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# 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 os
import random
from typing import Optional
import numpy as np
import torch
import torch.distributed
from megatron.core import mpu, tensor_parallel
from megatron.core.dist_checkpointing.mapping import ShardedObject
from verl.models.weight_loader_registry import get_weight_saver
from verl.utils.fs import is_non_local
from verl.utils.megatron_utils import (
get_hf_model_checkpoint_path,
get_model_checkpoint_path,
get_optimizer_checkpoint_path,
get_rng_states_checkpoint_path,
)
from .checkpoint_manager import BaseCheckpointManager
class MegatronCheckpointManager(BaseCheckpointManager):
"""
A checkpoint manager that saves and loads
- model
- optimizer
- lr_scheduler
- extra_states
in a SPMD way.
We save
- sharded model states and optimizer states
- full lr_scheduler states
- huggingface tokenizer/processor and config for ckpt merge
"""
def __init__(
self,
config,
model_config,
role,
model: torch.nn.ModuleList,
arch: str,
hf_config,
param_dtype: torch.dtype,
share_embeddings_and_output_weights: bool,
tokenizer,
optimizer,
use_distributed_optimizer: bool,
checkpoint_contents: Optional[list] = None,
**kwargs,
):
if checkpoint_contents is None:
checkpoint_contents = ["model", "optimizer", "extra"]
super().__init__(
model,
optimizer=optimizer,
lr_scheduler=None,
processing_class=tokenizer,
checkpoint_contents=checkpoint_contents,
)
self.arch = arch
self.config = config
self.role = role
self.is_value_model = False
if self.role in ["reward", "critic"]:
self.is_value_model = True
self.model_config = model_config
self.hf_config = hf_config
self.param_dtype = param_dtype
self.share_embeddings_and_output_weights = share_embeddings_and_output_weights
self.model_path = self.config.model.path
self.use_distributed_optimizer = use_distributed_optimizer
self.rank = torch.distributed.get_rank()
self.weight_saver = get_weight_saver(self.arch)
def get_rng_state(self, use_dist_ckpt: bool = False, data_parallel_random_init: bool = False):
"""collect rng state across data parallel ranks"""
rng_state = {
"random_rng_state": random.getstate(),
"np_rng_state": np.random.get_state(),
"torch_rng_state": torch.get_rng_state(),
"cuda_rng_state": torch.cuda.get_rng_state(),
"rng_tracker_states": tensor_parallel.get_cuda_rng_tracker().get_states(),
}
rng_state_list = None
if torch.distributed.is_initialized() and mpu.get_data_parallel_world_size() > 1 and data_parallel_random_init:
rng_state_list = [None for i in range(mpu.get_data_parallel_world_size())]
torch.distributed.all_gather_object(rng_state_list, rng_state, group=mpu.get_data_parallel_group())
else:
rng_state_list = [rng_state]
if use_dist_ckpt:
pp_rank = mpu.get_pipeline_model_parallel_rank()
pp_size = mpu.get_pipeline_model_parallel_world_size()
tp_rank = mpu.get_tensor_model_parallel_rank()
tp_size = mpu.get_tensor_model_parallel_world_size()
cp_rank = mpu.get_context_parallel_rank()
cp_size = mpu.get_context_parallel_world_size()
rng_state_list = ShardedObject(
"rng_state",
rng_state_list,
(pp_size, tp_size, cp_size),
(pp_rank, tp_rank, cp_rank),
replica_id=mpu.get_data_parallel_rank(with_context_parallel=True),
)
return rng_state_list
def get_checkpoint_name(
self,
checkpoints_path,
pipeline_parallel=None,
tensor_rank=None,
pipeline_rank=None,
cp_rank=None,
expert_parallel=None,
expert_rank=None,
return_base_dir=True,
basename="model.pt",
):
"""Determine the directory name for this rank's checkpoint."""
# Use both the tensor and pipeline MP rank.
if pipeline_parallel is None:
pipeline_parallel = mpu.get_pipeline_model_parallel_world_size() > 1
if tensor_rank is None:
tensor_rank = mpu.get_tensor_model_parallel_rank()
if pipeline_rank is None:
pipeline_rank = mpu.get_pipeline_model_parallel_rank()
if cp_rank is None:
cp_rank = mpu.get_context_parallel_rank()
if expert_parallel is None:
expert_parallel = mpu.get_expert_model_parallel_world_size() > 1
if expert_rank is None:
expert_rank = mpu.get_expert_model_parallel_rank()
