import List, Optional, Tuple, Union
+
+from transformers import (AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast)
+
+from vllm.lora.request import LoRARequest
+from vllm.utils import make_async, LRUCache
+from vllm.transformers_utils.tokenizers import *
+
+
+class TokenizerGroup:
+    """A group of tokenizers that can be used for LoRA adapters."""
+
+    def __init__(self, tokenizer: PreTrainedTokenizer, enable_lora: bool, max_num_seqs: int,
+                 max_input_length: Optional[int]):
+        self.enable_lora = enable_lora
+        self.max_input_length = max_input_length
+        self.tokenizer = tokenizer
+        if enable_lora:
+            self.lora_tokenizers = LRUCache(capacity=max_num_seqs)
+        else:
+            self.lora_tokenizers = None
+
+    def encode(self,
+               prompt: str,
+               request_id: Optional[str] = None,
+               lora_request: Optional[LoRARequest] = None) -> List[int]:
+        tokenizer = self.get_lora_tokenizer(lora_request)
+        return tokenizer.encode(prompt)
+
+    async def encode_async(self,
+                           prompt: str,
+                           request_id: Optional[str] = None,
+                           lora_request: Optional[LoRARequest] = None) -> List[int]:
+        tokenizer = await self.get_lora_tokenizer_async(lora_request)
+        return tokenizer.encode(prompt)
+
+    def get_lora_tokenizer(self, lora_request: Optional[LoRARequest]) -> "PreTrainedTokenizer":
+        if not lora_request or not self.enable_lora:
+            return self.tokenizer
+        if lora_request.lora_int_id not in self.lora_tokenizers:
+            # TODO(sgm): the lora tokenizer is also passed, but may be different
+            tokenizer = self.tokenizer
+            # tokenizer = (get_lora_tokenizer(
+            #     lora_request, **self.tokenizer_config) or self.tokenizer)
+            self.lora_tokenizers.put(lora_request.lora_int_id, tokenizer)
+            return tokenizer
+        else:
+            return self.lora_tokenizers.get(lora_request.lora_int_id)
+
+    # FIXME(sgm): for simplicity, we assign the special token here
+    @property
+    def pad_token_id(self):
+        return self.tokenizer.pad_token_id
+
+    @property
+    def eos_token_id(self):
+        return self.tokenizer.eos_token_id

verl/third_party/vllm/vllm_v_0_3_1/weight_loaders.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+# Copyright 2023 The vLLM team.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# Adapted from https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/models
+
+from typing import Dict
+import torch
+import torch.nn as nn
+
+
+# NOTE(shengguangming): replace the origin weight loader function in the class
+def parallel_weight_loader(self, param: torch.Tensor, loaded_weight: torch.Tensor) -> None:
+    """Parallel Linear weight loader."""
+    assert param.size() == loaded_weight.size(
+    ), 'the parameter size is not align with the loaded weight size, param size: {}, loaded_weight size: {}'.format(
+        param.size(), loaded_weight.size())
+    assert param.data.dtype == loaded_weight.data.dtype, "if we want to shared weights, the data type should also be the same"
+
+    param.data = loaded_weight.data
+
+
+def default_weight_loader(param: torch.Tensor, loaded_weight: torch.Tensor) -> None:
+    """Default weight loader."""
+    assert param.size() == loaded_weight.size()
+    assert param.data.dtype == loaded_weight.data.dtype, "if we want to shared weights, the data type should also be the same"
+
+    param.data = loaded_weight.data
+
+
+def gpt2_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module:
+    params_dict = dict(vllm_model.named_parameters(remove_duplicate=False))
+    for name, loaded_weight in actor_weights.items():
+        if "lm_head.weight" in name:
+            # GPT-2 ties the weights of the embedding layer and the final
+            # linear layer.
+            continue
+        if ".attn.bias" in name or ".attn.masked_bias" in name:
+            # Skip attention mask.
+            # NOTE: "c_attn.bias" should not be skipped.
+            continue
+        if not name.startswith("transformer."):
+            name = "transformer." + name
+        param = params_dict[name]
+        # The HF's GPT-2 implementation uses Conv1D instead of Linear.
+        # Because of this, we need to transpose the weights.
+        # Note(zhuohan): the logic below might break quantized models.
+        for conv1d_weight_name in ["c_attn", "c_proj", "c_fc"]:
+            if conv1d_weight_name not in name:
+                continue
+            if not name.endswith(".weight"):
+                continue
+            # TODO: check megatron
+            loaded_weight = loaded_weight.t()
+        weight_loader = getattr(param, "weight_loader", default_weight_loader)
+        weight_loader(param, loaded_weight)
+
+
+def llama_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module:
+    # NOTE(shengguangming): the megatron llama may have this prefix
+    prefix = '0.module.module.'
