88 lines
3.5 KiB
Python
88 lines
3.5 KiB
Python
# 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.
|
|
"""
|
|
Contains a resharding manager that binds weights from FSDP zero3 to XPerfGPT
|
|
"""
|
|
from typing import Optional
|
|
from .base import BaseShardingManager
|
|
|
|
import random
|
|
from torch.distributed.device_mesh import DeviceMesh
|
|
|
|
from verl.utils.torch_functional import allgather_dict_tensors
|
|
from verl.utils.ulysses import set_ulysses_sequence_parallel_group, get_ulysses_sequence_parallel_group
|
|
import numpy as np
|
|
|
|
import torch
|
|
import torch.distributed
|
|
|
|
from verl import DataProto
|
|
|
|
|
|
class FSDPUlyssesShardingManager(BaseShardingManager):
|
|
"""
|
|
Sharding manager to support data resharding when using FSDP + Ulysses
|
|
"""
|
|
|
|
def __init__(self, device_mesh: DeviceMesh):
|
|
super().__init__()
|
|
self.device_mesh = device_mesh
|
|
self.seed_offset = 12345
|
|
|
|
def __enter__(self):
|
|
if self.device_mesh is not None:
|
|
# We have a global SP group
|
|
# so we have to change to use model-specific sp group
|
|
self.prev_sp_group = get_ulysses_sequence_parallel_group()
|
|
set_ulysses_sequence_parallel_group(self.device_mesh['sp'].get_group())
|
|
# TODO: check how to set seed for each model
|
|
|
|
def __exit__(self, exc_type, exc_value, traceback):
|
|
# restore random states
|
|
if self.device_mesh is not None:
|
|
# revert to previous sp group
|
|
set_ulysses_sequence_parallel_group(self.prev_sp_group)
|
|
# TODO: check how to set seed for each model
|
|
|
|
def preprocess_data(self, data: DataProto) -> DataProto:
|
|
"""
|
|
AllGather data from sp region
|
|
This is because the data is first sharded along the FSDP dimension as we utilize the DP_COMPUTE
|
|
In Ulysses, we need to make sure the same data is used across a SP group
|
|
"""
|
|
if self.device_mesh is not None:
|
|
sp_size = self.device_mesh['sp'].size()
|
|
group = self.device_mesh['sp'].get_group()
|
|
|
|
prev_device = data.batch.device
|
|
data.batch = data.batch.cuda(device=torch.cuda.current_device())
|
|
data.batch = allgather_dict_tensors(data.batch.contiguous(), size=sp_size, group=group, dim=0)
|
|
data.batch = data.batch.to(prev_device)
|
|
# all gather non_tensor_batch
|
|
all_non_tensor_batch = [None for _ in range(sp_size)]
|
|
torch.distributed.all_gather_object(all_non_tensor_batch, data.non_tensor_batch, group=group)
|
|
data.non_tensor_batch = {
|
|
k: np.concatenate([d[k] for d in all_non_tensor_batch]) for k in data.non_tensor_batch
|
|
}
|
|
return data
|
|
|
|
def postprocess_data(self, data: DataProto) -> DataProto:
|
|
"""
|
|
Split the data to follow FSDP partition
|
|
"""
|
|
if self.device_mesh is not None:
|
|
sp_size = self.device_mesh['sp'].size()
|
|
sp_rank = self.device_mesh['sp'].get_local_rank()
|
|
data = data.chunk(chunks=sp_size)[sp_rank]
|
|
return data |