# Use both the tensor and pipeline MP rank. If using the distributed
# optimizer, then the optimizer's path must additionally include the
# data parallel rank.
# due to the fact that models are identical across cp ranks, cp rank is not used in the checkpoint path
if not pipeline_parallel:
common_path = os.path.join(checkpoints_path, f"mp_rank_{tensor_rank:02d}")
else:
common_path = os.path.join(checkpoints_path, f"mp_rank_{tensor_rank:02d}_{pipeline_rank:03d}")
if expert_parallel:
common_path = common_path + f"_{expert_rank:03d}"
os.makedirs(common_path, exist_ok=True)
if return_base_dir:
return common_path
return os.path.join(common_path, basename)
def load_optimizer(self, ckpt_path):
# TODO: Check Optimizer format and distributed optimizer
optimizer_path = get_optimizer_checkpoint_path(ckpt_path)
print(f"Loading optimizer from {optimizer_path}")
self.optimizer.load_parameter_state(optimizer_path)
def load_rng_states(self, ckpt_path, data_parallel_random_init=False, use_dist_ckpt=False):
rng_state_path = get_rng_states_checkpoint_path(ckpt_path, only_rank0_save=False)
print(f"Loading rng states from {rng_state_path}")
rng_state = torch.load(rng_state_path, weights_only=False)
# access rng_state for data parallel rank
if not use_dist_ckpt:
rng_state = rng_state[mpu.get_data_parallel_rank()] if data_parallel_random_init else rng_state[0]
random.setstate(rng_state["random_rng_state"])
np.random.set_state(rng_state["np_rng_state"])
torch.set_rng_state(rng_state["torch_rng_state"])
torch.cuda.set_rng_state(rng_state["cuda_rng_state"])
# Check for empty states array
if not rng_state["rng_tracker_states"]:
raise KeyError
tensor_parallel.get_cuda_rng_tracker().set_states(rng_state["rng_tracker_states"])
def load_checkpoint(self, local_path: str, hdfs_path: str = None, del_local_after_load=False):
if local_path is None:
return
if "model" in self.checkpoint_contents:
model_path = get_model_checkpoint_path(local_path)
ckpt_name = self.get_checkpoint_name(model_path, return_base_dir=False)
state_dicts = torch.load(os.path.join(ckpt_name), weights_only=False)
assert len(state_dicts) == len(self.model), f"state_dicts length: {len(state_dicts)} mismatch with model length: {len(self.model)}"
for vpp_rank, (state_dict, model) in enumerate(zip(state_dicts, self.model)):
model.load_state_dict(state_dict)
print(f"Loaded sharded model checkpoint from {model_path}")
if "optimizer" in self.checkpoint_contents:
self.load_optimizer(local_path)
if "extra" in self.checkpoint_contents:
self.load_rng_states(local_path)
if del_local_after_load:
try:
os.remove(local_path) if is_non_local(local_path) else None
except Exception as e:
print(f"[rank-{self.rank}]: remove local resume ckpt file after loading failed, exception {e} will be ignored")
def save_checkpoint(self, local_path: str, hdfs_path: str = None, global_step: int = 0, max_ckpt_to_keep=None):
# record the previous global step
self.previous_global_step = global_step
# remove previous local_path
if max_ckpt_to_keep and isinstance(max_ckpt_to_keep, int) and max_ckpt_to_keep > 0 and len(self.previous_saved_paths) >= max_ckpt_to_keep:
keep_start = len(self.previous_saved_paths) - max_ckpt_to_keep + 1
self.remove_previous_save_local_path(self.previous_saved_paths[:keep_start])
self.previous_saved_paths = self.previous_saved_paths[keep_start:]
local_path = self.local_mkdir(local_path)
# Save Model
if "model" in self.checkpoint_contents and mpu.get_data_parallel_rank() == 0:
state_dicts = []
for vpp_rank, model in enumerate(self.model):
state_dict = model.state_dict()
state_dicts.append(state_dict)
print(f"Saving sharded model checkpoint to {local_path}")
model_ckpt_path = get_model_checkpoint_path(local_path)
hf_model_ckpt_path = get_hf_model_checkpoint_path(local_path)
ckpt_name = self.get_checkpoint_name(model_ckpt_path, return_base_dir=False)
torch.save(state_dicts, os.path.join(ckpt_name))
self.processing_class.save_pretrained(hf_model_ckpt_path) # tokenizer will be saved to hf_model_ckpt_path
print(f"Saved checkpoint to {model_ckpt_path}")
if hdfs_path is not None:
print(f"Uploading checkpoint to {hdfs_path}")
from verl.utils import hdfs_io
hdfs_io.makedirs(hdfs_path, exist_ok=True)
hdfs_io.copy(src=model_ckpt_path, dst=hdfs_path, dirs_exist_ok=True)
if "hf_model" in self.checkpoint_contents:
# wait for everyone to dump to local
state_dict = self.weight_saver(
self.model,
self.hf_config,
dtype=self.param_dtype,
is_value_model=self.is_value_model,
tie_word_embeddings=self.share_embeddings_and_output_weights,
)
torch.distributed.barrier()
print(f"self.param_dtype: {self.param_dtype}")
for key in state_dict.keys():
print(f"state_dict[key].dtype: {key} {state_dict[key].dtype}")
torch.distributed.barrier()
if self.rank == 0:
hf_model_ckpt_path = get_hf_model_checkpoint_path(local_path)
import warnings
from accelerate import init_empty_weights
with init_empty_weights(), warnings.catch_warnings():
warnings.simplefilter("ignore")
if "mistral7b-rm" in self.config.model.path:
from transformers import MistralForSequenceClassification
model = MistralForSequenceClassification.from_pretrained(self.config.model.path) # use score head instead of lm_head
state_dict["score.weight"] = state_dict["score.weight"]
else:
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(self.config.model.path, torch_dtype="auto")
model.save_pretrained(hf_model_ckpt_path, state_dict=state_dict)
if hdfs_path is not None:
print(f"Uploading checkpoint to {hdfs_path}")
from verl.utils import hdfs_io
hdfs_io.makedirs(hdfs_path, exist_ok=True)
hdfs_io.copy(src=hf_model_ckpt_path, dst=hdfs_path, dirs_exist_ok=True)
# Save Optimizer
if "optimizer" in self.checkpoint_contents:
torch.distributed.barrier()
optimizer_path = get_optimizer_checkpoint_path(local_path)
self.optimizer.save_parameter_state(optimizer_path)
if self.rank == 0:
print(f"saving optimizer state to {optimizer_path}")
# Save RNG States
if "extra" in self.checkpoint_contents:
torch.distributed.barrier()
rng_state_path = get_rng_states_checkpoint_path(local_path, only_rank0_save=False)
rng_state = self.get_rng_state()
torch.save(rng_state, rng_state_path)
print(f"Rank {self.rank} saving rng states to {rng_state_path}")
self.previous_saved_paths.append(local_path)
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