+    params_dict = dict(vllm_model.named_parameters())
+    for name, loaded_weight in actor_weights.items():
+        if name[:len(prefix)] == prefix:
+            name = name[len(prefix):]
+        if "rotary_emb.inv_freq" in name:
+            continue
+        else:
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader", default_weight_loader)
+            weight_loader(param, loaded_weight)
+
+
+def mistral_weight_loader(actor_weights: Dict, vllm_model: nn.Module) -> nn.Module:
+    # TODO: need to implement a general way to deal with prefix
+    prefix = '0.module.module.'
+    params_dict = dict(vllm_model.named_parameters())
+    for name, loaded_weight in actor_weights.items():
+        if name[:len(prefix)] == prefix:
+            name = name[len(prefix):]
+        if "rotary_emb.inv_freq" in name:
+            continue
+        else:
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader", default_weight_loader)
+            weight_loader(param, loaded_weight)

verl/third_party/vllm/vllm_v_0_3_1/worker.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+# Copyright 2023 The vLLM team.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/worker/worker.py
+"""A GPU worker class."""
+import os
+import gc
+from typing import Dict, List, Tuple, Optional, Union, Set
+
+import torch
+import torch.distributed
+import torch.nn as nn
+
+from vllm.config import (CacheConfig, DeviceConfig, ModelConfig, ParallelConfig, SchedulerConfig, LoRAConfig)
+from vllm.model_executor import InputMetadata, set_random_seed
+from vllm.model_executor.parallel_utils.parallel_state import (initialize_model_parallel)
+from vllm.sampling_params import SamplingParams, SamplingType
+from vllm.sequence import SamplerOutput, SequenceData, SequenceGroupMetadata
+from vllm.worker.cache_engine import CacheEngine
+from vllm.model_executor.parallel_utils.custom_all_reduce import init_custom_ar
+from vllm.model_executor.parallel_utils.parallel_state import get_tensor_model_parallel_group
+
+from .model_runner import ModelRunner
+from .model_loader import load_weights
+from .parallel_state import initialize_model_parallel_from_megatron
+from vllm.lora.request import LoRARequest
+
+
+class Worker:
+    """A worker class that executes (a partition of) the model on a GPU.
+
+    Each worker is associated with a single GPU. The worker is responsible for
+    maintaining the KV cache and executing the model on the GPU. In case of
+    distributed inference, each worker is assigned a partition of the model.
+    """
+
+    def __init__(
+        self,
+        model: Union[nn.Module, Dict], # model itself or its parameter dict
+        model_config: ModelConfig,
+        parallel_config: ParallelConfig,
+        scheduler_config: SchedulerConfig,
+        device_config: DeviceConfig,
+        rank: Optional[int] = None,
+        distributed_init_method: Optional[str] = None,
+        lora_config: Optional[LoRAConfig] = None,
+        kv_cache_dtype: Optional[str] = "auto",
+    ) -> None:
+        # self.model = model  # will be replaced in the init_model
+        self.model_config = model_config
+        self.parallel_config = parallel_config
+        self.scheduler_config = scheduler_config
+        self.rank = rank
+        self.distributed_init_method = distributed_init_method
+        self.lora_config = lora_config
+
+        self.model_runner = ModelRunner(
+            model,
+            model_config,
+            parallel_config,
+            scheduler_config,
+            device_config,
+            lora_config=self.lora_config,
+            kv_cache_dtype=kv_cache_dtype,
+        )
+
+        # Uninitialized cache engine. Will be initialized by
+        # self.init_cache_engine().
+        self.cache_config = None
+        self.block_size = None
+        self.sliding_window = None
+        self.cache_engine = None
+        self.cache_events = None
+        self.gpu_cache = None
+
+        # For offloading inference engine params
+        self.cpu_model = None
+
+    def init_model(self, cupy_port: Optional[int] = None):
+        # torch.distributed.all_reduce does not free the input tensor until
+        # the synchronization point. This causes the memory usage to grow
+        # as the number of all_reduce calls increases. This env var disables
+        # this behavior.
+        # Related issue:
+        # https://discuss.pytorch.org/t/cuda-allocation-lifetime-for-inputs-to-distributed-all-reduce/191573
+        os.environ["TORCH_NCCL_AVOID_RECORD_STREAMS"] = "1"
+
+        # Env vars will be set by TORCHRUN.
+        self.rank = self.rank if self.rank is not None else int(os.getenv("RANK", "-1"))
+        local_rank = int(os.getenv("LOCAL_RANK", "0"))
+        self.device = torch.device(f"cuda:{local_rank}")
+        if self.rank < 0:
+            raise ValueError("Invalid or unspecified rank.")
+        torch.cuda.set_device(self.device)
+
+        _check_if_gpu_supports_dtype(self.model_config.dtype)
+
+        # Initialize the distributed environment.
+        # TODO: do not use cupy
+        _init_distributed_environment(self.parallel_config, self.rank, self.distributed_init_method)
+        if not self.parallel_config.disable_custom_all_reduce:
+            init_custom_ar()
+        # Initialize the model.
