easyr1/verl/workers/sharding_manager/fsdp_vllm.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 warnings
from typing import Dict, Iterable, Tuple, Union

import torch
import torch.distributed as dist
from torch.distributed._tensor import DTensor
from torch.distributed.device_mesh import DeviceMesh
from torch.distributed.fsdp.api import ShardedStateDictConfig, StateDictType
from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
from vllm import LLM
from vllm.distributed import parallel_state as vllm_ps

from ...protocol import DataProto, all_gather_data_proto
from ...utils.model_utils import print_gpu_memory_usage
from .base import BaseShardingManager


class FSDPVLLMShardingManager(BaseShardingManager):
    def __init__(
        self,
        module: FSDP,
        inference_engine: LLM,
        device_mesh: DeviceMesh = None,
    ):
        self.module = module
        self.inference_engine = inference_engine
        self.device_mesh = device_mesh
        with warnings.catch_warnings():
            warnings.simplefilter("ignore")
            FSDP.set_state_dict_type(
                self.module,
                state_dict_type=StateDictType.SHARDED_STATE_DICT,
                state_dict_config=ShardedStateDictConfig(),
            )

        self.world_size = dist.get_world_size()
        self.tp_size = vllm_ps.get_tensor_model_parallel_world_size()
        self.tp_rank = vllm_ps.get_tensor_model_parallel_rank()
        self.tp_group = vllm_ps.get_tensor_model_parallel_group().device_group

        # Record freed bytes to estimate memory usage correctly
        # https://github.com/vllm-project/vllm/pull/11743#issuecomment-2754338119
        self.freed_bytes = 0

        # 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 _make_weight_iterator(
        self, actor_weights: Dict[str, Union[torch.Tensor, DTensor]]
    ) -> Iterable[Tuple[str, torch.Tensor]]:
        for name, tensor in actor_weights.items():
            yield name, tensor.full_tensor() if self.world_size != 1 else tensor

    def __enter__(self):
        # NOTE: Basically, we only need `torch.cuda.empty_cache()` before vllm wake_up and
        # after vllm sleep, since vllm has its own caching memory allocator CuMemAllocator.
        # Out of vllm scope, we should avoid empty cache to let pytorch using caching memory
        # to speed up memory allocations.
        #
        # pytorch: https://pytorch.org/docs/stable/notes/cuda.html#memory-management
        # vllm: https://github.com/vllm-project/vllm/blob/v0.7.3/vllm/device_allocator/cumem.py#L103
        torch.cuda.empty_cache()
        print_gpu_memory_usage("Before state_dict() in sharding manager")
        actor_weights = self.module.state_dict()
        print_gpu_memory_usage("After state_dict() in sharding manager")

        self.inference_engine.wake_up()
        model = self.inference_engine.llm_engine.model_executor.driver_worker.worker.model_runner.model
        model.load_weights(self._make_weight_iterator(actor_weights))
        print_gpu_memory_usage("After sync model weights in sharding manager")

        del actor_weights
        torch.cuda.empty_cache()
        print_gpu_memory_usage("After del state_dict and empty_cache in sharding manager")
        # 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):
        print_gpu_memory_usage("Before vllm offload in sharding manager")
        free_bytes_before_sleep = torch.cuda.mem_get_info()[0]
        self.inference_engine.sleep(level=1)
        free_bytes_after_sleep = torch.cuda.mem_get_info()[0]
        self.freed_bytes = free_bytes_after_sleep - free_bytes_before_sleep
        print_gpu_memory_usage("After vllm offload in sharding manager")

        self.module.train()
        torch.cuda.empty_cache()  # add empty cache after each compute

        # 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 preprocess_data(self, data: DataProto) -> DataProto:
        """All gather across tp group to make each rank has identical input."""
        all_gather_data_proto(data, size=self.tp_size, group=self.tp_group)
        return data

    def postprocess_data(self, data: DataProto) -> DataProto:
        """Get chunk data of this tp rank since we do all gather in preprocess."""
        if self.tp_size > 1:
            data = data.chunk(chunks=self.tp_size)[self.tp_rank]

        return data