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# Copyright 2025 ModelBest Inc. 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.
# ==============================================================================
# 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 os
from typing import Union
import torch
import torch.distributed as dist
from sglang.srt.entrypoints.engine import Engine
from sglang.srt.entrypoints.verl_engine import VerlEngine
from sglang.srt.model_executor.model_runner import LocalSerializedTensor
from sglang.srt.utils import MultiprocessingSerializer
from torch.distributed.device_mesh import DeviceMesh
from torch.distributed.fsdp.api import FullStateDictConfig, ShardedStateDictConfig, StateDictType
from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
from torch.distributed.tensor import DTensor
from verl import DataProto
from verl.protocol import all_gather_data_proto
from verl.utils.debug import log_gpu_memory_usage
from verl.utils.fsdp_utils import fsdp_version, load_fsdp_model_to_gpu, offload_fsdp_model_to_cpu
from verl.utils.torch_functional import broadcast_dict_tensor, check_cuda_is_available
from .base import BaseShardingManager
# from vllm.distributed import parallel_state as sglang_ps
logger = logging.getLogger(__file__)
logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN"))
def _preprocess_tensor_for_update_weights(tensor: torch.Tensor):
if isinstance(tensor, DTensor):
return tensor.full_tensor()
return tensor
class FSDPSGLangShardingManager(BaseShardingManager):
@check_cuda_is_available()
def __init__(
self,
module: FSDP,
inference_engine: Union[VerlEngine, Engine],
model_config,
full_params: bool = False,
device_mesh: DeviceMesh = None,
offload_param: bool = False,
):
self.module = module
self.inference_engine = inference_engine
self.model_config = model_config
self.device_mesh = device_mesh
self.offload_param = offload_param
# Full params
self.full_params = full_params
if full_params and fsdp_version(self.module) == 1:
FSDP.set_state_dict_type(self.module, state_dict_type=StateDictType.FULL_STATE_DICT, state_dict_config=FullStateDictConfig())
elif fsdp_version(self.module) == 1:
FSDP.set_state_dict_type(
self.module,
state_dict_type=StateDictType.SHARDED_STATE_DICT,
state_dict_config=ShardedStateDictConfig(),
)
# Note that torch_random_states may be different on each dp rank
self.torch_random_states = torch.cuda.get_rng_state()
# get a random rng states
if self.device_mesh is not None:
gen_dp_rank = self.device_mesh["dp"].get_local_rank()
torch.cuda.manual_seed(gen_dp_rank + 1000) # make sure all tp ranks have the same random states
self.gen_random_states = torch.cuda.get_rng_state()
torch.cuda.set_rng_state(self.torch_random_states)
else:
self.gen_random_states = None
def __enter__(self):
torch.cuda.empty_cache()
log_gpu_memory_usage("Before state_dict() in sharding manager memory", logger=logger)
if self.offload_param:
load_fsdp_model_to_gpu(self.module)
params = self.module.state_dict()
log_gpu_memory_usage("After state_dict() in sharding manager memory", logger=logger)
device = torch.cuda.current_device() # used when fsdp2 set cpu_offload_policy
params = {k: v.to(device, non_blocking=True) if fsdp_version(self.module) == 2 else v for k, v in params.items()}
# Copy, not share memory
self.update_weights(params)
log_gpu_memory_usage("After sync model weights in sharding manager", logger=logger)
del params
if self.offload_param:
offload_fsdp_model_to_cpu(self.module)
torch.cuda.empty_cache()
log_gpu_memory_usage("After del state_dict and empty_cache in sharding manager", logger=logger)
# important: need to manually set the random states of each tp to be identical.
if self.device_mesh is not None:
self.torch_random_states = torch.cuda.get_rng_state()
torch.cuda.set_rng_state(self.gen_random_states)
def __exit__(self, exc_type, exc_value, traceback):
log_gpu_memory_usage("Before SGLang offload in sharding manager", logger=logger)
self.release_memory()
log_gpu_memory_usage("After SGLang offload in sharding manager", logger=logger)
self.module.train()
# add empty cache after each compute
torch.cuda.empty_cache()
# restore random states
if self.device_mesh is not None:
self.gen_random_states = torch.cuda.get_rng_state()
torch.cuda.set_rng_state(self.torch_random_states)
def update_weights(self, params):
self.inference_engine.resume_memory_occupation()
self.inference_engine.update_weights_from_tensor([(k, v) for k, v in params.items()], load_format=None)
def release_memory(self):
self.inference_engine.release_memory_occupation()
def preprocess_data(self, data: DataProto) -> DataProto:
"""All gather across tp group to make each rank has identical input."""
if self.device_mesh["infer_tp"].mesh.size()[0] == 1:
return data
# TODO: Current impl doesn't consider FSDP with torch micro-dp
group = self.device_mesh["infer_tp"].get_group()
all_gather_data_proto(data=data, process_group=group)
return data
def postprocess_data(self, data: DataProto) -> DataProto:
# TODO: Current impl doesn't consider FSDP with torch micro-dp
global_rank = self.device_mesh.get_rank()
tp_rank = self.device_mesh["infer_tp"].get_local_rank()
tp_size = self.device_mesh["infer_tp"].mesh.size()[0]
src_rank = global_rank // tp_size * tp_size
broadcast_dict_tensor(data.batch, src=src_rank, group=self.device_mesh["infer_tp"].get_group())
if tp_size > 1:
local_prompts = data.chunk(chunks=tp_size)
data = local_prompts[tp_rank]
return data
class FSDPAsyncSGLangShardingManager(FSDPSGLangShardingManager):
def __init__(
self,
module: FSDP,
inference_engine: Engine,
model_config,
full_params: bool = False,
device_mesh: DeviceMesh = None,
offload_param: bool = False,
):
super().__init__(module, inference_engine, model_config, full_params, device_mesh, offload_param)
def update_weights(self, params):
load_format = None if self.full_params else "dtensor"
if self.device_mesh["infer_tp"].get_local_rank() == 0:
self.inference_engine.resume_memory_occupation()
# Most naive implementation, can optimize a lot if it is bottleneck from sglang VerlEngine
named_tensors = [(k, v) for k, v in params.items()]
load_format = None
for tensor_index, (name, tensor) in enumerate(named_tensors):
serialized_tensor = MultiprocessingSerializer.serialize(_preprocess_tensor_for_update_weights(tensor))
if self.device_mesh["infer_tp"].get_local_rank() == 0:
gathered_serialized_tensors = [None for _ in range(self.device_mesh["infer_tp"].mesh.size()[0])]
else:
gathered_serialized_tensors = None
dist.gather_object(
obj=serialized_tensor,
object_gather_list=gathered_serialized_tensors,
dst=self.device_mesh["infer_tp"].mesh.tolist()[0],
group=self.device_mesh["infer_tp"].get_group(),
)
if self.device_mesh["infer_tp"].get_local_rank() == 0:
self.inference_engine.update_weights_from_tensor(
named_tensors=[
(
name,
LocalSerializedTensor(values=gathered_serialized_tensors),
)
],
load_format=load_format,
flush_cache=tensor_index == len(named_tensors) - 1,
)
def release_memory(self):
if self.device_mesh["infer_tp"].get_local_rank() == 0:
self.inference_engine.release_memory_occupation()
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