+        set_random_seed(self.model_config.seed)
+        # self.model = get_model(actor_model=self.model, model_config=self.model_config)
+
+    def load_model(self):
+        self.model_runner.load_model()
+
+    @torch.inference_mode()
+    def profile_num_available_blocks(
+        self,
+        block_size: int,
+        gpu_memory_utilization: float,
+        cpu_swap_space: int,
+        cache_dtype: str,
+    ) -> Tuple[int, int]:
+        # Profile the memory usage of the model and get the maximum number of
+        # cache blocks that can be allocated with the remaining free memory.
+        torch.cuda.empty_cache()
+        # torch.cuda.reset_peak_memory_stats()
+
+        # Execute a forward pass with dummy inputs to profile the memory usage
+        # of the model.
+        self.model_runner.profile_run()
+
+        # Calculate the number of blocks that can be allocated with the
+        # profiled peak memory.
+        torch.cuda.synchronize()
+        free_gpu_memory, total_gpu_memory = torch.cuda.mem_get_info()
+        peak_memory = total_gpu_memory - free_gpu_memory
+
+        cache_block_size = CacheEngine.get_cache_block_size(block_size, cache_dtype, self.model_config,
+                                                            self.parallel_config)
+        # NOTE(sgm) use the remaining memory
+        num_gpu_blocks = int((free_gpu_memory * gpu_memory_utilization) // cache_block_size)
+        # num_gpu_blocks = int((total_gpu_memory * gpu_memory_utilization - peak_memory) // cache_block_size)
+        num_cpu_blocks = int(cpu_swap_space // cache_block_size)
+        num_gpu_blocks = max(num_gpu_blocks, 0)
+        num_cpu_blocks = max(num_cpu_blocks, 0)
+        if self.model_runner.lora_manager:
+            self.model_runner.remove_all_loras()
+        gc.collect()
+        torch.cuda.empty_cache()
+        # Synchronize number of blocks with all the rank
+        num_gpu_blocks = torch.tensor([num_gpu_blocks], device='cuda')
+        num_cpu_blocks = torch.tensor([num_cpu_blocks], device='cuda')
+        torch.distributed.all_reduce(num_gpu_blocks,
+                                     op=torch.distributed.ReduceOp.MIN,
+                                     group=get_tensor_model_parallel_group())
+        torch.distributed.all_reduce(num_cpu_blocks,
+                                     op=torch.distributed.ReduceOp.MIN,
+                                     group=get_tensor_model_parallel_group())
+        num_gpu_blocks = num_gpu_blocks.item()
+        num_cpu_blocks = num_cpu_blocks.item()
+        return num_gpu_blocks, num_cpu_blocks
+
+    def init_cache_engine(self, cache_config: CacheConfig) -> None:
+        if self.cache_engine is None and self.gpu_cache is None:
+            self.cache_config = cache_config
+            self.cache_engine = CacheEngine(self.cache_config, self.model_config, self.parallel_config)
+            self.cache_events = self.cache_engine.events
+            self.gpu_cache = self.cache_engine.gpu_cache
+            self.model_runner.set_block_size(self.cache_engine.block_size)
+
+    def free_cache_engine(self):
+        # ensure `enforce_eager=True`
+        self.cache_engine = None
+        self.gpu_cache = None
+
+    def warm_up_model(self) -> None:
+        if not self.model_config.enforce_eager:
+            self.model_runner.capture_model(self.gpu_cache)
+        # Reset the seed to ensure that the random state is not affected by
+        # the model initialization and profiling.
+        set_random_seed(self.model_config.seed)
+
+    def cache_swap(
+        self,
+        blocks_to_swap_in: Dict[int, int],
+        blocks_to_swap_out: Dict[int, int],
+        blocks_to_copy: Dict[int, List[int]],
+    ) -> None:
+        # Issue cache operations.
+        issued_cache_op = False
+        if blocks_to_swap_in:
+            self.cache_engine.swap_in(blocks_to_swap_in)
+            issued_cache_op = True
+        if blocks_to_swap_out:
+            self.cache_engine.swap_out(blocks_to_swap_out)
+            issued_cache_op = True
+        if blocks_to_copy:
+            self.cache_engine.copy(blocks_to_copy)
+            issued_cache_op = True
+
+        cache_events = self.cache_events if issued_cache_op else None
+
+        # Wait for cache operations to finish.
+        # TODO(woosuk): Profile swapping overhead and optimize if needed.
+        if cache_events is not None:
+            for event in cache_events:
+                event.wait()
+
+    @torch.inference_mode()
+    def execute_model(
+        self,
+        seq_group_metadata_list: List[SequenceGroupMetadata],
+        blocks_to_swap_in: Dict[int, int],
+        blocks_to_swap_out: Dict[int, int],
+        blocks_to_copy: Dict[int, List[int]],
+    ) -> SamplerOutput:
+        num_seq_groups = len(seq_group_metadata_list)
+        self.cache_swap(blocks_to_swap_in, blocks_to_swap_out, blocks_to_copy)
+
+        # If there is no input, we don't need to execute the model.
+        if num_seq_groups == 0:
+            return {}
+        output = self.model_runner.execute_model(seq_group_metadata_list, self.gpu_cache)
+        return output
+
+        # # Prepare input tensors.
