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1faccd4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 | # 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 asyncio
from typing import Generator
import ray
import torch
from transformers import AutoModelForCausalLM
from verl.checkpoint_engine import CheckpointEngineRegistry, CheckpointEngineWorker
from verl.single_controller.base.decorator import Dispatch, register
from verl.single_controller.ray import RayClassWithInitArgs, RayResourcePool, RayWorkerGroup
from verl.utils.device import get_device_name
from verl.utils.fs import copy_to_local
from verl.workers.config import CheckpointEngineConfig, FSDPEngineConfig, HFModelConfig, RolloutConfig
from verl.workers.engine_workers import TrainingWorker, TrainingWorkerConfig
from verl.workers.rollout import BaseRollout, RolloutReplica
class TrainingWorkerTest(TrainingWorker):
def __init__(self, config: TrainingWorkerConfig, checkpoint_engine_config: CheckpointEngineConfig) -> None:
super().__init__(config)
backend = checkpoint_engine_config.backend
bucket_size = checkpoint_engine_config.update_weights_bucket_megabytes << 20
engine_kwargs = checkpoint_engine_config.engine_kwargs.get(backend, {})
if torch.distributed.get_rank() == 0:
engine_kwargs["is_master"] = True
self.checkpoint_engine = CheckpointEngineRegistry.new(backend, bucket_size=bucket_size, **engine_kwargs)
@register(dispatch_mode=Dispatch.ONE_TO_ALL, blocking=False)
async def update_weights(self, global_steps: int = None):
per_tensor_param, _ = self.engine.get_per_tensor_param()
await self.checkpoint_engine.send_weights(per_tensor_param)
@register(dispatch_mode=Dispatch.DP_COMPUTE, blocking=False)
def execute_checkpoint_engine(self, method: str, *args, **kwargs):
return getattr(self.checkpoint_engine, method)(*args, **kwargs)
class MockServerAdapter(BaseRollout):
def __init__(self, config: RolloutConfig, model_config: HFModelConfig, check_allclose: bool = True):
super().__init__(config, model_config, device_mesh=None)
self.check_allclose = check_allclose
self.model = None
self.received_weights: dict[str, torch.Tensor] = {}
async def resume(self, tags: list[str]):
raise NotImplementedError()
async def release(self):
raise NotImplementedError()
async def update_weights(
self,
weights: Generator[tuple[str, torch.Tensor], None, None],
**kwargs,
):
async for name, weight in weights:
weight = weight.clone()
if self.check_allclose:
self.received_weights[name] = weight.clone()
def check_weights(self):
if not self.check_allclose:
return
if self.model is None:
local_path = copy_to_local(self.model_config.path)
self.model = AutoModelForCausalLM.from_pretrained(local_path, torch_dtype=torch.bfloat16, device_map="cpu")
for name, weight in self.model.state_dict().items():
assert name in self.received_weights, f"weight {name} not received"
received = self.received_weights[name]
assert torch.allclose(weight.to(received.device), received), f"weight {name} not equal"
self.received_weights.clear()
class MockReplica(RolloutReplica):
async def init_hybrid(self, worker_group: RayWorkerGroup):
"""Init hybrid rollout server, rollout engine and training engine(fsdp/megatron) fused in same process.
Args:
worker_group: RayWorkerGroup, fused workers where training engine(fsdp/megatron) have been initialized.
"""
self.workers = worker_group.workers[
self.world_size * self.replica_rank : self.world_size * (self.replica_rank + 1)
]
def get_ray_class_with_init_args(self) -> RayClassWithInitArgs:
"""Get rollout worker actor class for colocated and standalone mode."""
raise NotImplementedError
async def launch_servers(self):
"""Launch http server in each node."""
raise NotImplementedError
class CheckpointEngineWorkerTest(CheckpointEngineWorker):
def __init__(
self, rollout_config: RolloutConfig, model_config: HFModelConfig, check_allclose: bool = True, *args, **kwargs
) -> None:
server_adapter = MockServerAdapter(rollout_config, model_config, check_allclose)
super().__init__(rollout_config, model_config, server_adapter, *args, **kwargs)
@register(dispatch_mode=Dispatch.ONE_TO_ALL)
def check_weights(self):
self.server_adapter.check_weights()
def create_trainer_worker_group(
resource_pool: RayResourcePool, model_config: HFModelConfig, checkpoint_engine_config: CheckpointEngineConfig
) -> RayWorkerGroup:
engine_config = FSDPEngineConfig(forward_only=True, fsdp_size=resource_pool.world_size, strategy="fsdp")
trainer_config = TrainingWorkerConfig(
model_type="language_model",
model_config=model_config,
engine_config=engine_config,
)
ray_cls_with_init = RayClassWithInitArgs(
cls=ray.remote(TrainingWorkerTest),
config=trainer_config,
checkpoint_engine_config=checkpoint_engine_config,
)
ray_cls_with_init.update_options(
{
"runtime_env": {
"env_vars": {
"PYTORCH_CUDA_ALLOC_CONF": "expandable_segments:True",
}
}
}
)
wg = RayWorkerGroup(resource_pool=resource_pool, ray_cls_with_init=ray_cls_with_init, device_name=get_device_name())
return wg
async def create_rollout_worker_group(
resource_pool: RayResourcePool,
model_config: HFModelConfig,
rollout_config: RolloutConfig,
check_allclose: bool = True,
) -> tuple[RayWorkerGroup, list[MockReplica]]:
# create rollout worker group
ray_cls_with_init = RayClassWithInitArgs(
cls=ray.remote(CheckpointEngineWorkerTest),
model_config=model_config,
rollout_config=rollout_config,
check_allclose=check_allclose,
)
wg = RayWorkerGroup(resource_pool=resource_pool, ray_cls_with_init=ray_cls_with_init, device_name=get_device_name())
# create rollout replicas
rollout_world_size = (
rollout_config.tensor_model_parallel_size
* rollout_config.data_parallel_size
* rollout_config.pipeline_model_parallel_size
)
num_replicas = wg.world_size // rollout_world_size
replicas = []
for replica_rank in range(num_replicas):
replica = MockReplica(
replica_rank=replica_rank,
config=rollout_config,
model_config=model_config,
)
replicas.append(replica)
await asyncio.gather(*[replica.init_hybrid(wg) for replica in replicas])
return wg, replicas
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