+        # # NOTE(shengguangming): currently we pad in our dataloader and unpad it in pre_process_input, j
+        # # we can just input un-padded sequence for better performance
+        # input_tokens, input_positions, input_metadata = self._prepare_inputs(seq_group_metadata_list)
+
+        # # Execute the model.
+        # output = self.model(
+        #     input_ids=input_tokens,
+        #     positions=input_positions,
+        #     kv_caches=self.gpu_cache,
+        #     input_metadata=input_metadata,
+        #     cache_events=cache_events,
+        # )
+        # return output
+
+    # assume the input is .state_dict()
+    def sync_model_weights(self, actor_weights: Dict):
+        load_weights(actor_weights, self.model_runner.model)
+
+    def offload_model_weights(self) -> None:
+        if self.cpu_model == None:
+            self.cpu_model = {}
+            for name, params in self.model_runner.model.named_parameters():
+                self.cpu_model[name] = torch.empty_like(params, device='cpu')
+                params.data = self.cpu_model[name]
+        else:
+            for name, params in self.model_runner.model.named_parameters():
+                params.data = self.cpu_model[name]
+
+    def add_lora(self, lora_request: LoRARequest) -> bool:
+        return self.model_runner.add_lora(lora_request)
+
+    def remove_lora(self, lora_id: int) -> bool:
+        return self.model_runner.remove_lora(lora_id)
+
+    def list_loras(self) -> Set[int]:
+        return self.model_runner.list_loras()
+
+
+def _init_distributed_environment(
+    parallel_config: ParallelConfig,
+    rank: int,
+    distributed_init_method: Optional[str] = None,
+) -> None:
+    """Initialize the distributed environment."""
+    if torch.distributed.is_initialized():
+        print('The distributed environment has been initialized before vLLM')
+    elif not distributed_init_method:
+        raise ValueError("distributed_init_method must be set if torch.distributed "
+                         "is not already initialized")
+    else:
+        torch.distributed.init_process_group(
+            backend="nccl",
+            world_size=parallel_config.world_size,
+            rank=rank,
+            # init_method=distributed_init_method,
+        )
+
+    # A small all_reduce for warmup.
+    torch.distributed.all_reduce(torch.zeros(1).cuda())
+    # TODO (shengguangming): maybe we should also flag the megatron is initialized
+    if torch.distributed.get_world_size() > 1:
+        initialize_model_parallel_from_megatron(tensor_model_parallel_size=parallel_config.tensor_parallel_size)
+    else:
+        initialize_model_parallel()
+
+
+def _pad_to_alignment(x: List[int], multiple_of: int, pad: int) -> List[int]:
+    return x + [pad] * ((-len(x)) % multiple_of)
+
+
+def _pad_to_max(x: List[int], max_len: int, pad: int) -> List[int]:
+    return x + [pad] * (max_len - len(x))
+
+
+def _check_if_gpu_supports_dtype(torch_dtype: torch.dtype):
+    # Check if the GPU supports the dtype.
+    if torch_dtype == torch.bfloat16:
+        compute_capability = torch.cuda.get_device_capability()
+        if compute_capability[0] < 8:
+            gpu_name = torch.cuda.get_device_name()
+            raise ValueError("Bfloat16 is only supported on GPUs with compute capability "
+                             f"of at least 8.0. Your {gpu_name} GPU has compute capability "
+                             f"{compute_capability[0]}.{compute_capability[1]}.")

verl/third_party/vllm/vllm_v_0_4_2/__init__.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

verl/third_party/vllm/vllm_v_0_4_2/arg_utils.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+# Copyright 2023 The vLLM team.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/engine/arg_utils.py
+
+import os
+import argparse
+import dataclasses
+from dataclasses import dataclass
+from typing import List, Optional, Union
+
+import torch.nn as nn
+
+from transformers import PretrainedConfig
+from .config import ModelConfig, LoadConfig
+
+from vllm.config import (CacheConfig, DecodingConfig, DeviceConfig, EngineConfig, LoRAConfig, ParallelConfig,
+                         SchedulerConfig, SpeculativeConfig, TokenizerPoolConfig, VisionLanguageConfig)
+from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
+from vllm.utils import str_to_int_tuple
+
+
+def nullable_str(val: str):
+    if not val or val == "None":
+        return None
+    return val
+
+
+@dataclass
+class EngineArgs:
+    """Arguments for vLLM engine."""
+    model_hf_config: PretrainedConfig = None
+    skip_tokenizer_init: bool = False
+    served_model_name: Optional[Union[str, List[str]]] = None  # TODO
+    download_dir: Optional[str] = None
+    load_format: str = 'auto'
+    dtype: str = 'auto'
+    kv_cache_dtype: str = 'auto'
+    quantization_param_path: Optional[str] = None
+    seed: int = 0
+    max_model_len: Optional[int] = None
+    worker_use_ray: bool = False
+    pipeline_parallel_size: int = 1
+    tensor_parallel_size: int = 1
+    max_parallel_loading_workers: Optional[int] = None
+    block_size: int = 16
+    enable_prefix_caching: bool = False
+    use_v2_block_manager: bool = False
+    swap_space: int = 4  # GiB
+    gpu_memory_utilization: float = 0.90
+    max_num_batched_tokens: Optional[int] = None
+    max_num_seqs: int = 256
+    max_logprobs: int = 5  # OpenAI default value
+    disable_log_stats: bool = False
+    revision: Optional[str] = None
+    code_revision: Optional[str] = None
+    tokenizer_revision: Optional[str] = None
+    quantization: Optional[str] = None
+    enforce_eager: bool = False
+    max_context_len_to_capture: Optional[int] = None
+    max_seq_len_to_capture: int = 8192
+    disable_custom_all_reduce: bool = False
+    tokenizer_pool_size: int = 0
+    tokenizer_pool_type: str = "ray"
+    tokenizer_pool_extra_config: Optional[dict] = None
+    enable_lora: bool = False
+    max_loras: int = 1
+    max_lora_rank: int = 16
+    fully_sharded_loras: bool = False
+    lora_extra_vocab_size: int = 256
+    lora_dtype = 'auto'
+    max_cpu_loras: Optional[int] = None
+    device: str = 'auto'
+    ray_workers_use_nsight: bool = False
+    num_gpu_blocks_override: Optional[int] = None
+    num_lookahead_slots: int = 0
+    model_loader_extra_config: Optional[dict] = None
+
+    # Related to Vision-language models such as llava
+    image_input_type: Optional[str] = None
+    image_token_id: Optional[int] = None
+    image_input_shape: Optional[str] = None
+    image_feature_size: Optional[int] = None
+    scheduler_delay_factor: float = 0.0
+    enable_chunked_prefill: bool = False
+
+    guided_decoding_backend: str = 'outlines'
+    # Speculative decoding configuration.
+    speculative_model: Optional[str] = None
+    num_speculative_tokens: Optional[int] = None
+    speculative_max_model_len: Optional[int] = None
+    ngram_prompt_lookup_max: Optional[int] = None
+    ngram_prompt_lookup_min: Optional[int] = None
+
+    @staticmethod
+    def add_cli_args(parser: argparse.ArgumentParser) -> argparse.ArgumentParser:
+        """Shared CLI arguments for vLLM engine."""
+        # Model arguments
+        # TODO(shengguangming): delete the unused args
+        parser.add_argument('--model',
+                            type=str,
+                            default='facebook/opt-125m',
+                            help='name or path of the huggingface model to use')
+        parser.add_argument('--tokenizer',
+                            type=str,
+                            default=EngineArgs.tokenizer,
+                            help='name or path of the huggingface tokenizer to use')
+        parser.add_argument('--revision',
+                            type=str,
+                            default=None,
+                            help='the specific model version to use. It can be a branch '
+                            'name, a tag name, or a commit id. If unspecified, will use '
+                            'the default version.')
+        parser.add_argument('--tokenizer-revision',
+                            type=str,
+                            default=None,
+                            help='the specific tokenizer version to use. It can be a branch '
+                            'name, a tag name, or a commit id. If unspecified, will use '
+                            'the default version.')
+        parser.add_argument('--tokenizer-mode',
+                            type=str,
+                            default=EngineArgs.tokenizer_mode,
+                            choices=['auto', 'slow'],
+                            help='tokenizer mode. "auto" will use the fast '
+                            'tokenizer if available, and "slow" will '
+                            'always use the slow tokenizer.')
+        parser.add_argument('--trust-remote-code', action='store_true', help='trust remote code from huggingface')
+        parser.add_argument('--download-dir',
+                            type=str,
+                            default=EngineArgs.download_dir,
+                            help='directory to download and load the weights, '
+                            'default to the default cache dir of '
+                            'huggingface')
+        parser.add_argument('--load-format',
+                            type=str,
+                            default=EngineArgs.load_format,
+                            choices=['auto', 'pt', 'safetensors', 'npcache', 'dummy'],
+                            help='The format of the model weights to load. '
+                            '"auto" will try to load the weights in the safetensors format '
+                            'and fall back to the pytorch bin format if safetensors format '
+                            'is not available. '
+                            '"pt" will load the weights in the pytorch bin format. '
+                            '"safetensors" will load the weights in the safetensors format. '
+                            '"npcache" will load the weights in pytorch format and store '
+                            'a numpy cache to speed up the loading. '
+                            '"dummy" will initialize the weights with random values, '
+                            'which is mainly for profiling.')
+        parser.add_argument('--dtype',
+                            type=str,
+                            default=EngineArgs.dtype,
+                            choices=['auto', 'half', 'float16', 'bfloat16', 'float', 'float32'],
+                            help='data type for model weights and activations. '
+                            'The "auto" option will use FP16 precision '
+                            'for FP32 and FP16 models, and BF16 precision '
+                            'for BF16 models.')
+        parser.add_argument('--max-model-len',
+                            type=int,
+                            default=None,
+                            help='model context length. If unspecified, '
+                            'will be automatically derived from the model.')
+        # Parallel arguments
+        parser.add_argument('--worker-use-ray',
+                            action='store_true',
+                            help='use Ray for distributed serving, will be '
+                            'automatically set when using more than 1 GPU')
+        parser.add_argument('--pipeline-parallel-size',
+                            '-pp',
+                            type=int,
+                            default=EngineArgs.pipeline_parallel_size,
+                            help='number of pipeline stages')
+        parser.add_argument('--tensor-parallel-size',
+                            '-tp',
+                            type=int,
+                            default=EngineArgs.tensor_parallel_size,
+                            help='number of tensor parallel replicas')
+        # KV cache arguments
+        parser.add_argument('--block-size',
+                            type=int,
+                            default=EngineArgs.block_size,
+                            choices=[8, 16, 32],
+                            help='token block size')
+        # TODO(woosuk): Support fine-grained seeds (e.g., seed per request).
+        parser.add_argument('--seed', type=int, default=EngineArgs.seed, help='random seed')
+        parser.add_argument('--swap-space',
+                            type=int,
+                            default=EngineArgs.swap_space,
+                            help='CPU swap space size (GiB) per GPU')
+        parser.add_argument('--gpu-memory-utilization',
+                            type=float,
+                            default=EngineArgs.gpu_memory_utilization,
+                            help='the percentage of GPU memory to be used for'
+                            'the model executor')
+        parser.add_argument('--max-num-batched-tokens',
+                            type=int,
+                            default=EngineArgs.max_num_batched_tokens,
+                            help='maximum number of batched tokens per '
+                            'iteration')
+        parser.add_argument('--max-num-seqs',
+                            type=int,
+                            default=EngineArgs.max_num_seqs,
+                            help='maximum number of sequences per iteration')
+        parser.add_argument('--disable-log-stats', action='store_true', help='disable logging statistics')
+        # Quantization settings.
+        parser.add_argument('--quantization',
+                            '-q',
+                            type=str,
+                            choices=['awq', None],
+                            default=None,
+                            help='Method used to quantize the weights')
+        return parser
+
+    @classmethod
+    def from_cli_args(cls, args: argparse.Namespace) -> 'EngineArgs':
+        # Get the list of attributes of this dataclass.
+        attrs = [attr.name for attr in dataclasses.fields(cls)]
+        # Set the attributes from the parsed arguments.
+        engine_args = cls(**{attr: getattr(args, attr) for attr in attrs})
+        return engine_args
+
+    def create_engine_config(
+        self,
+    ) -> EngineConfig:
+        device_config = DeviceConfig(self.device)
+        # NOTE(sgm): we only modify ModelConfig, other configs are import from vllm
+        model_config = ModelConfig(self.model_hf_config, self.dtype, self.seed, self.revision, self.code_revision,
+                                   self.tokenizer_revision, self.max_model_len, self.quantization,
+                                   self.quantization_param_path, self.enforce_eager, self.max_context_len_to_capture,
+                                   self.max_seq_len_to_capture, self.max_logprobs, self.skip_tokenizer_init,
+                                   self.served_model_name)
+        cache_config = CacheConfig(self.block_size, self.gpu_memory_utilization,
+                                   self.swap_space, self.kv_cache_dtype, self.num_gpu_blocks_override,
+                                   model_config.get_sliding_window(), self.enable_prefix_caching)
+        parallel_config = ParallelConfig(
+            self.pipeline_parallel_size, self.tensor_parallel_size, self.worker_use_ray,
+            self.max_parallel_loading_workers, self.disable_custom_all_reduce,
+            TokenizerPoolConfig.create_config(
+                self.tokenizer_pool_size,
+                self.tokenizer_pool_type,
+                self.tokenizer_pool_extra_config,
+            ), self.ray_workers_use_nsight)
+
+        # Use the world_size set by TORCHRUN
+        world_size = int(os.getenv("WORLD_SIZE", "-1"))
+        assert world_size != -1, "The world_size is set to -1, not initialized by TORCHRUN"
+        parallel_config.world_size = world_size
+
+        # TODO: spec config
+        speculative_config = SpeculativeConfig.maybe_create_spec_config(
+            target_model_config=model_config,
+            target_parallel_config=parallel_config,
+            target_dtype=self.dtype,
+            speculative_model=self.speculative_model,
+            num_speculative_tokens=self.num_speculative_tokens,
+            speculative_max_model_len=self.speculative_max_model_len,
+            enable_chunked_prefill=self.enable_chunked_prefill,
+            use_v2_block_manager=self.use_v2_block_manager,
+            ngram_prompt_lookup_max=self.ngram_prompt_lookup_max,
+            ngram_prompt_lookup_min=self.ngram_prompt_lookup_min,
+        )
+
+        scheduler_config = SchedulerConfig(
+            self.max_num_batched_tokens,
+            self.max_num_seqs,
+            model_config.max_model_len,
+            self.use_v2_block_manager,
+            num_lookahead_slots=(self.num_lookahead_slots
+                                 if speculative_config is None else speculative_config.num_lookahead_slots),
+            delay_factor=self.scheduler_delay_factor,
+            enable_chunked_prefill=self.enable_chunked_prefill,
+        )
+
+        lora_config = LoRAConfig(max_lora_rank=self.max_lora_rank,
+                                 max_loras=self.max_loras,
+                                 fully_sharded_loras=self.fully_sharded_loras,
+                                 lora_extra_vocab_size=self.lora_extra_vocab_size,
+                                 lora_dtype=self.lora_dtype,
+                                 max_cpu_loras=self.max_cpu_loras if self.max_cpu_loras and self.max_cpu_loras > 0 else
+                                 None) if self.enable_lora else None
+
+        load_config = LoadConfig(
+            load_format=self.load_format,
+            download_dir=self.download_dir,
+            model_loader_extra_config=self.model_loader_extra_config,
+        )
+
+        if self.image_input_type:
+            if (not self.image_token_id or not self.image_input_shape or not self.image_feature_size):
+                raise ValueError('Specify `image_token_id`, `image_input_shape` and '
+                                 '`image_feature_size` together with `image_input_type`.')
+            vision_language_config = VisionLanguageConfig(
+                image_input_type=VisionLanguageConfig.get_image_input_enum_type(self.image_input_type),
+                image_token_id=self.image_token_id,
+                image_input_shape=str_to_int_tuple(self.image_input_shape),
+                image_feature_size=self.image_feature_size,
+            )
+        else:
+            vision_language_config = None
+
+        decoding_config = DecodingConfig(guided_decoding_backend=self.guided_decoding_backend)
+
+        return EngineConfig(model_config=model_config,
+                            cache_config=cache_config,
+                            parallel_config=parallel_config,
+                            scheduler_config=scheduler_config,
+                            device_config=device_config,
+                            lora_config=lora_config,
+                            vision_language_config=vision_language_config,
+                            speculative_config=speculative_config,
+                            load_config=load_config,
+                            decoding_config=decoding_config)

verl/third_party/vllm/vllm_v_0_4_2/config.py

+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+# Copyright 2023 The vLLM team.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/config.py
+
+import enum
+import json
+from typing import List, Optional, Union
+from dataclasses import dataclass, field, fields
+
+from transformers import PretrainedConfig
+
+from vllm.logger import init_logger
+from vllm.model_executor.layers.quantization import get_quantization_config
+from vllm.transformers_utils.config import get_hf_text_config
+from vllm.utils import is_hip
+# Add for verl
+from vllm.config import ModelConfig, _get_and_verify_dtype, _get_and_verify_max_len
+
+GPTQMarlinConfig = get_quantization_config("gptq_marlin")
+
+logger = init_logger(__name__)
+
+_GB = 1 << 30
+
+
+class ModelConfig(ModelConfig):
+    """Configuration for the model.
+
+    Args:
+        model: Name or path of the huggingface model to use.
+        tokenizer: Name or path of the huggingface tokenizer to use.
+        tokenizer_mode: Tokenizer mode. "auto" will use the fast tokenizer if
+            available, and "slow" will always use the slow tokenizer.
+        trust_remote_code: Trust remote code (e.g., from HuggingFace) when
+            downloading the model and tokenizer.
+        download_dir: Directory to download and load the weights, default to the
+            default cache directory of huggingface.
+        load_format: The format of the model weights to load:
+            "auto" will try to load the weights in the safetensors format and
+                fall back to the pytorch bin format if safetensors format is
+                not available.
+            "pt" will load the weights in the pytorch bin format.
+            "safetensors" will load the weights in the safetensors format.
+            "npcache" will load the weights in pytorch format and store
+                a numpy cache to speed up the loading.
+            "dummy" will initialize the weights with random values, which is
+                mainly for profiling.
+        dtype: Data type for model weights and activations. The "auto" option
+            will use FP16 precision for FP32 and FP16 models, and BF16 precision
+            for BF16 models.
+        seed: Random seed for reproducibility.
+        revision: The specific model version to use. It can be a branch name,
+            a tag name, or a commit id. If unspecified, will use the default
+            version.
+        code_revision: The specific revision to use for the model code on
+            Hugging Face Hub. It can be a branch name, a tag name, or a
+            commit id. If unspecified, will use the default version.
+        tokenizer_revision: The specific tokenizer version to use. It can be a
+            branch name, a tag name, or a commit id. If unspecified, will use
+            the default version.
+        max_model_len: Maximum length of a sequence (including prompt and
+            output). If None, will be derived from the model.
+        quantization: Quantization method that was used to quantize the model
+            weights. If None, we assume the model weights are not quantized.
+        quantization_param_path: Path to JSON file containing scaling factors.
+            Used to load KV cache scaling factors into the model when KV cache
+            type is FP8_E4M3 on ROCm (AMD GPU). In the future these will also
+            be used to load activation and weight scaling factors when the
+            model dtype is FP8_E4M3 on ROCm.
+        enforce_eager: Whether to enforce eager execution. If True, we will
+            disable CUDA graph and always execute the model in eager mode.
+            If False, we will use CUDA graph and eager execution in hybrid.
+        max_context_len_to_capture: Maximum context len covered by CUDA graphs.
+            When a sequence has context length larger than this, we fall back
+            to eager mode (DEPRECATED. Use max_seq_len_to_capture instead).
+        max_seq_len_to_capture: Maximum sequence len covered by CUDA graphs.
+            When a sequence has context length larger than this, we fall back
+            to eager mode
+        skip_tokenizer_init: If true, skip initialization of tokenizer and
+            detokenizer.
+        served_model_name: The model name used in metrics tag `model_name`,
+            matches the model name exposed via the APIs. If multiple model 
+            names provided, the first name will be used. If not specified, 
+            the model name will be the same as `model`.
+    """
+
+    def __init__(
+        self,
+        hf_config: PretrainedConfig,
+        dtype: str,
+        seed: int,
+        revision: Optional[str] = None,
+        code_revision: Optional[str] = None,
+        tokenizer_revision: Optional[str] = None,
+        max_model_len: Optional[int] = None,
+        quantization: Optional[str] = None,
+        quantization_param_path: Optional[str] = None,
+        enforce_eager: bool = False,
+        max_context_len_to_capture: Optional[int] = None,
+        max_seq_len_to_capture: Optional[int] = None,
+        max_logprobs: int = 5,
+        skip_tokenizer_init: bool = False,
+        served_model_name: Optional[Union[str, List[str]]] = None,
+    ) -> None:
+        self.model = hf_config._name_or_path
+        self.tokenizer = hf_config._name_or_path
+        self.seed = seed
+        self.revision = revision
+        self.code_revision = code_revision
+        self.tokenizer_revision = tokenizer_revision
+        self.quantization = quantization
+        self.quantization_param_path = quantization_param_path
+        self.enforce_eager = enforce_eager
+        self.max_context_len_to_capture = max_context_len_to_capture
+        if self.max_context_len_to_capture is not None:
+            raise ValueError("`max_context_len_to_capture` is deprecated. "
+                             "Use `max_seq_len_to_capture` instead.")
+        self.max_seq_len_to_capture = (max_seq_len_to_capture or max_context_len_to_capture)
+        self.max_logprobs = max_logprobs
+        self.skip_tokenizer_init = skip_tokenizer_init
+
+        # self.hf_config = get_config(model, trust_remote_code, revision)
+        self.hf_config = hf_config
+        self.hf_text_config = get_hf_text_config(hf_config)
+        # TODO: for multimodal model
+        self.dtype = _get_and_verify_dtype(self.hf_config, dtype)
+        self.max_model_len = _get_and_verify_max_len(self.hf_config, max_model_len)
+        # self.served_model_name = get_served_model_name(model,
+        #                                                served_model_name)
+        # self._verify_load_format()
+        # self._verify_tokenizer_mode()
+        self._verify_quantization()
+        self._verify_cuda_graph()
+
+
+class LoadFormat(str, enum.Enum):
+    AUTO = 'auto'
+    MEGATRON = "megatron"
+    HF = "hf"
+    DTENSOR = 'dtensor'
+    DUMMY_HF = 'dummy_hf'
+    DUMMY_MEGATRON = 'dummy_megatron'
+    DUMMY_DTENSOR = 'dummy_dtensor'
+
+
+@dataclass
+class LoadConfig:
+    """
+        download_dir: Directory to download and load the weights, default to the
+            default cache directory of huggingface.
+        load_format: The format of the model weights to load:
+            "auto" will try to load the weights in the safetensors format and
+                fall back to the pytorch bin format if safetensors format is
+                not available.
+            "pt" will load the weights in the pytorch bin format.
+            "safetensors" will load the weights in the safetensors format.
+            "npcache" will load the weights in pytorch format and store
+                a numpy cache to speed up the loading.
+            "dummy" will initialize the weights with random values, which is
+                mainly for profiling.
+            "tensorizer" will use CoreWeave's tensorizer library for
+                fast weight loading.
+    """
+
+    load_format: Union[str, LoadFormat, "BaseModelLoader"] = LoadFormat.AUTO
+    download_dir: Optional[str] = None
+    model_loader_extra_config: Optional[Union[str, dict]] = field(default_factory=dict)
+
+    def __post_init__(self):
+        model_loader_extra_config = self.model_loader_extra_config or {}
+        if isinstance(model_loader_extra_config, str):
+            self.model_loader_extra_config = json.loads(model_loader_extra_config)
+        self._verify_load_format()
+
+    def _verify_load_format(self) -> None:
+        if not isinstance(self.load_format, str):
+            return
+
+        load_format = self.load_format.lower()
+        self.load_format = LoadFormat(load_format)
+
+        rocm_not_supported_load_format: List[str] = []
+        if is_hip() and load_format in rocm_not_supported_load_format:
+            rocm_supported_load_format = [
+                f for f in LoadFormat.__members__ if (f not in rocm_not_supported_load_format)
+            ]
+            raise ValueError(f"load format '{load_format}' is not supported in ROCm. "
+                             f"Supported load formats are "
+                             f"{rocm_supported_load_format}")